Classifications

• • 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
• • 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
  • A61B5/02—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
  • A61B5/021—Measuring pressure in heart or blood vessels
• • 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
  • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
  • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
• • 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
  • A61B5/4848—Monitoring or testing the effects of treatment, e.g. of medication
• • 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
  • A61B5/4869—Determining body composition
  • A61B5/4875—Hydration status, fluid retention of the body
• • 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
  • A61B5/4884—Other medical applications inducing physiological or psychological stress, e.g. applications for stress testing
• • 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
  • A61B5/7235—Details of waveform analysis
  • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
• • 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
  • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
  • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
• • 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
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • 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
• • 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
  • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
  • A61B5/6813—Specially adapted to be attached to a specific body part
  • A61B5/6825—Hand
  • A61B5/6826—Finger

Definitions

• Various embodiments can assess the physiological state of a patient, perhaps indicated by changes in a patient's compensatory reserve index ("CRI,” also referred to herein and in the Related Applications as “cardiac reserve index” or “hemodynamic reserve index” (“HDRI”), all of which should be considered synonymous for purposes of this disclosure) in response to a physical perturbation.
• CRM compensatory reserve index
• HDRI hemodynamic reserve index
• patient is used herein for convenience, that descriptor should not be considered limiting, because various embodiments can be employed both in a clinical setting and outside any clinical setting, such as by an athlete before, during, or after an athletic contest or training, a person during daily activities, a soldier on the battlefield, etc.
• the term, "patient,” as used herein, should be interpreted broadly and should be considered to be synonymous with "person."
• the assessments might be based on raw waveform data (e.g., PPG waveform data) captured by a sensor on the patent (such as the sensors described below and in the Related Applications, for example).
• a combination of waveform data and calculated/estimated CRI can be used to calculate the effectiveness of hydration and/or the amount of fluid needed for effective hydration.
• such functionality can be provided by and/or integrated with systems, devices (such as a cardiac reserve monitor and/or wrist-worn sensor device), tools, techniques, methods, and software described below and in the Related Applications.
• the tools provided by various embodiments include, without limitation, methods, systems, and/or software products.
• a method might comprise one or more procedures, any or all of which are executed by a computer system.
• an embodiment might provide a computer system configured with instructions to perform one or more procedures in accordance with methods provided by various other embodiments.
• a computer program might comprise a set of instructions that are executable by a computer system (and/or a processor therein) to perform such operations.
• software programs are encoded on physical, tangible and/or non-transitory computer readable media (such as, to name but a few examples, optical media, magnetic media, and/or the like).
• FIG. 1A is a schematic diagram illustrating a system for estimating compensatory reserve, in accordance with various embodiments.
• Fig. IB is a schematic diagram illustrating a sensor system that can be worn on a patient's body, in accordance with various embodiments.
• FIG. 2A is a process flow diagram illustrating a method of assessing effectiveness of hydration, in accordance with various embodiments.
• Fig. 2B illustrates a technique for assessing effectiveness of hydration, in accordance with various embodiments.
• FIG. 3A is a process flow diagram illustrating a method estimating a patient's compensatory reserve and/or dehydration state, in accordance with various embodiments.
• Fig. 3B illustrates a technique for estimating and/or predicting a patient's compensatory reserve index, in accordance with various embodiments.
• Fig. 4 is a process flow diagram illustrating a method of generating a model of a physiological state, in accordance with various embodiments.
• FIG. 5 is a generalized schematic diagram illustrating a computer system, in accordance with various embodiments.
• Figs. 6-8 are exemplary screen captures illustrating display features of a compensatory reserve monitor showing assessments of hydration effectiveness, in accordance with various techniques.
• Fig. 9A is a graphical diagram depicting an example a resuscitation interval overlaid over a blood draw profile, in accordance with various embodiments.
• Fig. 9B is a graphical diagram depicting intervals of ongoing bleeding between periods of non-ongoing bleeding during the example blood draw profile of Fig. 9 A, in accordance with various embodiments.
• Fig. 9C is a graphical diagram depicting periods during which fluid is needed and periods during which fluid is not needed during the example blood draw profile of Fig. 9A, in accordance with various embodiments.
• Figs. lOA-lOC are graphical diagrams depicting comparison between actual or reference blood draw resuscitation volume required versus estimated blood resuscitation volume needed (as calculated using the Fluid Volume Requirements ("FVR") Algorithm) for each of three subjects, in accordance with various embodiments.
• FVR Fluid Volume Requirements
• Fig. 11 is a graphical diagram illustrating receiver operating characteristic ("ROC") area under the curve (“AUC”) for Detection of Ongoing Fluid Loss (“DOFL”) Algorithm, in accordance with various embodiments.
• Fig. 12 is a graphical diagram illustrating receiver operating characteristic
• REIFR Immediate Fluid Requirements
• a set of embodiments provides methods, systems, and software that can be used, in many cases noninvasively, to quickly and accurately provide diagnostic information about a patient.
• these tools and techniques can be used to assess the effectiveness of hydration of a patient and/or the patient's hydration level.
• Such an assessment can include, without limitation, an estimate of the effectiveness at a current time, a prediction of the effectiveness at some point in the future, an estimate and/or prediction of a volume of fluid necessary for effective hydration, an estimate of the probability that a patient requires fluids, etc.
• a device which can be worn on the patient's body, can include one or more sensors that monitor a patient's physiological parameters. The device (or a computer in communication with the device) can analyze the data captured by the sensors and compare such data with a model (which can be generated in accordance with other embodiments) to assess the effectiveness of hydration, as described in further detail below.
• assessments can be performed in accordance with different embodiments.
• these assessments can be performed by calculating a patient's CRI multiple times before, during, and/or after a physical perturbation of the patient's body, and based on the variance of the calculated CRI values, assessing a physical state of the patient.
• a patient's body might be perturbed by the patient performing a squat exercise (including, without limitation, a deep squat exercise), either as a body weight exercise or while bearing additional weight, and multiple values of a patient's CRI might be calculated based on physiological parameters (such as those described below and in the Related
• an estimate of the patient's hydration state can be calculated.
• Another embodiment might employ a similar procedure, but using a different physical perturbation (including, without limitation, those described below and in the Related Applications). Further embodiments), and can make estimates or predictions about a number of physical states of the patient (including, without limitation, the estimations or predictions described in the Related Applications) based on calculations of CRI values based on physiological parameters measured before, during and/or after various types of physical perturbations.
• one mode of physically perturbing a subject's body is to apply lower body negative pressure (“LBNP”) to the body, using a LBNP chamber.
• Another mode of perturbation is to induce pressure changes (i.e., either positive pressure or negative pressure, or both) to the subject's airway or other body parts.
• An impedance threshold device in general, can be any device that restricts inspiratory airflow, which can result in negative intrathoracic pressure, an increase in venous return to the heart, and/or an increase in stroke volume/blood flow/circulation.
• An intrathoracic pressure regulating device can be considered a type of ITD, in that it functions in a similar way, on ventilated patients, by introducing an active vacuum source to the thorax through the respiratory circuit.
• the RESQGARDTM and RESQPODTM devices are examples of ITD devices that can be used to physically perturb a body, in accordance with certain embodiments.
• Other devices such as blood pressure cuffs and the like, can be used to induce positive pressure to desired areas of the body.
• various techniques that might not require any additional equipment can be used to perturb the body. For example, if the desired perturbation is to induce positive pressure on the subject's airway, a valsalva maneuver might be performed.
• positive pressure can be induced on an airway during spontaneous, unassisted breathing, or positive pressure can be induced on an airway during mechanical ventilation, e.g., through use of a ventilator.
• the delivery of medication and/or fluids can serve as a physical perturbation.
• the body may be perturbed through delivery of electrical current to the body of the subject (or a desired portion thereof, e.g., using an automatic implantable cardioverter defibrillator ("AICD").
• AICD automatic implantable cardioverter defibrillator
• a physical perturbation of the subject's body can be an action that imposes a physical change or stress on a subject's body (or a portion thereof) that results in a measurable change in a physiological parameter, such that the change can be used to enhance the medical monitoring process, or more particularly, facilitate the estimation and/or prediction of a physiological state of the subject.
• the body will perturb itself such as when performing an exercise (e.g., a squat exercise, a sit-up exercise, etc.), a change in position (e.g., sitting or standing up), taking a spontaneous breath, or when there is a premature ventricular contraction (PVC) of the heart.
• Position changes can impact the cardiovascular system, leading to increasing or decreasing venous return and/or a rise or fall in blood pressure.
• a spontaneous breath leads to greater negative intrathoracic pressure, an increase in venous return, greater filling of the heart, augmentation of cardiac stroke volume and/or a change in blood pressure.
• PVCs cause compensatory pauses in the cardiac cycle, which lead to greater filling of the heart, augmentation of the stroke volume and/or a change in blood pressure.
• These various perturbations of the cardiopulmonary system can be monitored, and the body's response measured, to continuously estimate the physiological state and/or clinical condition of the patient.
