WO2021226464A1 - Apparatus and methods for pulmonary monitoring - Google Patents
Apparatus and methods for pulmonary monitoring Download PDFInfo
- Publication number
- WO2021226464A1 WO2021226464A1 PCT/US2021/031306 US2021031306W WO2021226464A1 WO 2021226464 A1 WO2021226464 A1 WO 2021226464A1 US 2021031306 W US2021031306 W US 2021031306W WO 2021226464 A1 WO2021226464 A1 WO 2021226464A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- lung
- patient
- biomarker
- indication
- index
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000012544 monitoring process Methods 0.000 title claims description 15
- 230000002685 pulmonary effect Effects 0.000 title claims description 12
- 210000004072 lung Anatomy 0.000 claims abstract description 354
- 239000000090 biomarker Substances 0.000 claims abstract description 135
- 230000008569 process Effects 0.000 claims abstract description 15
- 230000036541 health Effects 0.000 claims description 42
- 238000012545 processing Methods 0.000 claims description 26
- 230000004044 response Effects 0.000 claims description 24
- 238000005259 measurement Methods 0.000 claims description 20
- 239000012530 fluid Substances 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 11
- 230000004199 lung function Effects 0.000 claims description 11
- 230000005750 disease progression Effects 0.000 claims description 5
- 239000007787 solid Substances 0.000 claims description 5
- 206010061818 Disease progression Diseases 0.000 claims description 4
- 238000002595 magnetic resonance imaging Methods 0.000 claims description 4
- 238000013178 mathematical model Methods 0.000 claims description 3
- 238000009530 blood pressure measurement Methods 0.000 claims 1
- 201000010099 disease Diseases 0.000 claims 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims 1
- 230000008859 change Effects 0.000 description 35
- 230000015654 memory Effects 0.000 description 18
- 238000006073 displacement reaction Methods 0.000 description 15
- 238000004891 communication Methods 0.000 description 13
- 238000003860 storage Methods 0.000 description 10
- 210000002345 respiratory system Anatomy 0.000 description 7
- 210000001519 tissue Anatomy 0.000 description 7
- 230000029058 respiratory gaseous exchange Effects 0.000 description 6
- 230000003068 static effect Effects 0.000 description 6
- 238000012546 transfer Methods 0.000 description 6
- 239000002131 composite material Substances 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 5
- 230000003434 inspiratory effect Effects 0.000 description 5
- 239000000203 mixture Substances 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 5
- 230000002123 temporal effect Effects 0.000 description 5
- 230000005291 magnetic effect Effects 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 208000006545 Chronic Obstructive Pulmonary Disease Diseases 0.000 description 3
- 230000009471 action Effects 0.000 description 3
- 239000012080 ambient air Substances 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 208000006673 asthma Diseases 0.000 description 3
- 230000006399 behavior Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000036772 blood pressure Effects 0.000 description 3
- 238000013500 data storage Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 239000001301 oxygen Substances 0.000 description 3
- 229910052760 oxygen Inorganic materials 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000011269 treatment regimen Methods 0.000 description 3
- 238000009423 ventilation Methods 0.000 description 3
- 206010016322 Feeling abnormal Diseases 0.000 description 2
- 208000019693 Lung disease Diseases 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 2
- 239000003570 air Substances 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 239000000470 constituent Substances 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000003748 differential diagnosis Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000002045 lasting effect Effects 0.000 description 2
- 230000033001 locomotion Effects 0.000 description 2
- 239000012528 membrane Substances 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 230000000241 respiratory effect Effects 0.000 description 2
- 238000013125 spirometry Methods 0.000 description 2
- 230000001225 therapeutic effect Effects 0.000 description 2
- 238000011282 treatment Methods 0.000 description 2
- 238000002604 ultrasonography Methods 0.000 description 2
- 241001580947 Adscita statices Species 0.000 description 1
- 206010011224 Cough Diseases 0.000 description 1
- 208000000059 Dyspnea Diseases 0.000 description 1
- 206010013975 Dyspnoeas Diseases 0.000 description 1
- 208000010412 Glaucoma Diseases 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 208000037656 Respiratory Sounds Diseases 0.000 description 1
- 206010047924 Wheezing Diseases 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 238000003915 air pollution Methods 0.000 description 1
- 239000013566 allergen Substances 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 210000000038 chest Anatomy 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 230000007850 degeneration Effects 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 230000035487 diastolic blood pressure Effects 0.000 description 1
- 208000002173 dizziness Diseases 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000004177 elastic tissue Anatomy 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 description 1
- 239000012212 insulator Substances 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 230000005823 lung abnormality Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 210000000214 mouth Anatomy 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 230000008529 pathological progression Effects 0.000 description 1
- 244000144985 peep Species 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000001902 propagating effect Effects 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 238000009613 pulmonary function test Methods 0.000 description 1
- 238000002601 radiography Methods 0.000 description 1
- 210000003019 respiratory muscle Anatomy 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000000276 sedentary effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 208000013220 shortness of breath Diseases 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 230000009885 systemic effect Effects 0.000 description 1
- 230000035488 systolic blood pressure Effects 0.000 description 1
- 238000011285 therapeutic regimen Methods 0.000 description 1
- 210000000115 thoracic cavity Anatomy 0.000 description 1
- 238000013334 tissue model Methods 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
- 239000003053 toxin Substances 0.000 description 1
- 231100000765 toxin Toxicity 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000001755 vocal effect Effects 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Classifications
-
- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/085—Measuring impedance of respiratory organs or lung elasticity
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/087—Measuring breath flow
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
-
- 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
-
- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
-
- 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
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/0515—Magnetic particle imaging
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
- A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
- A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data
Definitions
- the present disclosure generally relates to a new diagnostic tool for lung health that harnesses a non-invasive measure of lung material properties. This can allow pulmonary monitoring that can be fast, routine, affordable, and as repeatable as taking a patient's pulse or measuring blood pressure.
