EP3528694A1 - Systèmes et procédés de notation d'alerte précoce respiratoire - Google Patents

Systèmes et procédés de notation d'alerte précoce respiratoire

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Publication number
EP3528694A1
EP3528694A1 EP17867005.5A EP17867005A EP3528694A1 EP 3528694 A1 EP3528694 A1 EP 3528694A1 EP 17867005 A EP17867005 A EP 17867005A EP 3528694 A1 EP3528694 A1 EP 3528694A1
Authority
EP
European Patent Office
Prior art keywords
patient
respiratory
impedance
data
early warning
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP17867005.5A
Other languages
German (de)
English (en)
Other versions
EP3528694A4 (fr
Inventor
Jenny E. Freeman
Jordan BRAYANOV
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Respiratory Motion Inc
Original Assignee
Respiratory Motion Inc
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Filing date
Publication date
Application filed by Respiratory Motion Inc filed Critical Respiratory Motion Inc
Publication of EP3528694A1 publication Critical patent/EP3528694A1/fr
Publication of EP3528694A4 publication Critical patent/EP3528694A4/fr
Pending legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0024—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system for multiple sensor units attached to the patient, e.g. using a body or personal area network
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • This invention is directed to methods and devices for improving non-invasive ventilation therapy. Specifically, the invention is directed to methods and devices for adjusting non-invasive ventilation therapy based on impedance measurements of the patient.
  • Patient monitoring is essential because it provides warning to patient deterioration and allows for the opportunity of early intervention, greatly improving patient outcomes.
  • modern monitoring devices can detect abnormal heart rhythms, blood oxygen saturation, and body temperature, which can alert clinicians of a deterioration that would otherwise go unnoticed.
  • the first patient monitors were used on patients during surgery. As patient outcomes were shown to improve, monitoring of vital signs spread to other areas of the hospital such as the intensive care unit and the emergency department. For instance, pulse oximetry was first widely used in operating rooms as a method to continuously measure a patient's oxygenation non-invasively. Pulse oximetry quickly became the standard of care for the administration of general anesthetic and subsequently spread to other parts of the hospital, including the recovery room and intensive care units.
  • Physiological scores such as the Mortality Probability Model (MPM), the Acute Physiology and Chronic Health Education (APACHE), the Simplified Acute Physiological Score (SAPS) and the Therapeutic Intervention Scoring System (TISS) have shown significant improvements in patient outcomes. Monitoring sick patients by using MPM
  • MPM Mortality Probability Model
  • APACHE Acute Physiology and Chronic Health Education
  • SAPS Simplified Acute Physiological Score
  • TISS Therapeutic Intervention Scoring System
  • spirometry a patient's respiratory status is monitored with methods such as spirometry and end tidal C0 2 measurements. These methods are often inconvenient to use and inaccurate. While end tidal CO2 monitoring is useful during anesthesia and in the evaluation of intubated patients in a variety of environments, it is inaccurate for non- ventilated patients.
  • the spirometer and pneumotachometer are limited in their
  • Spirometry is the most commonly performed pulmonary function test.
  • the spirometer and pneumotachometer can give a direct measurement of respiratory volume. It involves assessing a patient's breathing patterns by measuring the volume or the flow of air as it enters and leaves the patient's body. Spirometry procedures and maneuvers are standardized by the American Thoracic Society (ATS) and the European Respiratory
  • ERS Society
  • Spirometry can provide important metrics for evaluating respiratory health and diagnosing respiratory pathologies.
  • the major drawback of mainstream spirometers is that they require the patient to breathe through a tube so that the volume and/or flow rate of his breath can be measured. Breathing through the apparatus introduces resistance to the flow of breath and changes the patient's breathing pattern. Thus it is impossible to use these devices to accurately measure a patient's normal breathing. Breathing through the apparatus requires a conscious, compliant patient.
  • ATS and ERS patients must undergo taxing breathing maneuvers, which excludes most elderly, neonatal, and COPD patients from being able to undergo such an examination.
  • the outcomes of the procedures are also highly variable dependent on patient effort and coaching, and operator skill and experience.
  • the ATS also recommends extensive training for healthcare professionals who practice spirometry. Also, many physicians do not have the skills needed to accurately interpret the data gained from pulmonary function tests.
  • Impedance-based respiratory monitoring fills an important void because current spirometry measurements are unable to provide continuous measurements because of the requirement for patient cooperation and breathing through a tube. Therefore there is a need for a device that provides near-real-time information over extended periods of time (vs. spirometry tests which last a minute or less) in non-intubated patients that can show changes in respiration related to a provocative test or therapeutic intervention.
  • Preoperative care is centered on identifying what patient characteristics may put the patient at risk during an operation and minimizing those risks.
  • Medical history, smoking history, age, and other parameters dictate the steps taken in preoperative care.
  • elderly patients and patients with pulmonary diseases may be at risk for respiratory complications when placed under a ventilator for surgery.
  • pulmonary function tests such as spirometry are performed which give the more information to determine whether the patient can utilize the ventilator.
  • Chest x-rays may also be taken. However, these tests cannot be replicated mid-surgery, or in narcotized patients or those who cannot or will not cooperate. Testing may be uncomfortable in a postoperative setting and disruptive to patient recovery.
  • End tidal CO2 is another useful metric for determining pulmonary state of a patient.
  • the value is presented as a percentage or partial pressure and is measured continuously using a capnograph monitor, which may be coupled with other patient monitoring devices. These instruments produce a capnogram, which represents a waveform of CO2
  • Capnography compares carbon dioxide concentrations within expired air and arterial blood. The capnogram is then analyzed to diagnose problems with respiration such as hyperventilation and hypoventilation. Trends in end tidal CO2 are particularly useful for evaluating ventilator performance and identifying drug activity, technical problems with intubation, and airway obstruction.
  • the American Society of Anesthesiologists (ASA) mandates that end-tidal CO2 be monitored any time an endotracheal tube or laryngeal mask is used, and is also highly encouraged for any treatment that involves general anesthesia. Capnography has also been proven to be more useful than pulse oximetry for monitoring of patient ventilation. Unfortunately, it is generally inaccurate and difficult to implement in the non-ventilated patient, and other complementary respiratory monitoring methods would have great utility.
  • Fenichel et al. determined that respiratory motion can cause interference with echocardiograms if it is not controlled for. Respiratory motion can block anterior echoes through pulmonary expansion and it chances the angle of incidence of the transducer ray relative to the heart. These effects on the echocardiography signal can decrease the accuracy of measurements recorded or inferred from echocardiograms. Combining echocardiography with accurate measurement of the respiratory cycle can allow an imaging device to compensate for respiratory motion.
  • Impedance pneumography is a simple method that can yield respiratory volume tracings without impeding airflow, does not require contact with the airstream, and does not restrict body movements. Furthermore, it may be able to make measurements that reflect functional residual capacity of the lungs.
  • transthoracic electrical impedance changed with respiration. They regarded the respiratory impedance changes as artifacts and asked the patients to stop breathing while measurements were made. In 1940, while also studying cardiac impedance, Nyboer noticed the same respiratory impedance artifact in his measurement. He confirmed the origin of the artifact by being the first person to relate changes in transthoracic impedance to changes in volume using a spirometer by simultaneously recording both. Goldensohn and Zablow took impedance pneumography a step further by being the first investigators to quantitatively relate respired volume and transthoracic impedance. They reported difficulty in separating the cardiac signal artifacts and also noted artifacts during body movements.
  • impedance is measured by the following equation, which is analogous to Ohm's law:
  • tissue layers that makeup the thorax and the abdomen all influence the measurement of transthoracic impedance. Each tissue has a different conductivity that influences the direction of current flow between electrodes. Beginning with the outermost layer, the surface of the body is covered by skin, which presents a high resistivity but is only about 1mm thick. Under the skin is a layer of fat, which also has a high resistivity. However, the thickness of this layer is highly variable and depends on body location and the body type of the subject. Moving posterior to anterior, below the layer of skin and fat are the postural muscles, which are anisotropic. They have a low resistivity in the longitudinal direction but a high resistivity in all other directions, which leads to a tendency to conduct current in a direction that is parallel to the skin.
  • impedance changes come from changes in thoracic circumference, changes in lung size, and movement of the diaphragm- liver mass. Measurements at lower thoracic levels are attributed to movement of the diaphragm and liver, and at higher thoracic levels they are attributed to aeration and expansion of the lungs. Therefore, the impedance signal is the sum of the change from the expansion and aeration of the lungs and the movement of the diaphragm-liver mass. Both the abdominal and thoracic components are needed in order to observe a normal respiratory signal. In addition, the different origins of impedance changes in the upper and lower thorax could explain why greater linearity is observed at higher thoracic levels.
  • Transthoracic impedance is measured with electrodes attached to the patient's skin. Geddes et al. determined that the electrode stimulation frequency should not be below 20 kHz because of physiological tissue considerations. It is a matter of safety and eliminating interference from bioelectric events. In addition, impedance measurements of a subject were found to differ depending on subject position, including sitting, supine, and standing. It was shown that for a given change in volume, laying supine yielded the greatest signal amplitude and lowest signal to noise during respiration.
  • Another potential signal artifact comes from subject movements, which may move electrodes and disturb calibrations. Furthermore, electrode movements may be more prevalent in obese and elderly patients, which may require repetitive lead recalibration during periods of long-term monitoring. Because of the calibration variability between trials, some have suggested that calibration should be performed for each individual for a given subject posture and electrode placement. However, a group was able to show that careful intrapatient electrode placement can reduce impedance differences between measurements to around 1%.
  • Electrodes attached to the mid-axillary line at the level of the sixth rib yielded the maximum impedance change during respiration.
  • the greatest linearity between the two variables was attained by placing the electrodes higher on the thorax.
  • large standard deviations of impedance changes during respiration have been reported.
  • acoustics in relationship to the lungs is to evaluate sounds that originate in the lungs acquired by the use of a stethoscope.
  • One frequently overlooked property of lung tissue is its ability to act as an acoustic filter. It attenuates various frequencies of sound that pass through them to different extents. There is a relationship between the level of attenuation and the amount of air in the lungs. Motion of the chest wall also results in frequency shift of acoustic signals passing through the thorax.
  • FVC forced vital capacity
  • FEVi forced expiratory volume in one second
  • the FVC and FEVI are two benchmark indicators typically measured by a spirometer and are used to diagnose and monitor diseases such as COPD, asthma, and emphysema.
  • impedance pneumography can also simultaneously record the electrocardiogram from the same electrodes.
  • the respiratory pattern is nonlinear, like any physiologic data, as it is influenced by a multitude of regulatory agents within the body.
  • breath-to- breath variability various entropy metrics are used to measure the amount of irregularity and reproducibility within the signal. These metrics can be used within the analysis of RVM tidal volume tracings in assessing not only breath-to-breath changes, but intrabreath variability, as well as magnitude, periodicity, and spatial location of the curve.
  • the respiratory trace produced by impedance pneumography as well as the average impedance of a subject can indicate states of decreased ventilation or changes in fluid volume in the thorax. This type of monitoring would be useful for the care of anesthetized patients. Respiratory monitoring with impedance pneumography in anaesthetized or immobile patients is shown to be accurate and reliable for long periods, especially during the critical period in the recovery room after surgery . Investigators have determined that fluid in the thorax or lungs can lead to measurable changes in impedance, which can be used to determine common problems for patients in the recovery room such as pulmonary edema or pneumonia.
  • Heart rate variability algorithms only report on variations in heart rate, beat-to-beat. It is desirable to use respiratory rate variability algorithms to incorporate variability in respiratory intensity, rate, and location of respiratory motion. Marked abnormalities in respiration as noted by changes in intensity, in rate, in localization of respiratory effort, or in variability of any of these parameters provide an early warning of respiratory or cardiovascular failure and may present an opportunity for early intervention.
  • a system that could provide a real time, quantitative assessment of work of breathing and analyze the trend of respiratory rate, intensity, localization, or variability in any or all of these parameters is needed for early diagnosis and intervention as well as therapeutic monitoring. Such a system is needed to judge the depth of anesthesia, or the adequacy or overdose of narcotic or other pain relieving medications.
  • PCA Patient Controlled Analgesia
  • the administration of opiates can suppress respiration, heart rate, and blood pressure, hence the need for careful and close monitoring.
  • the system comprises a computerized pump that contains pain medication that can be pumped into the patient' s IV line.
  • the patient may press a button to receive care in the form of additional medication.
  • patients are discouraged from pressing the button if they are getting too drowsy as this may prevent therapy for quicker recovery.
  • Pulse oximeters, respiratory rate and capnograph monitors may be used to warn of respiratory depression caused by pain medication and cut off the PCA dose, but each has serious limitations regarding at least accuracy, validity, and implementation.
  • COPD chronic obstructive pulmonary disease
  • emphysema a lung disease that makes it hard to breathe. It is caused by damage to the lungs over many years, usually from smoking. COPD is often a mix of two diseases: chronic bronchitis and emphysema. In chronic bronchitis, the airways that carry air to the lungs get inflamed and make a lot of mucus. This can narrow or block the airways, making it hard for you to breathe. In a healthy person, the tiny air sacs in the lungs are like balloons.
  • Cystic fibrosis also known as mucoviscidosis, is a genetic disorder that affects mostly the lungs but also the pancreas, liver, kidneys and intestine. Long-term issues include difficulty breathing and coughing up sputum as a result of frequent lung infections. Other symptoms include sinus infections, poor growth, fatty stool, clubbing of the finger and toes, and infertility in males among others.
  • HFCWO High- Frequency Chest Wall Oscillation
  • the HFCWO vest is an inflatable vest attached to a machine that vibrates it at high frequency. The vest vibrates the chest to loosen and thin mucus.
  • CPAP continuous positive airway pressure
  • BiPAP bilevel positive airway pressure
  • Other mechanical ventilation therapies include, but are not limited to cough assist systems, oxygen therapy, suction therapy, CHFO ("Continuous High Frequency Oscillation”), ventilators, medicated aerosol delivery systems, and other non-invasive ventilation methods.
  • the present invention overcomes the problems and disadvantages associated with current strategies and designs and provides new systems and methods of monitoring patients.
  • One embodiment of the invention is directed to an early warning scoring system.
  • the system comprises a computing device, a plurality of sensors for acquiring physiological signals from a patient, wherein the sensors are functionally connected to the computing device, and at least one alarm adapted to output an alert upon an early warning score (EWS) exceeding a predetermined level.
  • EWS early warning score
  • the computing device receives the physiological signals from the sensors, analyzes the physiological signals, and based on the analyzed signals, calculates the early warning score, and compares to the early waring score to predetermined limits and, if the score is outside the limits, triggers an alarm or actuates or modifies a treatment or medical intervention.
  • At least one sensor is a bioelectrical impedance sensor and the computing device provides an assessment of minute ventilation, tidal volume, and/or respiratory rate of the patient based on the bioelectrical impedance signal.
  • the EWS calculation includes at least one of the minute ventilation, tidal volume, and/or respiratory rate of the patient.
  • the EWS calculation includes minute ventilation and does not include respiratory rate.
  • the EWS is indicative of at least one of respiratory failure, sepsis, cardiac failure, congestive heart failure, renal failure, overhydration, pulmonary edema, hyper metabolic state, overexertion, traumatic brain injury, pulmonary embolus, opioid induced respiratory depression, over sedation.
  • the sensors preferably obtain patient data relating to at least one of minute ventilation, tidal volume, respiratory rate, oxygen saturation, temperature, blood pressure, pulse or heart rate, blood oxygen levels, and brain activity.
  • the at least one alarm is at least one of audible or visual.
  • at least two sensors are placed on the torso of the patient and a physiological bioelectrical impedance signal is measured transthoracically.
  • the computing device further obtains patient data comprising alertness, voice, pain, and unresponsiveness (AVPU) of the patient, and the EWS calculation includes the patient's AVPU data.