• perturbing any portion of a subject's body can be considered to be perturbing the patient's body itself. In some cases, physically perturbing the patient's body might involve the use of a perturbation device, although this is not necessary.
• the term "perturbation device,” as used herein, means any device, whether operated manually or automatically, that can be used to produce a physical perturbation of the subject's body (or a portion thereof), in accordance with various embodiments.
• Examples include, but are not limited to, an LBNP chamber, ITD, blood pressure cuffs, and/or any other device that can exert a positive and/or negative pressure on a subject's body (or portion of a subject's body, such as the airway, extremities, etc.); or a fluid and/or medication delivery system, such as an auto-infuser, intravenous pump or auto- infuser, etc., an electricity delivery system, such as an automatic implantable cardioverter defibrillator or a pacemaker, a heating/cooling blanket, and/or any other type of physical stimulant (e.g., an induced change in the subject's position, an audible sound, a change in temperature or lighting, etc.).
• a fluid and/or medication delivery system such as an auto-infuser, intravenous pump or auto- infuser, etc.
• an electricity delivery system such as an automatic implantable cardioverter defibrillator or a pacemaker, a heating/cooling blanket, and/
• physiological parameters from the patient can vary according to which parameters are measured (and which, according to the generated model, are found to be most predictive of the effectiveness of hydration, including the probability of the need for hydration and/or the volume of fluids needed).
• the parameters themselves e.g., continuous waveform data captured by a photoplethysmograph
• physiological parameters can be derived from the captured data, and these parameters can be used.
• direct physiological data can be used to estimate a set of values of CRI, and this set of values of CRI can be used to assess the effectiveness of hydration.
• the derived set of CRI values and raw sensor data can be used together to perform such an assessment.
• the '483 Application describes a compensatory reserve monitor (also described as a cardiac reserve monitor or hemodynamic reserve
• this monitor quickly, accurately and/or in real-time can determine the probability of whether a patient is bleeding.
• the device can simultaneously monitor the patient's compensatory reserve by tracking the patient's CRI, to
• the same device can also include advanced functionality to assess the effectiveness of hydration, based on the monitored CRI values, as explained in further detail below.
• CRI is a hemodynamic parameter that is indicative of the individual- specific proportion of intravascular fluid reserve remaining before the onset of hemodynamic decompensation.
• CRI has values that range from 1 to 0, where values near 1 are associated with normovolemia (normal circulatory volume) and values near 0 are associated with the individual-specific circulatory volume at which
• BLV(t) is, the intravascular volume loss (“BLV,” also referred to as “blood loss volume” in the Related Applications) of a person at time “t,” and BLV HDD is the intravascular volume loss of a person when they enter hemodynamic decompensation (“HDD"). Hemodynamic decompensation is generally defined as occurring when the systolic blood pressure falls below 70 mmHg. This level of intravascular volume loss is individual specific and will vary from subject to subject.
• BLV HDD A -LBNP photograph DD LBNP paragraph DD where LBNP(t) is the LBNP level that the individual is experiencing at time "t," and, L NP HDD is the LNPB level at which the individual will enter hemodynamic
• the effectiveness of hydration can be expressed as a value
• a general expression for the estimate of HE is as follows:
• HE f HE (CRI t , FV t , S t ) (Eq. 4)
• HE is a measure of hydration effectiveness
• f HE CRl t , FV t , S t is an algorithm embodied by a model generated empirically, e.g., using the techniques described with respect to Fig. 4 below, and/or in the Related Applications
• CRI t is a time history of
• CRI values (which can range from a single set of CRI values to many hours of sets of CRI values)
• FV t is a time history of fluid volume being given to the patient (which can range from a single value to many hours of values)
• S t is a time history of raw sensor values, such as physiological data measured by the sensors, as described
• Eq. 4 The functional form of Eq. 4 is similar to, but not limited to, the form of the CRI model in the sense that time histories of (CRI t , FV t , S t ) data gathered from human subjects at various levels of HE are compared to time histories of
• Eq. 4 is the general expression for HE
• various embodiments might use subsets of the parameters considered in Eq. 4. For instance, in one
• a model might consider only the volume of fluid and CRI data, without accounting for raw sensor input.
• HE can be calculated as follows:
• HE can be expressed thusly:
• the effectiveness of hydration can be assessed by estimating or predicting the volume, V, of fluid necessary for effective hydration of the patient.
• This volume, V can indicate a volume of fluid needed for full hydration if therapy has not yet begun, and/or it can indicate a volume remaining for fully
• V can be
• V can be expressed as the following:
• V f v (CRI t , FV t , S t ) (Eq. 7) where V is an estimated volume of fluid needed by a patient to prevent over or under - hydration, f v (CRI t , FV t , S t ) is an algorithm embodied by a model generated
• CRI t is a time history of CRI values
• FV t is a time history of fluid volume being given to the patient
• S t is a time history of physiological data received from the one or more sensors.
• V f v (CRI t , FV t ) (Eq. 8) or
• V f v (FV t , S t ) . (Eq. 9)
• the probability can estimate the likelihood that further hydration is
• this probability which can be expressed, e.g., as a
• various sensor data can be collected from test subjects before, during, and/or after hydration efforts, during hemorrhaging, or under other conditions that might
• a measure of CRI, HE, V, and/or Pf can be useful in a variety of clinical settings, including but not limited to: 1) acute blood loss volume due to injury or surgery; 2) acute circulatory volume loss due to hemodialysis (also called intradialytic hypotension); and 3) acute circulatory volume loss due to various causes of dehydration (e.g. reduced fluid intake, vomiting, dehydration, etc.).
• a change in CRI can also herald other conditions, including, without limitation, changes in blood pressure, general fatigue, overheating, and certain types of illnesses, etc.
• the tools and techniques for estimating and/or predicting CRI can have a variety of applications in a clinical setting, including, without limitation, diagnosing such conditions.
• measures of CRI, HE, V, and/or Pf can have applicability outside the clinical setting.
• an athlete can be monitored (e.g., using a wrist-wearable hydration monitor) before, during, or after competition or training to ensure optimal performance (and overall health and recovery).
• a person concerned about overall wellbeing can employ a similar hydration monitor to ensure that he or she is getting enough (but not too much) fluid, infants or adults can be monitored while ill to ensure that symptoms (e.g., vomiting, diarrhea, etc.) do not result in dehydration, and the like.
• soldiers in the field can be monitored to ensure optimal operational readiness.
• a hydration monitor, a compensatory reserve monitor, a wrist-wearable sensor device, and/or another integrated system can include, but is not limited to, some or all of the following functionality, as described in further detail herein and in the Related Applications:
• the patient's normalized compensatory reserve can be displayed on a continuum between the minimum and maximum values (perhaps labeled by different symbols and/or colors depending on where the patient falls on the continuum).
• F Estimating a patient's current blood pressure and/or predicting a patient's future blood pressure.
• K Estimating a hydration state of a patient or user.
• M Estimating and/or predicting a volume of fluid intake necessary for adequate hydration of a patient or user.
• N Estimating a probability that a patient is dehydrated.
• CRI, HE, V, and/or Pf estimates can be (i) based on a fixed time history of patient monitoring (for example a 30 second or 30 heart beat window); (ii) based on a dynamic time history of patient monitoring (for example, monitoring for 200 minutes, the system may use all sensor information gathered during that time to refine and improve CRI estimates, hydration
• Certain embodiments can also recommend treatment options, based on the analysis of the patient's condition (including, without limitation, the
• Treatment options can include, without limitation, such things as optimizing hemodynamics, ventilator adjustments, IV fluid adjustments (e.g., controlling the flow rate of an IV pump or the drip rate of an IV drip), transfusion of blood or blood products, infusion of volume expanders, medication changes, changes in patient position and surgical therapy, and/or the like.
• certain embodiments can be used to control an IV drip, IV pump, or rapid infuser, and/or the like, to infuse one or more of a crystalloid, colloid, or blood product into a patient.
• an embodiment might estimate the probability that a patient requires fluids and activate such a device in response to that estimate (or instruct a clinician to attach such a device to the patient and activate the device).
• the system might then monitor the progress of the hydration effort (through continual or periodic assessment of the effectiveness of hydration) and increase/decrease drip or flow rates accordingly.
• certain embodiments can be used as an input for a hemodialysis procedure. For example, certain embodiments can predict how much intravascular (blood) volume can be safely removed from a patient during a hemodialysis process. For example, an embodiment might provide instructions to a human operator of a hemodialysis machine, based on estimates or predictions of the patient's CRI. Additionally and/or alternatively, such embodiments can be used to continuously self-adjust the ultra-filtration rate of the hemodialysis equipment, thereby completely avoiding intradialytic hypotension and its associated morbidity.
• certain embodiments can be used to estimate and/or predict a dehydration state (and/or the amount of dehydration) in an individual (e.g., a trauma patient, an athlete, an elder living at home, etc.) and/or to provide treatment (either by providing recommendations to treating personnel or by directly controlling appropriate therapeutic equipment).
• an analytical model indicates a relationship between CRI (and/or any other physiological phenomena that can be measured and/or estimated using the techniques described herein and in the Related Applications) and dehydration state
• an embodiment can apply that model, using the techniques described herein, to estimate a dehydration state of the patient.