- Lung disease is a leading cause of death worldwide, and new threats are emerging from respiratory pandemics, vaping, and rising air pollution in many parts of the world.
- Current approaches to pulmonary examinations can be inaccurate, time-consuming, and inaccessible.
- lung health generally is not monitored unless symptomatic, an by then the damage can be permanent and degeneration can be irreversible.
- COPD chronic obstructive pulmonary disease
- the method can be medically transformative, enabling early detection, differential diagnosis, and treatment assessment.
- the method as disclosed herein has the potential to save lives, improve health outcomes, and save billions of dollars in diagnostic and treatment costs
- a method disclosed for pulmonary monitoring can include non-flow measurement of pressure evolution from an individual holding an inhaled breath.
- the method can be used as a standard screening procedure, similar to other clinically pervasive and revolutionary devices such as blood pressure cuffs and glaucoma tonometry in addition, the method can change the current pulmonary healthcare narrative by introducing a non ⁇ invasive, relatively fast, objective, and widely accessible assessment of lung health based on non-flow properties.
- viscoelasticity can evaluate lung health using signature pressure- time (P-T) features, such as lung biomarkers, to classify normal and abnormal lung function, differentiate between types of abnormalities, and continuously monitor disease progression.
- P-T signature pressure- time
- viscoelasticity can evaluate lung health using lung biomarkers to classify normal and abnormal lung function, differentiate between types of lung abnormalities, and continuously monitor lung disease progression.
- the whole-organ can be the entire lungs (i.e , right lung and left lung, or alternatively, only one lung if the patent or individual has only one lung) of a patient or individual
- a patient can inhale and hold his or her breath as long as possible (for example, less than 30 seconds) to generate a pressure-time (P-T) curve, such as using a mouthpiece 210 acting as a real-time pressure gauge (manometer) interfacing with control circuitry, such as including a computing machine running a computer software to record and store measurements, for contemporaneous or later analysis.
- P-T pressure-time
- the P-T curve can characterize a change in pressure over time, such as a decrease of pressure over time, that can be analyzed by rheological models to generate a lung biomarker
- a rheological model such as conceptually consisting of discrete elements (springs and dashpots), can be used to curve- lit the P-T curve, such as an exponentially decaying P-T curve.
- a lung biomarker can include a signature feature of the P-T curve, such as at least one of an indication of a peak pressure, an indication of asymptotic pressure, an indication of fractional relaxation, an Indication of a time-constant, an indication of degree of model non-linearity, or an indication of solid versus fluid proportional response (e.g , viscoelastic response).
- a lung biomarker can serve as an indication of patient lung health, such as a change in one or more lung biomarkers over time can indicate the risk for (or the presence of) a lung condition, such as in an asymptomatic patient.
- a patient can draw in and hold a breath, such as for a period of time to measure pressure evolution.
- Data obtained from the pressure evolution measurement can be applied to established theological models to generate characteristic or signature features of a temporal pressure-versus-time (P-T) curve, such as to allow for a comparison of features of healthy control, such as a ‘‘normal” lung, to diseased states, such as an “abnormal” lung. Differences between signature features of healthy control data and diseased state data can be used, such as to detect the abnormal lung state.
- the method disclosed herein can also be extended to additional diseased states, such as to explore possible differential diagnostic capabilities or for disease progression monitoring.
- a single characteristic feature of viscoelasticity e.g., percent pressure relaxation
- the asthmatic model exhibits notably decreased fractional (or percent pressure) relaxation, such a to indicate the presence of a lung condition in the tissue (e.g., asthma).
- Additional features such as peak and asymptotic pressure values, degrees of nonlinearity, time-constants, and/or solid versus fluid proportional response can yield further viscoelastic metrics, such as to allow a user to compare between healthy and diseased states, monitor disease progression, and provide differential diagnosis.
- the present inventors have recognized, among other things, that there is a need in the art for apparatus and methods that ca monitor or assess a patient lung.