  • the system is non-invasive.
  • the EWS calculation includes the patient's disease state and/or circumstance.
  • the EWS calculation includes the patient's age, demographics, condition, and/or data from the patient's electronic health records.
  • the system is a triage system, a mobilization protocol system, a training protocol system, or an activity and/or nutrition regimen system.
  • Another embodiment of the invention is directed to a method of calculating an early warning score (EWS).
  • the method comprises the steps of coupling a plurality of sensors for acquiring physiological signals to a patient, receiving the physiological signals from the sensors, analyzing the physiological signals, based on the analyzed signals, calculating the EWS, and comparing the early waring score to predetermined limits and, if the score is outside the limits, triggering an alarm or actuating or modifying a treatment or medical intervention.
  • At least one sensor is a bioelectrical impedance sensor and the method further provides an assessment of minute ventilation, tidal volume, and/or respiratory rate of the patient based on the bioelectrical impedance signal.
  • the EWS calculation includes at least one of the minute ventilation, tidal volume, and/or respiratory rate of the patient.
  • the EWS calculation includes minute ventilation and does not include respiratory rate.
  • the EWS is indicative of at least one of respiratory failure, sepsis, cardiac failure, congestive heart failure, renal failure, over-hydration, pulmonary edema, hyper metabolic state, overexertion, traumatic brain injury, pulmonary embolus, opioid induced respiratory depression, over sedation.
  • the sensors obtain patient data relating to at least one of minute ventilation, tidal volume, respiratory rate, oxygen saturation, temperature, blood pressure, pulse or heart rate, blood oxygen levels, and brain activity.
  • the alert is at least one of audible or visual.
  • at least two sensors are placed on the torso of the patient and a physiological bioelectrical impedance signal is measured transthorasically.
  • the method preferably further comprises obtaining patient data comprising alertness, voice, pain, and unresponsiveness (AVPU) of the patient, wherein the EWS calculation includes the patient's AVPU data.
  • the method is non-invasive.
  • the EWS calculation includes the patient's disease state and/or circumstance.
  • the EWS calculation includes the patient's age, demographics, condition, and/or data from the patient's electronic health records.
  • the method is a triage method, a mobilization protocol method, a training protocol method, or an activity and/or nutrition regimen method.
  • Figure 1 is a perspective view of a four-lead embodiment of the invention.
  • Figure 2 is a diagram of the Posterior Left to Right electrode configuration.
  • Figure 3 is a diagram of the Posterior Right Vertical electrode configuration.
  • Figure 4 is a diagram of the Anterior-Posterior electrode configuration.
  • Figure 5 is a diagram of the Anterior Right Vertical electrode configuration.
  • Figure 6 is a perspective view of two four-lead configurations connected to each other by a multiplexer.
  • Figure 7 is a diagram of the ICG electrode configuration.
  • Figure 8 is a perspective view of a four-lead embodiment of the invention connected to a spirometer.
  • Figure 9 is a perspective view of a four-lead embodiment of the invention connected to a ventilator.
  • Figure 10 is an RVM measurement (impedance) versus volume plot for slow, normal, and erratic breathing maneuvers.
  • Figure 11 is a set of RVM and volume plots against time for normal breathing.
  • Figure 12 is a set of RVM and volume plots against time for slow breathing.
  • Figure 13 is a set of RVM and volume plots against time for erratic breathing.
  • Figure 14 is a plot of calibration coefficients against BMI for four different electrode configurations.
  • Figure 15 is a spirometry plot that exhibits volume drift.
  • Figure 16 is a volume vs. impedance plot that is affected by volume drift.
  • Figure 17 is a spirometry plot that is corrected for volume drift.
  • Figure 18 is a plot of volume vs. impedance, comparing data that is uncorrected and corrected for volume drift.
  • Figure 19 is a flow chart that describes data analysis for the invention.
  • Figure 20 is a preferred embodiment of the invention that utilizes a speaker and a microphone.
  • Figure 21 is a preferred embodiment of the invention that utilizes a speaker and an array of microphones.
  • Figure 22 is a preferred embodiment of the invention that utilizes an array of speakers and a microphone.
  • Figure 23 is a preferred embodiment of the invention that utilizes a vest for the sensors.
  • Figure 24 is a preferred embodiment of the invention that utilizes an array built into a piece of cloth for the sensors.
  • Figure 25 is a preferred embodiment of the invention that utilizes a net of sensors.
  • Figure 26 is a preferred embodiment of the invention that utilizes a wireless transmitter and receiver.
  • Figure 27 shows graphs of impedance versus time and volume versus time for
  • Figure 28 illustrates an embodiment of a system of the invention.
  • Figure 29 illustrates an embodiment of the device of the invention.
  • FIGS 30-32 illustrate preferred embodiments of devices of the invention.
  • Figures 33-38 depict different embodiments of lead placement.
  • Figure 39 depicts an embodiment of a modified Howland circuit for compensating for parasitic capacitances.
  • Figure 40 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a HFCWO vest.
  • Figure 41 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a mechanical ventilation therapy device.
  • Figure 42 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a oxygenation therapy device.
  • Figure 43 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a suction therapy device.
  • Figure 44 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a cough assist device. Description of the Invention
  • One embodiment of the present invention is directed to a device for assessing a patient, individual or animal that collects impedance measurements by placing multiple electrode leads and/or speakers and microphones on the body.
  • a device for assessing a patient, individual or animal that collects impedance measurements by placing multiple electrode leads and/or speakers and microphones on the body.
  • at least one impedance measuring element and a microphone/speaker functionally connected to a programmable element, programmed to provide an assessment of at least one respiratory parameter of the subject.
  • the impedance measurement is based on a plurality of remote probe data sets, and wherein the programmable element is further programmed to enhance at least one of the plurality of remote probe data sets; or to stabilize at least one of the plurality of remote probe data sets; or to analyze each of the plurality of remote probe data sets for dynamic range and signal to noise ratio (SNR) values.
  • the device probes are maintained in several lead configurations. In one embodiment, variations in lead
  • the device maintains settings to identify valid lead configurations.
  • the device maintains settings to identify valid lead attachment.
  • the device or method as described in a protocol embedded in the machine instructs as to lead placement.
  • appropriate lead contact is verified by the device.
  • the device alerts the operator as to inadequate or inappropriate lead placement.
  • the device monitors continuously or intermittently and maintains alarms to indicate when a respiratory parameter reflects a loss in ventilation or other vital function.
  • the alarm is set based on a respiratory sufficiency index, on minute ventilation, on respiratory rate, on tidal volume, on an inspiratory volume or flow parameter, on an expiratory volume or flow parameter, on variability of respiratory rate, volume, flow or other parameter generated. For example, the alarm goes off if the monitor detects a decrease in either respiratory frequency or depth or minute ventilation associated with
  • An alarm is used on a hospital floor in comparing the patient's current respiratory status with a baseline level based on specific individual calibration to ventilator or spirometer. Preferably, the alarm is set based on parameters taken for the given individual from a ventilator or spirometer. More preferably the baseline level is based on one or more of the following: demographic, physiologic and body type parameters. An alarm is also used to alert for narcotic induced respiratory depression at a point that is determined to be detrimental to the patient.
  • the ranges of values beyond which alarms will be triggered are chosen by the physician or care giver for one or more of the following: respiratory rate, tidal volume, minute ventilation, respiratory sufficiency index, shape of the respiratory curve, entropy, fractal or other analysis parameters associated with respiratory variability or complexity.
  • the RVM measurements taken at any given point in time is recorded as baseline. These recorded values are correlated to subjective impression by a physician or other health care worker of patient status. Subsequently, RVM is monitored and an alarm set to alert health care staff if a 10%, 20% or other selected percentage change in respiratory volumes, minute ventilation curve characteristics, or variability is noted.
  • the invention preferably comprises an impedance pneumograph with integrated electronics to convert measured impedance values to volume and display the volume to an end-user through an electronic interface or printed reports employing numerical or graphical representations of the data.
  • the impedance measuring device comprises circuitry, at least one microprocessor and preferably at least four leads. Preferably, where at least two leads are used for injecting current into the subject's body and at least two are used for reading the voltage response of said patient's body.
  • the device preferably comprises an integrated module to simulate a patient and allow for automated system testing and demonstrations. Automated system tests improve the performance of the device and ensure that it is functioning correctly before use.
  • the device utilizes an analog divider to compensate for slight deviations in the injected current and increase the accuracy of acquired data.
  • the analog divider in the preferred embodiment would be placed after the demodulator and before the rectifier. In other embodiments the analog divider may be placed in other locations in the circuit including, but not limited to, after the precision rectifier or before the demodulator.
  • the device utilizes adaptive electronics driven by a microprocessor to maintain the appropriate gains on the different amplifiers in the circuit to prevent the signal from going out of range.
  • the microprocessor tracks the set gains at each of the hardware amplifiers and compensates appropriately during its calculations so that it always outputs an appropriate value.
  • the impedance measuring device is preferably connected to computer via a digital interface (e.g. USB, Fire wire, serial, parallel, or other kind of digital interface).
  • the digital interface is used to prevent data from corruption during transfer. Communication over this interface is preferably encrypted to further ensure data integrity as well as protect the invention from usage of counterfeit modules (either measuring device or computer).
  • an impedance plethysmograph comprising a radio frequency impedance meter 1, a programmable element 2 contained on a PC linked to the meter, which is connected to the patient by four leads, namely a first lead 3, a second lead 4, a third lead 5, and a fourth lead 6.
  • Each lead is preferably connected to a surface electrode, namely a first surface electrode, a second surface electrode, a third surface electrode, and a fourth surface electrode.
  • the electrodes can be made of a conductive material such as AgCl, coated with an adhesive, conductive material such as a hydrogel or hydrocolloid.
  • the leads can be made of any conductive material such as copper wire and are preferably coated with insulating material such as rubber.
  • wireless electrodes are utilized to provide current and collect and transmit data.
  • this lead composition is coupled with Bluetooth technology and a receiver.
  • Leads 1 and 4 are connected to a current source with a constant frequency preferably greater than 20 KHz, which is great enough to avoid interfering with biological signaling.
  • the amplitude of the current source is preferably less than 50 mA, and below the level that would cause fibrillation at the chosen frequency.
  • the differential voltage between leads 2 and 3 is used to calculate the impedance according to ohm's law.
  • the programmable element such as a PC
  • the device is calibrated by a method laid out herein to calculate the lung volumes and display them to an operator.
  • an exemplary and preferred system includes at least one general-purpose computing device 100, including a processing unit (CPU) 120, and a system bus 110 that couples various system components including the system memory such as read only memory (ROM) 140 and random access memory (RAM) 150 to the processing 25 unit 120.
  • system memory 130 may be available for use as well.
  • the invention preferably operates on a computing device with more than one CPU 120 or on a group or cluster of computing devices networked together to provide greater processing capability.
  • the system bus 110 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.
  • a basic input/output (BIOS) stored in ROM 140 or the like preferably provides the basic routine that helps to transfer information between elements within the computing device 100, such as during start-up.
  • the computing device 100 further preferably includes storage devices such as a hard disk drive 160, a magnetic disk drive, an optical disk drive, tape drive or the like.
  • the storage device 160 is connected to the system bus 110 by a drive interface.
  • the drives and the associated computer readable media provide nonvolatile storage of computer readable instructions, data structures, program modules and other data for the computing device 100.
  • the basic components are known to those of skill in the art and appropriate variations are contemplated depending on the type of device, such as whether the device is a small, handheld computing device, a desktop computer, a laptop computer, a computer server, a wireless devices, web-enabled devices, or wireless phones, etc.
  • the system is preferably controlled by a single CPU, however, in other embodiments, one or more components of the system is controlled by one or more microprocessors (MP). Additionally, combinations of CPUs and MPs can be used.
  • MP microprocessors
  • the MP is an embedded microcontroller, however other devices capable of processing commands can also be used.
  • an input device 190 represents any number of input mechanisms, such as a microphone for speech, a touch sensitive screen for gesture or graphical input, electrical signal sensors, keyboard, mouse, motion input, speech and so forth.
  • the device output 170 can be one or more of a number of output mechanisms known to those of skill in the art, for example, printers, monitors, projectors, speakers, and plotters.
  • the output can be via a network interface, for example uploading to a website, emailing, attached to or placed within other electronic files, and sending an SMS or MMS message.
  • multimodal systems enable a user to provide multiple types of input to communicate with the computing device 100.
  • the communications interface 180 generally governs and manages the user input and system output. There is no restriction on the invention operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.
  • Embodiments within the scope of the present invention may also include computer readable media for carrying or having computer-executable instructions or data structures stored thereon.
  • Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer.
  • Such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures.
  • a network or another communications connection either hardwired, wireless, or combination thereof
  • any such connection is properly termed a computer readable medium. Combinations of the above should also be included within the scope of the computer-readable media.
  • Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions.
  • Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments.
  • program modules include routines, programs, objects,
  • Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein.
  • the particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.
  • Networks may include the Internet, one or more Local Area Networks ("LANs"), one or more Metropolitan Area Networks ("MANs”), one or more Wide Area Networks ("WANs”), one or more Intranets, etc.
  • LANs Local Area Networks
  • MANs Metropolitan Area Networks
  • WANs Wide Area Networks
  • Intranets etc.
  • Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network.
  • program modules may be located in both local and remote memory storage devices.
  • FIG. 2 is a schematic of an embodiment of a system 200 of the invention.
  • the electrical source originates from signal source 205.
  • an adjustable function generator 210 e.g. a XR2206 chip
  • the function generator 210 is preferably adjustable via a microprocessor (MP) 275 or manually.
  • MP microprocessor
  • the function generator can be tuned in order to improve the signal. Tuning can occur once or multiple times. Bio-impedance spectroscopy can be used to detect levels of hydration at different frequencies, which can be used to calibrate function generator 210. Similarly, body fat percentages can be calculated.
  • Signal source 205 also comprises a current generator 215 (e.g. a Howland circuit).
  • Current generator 215 preferably keeps the source current constant despite changes in pad contact (unless the contact is totally broken). In the preferred embodiment, current generator 215 can be tuned to improve performance, which can be done manually or automatically by the MP 275.
  • the impedance measuring subsystem may utilize current generating components at one or more frequencies, which may be active simultaneously, or sequentially. Voltage measuring components may be functionally connected to one or more electrodes.
  • the impedance measuring subsystem may utilize non-sinusoidal current, such as narrow current pulses.
  • the system may integrate additional sensors, such as accelerometers, moisture and acoustics sensors, capnography or oximetry sensors.
  • the pad contact quality is monitored and a warning is produced when the pad contact is broken or too poor quality for the electronics to compensate.
  • Signal source 205 may also comprise a current monitor 220 to calculate impedance.
  • signal source 205 also comprises a patient simulator 225.
  • Patient simulator 225 can simulate changes in the impedance with parameters similar to a real patient. Patient simulator 225 can be used for testing system 200 as well as calibration of the circuitry.
  • the signal from signal source 205 passes through patient 230 and is received by sensor 235.
  • sensor 230 comprises an input amplifier 240.
  • Input amplifier 240 suppresses the effect of poor or variable pad contact on measurement.
  • the gain of input amplifier 240 is preferably controlled by the MP 275 to provide an enhanced signal to the other modules.
  • Sensor 230 preferably also comprises a signal filter 245 to remove interference from the power grid, etc.
  • Signal filter 245 may be a standard high-pass filter (as on Figure 30), a demodulator (as on Figure 31), or another signal filter. Synchronous demodulators are often used for detecting bio-impedance changes and stripping out motion artifacts in the signal.
  • the signal is split into two paths (as on Figure 32).
  • the first path demodulates the measured signal using the generator signal as a carrier.
  • the second path uses a 90-degree phase rotating circuitry before demodulation.