• the tools provided by various embodiments include, without limitation, methods, systems, and/or software products.
• a method might comprise one or more procedures, any or all of which are executed by a computer system.
• an embodiment might provide a computer system configured with instructions to perform one or more procedures in accordance with methods provided by various other embodiments.
• a computer program might comprise a set of instructions that are executable by a computer system (and/or a processor therein) to perform such operations.
• software programs are encoded on physical, tangible, and/or non-transitory computer readable media (such as, to name but a few examples, optical media, magnetic media, and/or the like).
• a hydration monitor might comprise one or more sensors to obtain physiological data from a patient and a computer system in communication with the one or more sensors.
• the computer system might comprise one or more processors and a computer readable medium in communication with the one or more processors.
• the computer readable medium might have encoded thereon a set of instructions that, when executed by the one or more processors, causes the computer system to: receive a first set of physiological data from the one or more sensors at a first time in relation to a physical perturbation of the patient; calculate a first set of compensatory reserve index ("CRI") values of the patient; receive a second set of physiological data from the one or more sensors at a second time in relation to the physical perturbation of the patient; calculate a second set of CRI values of the patient; analyze the first and second sets of CRI values against a pre-existing CRI model; based at least in part on a relationship between the first and second sets of CRI values, estimate a hydration state of the patient; and display on a display device, an estimate of the hydration state of the patient.
• the one or more sensors might comprise a finger cuff comprising a fingertip photoplethysmograph
• the computer system might comprise a wrist unit in communication with the fingertip
• the wrist unit further comprising a wrist strap.
• estimating a hydration state of the patient might comprise determining whether the patient needs more fluids. In some cases, determining whether the patient needs more fluids might comprise determining whether the patient has an ongoing, progressive increase in fluid requirements (i.e., if the patient's hydration state is deteriorating), and/or the like. In some instances, determining whether the patient needs more fluids might comprise estimating how much fluid is needed, in some cases, based at least in part on how much fluid the patient has received between the first time and the second time in relation to the physical perturbation of the patient.
• a method might comprise monitoring, with one or more sensors, physiological data of a patient and receiving a first set of physiological data from the one or more sensors at a first time in relation to a physical perturbation of the patient.
• the method might also comprise calculating a first set of
• CRI compensatory reserve index
• the physical perturbation of the patient might comprise performance of an exercise, which might comprise a squat exercise, a sit-up exercise, and/or the like.
• the physical perturbation might comprise delivery of fluid to the patient.
• the physical perturbation might comprise introduction of positive or negative pressure to an airway of the patient.
• the physiological state might be a hydration state of the patient.
• the physiological state might be an estimated point of cardiovascular collapse of the patient.
• calculating the first or the second set of CRI values of the patient might comprise estimating a set of CRI values by comparing the physiological data to the pre-existing CRI model, which is constructed
• BLV(t) is an intravascular volume loss of a test subject at time t
• BLVHDD is an intravascular volume loss at a point of hemodynamic
• the physiological data might comprise waveform data
• estimating a set of CRI values of the patient might comprise comparing the waveform data with one or more sample waveforms generated by exposing each of one or more test subjects to a state of hemodynamic decompensation or near hemodynamic decompensation or to a series of states progressing towards hemodynamic decompensation, and monitoring physiological data of the test subjects.
• the physiological data might comprise waveform data
• estimating a set of CRI values of the patient might comprise: comparing the waveform data with a plurality of sample waveforms, each of the sample waveforms corresponding to a different set of values of the compensatory reserve index to produce a similarity coefficient expressing a similarity between the waveform data and each of the sample waveforms; normalizing the similarity coefficients for each of the sample waveforms; and summing the normalized similarity coefficients to produce an estimated set of CRI values for the patient.
• At least one of the one or more sensors might comprise at least one of a blood pressure sensor, an intracranial pressure monitor, a central venous pressure monitoring catheter, an arterial catheter, an electroencephalograph, a cardiac monitor, a transcranial Doppler sensor, a transthoracic impedance plethysmograph, a pulse oximeter, a near infrared
• the physiological data might comprise blood pressure waveform data.
• the physiological data might comprise plethysmograph waveform data.
• the physiological data might comprise photoplethysmograph (“PPG”) waveform data.
• the method might further comprise generating the pre-existing CRI model.
• generating the pre-existing model might comprise: receiving data pertaining to one or more physiological parameters of a test subject to obtain a plurality of physiological data sets; directly measuring one or more physiological states of the test subject with a reference sensor to obtain a plurality of physiological state measurements; and correlating the received data with the physiological state measurements of the test subject.
• the one or more physiological states might comprise one or more states comprising at least one of reduced circulatory system volume, blood loss, added fluids to blood volume, dehydration, hydration state, cardiovascular collapse, near- cardiovascular collapse, euvolemia, or hypervolemia, and/or the like.
• correlating the received data with the physiological state measurements of the test subject might comprise: identifying a most predictive set of signals Sk out of a set of signals si, si, SD for each of one or more outcomes ⁇ %, wherein the most-predictive set of signals Sk corresponds to a first data set representing a first physiological parameter, and wherein each of the one or more outcomes ok represents a
• estimating a physiological state of the patient might comprise a hydration state of the patient and determining whether the patient needs more fluids.
• determining whether the patient needs more fluids might comprise determining whether the patient has an ongoing, progressive increase in fluid requirements (i.e., if the patient's hydration state is deteriorating), and/or the like.
• determining whether the patient needs more fluids might comprise estimating how much fluid is needed, in some cases, based at least in part on how much fluid the patient has received between the first time and the second time in relation to the physical perturbation of the patient.
• an apparatus might comprise a non-transitory computer readable medium having encoded thereon a set of instructions executable by one or more computers to: receive a first set of physiological data from one or more sensors at a first time in relation to a physical perturbation of a patient, the one or more sensors monitoring physiological data of the patient; calculate a first set of compensatory reserve index ("CRI") values of the patient; receive a second set of physiological data from the one or more sensors at a second time in relation to the physical perturbation of the patient; calculate a second set of CRI values of the patient; analyze the first and second sets of CRI values against a pre-existing CRI model; based at least in part on a relationship between the first and second sets of CRI values, estimate a physiological state of the patient; and display on a display device, an estimate of the physiological state of the patient.
• CRI compensatory reserve index
• a system might comprise a processor and a non- transitory computer readable medium having encoded thereon a set of instructions that, when executed by the processor, causes the system to: receive a first set of physiological data from one or more sensors at a first time in relation to a physical perturbation of a patient, the one or more sensors monitoring physiological data of the patient; calculate a first set of compensatory reserve index ("CRI") values of the patient; receive a second set of physiological data from the one or more sensors at a second time in relation to the physical perturbation of the patient; calculate a second set of CRI values of the patient; analyze the first and second sets of CRI values against a pre-existing CRI model; based at least in part on a relationship between the first and second sets of CRI values, estimate a physiological state of the patient; and display on a display device, an estimate of the physiological state of the patient.
• CRI compensatory reserve index
• Fig. 1A provides a general overview of a system provided by certain embodiments.
• the system includes a computer system 100 in communication with one or more sensors 105, which are configured to obtain physiological data from the subject (e.g., animal or human test subject or patient) 110.
• the computer system 100 comprises a Lenovo THINKPAD X200, 4GB of RAM with Microsoft WINDOWS 7 operating system and is programmed with software to execute the computational methods outlined herein.
• the computational methods can be implemented in MATLAB 2009b and C++ programming languages.
• a more general example of a computer system 100 that can be used in some embodiments is described in further detail below. Even more generally, however, the computer system 100 can be any system of one or more computers that are capable of performing the techniques described herein.
• the computer system 100 is capable of reading values from the physiological sensors 105, generating models of physiological state from those sensors, and/or employing such models to make individual-specific estimations, predictions, or other diagnoses, displaying the results, recommending and/or implementing a therapeutic treatment as a result of the analysis, and/or archiving (or learning) these results for use in future, e.g., model building and making predictions, etc.
• the sensors 105 can be any of a variety of sensors (including without limitation those described herein) for obtaining physiological data from the subject.
• An exemplary sensor suite might include a Finometer sensor for obtaining a noninvasive continuous blood pressure waveform, a pulse oximeter sensor, an Analog to Digital Board (National Instruments USB-9215A 16-Bit, 4 channel) for connecting the sensors (either the pulse oximeter and/or the finometer) to the computer system 100.
• one or more sensors 105 might obtain, e.g., using one or more of the techniques described herein, continuous physiological waveform data, such as continuous blood pressure or the like.
• Input from the sensors 105 can constitute continuous data signals and/or outcomes that can be used to generate, and/or can be applied to, a predictive model as described below.
• the structure might include a therapeutic device 115
• the therapeutic device (also referred to herein as a "physiological assistive device”), which can be controlled by the computer system 100 to administer therapeutic treatment, in accordance with the recommendations developed by analysis of a patient's physiological data.
• the therapeutic device might comprise hemodialysis equipment (also referred to as a hemodialysis machine), which can be controlled by the computer system 100 based on the estimated CRI of the patient, as described in further detail below.
• therapeutic devices in other embodiments can include, without limitation, a cardiac assist device, a ventilator, an automatic implantable cardioverter defibrillator (“AICD”), pacemakers, an extracorporeal membrane oxygenation circuit, a positive airway pressure (“PAP") device (including without limitation a continuous positive airway pressure (“cPAP”) device or the like), an anesthesia machine, an integrated critical care system, a medical robot, intravenous and/or intra- arterial pumps that can provide fluids and/or therapeutic compounds (e.g., through intravenous injection), intravenous drips, a rapid infuser, a heating/cooling blanket, and/or the like.
• a cardiac assist device e.g., a ventilator, an automatic implantable cardioverter defibrillator (“AICD”), pacemakers, an extracorporeal membrane oxygenation circuit, a positive airway pressure (“PAP”) device (including without limitation a continuous positive airway pressure (“cPAP”) device or the like), an anesthesia machine, an integrated critical care system,
• Fig. IB illustrates in more detail an exemplary sensor device 105, which can be used in the system 100 described above.
• the illustrated sensor device 105 is designed to be worn on a patient's wrist and therefore can be used both in clinical settings and in the field (e.g., on any person for whom monitoring might be beneficial, for a variety of reasons, including without limitation assessment of blood pressure and/or hydration during athletic competition or training, daily activities, military training or action, etc.).
• the sensor device 105 can serve as an integrated hydration monitor, which can assess hydration as described herein, display an indication of the assessment, recommend therapeutic action based on the assessment, or the like, in a form factor that can be worn during athletic events and/or daily activities.
• the exemplary sensor 105 device (hydration monitor) includes a finger cuff 125 and a wrist unit 130.
• the finger cuff 125 includes a fingertip sensor 135 (in this case, a PPG sensor) that captures data based on physiological conditions of the patient, such as PPG waveform data.
• the sensor 135 communicates with an input/output unit 140 of the wrist unit 130 to provide output from the sensor 135 to a processing unit 145 of the wrist unit 130.
• Such communication can be wired (e.g., via a standard— such as USB— or proprietary connector on the wrist unit 130) and/or wireless (e.g., via Bluetooth, such as Bluetooth Low Energy (“BTLE”), near field connection (“NFC”), WiFi, or any other suitable radio technology).
• BTLE Bluetooth Low Energy
• NFC near field connection
• WiFi or any other suitable radio technology
• the processing unit can have different types of functionality. For example, in some cases, the processing unit might simply act to store and/or organize data prior to transmitting the data through the I/O unit 140 to a monitoring computer 100, which might perform data analysis, control a therapeutic device 115, etc. In other cases, however, the processing unit 145 might act as a specialized computer (e.g., with some or all of the components described in connection with Fig. 5, below and/or some or all of the functionality ascribed to the computer 100 of Figs. 1 A and IB), such that the processing unit can perform data analysis onboard, e.g., to estimate and/or predict a patient's current and/or future blood pressure.
• a specialized computer e.g., with some or all of the components described in connection with Fig. 5, below and/or some or all of the functionality ascribed to the computer 100 of Figs. 1 A and IB
• the wrist unit 105 might include a display, which can display any output described herein, including, without limitation, estimated and/or predicted values (e.g., of CRI, blood pressure, hydration status, etc.), data captured by the sensor (e.g., heart rate, pulse ox, etc.), and/or the like.
• estimated and/or predicted values e.g., of CRI, blood pressure, hydration status, etc.
• data captured by the sensor e.g., heart rate, pulse ox, etc.
• the wrist unit 130 might include a wrist strap 155 that allows the unit to be worn on the wrist, similar to a wrist watch or a wrist-mounted fitness tracker.
• a wrist strap 155 that allows the unit to be worn on the wrist, similar to a wrist watch or a wrist-mounted fitness tracker.
• the sensor device 105 might not include all of the components described above, and/or various components might be combined and/or reorganized; once again, the embodiment illustrated by Fig. IB should be considered to be only illustrative, and not limiting, in nature.
• FIGs. 2A, 2B, 3A, 3B, and 4 illustrate methods in accordance with various embodiments. While the methods of Figs. 2A, 2B, 3A, 3B, and 4 are illustrated, for ease of description, as different methods, it should be appreciated that the various techniques and procedures of these methods can be combined in any suitable fashion, and that, in some embodiments, the methods depicted by Figs. 2A, 2B, 3 A, 3B, and 4 can be considered interoperable and/or as portions of a single method. Similarly, while the techniques and procedures are depicted and/or described in a certain order for purposes of illustration, it should be appreciated that certain procedures may be reordered and/or omitted within the scope of various
• Figs. 2A, 2B, 3A, 3B, and 4 can be implemented by (and, in some cases, are described below with respect to) the computer system 100 of Fig. 1 (or other components of the system, such as the sensor 105 of Figs. 1A and IB), these methods may also be implemented using any suitable hardware implementation.
• the computer system 100 of Fig. 1 (and/or other components of such a system) can operate according to the methods illustrated by Figs. 2A, 2B, 3A, 3B, and 4 (e.g., by executing instructions embodied on a computer readable medium), the system 100 can also operate according to other modes of operation and/or perform other suitable procedures.
• a method might comprise one or more procedures, any or all of which are executed by a computer system.
• an embodiment might provide a computer system configured with instructions to perform one or more procedures in accordance with methods provided by various other embodiments.
• a computer program might comprise a set of instructions that are executable by a computer system (and/or a processor therein) to perform such operations.
• software programs are encoded on physical, tangible and/or non-transitory computer readable media (such as, to name but a few examples, optical media, magnetic media, and/or the like).
• various embodiments can comprise a method for using sensor data to assess the effectiveness of fluid resuscitation of a patient and/or the hydration of a patient.
• Fig. 2 illustrates an exemplary method 200 in accordance with various embodiments.
• the method 200 might comprise generating a model, e.g., with a computer system, against which patient data can be analyzed to estimate and/or predict various physiological states (block 205).
• generating the model can comprise receiving data pertaining to a plurality of more physiological parameters of a test subject to obtain a plurality of physiological data sets.
• Such data can include PPG waveform data to name one example, and/or any other type of sensor data including, without limitation, data captured by other sensors described herein and in the Related Applications.
• Generating a model can further comprise directly measuring one or more physiological states of the test subject with a reference sensor to obtain a plurality of physiological state measurements.
• the one or more physiological states can include, without limitation, states of various volumes of blood loss and/or fluid resuscitation, and/or various states of hydration and/or dehydration.
• different states can include a state of hypervolemia, a state of euvolemia, and/or a state of cardiovascular collapse (or near-cardiovascular collapse), and/or can include states that have been simulated, e.g., through use of an LBNP apparatus or the like.
• Other physiological states that can be used to generate a model are described elsewhere herein and in the Related Applications.
• Fig. 4 provides a technique using a machine-learning algorithm to optimize the correlation between measured physiological parameters (such as PPG waveform data, to name one example) and physical states (e.g., various blood volume states, including states where a known volume of blood loss has occurred and/or a known volume of fluid resuscitation has been administered, various states of hydration and/or dehydration, etc.).
• measured physiological parameters such as PPG waveform data, to name one example
• physical states e.g., various blood volume states, including states where a known volume of blood loss has occurred and/or a known volume of fluid resuscitation has been administered, various states of hydration and/or dehydration, etc.
• any suitable technique or model may be employed in accordance with various embodiments.
• physiological states can be modeled, and a number of different conditions can be imposed on test subjects as part of the model generation.
• physiological states that can be induced (or monitored when naturally occurring) in test subjects include, without limitation, reduced circulatory system volume, known volume of blood loss, specified amounts of fluids added to blood volume, dehydration, cardiovascular collapse or near-cardiovascular collapse, euvolemia, hypervolemia, low blood pressure, high blood pressure, normal blood pressure, and/or the like.
• a number of physiological parameters of a plurality of test subjects might be measured.
• a subject might undergo varying, measured levels of blood loss (either real or simulated) or intravenous fluid addition.
• the system can determine which sensor information most effectively differentiates between subjects at different blood loss/addition volume levels.
• some embodiments might construct a model based on data that is derived from sensor data.
• one such model might use, as input values, CRI values of test subjects in different blood loss and/or volume addition conditions. Accordingly, the process of generating a model might first comprise building a model of CRI, and then, from that model, building a model of hydration effectiveness. (In other cases, a hybrid model might consider both raw sensor data and CRI data.)
• a CRI model can be generated in different ways. For example, in some cases, one or more test subjects might be subjected to LBNP.
• LBNP data is collected from human subjects being exposed to progressively lower levels of LBNP, until each subject experiences hemodynamic decompensation, at which time LBNP is released and the subject recovers.
• Each level of LBNP represents an additional amount of blood loss.
• physiological data including, without limitation, waveform data, such as continuous non-invasive blood pressure data, etc.
• waveform data such as continuous non-invasive blood pressure data, etc.