  • demodulated signals can be converted into RMS values using voltage-to-RMS converters. Measured separately, the signals are summed and then the square root is calculated. This allows for compensation for any phase shift in the subject and for separate measurements of resistance and reactance, which provides valuable information for motion artifact compensation as well as hydration levels, fat percentages, and calibration coefficient calculations.
  • sensor 230 may comprise an analog divider 250, which divides the measured voltage signal by the signal from the current monitoring circuit to calculate impedance.
  • Sensor 230 preferably also comprises a precision rectifier or root mean square to direct current (RMS-to-DC) chip 255 with a low pass filter to remove the carrier frequency.
  • the output of sensor 230 is preferably a DC signal proportional to the patient's impedance.
  • Sensor 230 may also comprise a band-pass filter 260 to select only the respiratory rates by filtering out the portion of the signal not corresponding to the respiration. Band-pass filter 260 may be calibrated manually or automatically by the MP 275.
  • sensor 230 comprises a multiplexor 265 controlled by the MP 275 to accommodate multiple probe pairs. Preferably there are 2 probe pairs, however more or fewer probe pairs are
  • Sensor 230 may also comprise an output amplifier 270.
  • Output amplifier 270 is preferably controlled by the MP 275 and provides a signal to an analog-to-digital converter (ADC) 280 for high precision digitization. Oversampling is used to reduce measurement noise which may originate from different sources (e.g., thermal, electronic, biological, or EM interference).
  • ADC analog-to-digital converter
  • MP 275 commands ADC to take measurements with as high a cadence as possible and then averages the obtained data over the time intervals corresponding to the sampling frequency.
  • the sampling frequency is the frequency of the impedance sampling as it is presented to the computer by the impedance measuring device. The frequency is preferably set sufficiently high to monitor all the minute features of respiration.
  • controllable gains and oversampling preferably allows the system to measure the impedance with extremely high effective precision (estimated 28-bit for current implementation, or 4 parts per billion).
  • MP 275 Both signal source 205 and sensor 230 are controlled by MP 275.
  • MP 275 preferably comprises at least one ADC 280 monitoring the signal processing, and at least one digital output 285 to control the digital potentiometers, multiplexors, op-amps, signal generator, and other devices.
  • MP 275 and a computer interface e.g., via a USB interface, a serial interface, or a wireless interface.
  • the MP computes values for respiratory rate (RR), tidal volume (TV) and minute ventilation (MV) as well as, tracks the trends in computed RR, TV, or MV values and performs statistical, factor, or fractal analysis on trends in real-time.
  • the MP may tracks instantaneous and cumulative deviations from predicted adequate values for RR, TV, or MV and computes a respiratory sufficiency index (RSI).
  • RSI respiratory sufficiency index
  • the device has the capability to measure and record other parameters including but not limited to: cardiac output, end tidal C02, oxygen perfusion, ECG and other electrophysologic measurements of the heart.
  • the impedance measuring device measures impedance cardiography and impedance
  • the additional parameters are displayed onscreen.
  • the respiratory impedance data are combined with the additional parameters in a meaningful way to act as an adjunct to diagnosis.
  • the impedance data alone, or combined with one or more additional parameters are used to provide a diagnosis of a disease state.
  • measurements are taken from each side of the chest
  • rib fractures where there can be changes attributed to damage including pulmonary contusion, decrease in motion due to splinting or pneumothorax where both sides of the chest are monitored independently to provide side specific data.
  • Other sources of localized pulmonary pathology can be evaluated including pneumonia, hydrothorax, chylothorax, hemothorax, hemo/pneumo thorax, atelectasis, tumor, and radiation injury.
  • information from the device is used with information from an echocardiogram, radionuclide study or other method of imaging the heart.
  • the device assists in the diagnosis of myocardial ischemia with one of the following: ekg, advanced electrophysiologic studies, cardiac catheterization,
  • the device provides information that is used to help with collection of other signals that vary with respiration such as respiratory sounds, cardiac information, radiation detection devices, radiation therapy devices, ablation devices.
  • the device can assist with the timing or data collection by another modality and/or using characteristics of the respiratory curve to correct data that is collected.
  • the device provides information about breath-to-breath variability or respiratory complexity to be used in conjunction with cardiac beat to beat variability or complexity to provide otherwise unavailable information about cardiac, pulmonary systems, or overall metabolic or neurologic status.
  • the proposed respiratory parameters evaluation technique relies on a highly linear relation between the parameters and measured impedance. It is not true for every electrode placement. Extensive research was conducted to select best electrode placement which preferably satisfies following conditions:
  • electrodes are attached horizontally to the mid-axillary line at the level of the sixth rib.
  • one electrode is placed at a stable location, such as immediately below the clavicle or at the sternal notch, and another electrode is place at the bottom of the ribcage or at the level of the xiphoid at the midaxillary line.
  • the electrodes can be placed higher or lower on the thorax.
  • electrodes may be placed in other locations and configurations (e.g.
  • At least one impedance measuring element is present on one or more electrode leads.
  • two or more electrodes are arranged in a linear array, grid-like pattern, or in an anatomically influenced configuration.
  • four remote probes are arranged in a linear array.
  • multiple electrode leads are arranged as a net, vest, or array.
  • the one or more probes, electrode leads or sensors are placed on the thorax or abdomen of the subject.
  • the device uses single use electrodes.
  • the electrodes are hydrogel, hydrocoUoids, or solid gels.
  • the electrode utilizes AgCl, nickel, or carbon sensors.
  • the electrodes come with soft cloth, foam, microporous tape, clear tape backing or another adhesive.
  • different, size appropriate electrodes exist for adults and neonates, with the adult electrodes larger than the neonatal ones, which are preferably 1" by 3/8" or less (2.54 cm by 0.95 cm or less).
  • sensor electrodes are the same as the probes that deliver electrical impulses to the body, or are different from the delivery electrodes, or are wireless and transmit data to a remote sensor.
  • the delivery probes are themselves sensors.
  • the stimulating electrode is battery powered.
  • the at least one respiratory parameter is recorded for a duration of 30 seconds, continuously, intermittently, for up to at least 3, 5, 10, 20, or 50 of the subject's breaths, for up to at least 100 of the subject's breaths, for up to at least 1000 of the subject's breaths, or for another duration.
  • the subject's impedance cardiogram is simultaneously recorded.
  • the at least one impedance measuring element comprises one or more remote probes or electrode leads, or leads similar to standard EKG leads or similar to the leads used for measuring cardiac impedance, and wherein the programmable element is further programmed to analyze one or more remote probe or electrode lead data sets collected from the one or more remote probes or electrode leads.
  • the impedance measurement subsystem reads impedance from multiple channels.
  • a secondary voltage sensing channel is arranged at an angle to a primary voltage sensing channel.
  • the two channels share current generating electrodes.
  • the two channels also share one of the voltage sensing electrodes. Data from the two or more channels may be used in an adaptive algorithm to determine and suppress noise from motion.
  • Lead configuration is critical for the performance of the device in any embodiment.
  • one or more leads are placed on the thorax.
  • leads are placed on the thorax and abdomen to measure breathing from different regions of the body such as the thorax or the abdomen. Differences in the location of body motion associated with breathing produces information that is useful clinically for diagnosis of physiologic state and monitoring of disease and can be compensated for in calculations.
  • Leads are placed on the thorax, neck and head in alternate configurations.
  • leads are placed in different configurations based on anatomic locations and spaced either according to specific measured distances or anatomic landmarks or a combination of both.
  • modifications of the spacing relative to body size are implemented. Preferably these modifications are related to anatomic landmarks.
  • the spacings remain relatively the same for patients of all sizes from neonates to obese patients, ranging from 250g to 400kg.
  • the spacings vary based on an algorithm reflecting body size and habitus.
  • Other configurations have the advantage of determining differential motion of one hemithorax vs. the other which is useful in diagnosing or monitoring unilateral or asymmetric pathology such as pneumothorax, hemothorax, empyema, cancer.
  • PLR Posterior Left to Right
  • the first electrode 7 is placed 6 inches to the left of the spine at the level of the xiphoid process
  • the second electrode 8 is placed 2 inches to the left of the spine at the level of the xiphoid process
  • the third electrode 9 is placed 2 inches to the right of the spine at the level of the xiphoid process
  • the fourth electrode 10 is placed six inches to the right of the spine level with the xiphoid process.
  • FIG. 3 there is shown the second specific electrode configuration called Posterior Vertical Right (PVR), in which the first electrode 11 is placed midway between the midaxillary line and the spine just beneath the scapula, the second electrode 12 is placed two inches beneath electrode 1, the third 13 electrode is placed two inches beneath electrode 2, and the fourth electrode 14 is placed beneath electrode 3.
  • PVR Posterior Vertical Right
  • FIG. 4 there is shown the third specific electrode configuration called Anterior to Posterior (AP), in which the first electrode 15 is placed 6 inches to the right of the right midaxillary line at the level of the xiphoid process, the second electrode 16 is placed 2 inches to the right of the right midaxillary line at the level of the xiphoid process, the third electrode 17 is placed 2 inches to the left of the right midaxillary line at the level of the xiphoid process, and the fourth electrode 18 is placed 2 inches to the left of the right midaxillary line at the level of the xiphoid process.
  • This position captures the most volume change, which is useful for determination of localization of breathing.
  • AVR Anterior Vertical Right
  • the first electrode 19 is placed immediately beneath the clavicle midway between the xiphoid and midaxillary line
  • the third electrode 20 is placed at the level of the xiphoid in line with the first electrode
  • the second electrode 21 is placed 4 inches above the third electrode
  • the fourth electrode 22 is placed 4 inches below the third electrode.
  • AVR Anterior Vertical Right
  • Other four-probe positions are placed vertically and horizontally on the abdomen and thorax, equidistant from each other or at specifically measured distances. Probe positions are also placed at physiological landmarks such as the iliac crest or third intercostal space. Probe placement on both the abdomen and thorax allows the relationship between chest and abdominal breathing to be determined. This relationship assists in diagnosis and monitoring of therapeutics.
  • these configurations can be modified to include more probes by adding probes equidistant between the positions, for example, by adding electrodes in between electrodes 1 and 2, 2 and 3, 3 and 4 in the AP configuration two inches from each electrode in line with the placement. With a large number of electrodes, they can be placed in a grid pattern equidistant from each other; this configuration will be further discussed below.
  • Other placements for 2 or more leads include around the thorax at equidistant points at a constant height such as the xiphoid process.
  • the specific placement for the 24 lead system is within a linear array with 12 leads equally spaced in a linear on the chest and back respectively.
  • Such a grid or array can be implemented within a net or vest to be worn by the patient.
  • the device provides a table describing lead placement alternatives and provides a measurement device to assist in probe placement.
  • measured distances between leads are confirmed automatically by the leads which have positioning sensors and/or sensors which can determine distance from one sensor to another sensor or sensors.
  • each lead is connected to several different electrodes by means of a multiplexer.
  • the advantage of this configuration is that it allows the device to digitally switch the electronic inputs and outputs of the DAS and effectively switch the electrode configuration in order to gather data on impedance in several directions nearly simultaneously.
  • a 12-electrode system is comprised of four different sets of leads, with the first set going to the corresponding first electrode in each configuration, the second set of leads going to the corresponding second electrode in each configuration, and so forth.
  • Electrode configurations are also made to correspond with anatomic positions on the thorax, abdomen, and limbs, such as a resting ICG position shown in Figure 7 where the first electrode 27 is place on the forehead, the second 28 above the left clavicle, the third 29 on the midaxillary line level with the xiphoid, and the fourth 30 on the midaxillary line immediately above the iliac crest.
  • Each electrode configuration will be affected by motion in different ways. For instance, movement of the right arm will cause a motion artifact on any lead placement which traces impedance across the right pectoral, latissimus, trapezius muscles, and other muscles of the chest and upper back. By noting differences between the shapes, derivatives or magnitudes of simultaneously recorded signals from different lead placements, local motion artifacts can be identified and subtracted from the impedance signal.
  • the probes are manufactured in a linear strip with a delivery and sensor pair at each end and having a fixed distance between the delivery and sensor electrode to form a discrete pad.
  • there is a compliant strip in- between the two pads that can be stretched to permit appropriate patient specific positioning based on anatomic landmarks.
  • the material, once stretched, will maintain its extended configuration.
  • the one or more remote probes which are embodied as surface electrodes, speakers and/or microphones, are integrated into a vest 46 connected to an impedance plethysmograph 47 using a cable.
  • the advantage of this embodiment is that the position of leads is determined by the manufacturer of the vest, and thus they are standardized. That is, the use of the vest eliminates operator error with respect to lead configuration.
  • the probes and actuators are wireless.
  • the vest also includes leads that cover the abdomen.
  • FIG. 24 there is shown an embodiment of the device in which the one or more remote probes are integrated into an array 48 where the electrodes are connected by a compliant piece of cloth or netting which is be pressed gently onto the patient's skin.
  • the benefit of this configuration is that the inter-electrode distance is standardized by the array manufacturer, thus lessening operator dependent error with respect to electrode configuration.
  • FIG. 25 there is shown an embodiment of the device in which the one or more remote probes are connected to each other by strings, forming a net 49 which can be applied to the patient's skin quickly and effectively.
  • the benefit of said embodiment is that the inter-electrode distance as well as the relative positions of electrodes to one another are standardized, thus lessening the effects of operator dependent error.
  • elastic stretch of the strings provides probe adjustment for different body habitus.
  • the stretch material would provide a measurement of the distance either to be read on the material or by relaying information relative to stretch to the device.
  • the strings would have attached displacement sensors such as linear
  • the programmable element is further programmed to account for changes in lead placement relayed to it from the displacement sensors.
  • FIG. 26 there is shown an embodiment of the device in which the one or more remote probes are functionally connected to a remote transmitter 50, and in which the programmable element 51 is connected to a remote receiver.
  • the communication protocols proposed for the system range from a limited scope to a vastly networked system of several nodes. This provides a foundation for an unlimited number of use cases.
  • a close range high frequency system such as Bluetooth v4.0 is used. This emulates a wireless solution of what a RS-232 wired connection would provide. This enables the communication of two devices in close range quickly and securely.
  • a roughly 802.11 compliant protocol is used to generate a mesh network comprised of the nearest devices.
  • This mesh network incorporates all of the devices in a given unit.
  • the unit size is without bound since the addition of individual nodes increases the range (range and unit size are directly proportional since the network is comprised and governed by the nodes themselves - no underlying infrastructure is required). Only a vast outlier is left out of this network. This means that in order for the outlier to be omitted the nearest currently connected node must be unequivocally out of range for the outlier to communicate with.
  • These services specifically the hardware, are capable of running / polling without the usage of a main CPU (minimizes battery usage). This is useful because when a device is not being read it can just act as a relay node. The nature of the system minimizes power requirements (increasing longevity of service), supports asymmetric links / paths, and enables each node to play multiple roles in order to benefit the network.
  • Another embodiment requires connection to a LAN or WAN network, the remote procedure is catalyzed by a user-driven event (button press, etc). This generates a unique identifier, for a digital receipt of the data transaction, on each phone coupled with device specific information. This information is supplemented with a GPS location to distinguish the devices locations. Since the data transmission was initiated by both parties at a precise time, coupled with GPS information, the system is capable of securely identifying both parties by location, UID, and device identifier. All methods are secured with anonymity heuristics and encryption. This will prevent snooping of data, a problem presented by a "man-in-the-middle" attack.
  • the device utilizes one or more electrical probes implanted in the body.
  • the implanted probes are connected to a cardiac pacemaker.
  • the implanted probes are connected to an internal automated defibrillator.
  • the implanted probes are connected to a phrenic nerve stimulator.
  • the implanted probes are connected to a delivery pump for pain medication, local anesthesia, baclofen, or other medication.
  • the implanted probes are connected to another implanted electronic device. Preferably the connections are wireless.
  • electrode configuration XidMar is show.
  • Configuration XidMar is a two channel configuration with electrode 1 on the xiphoid process and electrode 4 on the right midaxillary line, horizontally aligned with electrode 1.
  • Electrode 2a is 1 inch to the left of electrode 1, while electrode 3 a is 1 inch to the right of electrode 4. Electrodes 2a and 3a are used to record the voltage signal on channel a.
  • Channel b is recorded using electrodes 2b and 3b which are found 1 inch below the corresponding channel a electrodes.
  • Figure 34 shows the StnMar electrode configuration in which electrode 1 is located just below the sternal notch and electrode 4 is located on the right midaxillary line, horizontally aligned with the xiphoid process. Electrode 2a is located 1 inch below electrode 1, and electrode 3 a is located 1 inch to the right of electrode 4. Channel b is at an angle approximately 45 degrees to channel a. Electrode 2b is located on the xiphoid process and electrode 3b is located 1 inch below electrode 3a.
  • Figure 35 shows the StnlMar electrode location in which electrode 1 is located just below the sternal notch and electrode 4 is located on the inferior right midaxillary line at the bottom of the rib cage. Electrode 2a is located 1 inch below electrode 1, and 3a is located 1 inch to the right of 4. Electrode 2b is located on the xiphoid process and electrode 3b is located 1 inch below electrode 3a.
  • Figure 36 shows the McrMar electrode configuration in which electrode 1 is located on the right midclavicular line just below the clavicle and electrode 4 is located on the right midaxillary line horizontally aligned with the xiphoid process. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Electrode 2b is located on the xiphoid process, and electrode 3b is located 1 inch below electrode 3 a.
  • Figure 37 shows the McrlMar electrode configuration in which electrode 1 is located on the right midclavicular line just below the clavicle and electrode 4 is located on the inferior midaxillary line approximately at the bottom of the ribcage. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Electrode 2b is located on the xiphoid process and electrode 3b is located 1 inch below electrode 3 a.
  • Figure 38 shows the MclMar electrode configuration in which electrode 1 is located on the left mixclavicular line just below the clavicle and electrode 4 is located on the right midaxillary line, horizontally aligned with the xiphoid process. Electrode 2a is located 1 inch below electrode 1 and electrode 3a is located 1 inch to the right of electrode 4. Electrode 2b is located on the xiphoid process and electrode 3b is located 1 inch below electrode 3 a.
  • the electrode configurations shown in Figures 34-38 can utilize either channel a, channel b, or both simultaneously to measure data.
  • the system is adapted to perform an impedance tomography scan utilizing a one or more pairs of source electrodes and one or more voltage sensing electrodes.
  • the scan is completed by taking a series of
  • the movable electrode forms a voltage measuring pair for impedance reading with at least one other electrode.
  • the movable electrode may be coated in hydrogel which may be applied multiple times.
  • the electrode contains a hydrogel dispenser for each application.
  • hydrogel is stored in an internal pouch or syringe and there are devices, such as a mechanical button or squeeze tube, which allows the user to dispense hydrogel onto the electrode.
  • the system directs the user to sweep the movable electrode between predetermined points on the body as indicated on the user interface or on a reference card.
  • the user may place the movable electrode from point to point and the system senses the location of the electrode using a camera, sonar, radar or other device.
  • the secure adhesion of electrodes is determines the quality of impedance readings.
  • the system detects the quality of adhesion and reports an index of adhesion to the user. In another embodiment, the system reports problems with adhesion if the index crosses a specific threshold.
  • Electrode C is not placed in a straight line with the other pairs of electrodes.
  • impedance is measured on channels B-C and B-D.
  • the ratio between the impedance on the two channels Z B C and Z BD is used to determine the index of adhesion quality.
  • the current driven through electrodes A and E is measured. The current measurement or variability in the current measurement can be used to determine the index of adhesion for electrodes A and E.
  • the system compensates for the capacitance of cables, leads or other electrical connection between the impedance measuring subsystem and the patient-connected electrodes. In one embodiment, this is accomplished by an inductor within the impedance measuring subsystem. In another embodiment, a compensating inductor is integrated into a patient cable or leads which connect the impedance measuring subsystem to the patient-connected electrode pads. In another embodiment a compensating inductor is embedded into an integrated electrode PadSet. In another embodiment, a modification of a Howland circuit which consists of capacitors Ci and C 2 with values chosen to compensate for parasitic capacitances Cc is used (see Figure 39).
  • the impedance measurement subsystem should to be able to determine small variations in patient impedances on top of a relatively high baseline background with a high resolution. Therefore, there are stringent requirements on the absolute and relative impedance measurement errors.
  • the electronic design can be based on high precision/low temperature drift electronic components; (2) a high precision analog divider can be used to obtain the ratio between measured voltage and monitored source current, compensating for variations in the source current; (3) the same voltage can be used for source current generation and as an ADC reference, compensating for variations in the reference voltage; (4) external calibrated impedance standards can be used to calibrate and verify the impedance measurement subsystem performance.
  • the calibrated system is preferably connected to the impedance standard with the same trunk cables used for patient
  • the impedance measuring subsystem can have a built-in calibrated impedance standard, allowing on-site verification and recalibration.
  • built-in standard is attached to the system via an external service port. The calibration is conducted by connecting the
  • the calibration can be compleated by varying impedance of the built-in standard over the whole range of the measured patient impedances to derive a device model, which can be used during patient measurements to achieve high-precision results.
  • the temperature model of the device can be derived by placing the device into a thermostat and measuring drift in the measured value as a function of internal device temperature. The internal device temperature can be monitored via a built-in thermal sensor. During patient measurement, a measurement correction is calculated using the thermal sensor's reading and applied to the measured values.
  • the device comprises at least one speaker and at least one microphone.
  • the at least one speaker and microphone are arranged as a net, vest, or array.
  • the at least one speaker switches between discrete frequencies or broadcasts broad spectrum noise.
  • numerous speakers are active simultaneously, broadcasting different acoustic signals.
  • numerous microphones are active simultaneously and record the measured acoustic properties of the thorax which can be correlated to lung volume as well pathologies of the lungs.
  • the microphones also record sounds that originate in the lungs such as wheezing, squawks, and crackles, which can be indicators of numerous chronic and acute pulmonary diseases.
  • the lung sounds are recorded and identified as they are modified by the active signal.
  • an algorithm analyzes the number and position of wheezes, squawks, and crackles to predict asthma and other pulmonary diseases.
  • acoustic data are combined with impedance data to help time the acoustic measurements relative to the respiratory cycle.
  • acoustic data are combined with impedance data for the purposes of diagnosis or monitoring of disease.
  • An example of this is congestive heart failure where stiffness creates characteristic changes in impedance curves and there are also changes in lung sounds associated with congestive heart failure. Combination of the data provides additional information.
  • a speaker 38 is attached to the chest of a patient, and insulated with sound dampening foam 39.
  • a microphone 40 is attached to the patient's back and is insulated with sound dampening foam.
  • Both the speaker and the microphone are functionally connected to a programmable element 41, for example a computer with installed analysis software such as MATLAB.
  • the output element provides data relating to the patient's respiration to the operator in real time.
  • the speaker generates an acoustic signal which is recorded by the microphone. Signal generation and recording are timed and synchronized by the programmable element.
  • Analysis software uses features of the recorded sound wave to evaluate the acoustic properties of the thorax, which can be used to estimate lung volume.
  • Said signal features include but are not limited to: frequency-dependent phase shift, and amplitude attenuation.
  • the speaker switches between discrete frequencies of sound or generates broad spectrum white noise.
  • the microphone is also used to detect sounds which originate within the lungs such as crackles, squawks and wheezes.
  • the programmable element of the device will employ software algorithms to detect associate acoustic patterns and inform physicians.
  • the acoustic system will interface with an impedance based system as well.
  • an array of microphones 42 is used to record transmitted sound from different regions of the thorax.
  • microphones record simultaneously.
  • the programmable element 43 selects the microphone with the best signal to noise ratio for analysis.
  • the programmable element combines the data from different channels in order to maximize the accuracy of lung volume estimates and localize pathologies of the lungs including tumor formation, bleeding, and tissue degradation.
  • the programmable element 45 controls each of the speakers individually, and switches between speakers to allow the device to measure acoustic properties of the thorax in many different directions.
  • the programmable element will activate each speaker simultaneously with signals of unique frequencies so that the signal from each speaker can be separated in the recorded signals.
  • the programmable element combines the data from different channels in order to maximize the accuracy of lung volume estimates and localize pathologies of the lungs including tumor formation, bleeding, and tissue degradation.
  • the device software maintains a user-friendly GUI (Graphical User Interface).
  • GUI Graphic User Interface
  • the GUI contains a color coding system to aid operators in quickly making diagnoses and decisions for patient care.
  • the GUI presents a numerical RVM measurement.
  • the GUI presents a respiratory sufficiency index (RSI).
  • RSI respiratory sufficiency index
  • the GUI presents a respiratory waveform.
  • patient data is preferably recorded by the user prior to testing.
  • the user is prompted to enter patient data.
  • the data recorded includes any or all of the following: patient height, weight, chest circumference during maximum inspiration, chest circumference during normal end-expiration, age, gender, ethnicity, and smoking history.
  • posture when testing is also input into the device within the programmable GUI. Variations in posture may lead to different breathing patterns and tidal volumes.
  • the device accepts posture inputs such as supine and seated and standing. The ability to test patients in multiple postures is helpful with noncompliant patients such as neonates or obtunded patients.
  • the device calculates BMI.
  • an algorithm in the device or on a look up table calculates a "calibration coefficient" that corrects for patient size and body habitus to provide a universal calibration to deliver an absolute measurement.
  • the calibration coefficient may be obtained by combining patient information with the data recorded off the probes applied. Preferably, the physical location of the probes is also entered.
  • the calibration algorithm may validate the data and their consistency with the patient information entered, and may suggest combination of the input parameters that is most consistent with the data recorded, as well as a suggestion for the operator to re-check the patient's information.
  • the calibration algorithm may suggest and/or perform re-adjustment based on signal pattern recorded off probes, and/or provided by an operator as normal or abnormal.
  • the device calculates BSA or another index of body shape or size.
  • the system displays predictive values for patient results based on the aforementioned patient data.
  • the device also provides a percentage comparison against these values within displayed results to further inform the clinician of patient parameters or condition based on standard tables of spirometric data created by Knudsen, Crapo, or others.
  • the patient's demographics and/or body measurements are entered and the device suggests the lead configuration and/or the spacing of the leads and/or the size or characteristics of the lead for that patient.
  • the device assesses signal variation and adjusts display parameters, calibration parameters and or intermediate calculations in response to the variation. In one embodiment the device assesses variation in one or more features of the signal including baseline, mean, minimum, maximum, dynamic range, amplitude, rate, depth, or second or third order derivatives of any items in the list.
  • the device calculates a calibration coefficient to convert a raw or processed impedance trace to a respiratory volume trace.
  • the calibration coefficient is calculated from a range of physiological and demographic parameters.
  • the device of the invention automatically adjusts the calibration coefficient in response to variation in the parameters.
  • the device automatically adjusts the calibration coefficient in response to one or more of: respiratory rate, baseline impedance, or mean impedance.
  • the device includes one or more of, respiratory rate, baseline impedance, or mean impedance in the calculation of the coefficient, or a correction factor for the calibration coefficient.
  • the calibration coefficient is based on a time-variable parameter, such as respiratory rate, baseline impedance or mean impedance, the device automatically adjusts the calibration coefficient to account for the variation in the parameter.
  • the device adjusts the calibration coefficient based on the assessment of variation in the signal. In one embodiment where the calibration coefficient is used to convert a raw impedance signal to a respiratory volume trace, the calibration coefficient is based partially on respiratory rate.
  • the device adjusts the display of a dataset in response to variation in the dataset.
  • the dataset is made up of a raw signal from a sensor, a processed signal from a sensor, or the calculated metrics or parameters.
  • the device adjusts the minimum of the y-axis on a displayed chart in response to variation in the dataset.
  • the minimum of the y-axis on a displayed chart is equal to the minimum of the dataset.
  • the minimum of the y-axis on the displayed chart is equal to the minimum of the dataset within a specific window.
  • the window over which the relevant minimum of the dataset is calculated is the same as the window over which the data is displayed.
  • the minimum of the y-axis on the displayed chart is equal to the minimum of the dataset within the display window minus a coefficient or percentage of the minimum value.
  • the device adjusts the range of the y-axis of the displayed dataset is to account for variation in the dataset.
  • the range of the y-axis of a displayed dataset is equal to the dynamic range of the dataset.
  • the range of the y-axis of the displayed dataset is equal to the dynamic range of the dataset within a specific window.
  • the y-axis of the displayed dataset is equal to the dynamic range of the dataset within a specific window, plus a constant, or a percentage of the dynamic range.
  • the device adjusts the range of the y-axis of a displayed dataset based on statistics of a feature of the dataset. In one embodiment, the device sets the range of the y-axis to be equal to the mean amplitude of the signal plus the standard deviation of the amplitude of the signal within a specified window multiplied by a coefficient. In one embodiment, the device adjusts the range of the y-axis of a displayed dataset to be equal to the mean amplitude of the signal plus the variance of the amplitude of the signal within a specified window multiplied by a coefficient. In one embodiment, the device calculates the amplitude of respirations in the dataset.
  • the device then removes outliers at the high end, low end or which have features which appear unrelated to the intended measured parameter.
  • the device then adjusts the range of the y-axis to be equal to the mean of the amplitude of the dataset plus the standard deviation of the dataset multiplied by a coefficient.
  • the device automatically adjusts the midpoint of the y-axis of a chart of a dataset in response to variation in the dataset. In one embodiment, the device sets the y-axis to be equal to the mean of the dataset within a specific window. In another embodiment, the device sets the y-axis to the equal to the median of the dataset within a specific window. In one embodiment, the device sets the midpoint of the y-axis to the result of a function of the statistics of the dataset.
  • the calibration coefficient is calculated in a novel way.
  • the preferred embodiment is a novel way.
  • the device contains circuitry and software that automatically calibrates the device.
  • calibration is aided by data acquired through bioelectrical impedance analysis, a process which measures tissue impedance on one or more channels at various frequencies.
  • data from bioelectrical impedance analysis may be used to calculate certain characteristics of the subject including, but not limited to, hydration level, baseline impedance and body composition.
  • a low level of hydration causes the electrical impedance of the body to be greater.
  • a high level of fat in the body would also cause an increase in the average electrical impedance of the body, but likely a decrease in overall impedance as electricity passes through the path of least resistance.
  • Muscle is much more vascular than fat and contains more conductive electrolytes, so a muscular patient's body would have much lower electrical impedance than a similarly size person who was not very muscular. Scaling the calibration factor based on these inputs makes it more accurate.
  • Calibration of the device of the invention preferably comprises predictions for respiratory rate, tidal volume and minute ventilation based on the metabolic requirements of body tissue. Predictions preferably involve multiplying the patient's measured body weight, or ideal body weight by a volume of air, or volume of air per minute required by a unit of body weight.
  • the ideal body weight is determined from a patient's height, race, and/or age and may further be determined with one or more of the Devine, Robinson, Hamwi, and Miller formulas.
  • the calibration coefficient is calculated from a patient's demographic information, including but not limited to: sex, age, and race.
  • the calibration coefficient is calculated from a patient' s physiological measurements including but not limited to body type, height, weight, chest circumference measured at different points of the respiratory cycle, body fat percent, body surface area, and body mass index.
  • the calibration coefficient is calculated based on the measured value of the ECG signal recorded at different points.
  • the ECG is recorded by electrodes at various locations on the thorax and abdomen.
  • the differential voltage recordings at different electrodes are used to calculate the average baseline impedance and estimate the resistivity of the patient's thorax in various directions.
  • the calibration coefficient is calculated based on the patient's baseline impedance to an external current source as measured between electrodes in a bipolar configuration, tetrapolar configuration or other configuration comprising 2 or more leads. The locations of these electrodes are placed in a range of configurations over the whole body.