• a relationship (as expressed by Equation 2) can be identified between LBNP and intravascular volume loss, and this relationship can be used to estimate CRI.
• Equation 2 a relationship (as expressed by Equation 2) can be identified between LBNP and intravascular volume loss, and this relationship can be used to estimate CRI.
• LBNP studies form a framework (methodology) for the development of the hemodynamic parameter referred to herein as CRI and can be used to generate models of
• LBNP blood pressure
• dehydration can be used to induce this condition as well.
• Other techniques are possible as well.
• data collected from a subject in a state of euvolemia, dehydration, hypervolemia, and/or other states might be used to generate a CRI model in different embodiments.
• the method 200 comprises monitoring, with one or more sensors, physiological data of a patient.
• physiological data a variety of physical parameters can be monitored, invasively and/or non-invasively, depending on the nature of the anticipated physiological state of the patient.
• monitoring the one or more physical parameters might comprise receiving, e.g., from a physiological sensor, continuous waveform data, which can be sampled as necessary.
• continuous waveform data can include, without limitation, plethysmograph waveform data, PPG waveform data (such as that generated by a pulse oximeter), and/or the like.
• the method comprises perturbing the patient's body, or a portion thereof.
• physically perturbing any portion of a subject's body can be considered to be perturbing the patient's body itself.
• physically perturbing the patient's body might involve the use of a perturbation device, as described above, although this is not necessary.
• the body might perturb itself through a change in position, a spontaneous breath, or a premature ventricular contraction or other cardiac dysrhythmia, to name a few examples.
• perturbing the patient's body might simply comprise monitoring and/or detecting the self-perturbation of the body.
• Many different types of physical perturbations are described above, and any of these, or other modes of perturbation may be employed in accordance with the method 200.
• the method 200 might further comprise analyzing, with a computer system (e.g., a monitoring computer 100 and/or a processing unit 135 of a sensor unit, as described above), the physiological data (block 220).
• a computer system e.g., a monitoring computer 100 and/or a processing unit 135 of a sensor unit, as described above
• the physiological data is analyzed against a pre-existing model (which might be generated as described above and which in turn, can be updated based on the analysis, as described in further detail below and in the Related Applications).
• sensor data can be analyzed directly against a generated model to assess the effectiveness of hydration (which can include estimating current values, and/or predicting future values for any or all of HE, V, and/or Pf, as expressed above.
• the sensor data can be compared to determine similarities with models that estimate and/or predict any of these values.
• an input waveform captured by a sensor from a patient might be compared with sample waveforms generated by models for each of these values.
• the sensor data is analyzed to determine a patient response to the one or more physical perturbations (block 225).
• This analysis might, comprise, for example, identifying a change in a patient's CRI values (which can be calculated, e.g., using the technique described with respect to the method 300, discussed below with regard to Fig. 3A), based on data samples taken before the perturbation, at one or more points during the perturbation, and/or after the perturbation.
• the analysis might comprise analyzing the input data against a model of a specified physiological state.
• the model is pre- existing (i.e., generated prior to receiving the input data), although, as noted above, the model can be refined using the input data and the results of the analysis itself.
• the model provides an algorithm to which the input data can be applied, to produce output data relating to an estimated/predicted physiological state of the patient that corresponds to the patient's measured physiological parameters.
• the technique 265 (as shown in Fig. 2B) provides one method for deriving an estimate of HE in accordance with some embodiments.
• the technique 265 is presented as an example only, and that while this technique 265 estimates HE from raw sensor data, similar techniques can be used to estimate or predict HE, V, and/or Pf from raw sensor data, from CRI data, and/or from a combination of both.
• one model might produce a first estimate of HE from raw sensor data, produce a second estimate of HE from estimated CRI values, and then combine those estimates (in either weighted or unweighted fashion) to produce a hybrid HE estimate.
• this technique 265 can be used to estimate HE (or any other physical state disclosed in the Related Applications) from variations in CRI data (which variations themselves can be used as input data to the technique 265).
• the illustrated technique 265 comprises sampling waveform data (e.g., any of the data described herein and in the Related Applications, including without limitation arterial waveform data, such as continuous PPG waveforms and/or continuous noninvasive blood pressure waveforms) for a specified period, such as 22 or 32 heartbeats (block 270).
• waveform data e.g., any of the data described herein and in the Related Applications, including without limitation arterial waveform data, such as continuous PPG waveforms and/or continuous noninvasive blood pressure waveforms
• a specified period such as 22 or 32 heartbeats
• multiple CRI data points can be used as the data sample. That sample is compared with a plurality of waveforms (or data points) of reference data corresponding to HE values (block 275), which in this case range from 0 to 1 using the scale described above (but alternatively might use any appropriate scale).
• any number of sample waveforms can be used for the comparison; for example, if there is a nonlinear relationship between the measured sensor data and the HE values, more sample waveforms might provide for a better comparison.
• a similarity coefficient is calculated (e.g., using a least squares or similar analysis) to express the similarity between the sampled waveform and each of the reference waveforms (block 280).
• These similarity coefficients can be normalized (if appropriate) (block 285), and the normalized coefficients can be summed (block 390) to produce an estimated HE value of the patient.
• Similar techniques can be used to analyze data against a model based on parameters derived from direct sensor measurements.
• such operations can be iterative in nature, by generating the derived parameters—such as CRI, to name one example— by analyzing the sensor data against a first model, and then analyzing the derived parameters, and in particular cases, variance in derived parameters, such as CRI, against a second model.
• Fig 3A illustrates a method 300 of calculating a patient's
• the method 300 includes generating a model of CRI (block 305), monitoring
• the method 300 includes estimating, with the computer system, a compensatory reserve (or CRI) of the patient, based on analysis of the physiological data (block 320).
• the method might further comprise predicting, with the computer system, the compensatory reserve (or CRI) of the patient at one or more time points in the future, based on analysis of the physiological data (block 325).
• the operations to predict a future value of a parameter can be similar to those for estimating a current value; in the prediction context, however, the applied model might correlate measured data in a test subject with subsequent values of the diagnostic parameter, rather than contemporaneous values.
• the same model can be used to both estimate a current value and predict future values of a physiological parameter.
• the estimated and/or predicted compensatory reserve of the patient can be based on several factors.
• the estimated/predicted compensatory reserve (or CRI) can be based on a fixed time history of monitoring the physiological data of the patient and/or a dynamic time history of monitoring the physiological data of the patient.
• the estimated/predicted compensatory reserve can be based on a baseline estimate of the patient's compensatory reserve established when the patient is euvolemic.
• the estimate and/or prediction might not be based on a baseline estimate of the patient's compensatory reserve established when the patient is euvolemic.
• Fig. 3B illustrates one technique 370 for deriving an estimate of CRI in accordance with some embodiments similar to the technique 265 described above with respect to Fig. 2B for deriving an assessment of hydration effectiveness directly from sensor data (and, in fact, CRI can be derived as described herein, and that derived set of values can be used, alone or with raw sensor data, to assess such effectiveness).
• the illustrated technique comprises sampling waveform data (e.g., any of the data described herein and in the Related Applications, including without limitation arterial waveform data, such as continuous PPG waveforms and/or continuous noninvasive blood pressure waveforms) for a specified period, such as 32 heartbeats (block 375).
• That sample is compared with a plurality of waveforms of reference data corresponding to different sets of CRI values (block 380).
• These reference waveforms might be derived using the algorithms described in the Related Applications, might be the result of experimental data, and/or the like).
• the sample might be compared with waveforms corresponding to a CRI of 1 (block 380a), a CRI of 0.5 (block 380b), and a CRI of 0 (block 380c), as illustrated.
• a similarity coefficient is calculated (e.g., using a least squares or similar analysis) to express the similarity between the sampled waveform and each of the reference waveforms (block 385).
• These similarity coefficients can be normalized (if appropriate) (block 390), and the normalized coefficients can be summed (block 395) to produce an estimated value or set of values of the patient's CRI.
• the method 300 can comprise estimating and/or predicting a patient's dehydration state (block 330).
• the patient's state of dehydration can be expressed in a number of ways. For instance, the state of dehydration might be expressed as a normalized value (for example, with 1.0 corresponding to a fully hydrated state and 0.0 corresponding to a state of morbid dehydration). In other cases, the state of dehydration might be expressed as a missing volume of fluid or as a volume of fluid present in the patient's system, or using any other appropriate metric.
• estimating a dehydration state of the patient might comprise estimating the compensatory reserve (e.g., CRI) of the patient, and then, based on that estimate and the known relationship, estimating the dehydration state.
• a predicted value of compensatory reserve (or CRI) at some point in the future can be used to derive a predicted dehydration state at that point in the future.
• Other techniques might use a parameter other than CRI to model dehydration state.
• the method 300 might further comprise normalizing the results of the analysis (block 335), such as the compensatory reserve, dehydration state, and/or probability of bleeding, to name a few examples.
• the estimated/predicted compensatory reserve of the patient can be normalized relative to a normative normal blood volume value corresponding to euvolemia, a normative excess blood volume value corresponding to circulatory overload, and a normative minimum blood volume value corresponding to cardiovascular collapse. Any values can be selected as the normative values.