  • demographic characteristics are combined with baseline impedance measurements for calibration.
  • anatomic information is combined with baseline impedance measurements for calibration.
  • known volumes recorded on a spirometer or ventilator are combined with demographic information and baseline impedance.
  • the system may simultaneously measure impedance and volume (using a spirometer, ventilator, or other similar device). The system then computes a specific transformation between impedance and volume as an input to the conversion algorithm
  • a dynamic calibration based on additional parameters obtained using the impedance measuring subsystem and consisting of overall patient impedance (including skin and fat layer impedances), internal organs impedance (baseline impedance) and its variations, and the shape of the respiratory curve is implemented.
  • calibration is recalculated with the recording of each sample.
  • the device is regularly recalibrated based on a timer function.
  • the device is recalibrated whenever the baseline impedance varies from the baseline by a certain threshold such as 10%.
  • the device is recalibrated whenever tidal volume or minute volume varies from baseline levels or predicted levels by a certain threshold, such as 20%, where predicted values are calculated using the formulas published by Krappo, Knudson, and others.
  • Ongoing or intermittent checks of calibration may be undertaken. Preferably this involves an internal check to internal phantom.
  • ongoing or intermittent checks of baseline impedance are be used to recalibrate or reaffirm calibration.
  • ongoing or intermittent readings from each hemithorax individually or in combination are used to recalibrate or provide data for recalibration.
  • recalibration is performed automatically or by alerting a caregiver of required modification or requiring additional steps to be taken by the caregiver, such as recalibrating with a ventilator or spirometer.
  • calibration is done through measurement electrode pairs. In another embodiment, calibration is done through additional electrodes. In another embodiment, calibration is done all or in part by repurposing measurement electrodes and using the sensor as the delivery electrodes and the delivery electrodes as the sensor electrodes.
  • the calibration electrodes are placed in specific locations and/or at specific distances apart on the abdomen and thorax. In another embodiment, one or more of the leads are placed a specified distance apart on the forehead. In another embodiment of the device, the magnitude of the ICG signal across an acceptable electrode configuration with or without an estimation of the heart volume is used to determine the baseline impedance and calibrate the RVM data to respiratory volume. Preferably the calibration coefficient is calculated using a combination of the 5 previously mentioned methods. Universal Calibration
  • the "scaling factor" varies between patients by about an order of magnitude. In a preferred embodiment, this factor can be determined precisely by preliminary calibration with a spirometer or ventilator data or other data set. In a preferred embodiment, the RMV device is used for measurement of respiratory parameters without preliminary calibration. Preferably, a reliable procedure of deducing this factor from measurable patient physiological parameters is used for calibration. Such procedure allows the determination of the "scaling parameter" with sufficient precision to satisfy measurement requirements for all proposed device applications.
  • measurements of respiratory motion derived from a technology including impedance plethysmography, accelerometers placed on the body, video images, acoustic signals or other means of tracking motion of the thorax, abdomen or other body parts is calibrated or correlated with another technology that assesses respiratory status.
  • respiratory motion detection derived from impedance measurements is calibrated with spirometry.
  • respiratory motion detection is calibrated or correlated with end tidal C02 measurements.
  • respiratory motion detection is calibrated or correlated with ventilator measurements of flow and/or volume.
  • respiratory motion is calibrated with a full-body plethysmograph.
  • baseline RVM measurements of a given patient are taken in conjunction with standard spirometry measurements and a calibration coefficient for that particular patient is derived. Later in the postoperative period or otherwise, the calibration coefficients are used to obtain quantitative lung volume measurements for that patient.
  • the calibration coefficients are used to obtain quantitative lung volume measurements for that patient.
  • such calibration coefficients are combined with current baseline impedance or other physiologic measurements for ongoing or intermittent calibration.
  • preoperative measurements are used to derive a calibration coefficient which is then used, alone or in combination with other data, to obtain quantitative lung volume measurements to use in management of the patient after surgery or in other situations.
  • the calibration coefficient is derived from lung volume or flow measurements obtained on an intubated patient from measurements recorded from a mechanical ventilator.
  • the device is linked to a spirometer, ventilator or pneumotachometer to provide volume or flow calibration.
  • the device is linked to a spirometer or ventilator or pneumotachometer to provide volume calibration.
  • the operator will run the patient through a brief breathing test regimen of one or more of the following: at least one tidal breathing sample, at least one forced vital capacity (FVC) sample, at least one measurement of minute ventilation sample, and at least one maximum voluntary ventilation (MVV) sample.
  • FVC forced vital capacity
  • MVV maximum voluntary ventilation
  • the device will be calibrated based on the results of the spirometer tests relative to the impedance measurements.
  • calibration will be implemented from measurements taken during tidal breathing. In particular, for patients who are unable to comply with the procedure, a simple tidal breathing sample will be taken, which requires no coaching or compliance.
  • the tidal breathing sample is collected over 15 seconds, 30 seconds, 60 seconds, or another time frame.
  • a calibration coefficient for a given individual is calculated based on combined spirometry and RVM data and applied to deliver an absolute volume measurement for RVM measurements taken at a future time.
  • this absolute volume measurement will be validated or modified at the future time using calibration capabilities intrinsic to the hardware and current measurements derived from the device.
  • an algorithm is applied to RVM data based on patient
  • the device may be used in conjunction with ECG or ICG data to produce further calibration of impedance data by utilizing parameters derived ECG and ICG such as heart rate and SNR.
  • ECG or ICG data will help validate proper electrode placement.
  • the electrical activity of the heart is used to enhance the device calibration.
  • the device can measure the following cardiac, pulmonary and other physiology parameters and features: Heart Rate (HR), baseline impedance, impedance magnitude, Pre-ejection Period (PEP), Left Ventricular Ejection Time (LVET), Systolic Time Ration (STR), Stroke Volume (SV), Cardiac Output (CO), Cardiac Index (CI), Thoracic Fluid Content (TFC), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), Mean Central Venous Pressure (CVP), Systemic Vascular Resistance (SVR), Rate Pressure Product (RPP), Heather Index (HI), Stroke Volume Index (SVI), and Waveform Accuracy Value (WAV).
  • HR Heart Rate
  • PEP Pre-ejection Period
  • LVET Left Ventricular Ejection Time
  • STR Systolic Time Ration
  • SV Stroke Volume
  • CO Cardiac Index
  • TFC Thoracic Fluid Content
  • SBP Systolic Blood Pressure
  • DBP Diasto
  • RVM data can be used to enhance accuracy or utility of ICG data such as Heart Rate (HR), baseline impedance, impedance magnitude, Pre-ejection Period (PEP), Left Ventricular Ejection Time (LVET), Systolic Time Ration (STR), Stroke Volume (SV), Cardiac Output (CO), Cardiac Index (CI), Thoracic Fluid Content (TFC), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), Mean Central Venous Pressure (CVP), Systemic Vascular Resistance (SVR), Rate Pressure Product (RPP), Heather Index (HI), Stroke Volume Index (SVI), and Waveform Accuracy Value (WAV).
  • HR Heart Rate
  • PEP Pre-ejection Period
  • LVET Left Ventricular Ejection Time
  • STR Systolic Time Ration
  • SV Stroke Volume
  • CO Cardiac Output
  • CI Cardiac Index
  • TFC Thoracic Fluid Content
  • SBP Systolic Blood
  • FIG 8 there is shown an impedance plethysmograph 31 and a spirometer 32 both functionally connected to the same programmable element 33. Volume data from the spirometer is preferably sampled simultaneously or nearly simultaneously with the impedance reading of the impedance plethysmograph.
  • Figure 9 there is shown a patient who is connected to a ventilator 34 as well as the impedance plethysmograph 35, both functionally connected to a programmable element 36. The volume of the ventilator is sampled simultaneously with the impedance reading of the impedance plethysmograph.
  • FIG 10 there is shown a graph of volume versus impedance for a given patient undergoing various breathing maneuvers while data was simultaneously collected using the impedance plethysmograph and a spirometer.
  • the trace represented by Figure 11 with volume over time is normal breathing.
  • the trace represented by Figure 12 is slow breathing and the trace represented Figure 13 is erratic breathing.
  • the slope of the line of best fit 37 is used as the RVM calibration coefficient to compute volume from impedance.
  • an algorithm utilizing the slope, shape and/or other curve characteristics and/or other demographic or body habitus characteristics of the patient is used to calculate the calibration coefficient.
  • a simple numerical value is obtained from a ventilator or spirometer for tidal volume or minute ventilation for use in calibration of the device.
  • One embodiment is comprised of a combined system in which RVM and volume measurements are taken simultaneously, nearly simultaneously, or sequentially by means of a spirometer, pneumotachometer, ventilator or similar device and the combined data utilized to create an individual calibration coefficient for the calculation of absolute volume from RVM measurements for a given individual.
  • Electrodes were positioned at the Posterior Left to Right, Posterior Right Vertical, and Anterior-Posterior, and ICG configuration discussed above. The four probes of the impedance measurement device were connected to the electrodes that corresponded to one of the configurations above. The ICG position was connected first and only used to measure resting ICG of the subject in a supine position.
  • the leads were then reconfigured to connect to the Posterior Left to Right position.
  • the subject performed breathing tests which were measured simultaneously by the impedance measurement device and a spirometer for a sampling time of about 30 seconds.
  • the breathing tests performed were normal tidal breathing (3 runs), erratic breathing (2 runs), slow breathing (2 runs), Forced Vital Capacity (FVC) (3 runs), and Maximum Ventilatory Volume (MVV) (2 runs).
  • FVC and MVV were performed according to ATS procedures. Normal, erratic, and slow tests were measured by a bell spirometer, and FVC and MVV were measured by a turbine spirometer.
  • the calibration can be run all together on any type of spirometer that meets ATS standards. Once all breathing tests were complete, the leads were repositioned to a new configuration, and the tests were run again until all configurations had been tested.
  • the data was collected on PC for the impedance data and turbine spirometer data, and on another PC for the bell spirometer data. The data was then merged onto one PC and loaded into MATLAB. Preferably, MATLAB or other software packages that utilize signal processing are used. Preferably, the data is loaded onto a PC or other computing station. Once the data was merged, the impedance and volume data from each breathing test were matched together using a GUI-based program.
  • Correlation coefficients and calibration coefficients were produced for each of the test runs by comparing the impedance and volume traces using MATLAB. This data then was utilized in Excel to predict calibration coefficients based on patient characteristics. Preferably, the data can be imported into and analyzed in any software with a statistical package.
  • FIG 14 depicted is a graph of BMI versus the calibration coefficient for 7 patients.
  • BMI is shown on the x-axis
  • calibration coefficient is shown on the y-axis.
  • the linear relationship between height and the calibration coefficient in configuration D is indicative of its utility in determining the calibration coefficient.
  • Other physiological parameters such as height weight, body surface area, race, sex, chest circumference, inter-mammary distance, age also have important relationships with the calibration coefficient, and in one embodiment any or all of these parameters aid in accurate determination of the calibration coefficient.
  • a combination of statistical analysis and an expert system is used to determine a given patient's correlation coefficient based on the input of said physiological parameters.
  • test data from a pilot study is used to train the expert systems.
  • existing data regarding patient demographics and pulmonary function are used to train the expert system.
  • a combination of test data from a pilot study and existing pulmonary function datasets are use to train the expert system.
  • volume drift One problem that is encountered with some spirometers is volume drift, where a greater amount of air is inspired rather than expired. Additionally, prolonged spirometry testing provides increase in resistance to pulmonary flow that can alter the physiology and/or can change the respiratory flows and/or volumes. These patterns can disrupt the correlation coefficient for the test by altering the volume so that it trends downwards while the impedance trace stays constant.
  • Figure 15 shows a volume curve that exhibits volume drift.
  • Figure 16 shows a volume versus impedance curve for that set where the volume drift damages the fit of the plot. In one embodiment, the device corrects for the problem by subtracting out a line with a constant slope value.
  • volume drift subtraction is used in calibration.
  • volume drift subtraction is used in deriving the calibration coefficient. The same utility is also achieved by differentiating the volume curve to get flow, subtracting the DC offset between intervals that have the same lung volume at the start and end point, and then integrating to get flow without the drift artifact.
  • the calibration coefficient is determined by comparing the RVM data trace and calculated values compared to predicted values for the patient's tidal volume, FVC, FEVl etc. based on standard tables of spirometric data created by Knudsen, Crapo, or others known to those skilled in the art.
  • FIG. 19 there is shown a flow chart that displays the progression of data through the analysis software.
  • Raw data is recorded by the impedance meter, digitized using an analog to digital converter, and inputted to the programmable element through a standard data port.
  • Data processing strips the signal of noise and motion artifacts.
  • Analysis algorithms calculate the volume trace as well as medically relevant information including but not limited to: frequency and time domain plots of the impedance and/or calculated volume traces, respiratory rate, tidal volume, and minute ventilation.
  • the analysis algorithm to convert impedance into volume traces utilizes either calibration in conjunction with spirometer or ventilator data, or in another embodiment, calibration based on physiological parameters.
  • the algorithm produces a correlation coefficient which, when multiplied with the impedance data, converts the impedance scale into a volume scale.
  • the algorithms take variability of the above metrics into account and automatically calculate a standardized index of respiratory sufficiency (RSI).
  • This RSI contains information that integrates information from one or more measurements and/or utilizes the range of acceptable values of the following measurements individually and in combination to provide a single number related to respiratory sufficiency or insufficiency: respiratory rate, respiratory volume, respiratory curve characteristics, respiratory variability or complexity as previously prescribed.
  • one of the following methods are used in calculation of the RSI: change in patient status from previous measurement, second derivative of change in patient status from previous measurements, multivariate analysis, pattern analysis, spectral analysis, neural networks, self-teaching system for individual, self-teaching system for patient population.
  • the RSI also includes data from the following: oxygen saturation, Tcp02, TcpC02, end tidal C02, sublingual C02, heart rate, cardiac output, oncotic pressure, skin hydration, body hydration, and BMI.
  • oxygen saturation Tcp02, TcpC02, end tidal C02, sublingual C02, heart rate, cardiac output, oncotic pressure, skin hydration, body hydration, and BMI.
  • the output module which may be embodied as a printer or displayed on a screen or delivered by oral, visual, or textual messaging.
  • the device notes a pattern in the curve recorded during the inspiratory or expiratory phase of respiration. In one embodiment, the device notes a pattern in the respiratory variability in rate, volume and/or location of respiration. In one
  • the pattern is noted in the shape of the respiratory curve.
  • the pattern analysis includes the values derived from the slope of inspiration.
  • the pattern analysis includes the values derived from the slope of expiration.
  • the pattern analysis includes a combination of parameters which could include any or all of the following: respiratory rate, minute ventilation, tidal volume, slope of inspiration, slope of expiration, respiratory variability. In one embodiment, these parameters are used within the calculation of a Respiratory Health Index (RHI) that provides a standardized quantitative measure of adequacy of ventilation.
  • the RHI is coupled with alarms that sound either when respiration falls below what is deemed as adequate, or within the range that is deemed adequate, if the patient experiences a very sudden change.
  • the device provides information to calculate an RHI. Preferably the device calculates and displays the RHI. In one embodiment, the Respiratory Health Index is compared against a universal calibration based on patient characteristics. In one embodiment, the RHI provides quantitative data with the system calibrated to a specific patient.
  • phase lag between volume and impedance signals is an important issue that is addressed in one embodiment.
  • this phase difference is used as a measure of lung stiffness and airway resistance. Frequency phase analysis allows the user to find the phase angle. A larger phase offset is indicative of a high degree of airway resistance to motion.
  • Calculation of the phase angle is accomplished by comparing simultaneously recorded and synchronized RVM curves with flow, volume or pressure curves recorded by a spirometer, pneumotachometer, ventilator or similar device.