• the normative excess blood volume value is >1
• the normative normal blood volume value is 1
• the normative minimum blood volume value is 0.
• the normative excess blood volume value might be defined as 1, the normative normal blood volume value might be defined as 0, and the normative minimum blood volume value at the point of cardiovascular collapse might be defined as -1.
• different embodiments might use a number of different scales to normalize CRI and other estimated parameters.
• normalizing the data can provide benefits in a clinical setting, because it can allow the clinician to quickly make a qualitative judgment of the patient's condition, while interpretation of the raw estimates/predictions might require additional analysis.
• that estimate might be normalized relative to a normative normal blood volume value corresponding to euvolemia and a normative minimum blood volume value corresponding to cardiovascular collapse.
• any values can be selected as the normative values.
• the normative normal blood volume is defined as 1, and the normative minimum blood volume value is defined as 0, the normalized value, falling between 0.0 and 1.0 can quickly apprise a clinician of the patient's location on a continuum between euvolemia and cardiovascular collapse. Similar normalizing procedures can be implemented for other estimated data (such as probability of bleeding, dehydration, and/or the like).
• the method 300 might further comprise displaying data with a display device (block 340).
• data might include an estimate and/or prediction of the compensatory reserve (or CRI) of the patient and/or an estimate and/or prediction of the patient's dehydration state.
• CRI compensatory reserve
• a variety of techniques can be used to display such data.
• displaying the estimate of the compensatory reserve (or CRI) of the patient might comprise displaying the normalized estimate of the compensatory reserve (or CRI) of the patient.
• displaying the normalized estimate of the compensatory reserve of the patient might comprise displaying a graphical plot showing the normalized excess blood volume value, the normalized normal blood volume value, the normalized minimum blood volume value, and the normalized estimate of the compensatory reserve (e.g., relative to the normalized excess blood volume value, the normalized normal blood volume value, the normalized minimum blood volume value).
• the method 300 might comprise repeating the operations of monitoring physiological data of the patient, analyzing the physiological data, and estimating (and/or predicting) the compensatory reserve of the patient, to produce a new estimated (and/or predicted) compensatory reserve of the patient.
• displaying the estimate (and/or prediction) of the compensatory reserve of the patient might comprises updating a display of the estimate of the compensatory reserve to show the new estimate (and/or prediction) of the compensatory reserve, in order to display a plot of the estimated compensatory reserve over time.
• the patient's compensatory reserve can be repeatedly estimated and/or predicted on any desired interval (e.g., after every heartbeat), on demand, etc.
• the method 300 can further comprise determining a probability that the patient is bleeding, and/or displaying, with the display device, an indication of the probability that the patient is bleeding (block 345). For example, some embodiments might generate a model based on data that removes fluid from the circulatory system (such as LBNP, dehydration, etc.). Another embodiment might generate a model based on fluid removed from a subject voluntarily, e.g., during a blood donation, based on the known volume (e.g., 500cc) of the donation. Based on this model, using techniques similar to those described above, a patient's physiological data can be monitored and analyzed to estimate a probability that the patient is bleeding (e.g., internally).
• data that removes fluid from the circulatory system such as LBNP, dehydration, etc.
• Another embodiment might generate a model based on fluid removed from a subject voluntarily, e.g., during a blood donation, based on the known volume (e.g., 500cc) of the donation. Based
• the probability that the patient is bleeding can be used to adjust the patient's estimated CRI (block 350). Specifically, give a probability of bleeding expressed as Pr_Bleed at a time t, the adjusted value of CRI can be expressed as:
• the estimated CRI can be adjusted to produce a more accurate diagnosis of the patient's condition at a given point in time.
• the method 300 might comprise selecting, with the computer system, a recommended treatment option for the patient, and/or displaying, with the display device, the recommended treatment option (block 355).
• the recommended treatment option can be any of a number of treatment options, including, without limitation, optimizing hemodynamics of the patient, a ventilator adjustment, an intravenous fluid adjustment, transfusion of blood or blood products to the patient, infusion of volume expanders to the patient, a change in medication administered to the patient, a change in patient position, and surgical therapy.
• the method 300 might comprise controlling operation of hemodialysis equipment (block 360), based at least in part on the estimate of the patient's compensatory reserve.
• a computer system that performs the monitoring and estimating functions might also be configured to adjust an ultra-filtration rate of the hemodialysis equipment in response to the estimated CRI values of the patient.
• the computer system might provide instructions or suggestions to a human operator of the hemodialysis equipment, such as instructions to manually adjust an ultra-filtration rate, etc.
• the method 300 might include assessing the tolerance of an individual to blood loss, general volume loss, and/or dehydration (block 365). For example, such embodiments might include estimating a patient's CRI based on the change in a patient's position (e.g., from lying prone to standing, lying prone to sitting, and/or sitting to standing). Based on changes to the patient's CRI in response to these maneuvers, the patient's sensitivity to blood loss, volume loss, and/or dehydration can be measured.
• this measurement can be performed using a CRI model generated as described above; the patient can be monitored using one or more of the sensors described above, and the changes in the sensor output when the subject changes position can be analyzed according to the model (as described above, for example) to assess the tolerance of the individual to volume loss.
• Such monitoring and/or analysis can be performed in real time.
• the method 200 can include assessing the effectiveness of hydration of the patient (block 230), based on analysis of the patient's physiological data against the model.
• assessing effectiveness of hydration can include estimating or predicting a number of values, such as the estimated effectiveness, HE, of the hydration effort, the volume, V, of fluid necessary for effective hydration, the probability, Pf, that the patient needs fluids, and/or the like.
• the assessment of the effectiveness of hydration will be based on the analysis of a plurality of measured (or derived) values of a particular physiological parameter (or plurality of parameters).
• the analysis of the data might be performed on a continuous waveform, either during or after measurement of the waveform with a sensor (or both), and the assessment of the effectiveness can be updated as hydration efforts continue.
• the amount of fluids added to the patient's blood volume can be measured directly, and these direct measurements (at block 235) can be fed back into the model to update the model and thereby improve performance of the algorithms in the model (e.g., by refining the weights given to different parameters in terms of estimative or predictive value).
• the updated model can then be used to continue assessing the treatment (in the instant patient and/or in a future patient), as shown by the broken lines on Fig. 2A.
• the method 200 comprises displaying data (block 240) indicating the assessment of the effectiveness of hydration.
• the data might be displayed on a display of a sensor device (such as the device 105 illustrated by Fig. IB).
• the data might be displayed on a dedicated machine, such as a compensatory reserve monitor, or on a monitor of a generic computer system.
• the data might be displayed alphanumerically, graphically, or both.
• the method 200 can include selecting and/or displaying treatment options for the patient (block 245) and/or controlling a therapeutic device (block 250) based on the assessment of the effectiveness of hydration of the patient.
• a display might indicate to a clinician or the patient himself or herself that the patient is becoming (or has become) dehydrated, that fluid resuscitation therapy should be initiated, an estimated volume of fluid to drink, infuse, or otherwise consume, a drip rate for an IV drip, a flow rate for an IV pump or infuser, or the like.
• the system might be configured to control operation of a therapeutic device, such as dispensing a fluid to drink from an automated dispenser, activating or adjusting the flow rate of an IV pump or infuser, adjusting the drip rate of an IV drip, and/or the like, based on the assessment of the effectiveness of hydration.
• a therapeutic device such as dispensing a fluid to drink from an automated dispenser, activating or adjusting the flow rate of an IV pump or infuser, adjusting the drip rate of an IV drip, and/or the like, based on the assessment of the effectiveness of hydration.
• a water bladder e.g., a backpack-based hydration pack, such as those available from Camelbak Products LLC
• the hydration monitor could communicate with and/or control operation of such a dispensing device (e.g., to cause the device to dispense a certain amount of fluid, to cause the device to trigger an audible alarm, etc.).
• the method 200 can include functionality to help a clinician (or other entity) to monitor hydration, fluid resuscitation and/or blood volume status.
• a clinician or other entity
• any measure of effectiveness outside of the normal range would set off various alarm conditions, such as an audible alarm, a message to a physician, a message to the patient, an update written automatically to a patient's chart, etc.
• Such messaging could be accomplished by electronic mail, text message, etc., and a sensor device or monitoring computer could be configured with, e.g., an SMTP client, text messaging client, or the like to perform such messaging.
• a hydration monitor might be configured to send monitoring results (e.g., any of the assessments, estimates and/or predictions described herein) to another device or computer, either for personal monitoring by the patient or for monitoring by another. Examples could include transmitting such alarms or data (e.g., by Bluetooth, NFC, WiFi, etc.) to a wireless phone, wearable device (e.g., smart watch or glasses) or other personal device of the patient, e.g., for inclusion in a health monitoring application.
• monitoring results e.g., any of the assessments, estimates and/or predictions described herein
• Examples could include transmitting such alarms or data (e.g., by Bluetooth, NFC, WiFi, etc.) to a wireless phone, wearable device (e.g., smart watch or glasses) or other personal device of the patient, e.g., for inclusion in a health monitoring application.
• such information could be sent to a specified device or computer (e.g., via any available IP connection), for example to allow a parent to monitor a child's (or a child to monitor an elderly parent's) hydration remotely, to allow a coach to monitor a player's hydration remotely, and/or to allow a superior officer to monitor a soldier's hydration remotely.
• a coach or superior officer an application might aggregate results from a plurality of hydration monitors, to allow the supervisor to view (e.g., in a dashboard-type configuration), hydration effectiveness (and/or any other data, such as CRI, blood pressure, etc.) for a group of people.
• Such a display might employ, for example, a plurality of "fuel gauge” displays, one (or more) for each person in the group, allowing the supervisor to quickly ascertain any unusual results (e.g., based on the color of the gauge, etc.).
• an alarm condition were met for another physiological parameter (such as blood pressure, which can be estimated as described in the '171 Application, for example), that alarm could trigger an assessment of hydration effectiveness via this the method 200, to determine whether the first alarm condition has merit or not. If not, perhaps there could be an automated silencing of the original alarm condition, since all is well at present. More generally, the assessment techniques could be added to an ecosystem of monitoring algorithms (including without limitation those described in the Related Applications), which would inform one another or work in combination, to inform one another about how to maintain optimal physiological stability.
• another physiological parameter such as blood pressure, which can be estimated as described in the '171 Application, for example
• Fig. 4 illustrates a method 400 of employing such a self-learning predictive model (or machine learning) technique, according to some embodiments.
• the method 400 can be used to correlate physiological data received from a subject sensor with a measured physiological state. More specifically, with regard to various embodiments, the method 400 can be used to generate a model for assessing, predicting and/or estimating various physiological parameters, such as blood loss volume, effectiveness of hydration or fluid resuscitation efforts, estimated and/or predicted blood pressure, CRI, the probability that a patient is bleeding, a patient's dehydration state, and/or the like, from one or more of a number of different physiological parameters, including without limitation those described above and in the Related Applications.
• various physiological parameters such as blood loss volume, effectiveness of hydration or fluid resuscitation efforts, estimated and/or predicted blood pressure, CRI, the probability that a patient is bleeding, a patient's dehydration state, and/or the like, from one or more of a number of different physiological parameters, including without limitation those
• the method 400 begins at block 405 by collecting raw data measurements that may be used to derive a set of D data signals s lt ... , s D as indicated at block 410 (each of the data signals s being, in a particular case, input from one or many different physiological sensors).
• data signals can be retrieved from a computer memory and/or can be provided from a sensor or other input device.
• the data signals might correspond to the output of the sensors described above (which measure the types of waveform data described above, such as continuous, non-invasive PPG data and/or blood pressure waveform data).
• a set of K current or future outcomes o (o ⁇ ... , o K ) is hypothesized at block 415 (the outcomes o being, in this case, past and/or future physiological states, such as probability that fluids are needed, volume of fluid needed for effective hydration or fluid resuscitation, HE, CRI, dehydration state, probability of bleeding, etc.).
• the method autonomously generates a predictive model M that relates the derived data signals s with the outcomes o.
• “autonomous,” means "without human intervention.”
• this is achieved by identifying the most predictive set of signals Sk , where Sk contains at least some (and perhaps all) of the derived signals s lt ... , s D for each outcome Ok , where k G ⁇ 1, ... , K ⁇ .
• the method 400 loops so that the data are added incrementally to the model for the same or different sets of signals Sk, for all k G ⁇ 1, ... , K ⁇ .
• a linear model framework may be used to identify predictive variables for each new increment of data.
• a linear model may be constructed that has the form, for all k G ⁇ 1, ... , K ⁇ ,
• fk is any mapping from one input to one output
• ( Q, ( I ... ⁇ are the linear model coefficients.
• the framework used to derive the linear model coefficients may estimate which signals s, s lt ... , s d are not predictive and accordingly sets the corresponding coefficients a 0 , a x , ... , a d to zero.
• the model builds a predictive density model of the data
• a prediction system can be implemented that can predict future results from previously analyzed data using a predictive model and/or modify the predictive model when data does not fit the predictive model.
• the prediction system can make predictions and/or to adapt the predictive model in real-time.
• a prediction system can use large data sets not only to create the predictive model, but also predict future results as well as adapt the predictive model.
• a self-learning, prediction device can include a data input, a processor and an output. Memory can include application software that when executed can direct the processor to make a prediction from input data based on a predictive model. Any type of predictive model can be used that operates on any type of data.
• the predictive model can be implemented for a specific type of data.
• the predictive model when data is received the predictive model can determine whether it understands the data according to the predictive model. If the data is understood, a prediction is made and the appropriate output provided based on the predictive model. If the data is not understood when received, then the data can be added to the predictive model to modify the model. In some embodiments, the device can wait to determine the result of the specified data and can then modify the predictive model accordingly. In some embodiments, if the data is understood by the predictive model and the output generated using the predictive model is not accurate, then the data and the outcome can be used to modify the predictive model. In some embodiments, modification of the predictive model can occur in real-time.
• Particular embodiments can employ the tools and techniques described in the Related Applications in accordance with the methodology described herein perform the functions of a cardiac reserve monitor, a wrist-wearable sensor device, and/or a monitoring computer, as described herein (the functionality of any or all of which can be combined in a single, integrated device, in some embodiments).
• These functions include, but are not limited to, assessing fluid resuscitation of a patient, assessing hydration of a patient, monitoring, estimating and/or predicting a subject's (including, without limitation, a patient's) current or future blood pressure and/or compensatory reserve, estimating and/or determining the probability that a patient is bleeding (e.g., internally) and/or has been bleeding, recommending treatment options for such conditions, and/or the like.
• Fig. 5 provides a schematic illustration of one embodiment of a computer system 500 that can perform the methods provided by various other embodiments, as described herein, and/or can function as a monitoring computer, CRI monitor, processing unit of sensor device, etc. It should be noted that Fig. 5 is meant only to provide a generalized illustration of various components, of which one or more (or none) of each may be utilized as appropriate. Fig. 5, therefore, broadly illustrates how individual system elements may be implemented in a relatively separated or relatively more integrated manner.
• the computer system 500 is shown comprising hardware elements that can be electrically coupled via a bus 505 (or may otherwise be in communication, as appropriate).
• the hardware elements may include one or more processors 510, including, without limitation, one or more general-purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like).
• processors 510 including, without limitation, one or more general-purpose processors and/or one or more special-purpose processors (such as digital signal processing chips, graphics acceleration processors, and/or the like).
• embodiments can employ as a processor 510 any device (or combination of devices) that can operate to execute instructions to perform functions as described herein.
• any microprocessor also sometimes referred to as a central processing unit, or "CPU” can be used as the processor 510, including, without limitation, one or more complex instruction set computing (“CISC”) microprocessors, such as the single core and multicore processors available from Intel CorporationTM and others, such as Intel's X86 platform, including, e.g., the PentiumTM, CoreTM, and XeonTM lines of processors.
• CISC complex instruction set computing
• RISC reduced instruction set computing
• processors such as the IBM PowerTM line of processors, processors employing chip designs by ARM HoldingsTM, and others can be used in many embodiments.
• a processor 510 might be a
• SoC system on a chip
• processor can mean a single processor or processor core (of any type) or a plurality of processors or processor cores (again, of any type) operating individually or in concert.
• the computer system 500 might include a general-purpose processor having multiple cores, a digital signal processor, and a graphics acceleration processor.
• the computer system 500 might include a CPU for general purpose tasks and one or more embedded systems or microcontrollers, for example, to run real-time functions.
• the functionality described herein can be allocated among the various processors or processor cores as needed for specific implementations.
• processors have been described herein for illustrative purposes, these examples should not be considered limiting.
• the computer system 500 can also include (or be in communication with) one or more input devices 515, which can include, without limitation, a mouse, a keyboard, a touch screen, a trackpad, and/or the like; and one or more output devices 520, which can include, without limitation, a display device, a printer and/or the like.
• input devices 515 can include, without limitation, a mouse, a keyboard, a touch screen, a trackpad, and/or the like
• output devices 520 which can include, without limitation, a display device, a printer and/or the like.
• the computer system 500 may further include (and/or be in communication with) one or more storage devices 525, which can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an optical storage device, solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like.
• storage devices 525 can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an optical storage device, solid-state storage device such as a random access memory (“RAM”) and/or a read-only memory (“ROM”), which can be programmable, flash-updateable and/or the like.
• RAM random access memory
• ROM read-only memory
• Such storage devices may be configured to implement any appropriate data stores, including, without limitation, various file systems, database structures, and/or the like.
• the computer system 500 might also include a communications subsystem 530, which can include, without limitation, a modem, a network card (wireless or wired), an infra-red communication device, a wireless communication device and/or chipset (such as a BluetoothTM device, an 802.11 device, a WiFi device, a WiMax device, a WW AN device, cellular communication facilities, etc.), and/or the like.
• the communications subsystem 530 may permit data to be exchanged with a network (such as the network described below, to name one example), with other computer systems, and/or with any other devices described herein.