  • the phase lag between volume and impedance signals is a component of the algorithm that is used to calibrate the system to a given individual.
  • the phase lag is used to calibrate the system for a universal calibration.
  • the leading curve is shifted by the magnitude of the phase lag so as to correlate temporally with the trailing curve. This embodiment increases the accuracy of the calibration algorithm.
  • no external pressure, flow, or volume measuring device is used for calibration, a virtual phase lag is calculated based on patient characteristics, including demographic information, physiological measurements, and pulmonary function test metrics.
  • phase lag is corrected for by RVM algorithms in aligning both impedance and volume.
  • phase lag data is presented independently as a standardized index to demonstrate a measure of lung compliance and stiffness.
  • phase lag data is integrated within the Respiratory Health Index as a measure of respiratory status.
  • frequency domain analysis is applied to the RVM measurements.
  • at least one frequency domain plot such as a Fourier transform is displayed to the operator.
  • at least one 2-dimensional frequency domain image of the RVM data such as a spectrograph is displayed to the operator, where one dimension is frequency and the other is time, and the magnitude of the signal at each location is represented by color.
  • the frequency domain information is used to assess respiratory health or pathologies.
  • an alarm will alert a medical professional if the frequency domain data indicates rapid deterioration of patient health.
  • RVM measurements are used as the basis for complexity analysis.
  • complexity analysis is performed on the RVM signal alone.
  • RVM measurements are used in combination with other physiologic
  • measurements such as heart rate, urine output, EKG signal, impedance cardiogram, EEG or other brain monitoring signal.
  • RVM measurements are utilized as a component of complexity analysis in combination with data provided by a device used to treat or monitor the patient including: the ventilator measurement of the patient generated respiratory pressure, the ventilator measurement of the patient generated respiratory flow, the ventilator measurement of the patient generated respiratory volume, the ventilator measurement of the ventilator generated respiratory pressure, the ventilator measurement of the ventilator generated respiratory flow, the ventilator measurement of the ventilator generated respiratory volume an infusion pump, or other devices used to treat the patient, RVM measurements may be used to quantify breath-to-breath variability.
  • One embodiment of the device is used to define a specified point along the respiratory curve with which to calculate breath-to-breath variability in respiratory rate such as the peak of inspiration or nadir of expiration.
  • the device provides data with describing breath-to-breath variability in volume inspired. In one embodiment, the device provides data describing breath-to-breath variability or complexity in the slope or other characteristics of the respiratory volume or flow curve. In one embodiment, the device provides data with which to calculate variability or complexity associated with the location of respiratory effort, such as chest vs. abdominal or one hemithorax vs. the other, by collecting data from different locations on the body with the same or different electrode pairings. Preferably, the device calculates breath-to-breath variability or complexity of one or more of these parameters. Preferably, the device presents the variability or complexity analysis in a form that is easy to interpret by the user.
  • the device combines data from more than one source of variability or complexity among the following: respiratory rate, respiratory volume, location of respiratory effort, slope or other characteristic of the respiratory volume or flow curves, to provide an advanced assessment of respiratory function.
  • the device analyzes the variability or complexity data intermittently or continuously and presents the data at intervals such as every 10 minutes, every 30 minutes, or every hour.
  • the device presents the variability analysis in less than 10 minutes, less than 5 minutes, less than 1 minute, or in near real time.
  • the variability or complexity of any of the respiratory parameters may be quantified by linear or nonlinear analysis methods.
  • the variability or complexity of any of the respiratory parameters may be quantified by nonlinear dynamical analysis.
  • approximate entropy is used by the device for data analysis.
  • variability or complexity analysis of the data is combined with volume data to provide a combined index of respiratory function.
  • variability or complexity analysis data is combined with other parameters and presented as a Respiratory Sufficiency Index or a Respiratory Health Index.
  • RVM measurements or the complexity analysis of the RVM signal is utilized as at least a part of the information used in goal directed therapy. In a preferred embodiment, RVM measurements or the complexity analysis of the RVM signal provide information for decision support. In a preferred embodiment RVM measurements or the complexity analysis of RVM signal is utilized as at least a part of the patient data required for a controlled loop system.
  • the respiratory cycle is measured by one or more methods including but not limited to impedance pneumography, end tidal C0 2 , or pulse oximetry while the heart is imaged or otherwise measured using echocardiography which may be embodied as 2D echo, 3D echo or any other type of echocardiography.
  • Echocardiography which may be embodied as 2D echo, 3D echo or any other type of echocardiography.
  • Time series data from the echocardiogram is marked as having a certain accuracy rating based on the respiratory motion recorded by the respiratory monitor.
  • echocardiography data below an accuracy threshold is discarded.
  • echocardiography data is weighted based on its accuracy rating where the least accurate data is weighted lowest.
  • the device generates a composite image or video of the heart and cardiac motion based on the most accurate echocardiogram data.
  • echocardiography data is recorded over more than one cardiac cycle, then after analysis and accuracy rating, the best data is used for generating a composite image of the heart or video of the cardiac cycle.
  • Other embodiments include combining respiratory cycle measurement and quantification with other cardiac imaging techniques for the purpose of improving accuracy.
  • the methods of cardiac imaging may include Doppler flow measurements, radionuclide study, gated CT, and gated MRI.
  • Other embodiments include combining respiratory cycle measurement by RVM with other diagnostic or therapeutic modalities of the chest, abdomen, and other body parts, including diagnostic CT or MRI, catheter directed therapy, directed cardiac ablation, radioablation of tumor, radiation of tumor.
  • RVM and cardiac impedance data are utilized together for timing of data collection or data analysis of diagnostic imaging or anatomically directed therapy.
  • the respiratory impedance measurements or data from complexity analysis of RVM measurements are used to generate an image of the lungs.
  • data from complexity analysis of RVM measurements and cardiac impedance measurements are used to generate an image of the heart and lungs.
  • the heart and lungs are imaged
  • the device is used for generating 2D images, videos, or models of the heart and/or lungs.
  • the device generates 3D images, videos or models of the heart and/or lungs.
  • the device provides RVM data which, with our without variability or complexity analysis, is used to aid in decision making such as extubation or intubation for mechanical ventilation.
  • the device provides RVM data which, with or without variability or complexity analysis, aids in decision making regarding drug administration or other therapeutic intervention.
  • the device uses variability or complexity information alone or with volume data as part of an open or closed loop control system to adjust ventilatory settings.
  • the device uses variability or complexity information, alone or with volume data or other analysis of the respiratory curve provided by RVM, as part of an open or closed loop control system to adjust doses of medications. This embodiment is useful for premature infants to optimize the management of a pressure ventilator, and for patients with uncuffed endotrachial tubes.
  • the device uses variability or complexity information, alone or with volume data or other analysis of the respiratory curve provided by RVM, as part of a patient management system that monitors patient status, recommends medication delivery, and, then, reassesses the patient to direct further action.
  • the device uses variability or complexity analysis of the RVM signal alone, volume data alone, curve analysis alone, or any of these in combination to trigger alarms indicating change in patient status.
  • symbol- distribution entropy and bit-per-word entropy are used to measure the probability of patterns within the time series.
  • similarity of distributions methodology is used.
  • the device sounds an alarm when it detects a change in respiratory complexity or a respiratory complexity below a specified threshold or more constrained breathing patterns associated with pulmonary pathology or disease states.
  • the device sounds an alarm when it detects a change in a combined
  • EWS early warning score
  • EWS scores are tools used by hospital care teams to recognize the early signs of clinical deterioration in order to initiate early intervention and management, such as increasing nursing attention, informing the provider, or activating a rapid response or medical emergency team. These tools typically involve assigning a numeric value to several physiologic parameters (e.g., systolic blood pressure, heart rate, oxygen saturation, respiratory rate, level of consciousness, and urine output) to derive a composite score that can be used to identify a patient at risk of deterioration.
  • physiologic parameters e.g., systolic blood pressure, heart rate, oxygen saturation, respiratory rate, level of consciousness, and urine output
  • MV, TV and/or RR become part of an existing early warning scoring system, either as a replacement of or in addition to the standard respiratory rate that is currently a part of most EWS systems, for use to help prevent or predict evolving patient compromise, disease state, or distress.
  • Such compromise, disease state, or distress may be one or more of respiratory failure, sepsis, cardiac failure, congestive heart failure, renal failure, over-hydration, pulmonary edema, hyper metabolic state, overexertion, traumatic brain injury, pulmonary embolus, opioid induced respiratory depression, over sedation.
  • the device is attached to the patient using one or more sensors to obtain one or more of the patient's impedance levels (used to determine MV, TV and/or RR as described herein), MV, TV and/or RR, oxygen saturation, temperature, blood pressure, pulse or heart rate, blood oxygen levels, brain activity, blood lab tests (e.g.
  • CBC complete blood count
  • the incoming data from the sensors is collected an analyzed to output an early warning score for the patient. If the score exceeds a predetermined level, one or more alarms (audible and/or visual) may be activated. Additionally, clinicians may input information about the patient's condition, including, but not limited to alertness, voice, pain, and unresponsiveness (commonly referred to as
  • AVPU AVPU
  • minute ventilation is used instead of or in conjunction with respiration rate and in combination with other sensor data to derive the patients state and output the early warning score.
  • the sensors do not impede breathing or obstruct the patient's airways.
  • the sensors are non-invasive.
  • MV, TV and/or RR become one of the foundational pieces of a new, improved early warning scoring system upon which a predictive algorithm is based for use to help prevent or predict evolving patient compromise, disease state, or distress.
  • compromise, disease state, or distress may be one or more of respiratory failure, sepsis, cardiac failure, congestive heart failure, renal failure, overhydration, pulmonary edema, hyper metabolic state, overexertion, traumatic brain injury, pulmonary embolus, opioid induced respiratory depression, over sedation.
  • the device is attached to the patient using one or more sensors to obtain one or more of the patient's impedance levels (used to determine MV, TV and/or RR as described herein), MV, TV and/or RR, oxygen saturation, temperature, blood pressure, pulse or heart rate, blood oxygen levels, brain activity, blood lab tests (e.g. complete blood count (CBC)) or another physiological status.
  • the incoming data from the sensors is collected and analyzed to output an early warning score for the patient. If the score exceeds a predetermined level, one or more alarms (audible and/or visual) may be activated.
  • clinicians may input information about the patient's condition, including, but not limited to alertness, voice, pain, and unresponsiveness (commonly referred to as AVPU).
  • minute ventilation is used instead of or in conjunction with respiration rate and in combination with other sensor data to derive the patients state and output the early warning score.
  • the sensors do not impede breathing or obstruct the patient's airways.
  • the sensors are noninvasive.
  • the weighting of the algorithm is modified and then applied for use to help prevent or predict evolving patient compromise, disease state, or distress.
  • compromise, disease state, or distress may be one or more of respiratory failure, sepsis, cardiac failure, congestive heart failure, renal failure, over-hydration, pulmonary edema, hyper metabolic state, overexertion, traumatic brain injury, pulmonary embolus, opioid induced respiratory depression, over sedation.
  • the device is attached to the patient using one or more sensors to obtain one or more of the patient's impedance levels (used to determine MV, TV and/or RR as described herein), MV, TV and/or RR, oxygen saturation, temperature, blood pressure, pulse or heart rate, blood oxygen levels, brain activity, blood lab tests (e.g. complete blood count (CBC)) or another physiological status.
  • the incoming data from the sensors is collected and analyzed to output an early warning score for the patient. If the score exceeds a
  • one or more alarms may be activated.
  • clinicians may input information about the patient' s condition, including, but not limited to alertness, voice, pain, and unresponsiveness (commonly referred to as AVPU).
  • minute ventilation is used instead of or in conjunction with respiration rate and in combination with other sensor data to derive the patients state and output the early warning score.
  • the sensors do not impede breathing or obstruct the patient's airways.
  • the sensors are non-invasive.
  • the device provides information for the early detection of sepsis or other infections, by using MV, TV, and/or RR measurements in conjunction with other factors, like temperature, blood pressure, pulse rate, blood work (e.g. CBC), etc. but reduces the emphasis commonly associated with temperature and pulse rate while increasing the emphasis on other (e.g. respiratory) parameters (e.g. MV, TV, and/or RR).
  • other factors like temperature, blood pressure, pulse rate, blood work (e.g. CBC), etc. but reduces the emphasis commonly associated with temperature and pulse rate while increasing the emphasis on other (e.g. respiratory) parameters (e.g. MV, TV, and/or RR).
  • different physiologic components are added to the EWS.
  • one or more of the existing parameters in the EWS is removed from the formula used to help prevent or predict evolving patient compromise, disease state, or distress.
  • compromise, disease state, or distress may be one or more of respiratory failure, sepsis, cardiac failure, congestive heart failure, renal failure, over-hydration, pulmonary edema, hyper metabolic state, overexertion, traumatic brain injury, pulmonary embolus, opioid induced respiratory depression, over sedation.
  • the device is attached to the patient using one or more sensors to obtain one or more of the patient's impedance levels (used to determine MV, TV and/or RR as described herein), MV, TV and/or RR, oxygen saturation, temperature, blood pressure, pulse or heart rate, blood oxygen levels, brain activity, blood lab tests (e.g. complete blood count (CBC)) or another physiological status.
  • the incoming data from the sensors is collected and analyzed to output an early warning score for the patient. If the score exceeds a predetermined level, one or more alarms (audible and/or visual) may be activated.
  • clinicians may input information about the patient's condition, including, but not limited to alertness, voice, pain, and unresponsiveness (commonly referred to as AVPU).
  • minute ventilation is used instead of or in conjunction with respiration rate and in combination with other sensor data to derive the patient's state and output the early warning score.
  • the sensors do not impede breathing or obstruct the patient's airways.
  • the sensors are noninvasive.
  • pre-defined criteria and ranges for the early warning scoring system are based on (and may be adjusted according to) patient's disease state (obstructive sleep apnea (OSA), congestive heart failure (CHF), systemic inflammatory response syndrome (SIRS), sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an electronic health record (EHR))) or as determined by the device itself.
  • pre-defined criteria and ranges for the early warning scoring system are based on (and may be adjusted according to) the patient's circumstance
  • the early warning score may be adjusted based on the patient's age, demographics, condition (e.g. pregnancy), or other feature.
  • pre-defined criteria and ranges for the early warning scoring system are based on (and may be adjusted according to) a combination of patient's circumstance (endoscopic procedure, surgery, post-operative state, etc.) and disease state (OSA, CHF, COPD, pulmonary fibrosis, asthma, sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • MV, TV and/or RR become part of a triage system (such as the Aldrete scoring system used for discharge criteria from a PACU) to help make decisions regarding patient care, patient medication or patient nutrition.
  • the triage system preferably is similar to the early warning scoring system.
  • pre-defined criteria and ranges for the triage system are based on (and may be adjusted according to) a combination of a triage criteria (Aldrete score, etc.) and disease state (OSA, CHF, sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • MV, TV, and/or RR become part of a modified early warning scoring system (which computes a modified early warning score used to trigger an alarm and/or actuates an external system, which delivers or controls treatment or medical intervention) used for detecting changes in patient condition, disease state (e.g. CHF, COPD, OSA, asthma, sepsis, cranial hemorrhage, ARDS, etc.), identify patients at-risk or in need of additional or advanced care, help initiate or modify patient care, determine the effectiveness or ineffectiveness of interventions.
  • the modified early scoring system preferably is similar to the early warning system.
  • pre-defined ranges and criteria for the modified early scoring system are based on (and may be adjusted according to) a combination of a triage criteria (Aldrete score, etc.) and disease state (OSA, asthma, pulmonary fibrosis, COPD, CHF, sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • the modified early warning scoring system may be adjusted based on the patient's age, demographics, condition (e.g. pregnancy), or other feature.
  • MV, TV, and/or RR become part of a pediatric early warning scoring system (which computes a pediatric early warning score used to trigger an alarm and/or actuate an external system, which delivers or controls treatment or medical intervention) used for detecting changes in patient condition, disease state (e.g.
  • the pediatric early scoring system preferably is similar to the early warning system.
  • pre-defined ranges and criteria for the pediatric early scoring system are based on (and may be adjusted according to) a combination of a triage criteria (Aldrete score, etc.) and disease state (OSA, CHF, sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • the modified early warning scoring system may be adjusted based on the patient's age, demographics, condition (e.g. pregnancy), or other feature.
  • MV, TV and/or RR become part of a PACU/ICU/hospital floor/home/rehab/nursing home mobilization protocol to help make decisions regarding patient care or patient nutrition.
  • the mobilization protocol system preferably is similar to the early waring scoring system.
  • pre-defined criteria and ranges for the mobilization protocol system are based on (and may be adjusted according to) a
  • PACU/ICU/hospital floor/home/rehab/nursing home mobilization protocol and disease state either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • MV, TV and/or RR become part of a
  • the training protocol system preferably is similar to the early waring scoring system.
  • pre-defined criteria and ranges are based on (and may be adjusted according to) a combination of fitness/wellness/rehab/athletic training/performance protocol and disease state (OSA, CHF, sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • OSA fitness/wellness/rehab/athletic training/performance protocol and disease state
  • MV, TV and/or RR become part of a system for monitoring or adjusting patient care based on activity and/or nutrition for patients with different metabolic states such as diabetes, cachexia, obesity, sepsis, anabolism, catabolism etc. to help make decisions regarding modification of activity or nutrition regimens.
  • Regimens can be modified continuously with an open or closed-loop feedback system, or intermittently on a pre-defined schedule, or as alerted by the system.
  • the activity or nutrition regimen system preferably is similar to the early warning scoring system.
  • pre-defined criteria and ranges are based on (and may be adjusted according to) a combination of a system for monitoring or adjusting patient care based on activity and/or nutrition for patients with different metabolic states such as diabetes, cachexia, obesity, sepsis, etc. and disease state (OSA, CHF, sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • metabolic states such as diabetes, cachexia, obesity, sepsis, etc. and disease state (OSA, CHF, sepsis, renal disease, etc.) either externally entered (manually or automatically (e.g. from an EHR)) or as determined by the device itself.
  • an activity is considered to be "normal” over a given time frame if outputs from it (measured or computed) fall within predefined or adjustable limits.
  • activity is structured to elicit or enhance certain measurement, for example MV, and thus used to evaluate metabolic state with specific active stimulus.
  • RVM measurements are integrated into an open or closed feedback loop to report adequacy of ventilation by ensuring safe dosage of medication by monitoring ventilation for warning signs of respiratory arrest.
  • RVM is integrated into a system with a ventilator providing an open or closed feedback loop by which ventilator adjustments are made. Differences between RVM measurements and ventilator or spirometer generated volume or flow measurements can be used to provide information for diagnosis and guidance of therapy.
  • RVM monitoring with or without additional information from end tidal C0 2 or pulse oximetry measurements, this embodiment automatically weans the patient by gradually decreasing ventilatory support and observing RVM and other parameters and alerts the physician of readiness for extubation, or alerts for failure to progress.
  • This combined system with either pulse oximetry or ETC02 or both could be used as an open or closed loop system to deliver narcotics or other respiratory depressant drugs such as benzodiazepines or propofol.
  • the analysis algorithm detects the presence of specific respiratory patterns maintained in the expert system database and informs the physician or other health care provider about the possibility of associated pathology.
  • the respiratory pattern for a given pathology is recognized and in a preferred embodiment, quantified.
  • the pathology is localized.
  • the device recognizes a specific patterns related to respiratory volume, curve, variability or complexity or other analysis of RVM data.
  • the device recognizes the pattern associated with impending respiratory failure or respiratory arrest and delivers an audible and/or visible alert or warning. In one embodiment, the device analyzes the respiratory data or the trend in the data and makes a recommendation for intubation and mechanical ventilation. In one
  • the device analyses the respiratory pattern data and adjusts the level of infusion of a narcotic or other respiratory depressant drug such as propafol.
  • the device recognizes the respiratory pattern associated with a specific disease entity or pathology such as congestive heart failure, or asthma or COPD or narcotic induced respiratory depression or impending respiratory failure.
  • a specific disease entity or pathology such as congestive heart failure, or asthma or COPD or narcotic induced respiratory depression or impending respiratory failure.
  • the device alerts the physician to this pathology. In one embodiment the device quantifies the degree of the pathology. In one embodiment, the device recognizes a pattern of congestive heart failure and provides data regarding the trending toward improvement or deterioration with time or as associated therapeutic intervention.
  • the impedance measuring element of the device can produce Impedance Cardiograph (ICG) measurements.
  • ICG Impedance Cardiograph
  • the device detects impedance variability associated with heart rate variability.
  • the device detects impedance variability associated with variability of the respiratory waveform or other respiratory parameter and utilizes the heart rate and respiratory rate, volume or waveform variability to predict cardiac, respiratory and pulmonary complications.
  • the device maintains alarms for predetermined limits associated with unsafe pulmonary variability or complexity or combined heart rate and respiratory variability or complexity.
  • End Tidal C0 2 (ETCO2) is used in addition to or instead of subjective assessment to determine the RVM baseline.
  • RVM is coupled with ETCO2 measurements to provide additional information regarding respiratory status.
  • RVM is coupled with pulse oximetry to provide information about both ventilation/respiration and oxygenation.
  • a more complex RVM system couples standard RVM measurements with both or either ETCO2 or pulse oximetry. This combined device provides further information about breathing for sedated patients and enhances patient monitoring.
  • measurments of lung volumes and minute ventilation are used to assess the adequacy of the patient after extubation in a quantitative way. Minute ventilation is specifically used for patients undergoing surgery.
  • a preoperative measurement of tidal volume or minute ventilation is obtained as a baseline for the specific patient.
  • the baseline is used post-operatively as a comparison between preoperative and postoperative respiratory status.
  • the trend of tidal volume or minute ventilation is used to monitor a patient during surgery or a procedure or during postoperative recovery in the Post Anesthesia Care Unit, in the Intensive Care Unit, or on the hospital floor. This trend gives an accurate measure of differences and changes in the patient's breathing from preprocedure baseline and can denote when the patient returns to a baseline level of breathing.
  • the device directly aids the physician to make an appropriate extubation decision by defining an adequate level of breathing specific to that patient.
  • absolute lung volumes are compared with precalibrated data derived from patient characteristics, and are used in determining the presence of restrictive and/or obstructive lung disease and other respiratory conditions.
  • Absolute volume data can be especially useful within the PACU and ICU as a complement to existing quantitative data.
  • One use of the device is to use cardiac and/or respiratory data measured and recorded by one, several, or a combination of the technologies listed herein, to determine the effect of one or more drugs or other medical interventions on the patient.
  • the respiratory monitor is used to judge the side effects of analgesic drugs on the body and prevent or assist in the prevention of respiratory failure or other compromises due to adverse reaction or overdose.
  • the device of the invention communicates with an electronic PCA system, or by an integrated monitor/PCA system or by a setting in the monitor indicating that the patient is being administered PCA.
  • the administration of analgesia or anesthesia is limited based on the risk of respiratory or other complications predicted by the device. If the PCA system is not electronic, or analgesic drugs are being delivered by personnel, the device makes recommendations as to when the risk of respiratory complication is high and the dosage should be lowered.
  • Another embodiment of the device of the invention is a diagnostic/therapeutic platform.
  • the monitoring device is paired with one or more of the following:
  • pharmaceutical regimens therapeutic regimens, use of inhaler, use of nebulizer, use of pharmaceutical targeting respiratory system, use of pharmaceutical targeting cardiovascular system, use of pharmaceutical targeting asthma, COPD, CHF, cystic fibrosis,
  • This embodiment of the device is used to judge the effectiveness of possible medical and nonmedical interventions on respiratory state or respiratory health and suggest changes in regimen for optimization and/or suggest appropriate interventions when the patient is at risk for complications.
  • RVM is paired with behavioral algorithms or algorithm that includs information about any of the following patient medical status, environmental factors, and behavioral factors of a demographic group or of the patient in general.
  • one of the algorithms described above could denote the necessity for obtaining an RVM measurement.
  • the RVM measurements are used in conjunction with behavioral/medical/environmental algorithmic data to provide information to indicate action or therapy.
  • An example of the use of this embodiment of the device would be an algorithm which includes the patient's previous respiratory complications or chronic respiratory illness, and/or allergies as inputs along with behavioral events known to exacerbate said conditions.
  • the system recommends that he take an RVM measurement then makes recommendations about whether to maintain normal dosing of medication or increase it.
  • the software can also recommend that the patient bring medication with him to the event, and generally remind the patient to take his medication.
  • RVM data could be utilized to assess the severity of this attack by any of the measured parameters including minute ventilation, tidal volume, time for inspiration vs. expiration (i.e. ratio), shape of the respiratory curve during normal breathing, shape of the respiratory curve during the deepest possible breath or other respiratory maneuver.
  • the data could then prompt independently or be used in conjunction with other information to make a decision for the patient to perform an action including one of the following: do nothing, rest, use an inhaler, take a pharmaceutical, use a nebulizer, go to the hospital.
  • Information as to the action required could be part of a behavioral or other algorithm designed for the specific patient or a group of patients with a similar disorder, patients with a similar demographic, patients with a specific medical, anatomic or behavioral profile or patients in general.
  • the patient is instructed to repeat the RVM measurement to assess the adequacy of therapy.
  • his repeat measurement is compared to the measurement before the therapy or other intervention and changes are noted. Additional information from this comparison or just data taken after therapy is used alone or in combination with other patient data to make further medical decisions or recommendations for action.
  • an asthmatic is having symptoms and decides to or is instructed by a disease management algorithm to obtain an RVM measurement.
  • the RVM data is analyzed by the device, utilized independently or compared to his historic baseline or the last measurement taken. Based on these, with or without other patient specific inputs such as heart rate, the device recommends he use his inhaler. A second set of RVM data is then taken. The RVM data is compared to the previous RVM data taken prior to treatment. The device then follows a decision tree and tells the patient he has improved and needs no further therapy, that he needs to repeat the dosage, that he needs to call his physician, or that he immediately needs to go to the hospital.
  • the RVM data is combined with behavioral algorithms developed for a demographic or for a specific patient to optimize recommendations for the patient.
  • the device is used within a Postoperative Anesthesia Care Unit
  • RVM volume is calculated and compared against pre-calibrated data derived taking into account BMI, height, weight, chest circumference, and other parameters.
  • the device is used to complement existing quantitative data that supports decision making within the PACU.
  • RVM data is correlated with end tidal carbon dioxide measurements to provide a more comprehensive assessment of respiratory status.
  • RVM derived measurements including minute ventilation are used to compare a patient's status before, during, and after surgery or a procedure and to document the effect of anesthesia/narcotic induced respiratory depression.
  • RVM is used to support more subjective assessments made by clinicians in the PACU by providing a quantitative justification for certain decisions, including the decision to re-intubate.
  • the device also supports subjective assessment regarding patients on the hospital floor as a monitor for decline in respiratory status and an alarm for the need to re- intubate or perform another intervention to improve respiratory status.
  • RVM measurements will assist in regulation of narcotic pain medication, sedative drugs such as benzodiazepines, or other drugs with respiratory depressive effects.
  • the above mentioned uses regarding the RVM in a PACU setting are implemented within the ICU setting such as a Neonatal ICU, Surgical ICU, Medical ICU, Pulmonary ICU, Cardiac ICU, Coronary Care Unit, Pediatric ICU, and Neurosurgical ICU.
  • the RVM device is used in the setting of a step down unit or standard hospital bed to follow respiratory status.
  • RVM monitoring identifies problems that are commonly associated with ventilators, such as poor endotracheal tube positioning, hyperventilation, hypoventilation, rebreathing and air leaks. The system also identifies air leaks through a chest tube or cuffless tube.
  • Air leaks would cause a downward trend to appear on any direct volume measurement which would not be present on the impedance trace, thus the device can detect and report air leaks in devices which directly measure volume or flow.
  • the system identifies abnormalities and trends specific to a hemithorax such as those related to the following pathologies: pneumothorax, pulmonary contusion, rib fractures, hemothorax, chylothorax, hydrothorax, and pneumonia.
  • the device is used during Monitored Anesthesia Care (MAC) to monitor respiratory status, assist in drug and fluid administration, provide indication of impending or existing respiratory compromise or failure, and assist in the decision to intubate if necessary.
  • MAC Monitored Anesthesia Care
  • RVM monitoring identifies problems that are commonly associated with ventilators, such as poor endotracheal tube positioning, hyperventilation, hypoventilation, rebreathing and air leaks.
  • RVM measurements are combined with data derived from the ventilator to provide additional data regarding physiology. An example of this is that differences can be recorded in RVM measurements vs. inspired or expired flows or volumes measured on the ventilators to assess "work of breathing" in a quantitative fashion.
  • RVM measurements are taken after surgery in a patient who is still under the effects of anesthesia or pain medication to monitor patient recovery.
  • a baseline tidal volume curve for a patient during normal preoperative conditions provides a comparison baseline for monitoring during and after surgery.
  • a similar tidal volume curve is one signal of respiratory recovery after being taken off a ventilator.
  • the device is used to evaluate the success of extubation and determine if reintubation is necessary.
  • the invention described herein allows these measurements to be taken noninvasively and without being in the stream of inspired/expired air or impeding airway flow or contaminating the airway circuit.
  • the device is used within outpatient surgicenters, specifically geared towards patients receiving Monitored Anesthesia Care, including patients undergoing orthopedic procedures, cataract surgery and endoscopy of the upper and lower GI tract. Diagnostic Usage
  • the device is used to quantify respiratory parameters during performance based tests. In a preferred embodiment, the device is used to quantify respiratory parameters in tests of cardiovascular function including stress tests. In a preferred embodiment, the device is used in combination with one of the following tests to assess impact of the test on respiration. In a preferred embodiment, the device reports effects of exercise or a particular drug like dopamine on the overall physiology or metabolism of the body as reflected by changes in respiratory volumes, patterns, rate or combinations thereof including advanced analysis of breath-to-breath variability/complexity, fractal or entropy based analyses as described elsewhere. In a preferred embodiment, the device is used to evaluate the safety of a given level of exercise or pharmacologic stress.
  • RVM variability or complexity analysis
  • RVM measurements is undertaken in concert with standard pulmonary function testing.
  • variability or complexity analysis of RVM measurements is undertaken with or without heart rate variability/complexity analysis in concert with standard cardiovascular physiology testing such as stress testing, walking tests for claudication, or other performance based testing.
  • the device is used to evaluate the effects of drugs on the respiratory system including bronchodilators for diagnostic purposes, monitoring of therapeutics, optimization including effects on both heart and lungs. More preferably, the device above combines respiratory information obtained by impedance or other methods described with EKG information about heart rate, heart rate variability, EKG evidence of ischemia or arrhythmia. In a preferred embodiment, the device is used to evaluate the effects of bronchoconstrictors as in a provocative test. In various embodiments, the device obtains continuous or intermittent RVM measurements. In a preferred embodiment, the device provides trending of RVM data.
  • the device is used to evaluate the effects of metabolic stimulants, cardiovascular drugs including beta blockers, alpha adrenergic agonists or blockers, beta adrenergic agonists or blockers.
  • the device is used during a stress test to demonstrate level of effort placed or to demonstrate an unsafe condition relative to the pulmonary system to terminate or modify the test. Stress Introduced to the patient is created by various means including but not limited to, exercise and/or the delivery of a drug.
  • the device indicates or works with other technologies described earlier to indicate the level of overall exercise.
  • the device is used as a free-standing device for measuring the effects of exercise or other stimulant on the pulmonary system.
  • the respiratory information is combined with cardiac information to define the level of exertion related to EKG changes associated with cardiac disease.