• the computer system 500 will further comprise a working memory 535, which can include a RAM or ROM device, as described above.
• the computer system 500 also may comprise software elements, shown as being currently located within the working memory 535, including an operating system 540, device drivers, executable libraries, and/or other code, such as one or more application programs 545, which may comprise computer programs provided by various embodiments, and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein.
• application programs 545 may comprise computer programs provided by various embodiments, and/or may be designed to implement methods, and/or configure systems, provided by other embodiments, as described herein.
• code and/or instructions can be used to configure and/or adapt a general purpose computer (or other device) to perform one or more operations in accordance with the described methods.
• a set of these instructions and/or code might be encoded and/or stored on a non-transitory computer readable storage medium, such as the storage device(s) 525 described above.
• the storage medium might be incorporated within a computer system, such as the system 500.
• the storage medium might be separate from a computer system (i.e., a removable medium, such as a compact disc, etc.), and/or provided in an installation package, such that the storage medium can be used to program, configure and/or adapt a general purpose computer with the instructions/code stored thereon.
• These instructions might take the form of executable code, which is executable by the computer system 500 and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer system 500 (e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code.
• some embodiments may employ a computer system (such as the computer system 500) to perform methods in accordance with various embodiments of the invention. According to a set of embodiments, some or all of the procedures of such methods are performed by the computer system 500 in response to processor 510 executing one or more sequences of one or more instructions (which might be incorporated into the operating system 540 and/or other code, such as an application program 545) contained in the working memory 535. Such instructions may be read into the working memory 535 from another computer readable medium, such as one or more of the storage device(s) 525. Merely by way of example, execution of the sequences of instructions contained in the working memory 535 might cause the processor(s) 510 to perform one or more procedures of the methods described herein.
• a computer system such as the computer system 500
• machine readable medium and “computer readable medium,” as used herein, refer to any medium that participates in providing data that causes a machine to operate in a specific fashion.
• various computer readable media might be involved in providing instructions/code to processor(s) 510 for execution and/or might be used to store and/or carry such instructions/code (e.g., as signals).
• a computer readable medium is a non-transitory, physical, and/or tangible storage medium.
• a computer readable medium may take many forms, including, but not limited to, non-volatile media, volatile media, or the like.
• Non-volatile media includes, for example, optical and/or magnetic disks, such as the storage device(s) 525.
• Volatile media includes, without limitation, dynamic memory, such as the working memory 535.
• a computer readable medium may take the form of transmission media, which includes, without limitation, coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 505, as well as the various components of the communication subsystem 530 (and/or the media by which the communications subsystem 530 provides communication with other devices).
• transmission media can also take the form of waves (including, without limitation, radio, acoustic, and/or light waves, such as those generated during radio- wave and infra-red data communications).
• Common forms of physical and/or tangible computer readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, a RAM, ROM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read instructions and/or code.
• Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor(s) 510 for execution.
• the instructions may initially be carried on a magnetic disk and/or optical disc of a remote computer.
• a remote computer might load the instructions into its dynamic memory and send the instructions as signals over a transmission medium to be received and/or executed by the computer system 500.
• These signals which might be in the form of electromagnetic signals, acoustic signals, optical signals and/or the like, are all examples of carrier waves on which instructions can be encoded, in accordance with various embodiments of the invention.
• the communications subsystem 530 (and/or components thereof) generally will receive the signals, and the bus 505 then might carry the signals (and/or the data, instructions, etc. carried by the signals) to the working memory 535, from which the processor(s) 505 retrieves and executes the instructions.
• the instructions received by the working memory 535 may optionally be stored on a storage device 525 either before or after execution by the processor(s) 510.
• Figs. 6-8 illustrate exemplary screen captures from a display device of a compensatory reserve monitor, showing various features that can be provided by one or more embodiments. Similar screens could be shown by other monitoring devices, such as a display of a wrist- wearable sensor device, a display of a monitoring computer, and/or the like. While Figs. 6-8 use HE as an example condition for illustrative purposes, other embodiments might also display values for the volume, V, the volume of fluid necessary for effective hydration, or the probability, Pf, that the patient needs fluid (including additional fluid, if hydration efforts already are is underway).
• FIG. 6 illustrates an exemplary display 600 of a compensatory reserve monitor implementation where a normalized hydration effectiveness ("HE") of " 1 " implies that the hydration efforts have been or are completely effective, and "0” implies that the hydration efforts have been or are completely ineffective. Values in between “0” and “ 1” imply a continuum of effectiveness.
• HE normalized hydration effectiveness
• Fig. 7A illustrates four screen captures 700 of a display of a compensatory reserve monitor implementation that displays HE as a "fuel gauge" type bar graph for a person undergoing central volume blood loss and subsequent hydration efforts. While Fig. 6 illustrates a trace of HE over time, the bar graphs of Figure 7A provide snapshots of HE at the time of each screen capture. (In the illustrated implementation, the bar graphs are continuously and/or periodically updated, such that each bar graph could correspond to a particular position on the X- axis of Figure 6.)
• Fig. 7B illustrates similar "fuel gauge” type displays, but the displays 705 of Fig. 7B feature bars of different colors - for example, green (illustrated by diagonal cross-hatching), yellow (illustrated by a checked or checkered pattern), and red (illustrated by gray shading) corresponding to different levels of HE, along with arrows 710 indicating trending in the HE values (e.g., rising, declining, or remaining stable).
• a "fuel gauge” display (or other indicator of HE and/or different physiological parameters) can be incorporated in a more comprehensive user interface.
• Fig. 8 illustrates an exemplary display 800 of a monitoring system.
• the display 800 includes a graphical, color-coded "fuel gauge” type display 805 of the current estimated HE (similar to the displays illustrated by Fig. 7B), along with a historical display 810 of recent CRI estimates; in this example, each bar on the historical display 810 might correspond to an estimate performed every minute, but different estimate frequencies are possible, and in some embodiments, the operator can be given the option to specify a different frequency.
• the display 800 also includes numerical display 815 of the current HE as well as a trend indicator 820 (similar to that indicated above).
• the display 800 can include additional information (and, in some cases, the types of information displayed and/or the type of display can be configured by the operator).
• the exemplary display 800 includes an indicator 825 of the patient's current heart rate and an indicator 830 of the patient's blood oxygen saturation level (Sp02).
• the exemplary display 800 also includes an indicator of the estimated volume, V, necessary for effective hydration, as well as a numerical indicator 840, a trend indicator 845, and a similar color coded "fuel gauge" display 850 of the current set of CRI values.
• Other monitored parameters might be displayed as well, such as an ECG tracing, blood pressure, probability of bleeding estimates, and/or the like.
• FVR Algorithm Fluid Volume Requirements
• DOFL Algorithm Detection of Ongoing Fluid Loss
• REIFR Algorithm Rapid Estimation of Immediate Fluid Requirements
• Resuscitation was immediately given if the subject experienced symptoms due to blood loss, defined as a systolic blood pressure ⁇ 80mmHg or MAP 30% below baseline. After blood removal was complete, the removed volume was reinfused. Subjects were monitored continuously with the Nonin 9560 fingertip pulse oximeter. PPG signals were recorded and synchronized with blood draws and reinfusion. The FVR algorithm was applied to 5-minute moving time windows during the
• resuscitation blood reinfusion, shown as the thick gray curve 910 overlaid on the blood draw profile 905 in Fig. 9A
• Ongoing fluid loss was the blood draw period in the study (shown as the thick gray line segments 915 overlaid on the non-ongoing periods of the blood draw period (shown by the dashed line curve 920) in Fig. 9B).
• the DOFL algorithm was applied to 5-minute moving time windows during the study and gave real-time classification of ongoing bleeding. Finally, the subject was classified as needing fluid once 5% volume loss was achieved.
• 9C depicts the period during which fluid is needed (shown by thick gray line 925 overlaid on the dashed line segments 930, which indicates that fluid is not needed).
• the REIFR Algorithm was applied to 1 -minute moving time windows and gave real-time classification of (yes/no determinations of) needing fluids.
• Figs. lOA-lOC (collectively, "Fig. 10"), where the dash line curve 1005 (Fig. 10A), 1015 (Fig. 10B), and 1025 (Fig. IOC) each shows the FVR monitor's estimate of fluid requirements, while the actual fluid requirements are shown by the thick gray curve 1010 (Fig. 10A), 1020 (Fig. 10B), and 1030 (Fig. IOC), for patients 1, 17, and 10, respectively.
• Both the DOFL and REIFR Algorithms achieved ROC area under the curve of better than 0.9 (sensitivity and specificity of > 0.9) in identifying ongoing bleeding and flagging when no additional fluids were needed.
• the DOFL Algorithm achieved ROC area under the curve of 0.9697, sensitivity of 0.9192, and specificity of 0.9028 in identifying ongoing bleeding, as shown in Fig. 11.
• the REIFR Algorithm achieved ROC area under the curve of 0.9724, sensitivity of 0.9330, and specificity of 0.9000 in determining when no additional fluids were needed, as shown in Fig. 12.

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