  • the system combines respiratory information with cardiac information to determine the level of exertion of an athlete.
  • the device provides warning of potential negative impact of the level of exercise on overall health or on cardiac status, with or without pairing respiratory signals with cardiac impedance or EKG measurements in the home, athletic field, military environment or out of hospital setting.
  • One embodiment of the device is a holter monitor which outputs values for one or more of the following: respiratory effort, level of activity, state of physiology, or metabolism associated with different rhythms, depolarization or other cardiac pathophysiology.
  • One embodiment of the invention is similar to a holter monitor which monitors one or more physiological parameters over hours to days in a hospital, home, or other setting.
  • One embodiment of the device is combined with a holter monitor or critical care monitor which specifically monitors decompensation effects related to heart failure.
  • a similar embodiment of the device monitors and outputs measurements of "lung water”.
  • the device is included in a disease management system for congestive heart failure.
  • the device provides a continuous measurement which can be run for long periods of time and can deliver a time curve demonstrating the effects of exercise or a drug for diagnosis, therapeutic monitoring or drug development.
  • One embodiment of the device provides trending data over minutes to hours to days for patients with a variety of disease states including chronic obstructive pulmonary disease, congestive heart failure, pulmonary hypertension, pulmonary fibrosis, cystic fibrosis, interstitial lung disease, restrictive lung disease, mesothelioma, post thoracic surgery, post cardiac surgery, post thoracotomy, post thoracostomy, post rib fracture, post lung contusion, post pulmonary embolus, cardiac ischemia, cardiomyopathy, ischemic cardiomyopathy, restrictive cardiomyopathy, diastolic cardiomyopathy, infectious cardiomyopathy, hypertrophic cardiomyopathy.
  • the device provides information about changes in respiration in these disease states related to interventions or provocative testing procedures.
  • the system is used to diagnose various diseases.
  • the device is used to assess the risk of developing pneumonia.
  • the device is used to assess the risk that a pneumonia therapy is not effective, and suggest corrective action.
  • Another embodiment of the invention is used for the evaluation of functional deterioration or recovery associated with diseases including but not limited to: pneumonia, heart failure, cystic fibrosis, interstitial fibrosis, hydration levels, congestion due to heart failure, pulmonary edema, blood loss, hematoma, hemangioma, buildup of fluid in the body, hemorrhage, or other diseases.
  • This information may be used for diagnosis as above or be integrated with respiratory volume measurements or other physiological measurements that may be measured by the device or input into the device to provide a comprehensive respiratory sufficiency index (cRSI).
  • cRSI comprehensive respiratory sufficiency index
  • disease specific modules can be created to gather disease specific information, employ disease specific algorithms and deliver either optimized respiratory volume data or respiratory diagnostic data related to the specific disease.
  • respiratory curve analysis is used to diagnose medical conditions.
  • the system utilizes provocative tests to determine measurements or estimates of one or more of the following: tidal volume, residual volume, expiratory reserve volume, inspiratory reserve volume, inspiratory capacity, inspiratory vital capacity, vital capacity, functional residual capacity, residual volume, forced vital capacity, forced expiratory volume, forced expiratory flow, forced inspiratory flow peak expiratory flow, and maximum voluntary ventilation.
  • diagnostic tools such as flow volume loops are generated by software running on the system for diagnosis of various cardiopulmonary or other disorders.
  • Respiratory curve analysis can also be used to assess cardiopulmonary or other disorders without provocative tests.
  • an algorithm monitors trends in TV, MV and RR to provide a metric of respiratory sufficiency or respiratory sufficiency index (RSI).
  • an algorithm analyzes individual breaths as an input to diagnose respiratory conditions.
  • one or more of the following parameters are calculated on a breath by breath basis: inspiratory time (I t ), expiratory time (E t ), It:E t ratio, percent inspiratory time, tidal impedance, tidal volume and area under the curve.
  • the various parameters are outputted through the system's user interface or printable report for the user to assess respiratory disease state.
  • an algorithm analyzes the parameters to act as a diagnostic aid.
  • the system outputs an index of disease severity or a positive/negative reading for the disease.
  • the device is implanted.
  • the device is powered from a pacemaker-like battery.
  • the device is combined with a pacemaker or defibrillator.
  • the device is adjusted or calibrated or interrogated using an external component.
  • Figure 40 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a High-Frequency Chest Wall Oscillation
  • HFCWO (“HFCWO") vest. It has recently been observed that during vest oscillation therapy, the Minute Ventilation of a patient is reduced by up to 50%. The improvement in efficiency may provide significant health benefits for a patient who is having difficulty providing oxygenation of their bloodstream during breathing.
  • the Minute Ventilation of a patient is reduced by up to 50%.
  • the improvement in efficiency may provide significant health benefits for a patient who is having difficulty providing oxygenation of their bloodstream during breathing.
  • HFCWO vest automatically provides therapy levels (frequency, intensity, length) which have been developed to optimize the 02 to C02 transfer in the lungs.
  • the goal is to optimize the oxygen and C02 transfer by the use of the HFCWO vest.
  • By increasing the turbulence in the lungs during inhalation and exhalation better oxygen and C02 transfer can be achieved.
  • a decrease in work of breathing decreases the chance of respiratory failure.
  • patients who are receiving oxygen therapy could combine the oxygen therapy with the HFCWO vest therapy to maximize oxygenation, improve C02 removal and decrease work of breathing, thereby preferably extending life.
  • HFCWO vest therapy provides for a 10 min treatment to eliminate exudate.
  • the use of this product preferably allows for better oxygenation.
  • the use of the product could be continuously up to 24hrs/day.
  • the system could be customized to activate when the patient requires the additional oxygenation efficiency, e.g. during active times such as walking.
  • the parameters of oscillation could be optimized to minimize patient discomfort while maximizing oxygen transfer in the lungs.
  • a sensor for acquiring a physiological bioelectrical impedance signal from the patient is preferably functionally connected to a computing device.
  • the computing device preferably analyzes the physiological bioelectrical impedance signal, and provides an assessment of minute ventilation and tidal volume of the patient based on the analyzed bioelectrical impedance signal.
  • the computing device also preferably monitors the signal over time and provides a signal to the HFCWO vest.
  • the HFCWO vest automatically adjusts therapy levels (frequency, intensity, length) based on the levels of physiologic parameters including tidal volume, minute ventilation, and respiratory rate during therapy as determined by the computing device.
  • the general session-to-session lung performance can be tracked (TV, RR, MV) to demonstrate effectiveness of the therapy and the need to extend or modify the therapy levels.
  • the goal is to optimize the oxygen and C02 transfer by the use of the HFCWO vest to increase the turbulence in the lungs during inhalation and exhalation.
  • the shape of the bioimpedance exhalation/inhalation curve can be an indicator of the success of the therapy.
  • Appropriate curves for maximizing oxygen transfer can be identified and the levels of the HFCWO vest (frequency, intensity, length of therapy, Baseline compression) can be adjusted to get the desired respiratory curve and necessary oxygenation and/or C02 extraction and to minimize the work of breathing.
  • a pulse oximeter can be added to the system as an indicator of the success of the enhanced compression therapy and improved oxygenation.
  • the levels of therapy can be optimized by watching the oxygenation response over time.
  • C02 monitoring can be added to the system with either end tidal or transcutaneous C02 monitoring.
  • patients who are receiving oxygen therapy could combine the oxygen therapy with the HFCWO vest therapy to preferably maximize oxygenation, improve C02 removal, decrease work of breathing, and extend life.
  • Figure 41 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a mechanical ventilation therapy device.
  • the mechanical ventilation therapy device may be a CHFO system, a ventilator, a CPAP, a BiPAP, a CPEP (Continuous Positive Expiratory Pressure), or another non-invasive ventilation device.
  • the system includes a sensor for acquiring a physiological bioelectrical impedance signal from a patient and is functionally connected to a computing device.
  • the computing device preferably analyzes the physiological bioelectrical impedance signal and outputs an assessment of minute ventilation and tidal volume of the patient based on the analyzed bioelectrical impedance signal.
  • the system may also monitor the signal over time and provide a signal to the mechanical ventilation device.
  • the mechanical ventilation device preferably causes better oxygenation efficiency in the lungs.
  • the mechanical ventilation device preferably can adjust the frequency, intensity, of the oscillations and/or the base line inhalation and exhalation pressures.
  • a bioelectric feedback signal provides indication for the success of oxygenation.
  • the characteristic values for tidal volume, minute volume, and respiratory rate will change.
  • the system can automatically adjust the mechanical ventilation device's parameters to optimize physiological response and the efficiency of the system.
  • a pulse oximeter can be added to the system as an indicator of the success of the mechanical ventilation therapy.
  • Improved oxygenation and C02 transfer can preferably be achieved or a decrease in work of breathing can preferably be achieved to decrease the chance of respiratory failure.
  • the levels of therapy can be further optimized by watching the oxygenation response over time.
  • the overall length of therapy can be adjusted.
  • the general session-to- session lung performance can be tracked (TV, RR, MV) to demonstrate effectiveness of the ventilation and the need to extend or modify the therapy levels.
  • the characteristic shape of the bioimpedance inhalation and exhalation curve is an indicator of the success of the therapy.
  • the system can optimize oxygenation efficiency. Appropriate curves for maximizing ventilation can be determined and the adjustment levels of the Ventilator (frequency, intensity, length of therapy, Baseline Pressure) can be adjusted to get the desired respiratory curve.
  • the Ventilator frequency, intensity, length of therapy, Baseline Pressure
  • patients who are receiving oxygen therapy could combine oxygen therapy with mechanical ventilation therapy to maximize oxygenation and extend life.
  • the level of compliance to using the system and getting the adequate therapy can be monitored by analyzing the volume of air coming in and out of the lungs.
  • Mechanical ventilation therapy can be combined with aerosol delivery to provide an additional therapy regimen.
  • aerosol will inherently modify the impedance characteristic of the lung
  • the level of respiration and the effect of these two combined treatments can also be optimized.
  • Tidal Volume and the characteristic inhalation and expulsion curves can be monitored before, during, and after treatment to ensure appropriate optimization of the positive expiratory presssure on expansion of the lung and airways or an adequately cleared lung.
  • Figure 42 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with an oxygenation therapy device.
  • the system preferably includes a sensor for acquiring a physiological bioelectrical impedance signal from a patient and is functionally connected to a computing device.
  • the computing device preferably analyzes the physiological bioelectrical impedance signal and provides outputs an assessment of minute ventilation and tidal volume of the patient based on the analyzed bioelectrical impedance signal.
  • the computing device additionally preferably monitors the signal over time and provides a signal to an oxygen therapy system.
  • the oxygen therapy provides oxygen via a mask or nose cannula.
  • the bioelectric feedback signal provides indication for the success of the level of the expansion of the airways.
  • the characteristic shape of the bioimpedance expansion curve is an indicator that the air is getting into the lungs.
  • the oxygenation therapy system can synchronize the delivery of oxygen to the cannula to ensure optimal oxygen uptake through the nose cannula.
  • the feedback mechanism of the oxygen delivery can be optimized as well.
  • the oxygen system can more reliably determine how well the mask is applied to the patient and how well the circuit is maintained (kink free and leak free).
  • Figure 43 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a suction therapy device.
  • the system preferably includes a sensor for acquiring a physiological bioelectrical impedance signal from a patient and is functionally connected to a computing device.
  • the computing device preferably analyzes the physiological bioelectrical impedance signal and provides an output of an assessment of minute ventilation and tidal volume of the patient based on the analyzed bioelectrical impedance signal.
  • the computing device preferably also monitors the signal over time and provides a signal to the suction therapy device.
  • Suction therapy preferably causes the mobilization of fluid in the lungs.
  • the suction therapy can be adjusted for frequency and intensity of the oscillations. Also, the base line inhalation and exhalation pressures can be adjusted and the overall length of therapy can be adjusted.
  • the bioelectric feedback signal preferably provides an indication for the success of the mobilization of secretions.
  • the characteristic values for tidal volume, minute volume, and respiratory rate will change.
  • the system can preferably automatically adjust the suction parameters to optimize physiological response.
  • the characteristic shape of the bioimpedance expulsion curve is an indicator of the success of the therapy. By tailoring the therapy to get the desired expulsion curve the system can optimize the mobilization of fluid from the patient.
  • Fluid clearance can be combined with aerosol delivery to provide another therapy regimen.
  • aerosol will inherently modify the impedance characteristic of the lung
  • the level of respiration and the effect of these two combined treatments can also be optimized.
  • the tidal volume and the characteristic inhalation and expulsion curves can be monitored before, during, and after treatment to ensure appropriate outcome of an adequately cleared lung.
  • Figure 44 depicts an embodiment of the invention wherein the impedance measuring device is in data communication with a cough assist device.
  • the system preferably includes a sensor for acquiring a physiological bioelectrical impedance signal from a patient and is functionally connected to a computing device.
  • the computing device preferably analyzes the physiological bioelectrical impedance signal and provides an output of an assessment of minute ventilation and tidal volume of the patient based on the analyzed bioelectrical impedance signal.
  • the computing device preferably also monitors the signal over time and provides a signal to the cough assist device.
  • the cough assist device is preferably a non-invasive therapy that stimulates a cough to remove secretions in patients with compromised peak cough flow. It is designed to keep lungs clear of mucus. Retained secretions collect in the lungs, creating an environment for infection.
  • Mechanical Insufflation/Ex- sulflation (MVE) therapy products are important for patients who have weakened cough and are unable to remove secretions from the large airways without assistance.
  • the system supplies positive pressure (inhale) to inflate the lungs, then quickly shifts to supply negative pressure (exhale), during this process secretions are sheared and can be expectorated or removed with suction. After the exhale, the system pauses and maintains a resting positive pressure flow to the patient.
  • a facemask or mouthpiece can be used on endotracheal and tracheostomy (i.e. for patients with an appropriate adapter).
  • the cough assist device automatically adjusts characteristic therapy levels
  • Graphs could be provided to document breathing characteristics of the patient and to demonstrate improvement to the patient over time.
  • the characteristic shape of the bioimpedance expansion curve is an indicator of the success of each individual cough.
  • Appropriate curves for maximizing exudate removal can be identified and the adjustment levels of the Cough assist System (frequency, intensity, length of therapy, inhalation pressure, and exhalation pressure) can be adjusted to get the desired cough expulsion curve.
  • Characteristics of the cough assist can be adjusted to ensure the optimal results are provided for each individual patient.

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Abstract

La présente invention concerne un système et un procédé de notation d'alerte précoce. Le système comprend un dispositif informatique, une pluralité de capteurs pour acquérir des signaux physiologiques provenant d'un patient, les capteurs étant fonctionnellement connectés au dispositif informatique, et au moins une alarme conçue pour émettre une alerte lorsqu'un score d'alerte précoce (EWS) dépasse un niveau prédéterminé. Le dispositif informatique reçoit les signaux physiologiques provenant des capteurs, analyse les signaux physiologiques, et sur la base des signaux analysés, calcule le score d'alerte précoce, et compare le score d'alerte précoce à des limites prédéterminées et, si le score est en dehors des limites, déclenche une alarme ou actionne ou modifie un traitement ou une intervention médicale.
EP17867005.5A 2016-11-02 2017-11-02 Systèmes et procédés de notation d'alerte précoce respiratoire Pending EP3528694A4 (fr)

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BR112019010552A2 (pt) 2019-09-17
IL266388B2 (en) 2023-08-01
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IL266388B1 (en) 2023-04-01
EP3528694A4 (fr) 2020-10-14
KR20190071808A (ko) 2019-06-24
MX2019005195A (es) 2019-09-19
CN109996488B (zh) 2022-11-01
JP2020503085A (ja) 2020-01-30
IL266388A (en) 2019-06-30
CN109996488A (zh) 2019-07-09
WO2018085563A1 (fr) 2018-05-11

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