- the apparatus and methods can include control circuitry, such as capable of running software, configured to process a lung biomarker from patient data. Further, the control circuitry can be configured to generate a lung index, such as to characterize a signature feature of the patient lung to monitor or assess the patient lung. In an example, the lung index can be based at least in part on the lung hiontarker, such as to characterize the patient lung.
- FIG 1 shows an example of an apparatus, such as to sense an indication of pressure evolution in a patient lung.
- FIG. 2 shows an example mouthpiece including an optional volumetric infiator.
- FIG. 3 shows an example fi-T curve.
- FIG 4 shows an example method for using an apparatus to monitor a patient, such as to monitor a lung condition of the patient.
- FIG. 5 shows an example computing machine.
- Pulmonary monitoring can be described as a method to track the health of a patient lung, such as by at least one of tracking, charting, or checking performance of lung function over time.
- pulmonary monitoring can be used to identity a change in a physiological parameter of the patient lung.
- a physiological parameter of the patient lung can include any parameter that can describe a characteristic of the patient lung, such as a viscoelastic characteristic of a patient Sung.
- An indication of the physiological parameter can be represented by patient data, such as data collected from the patient with a writte questionnaire or measured from the patient with a sensor.
- a change in a physiological parameter can indicate at least one of an onset of a lung condition, such as when a value of the physiological parameter deviates from a “normal” patient value of the physiological parameter, or a change in patient lung function, such as indicative of progression of an abnormal lung condition in an example
- a “normal” lung condition can include a state of a patient lung where a medical professional would not recommend a therapeutic intervention, such as based on a physiological parameter of the patient lung in an example
- an “abnormal” lung condition can include a state of a patient lung where a medical professional would recommend a therapeutic intervention, such as based on a physiological parameter of the patient lung in an example
- t e term “lung condition” can refer to either of a “normal” or “abnormal” lung condition, such as based on the context in which the term is used jd026j in an example
- pulmonary monitoring can include at least one of early detection of a lung condition, diagnosis of a lung condition, or assessing patient response to a treatment regimen
- Pressure evolution can be described as a change, such as a change in pressure experienced in a patient respiratory tract or a patient lung over time.
- pressure evolution can also refer to at least one of temporal pressure evolution, temporal pressure dissipation, or temporal pressure relaxation, such as experienced in at least a portion of the patient respiratory tract or the patient lung.
- Pressure evolution can be understood as stress- relaxation response of the tissue, such as the stress-relaxation response of the lung to an inspiratory breath held by the patient for a period of time.
- the pressure evolution response can include an indication of a physiological parameter, such as related to the patient lung.
- the pressure evolution response can characterize the physiological parameter, such as at least one of a change in patient lung pressure or a change in distance (e.g , displacement) between two landmarks on the patient lung.
- An indication of pressure evolution response can be obtained from patient data, such as patient data related to the physiological parameter sensed from the patient with a sensor.
- the indication of pressure evolution response can be related to a dynamic (or flow) measurement of fluid, such as fluid flow into (e.g., inspiration) or out of (e.g., expiration) the patient lung in an example, the indication of pressure evolution can be related to a static (or non-flow) measurement of fluid, such as an indication associated with pressure evolution from a patient holding an inhaled breath (e.g , a held breath)
- Respiration can include a physiological process where an organism, such as a human, can extract oxygen from the environment, such as by inhaling a gas mixture including ambient air into a lung of the human.
- respiration can include receiving a breath, such as can include the act of breathing.
- Breathing can include a passive process, such as at least one of inspiration or expiration through a combination of at least one of relaxation of the respiratory muscles or the elastic recoil of the lungs and thorax.
- the volume of the lungs can dictate the inspiratory volume (e.g , the inhaled breath) a patient can receive within the lungs, such as inspiratory volume can be related to at least one of the elasticity of lung tissue or the volume of the thoracic cavity ,
- FIG. 1 shows an example of an apparatus 100, such as to sense an indication of pressure evolution response in a patient lung.
- the apparatus 100 can include control circuitry 120 and, optionally, a sensor 130, such as connected to the control circuitry 120 with a connector 140.
- the apparatus 1 0 can include control circuitry 120 configured to receive patient data, such as patient data related to a physiological parameter of a patient including an indication of pressure evolution from the patient, and process the patient data, such as to process the indication of pressure evolution response to form a lung biomarker in an example, the apparatus 1 0 can include the sensor 130, such as the sensor 130 configured to sense patient data, such as an indication of the patient lung, including an indication of pressure evolution response from the patient, and the control circuitry 120, such as configured to receive and process the indication of pressure e volu tion from the sensor 130. (0030 . 1
- the control circuitry 120 can facilitate and coordinate operation of the apparatus 100.
- control circuitry 120 can be coupled to, such as in at least one of mechanical or electrical communication with, the sensor 130.
- mechanical communication can include the apparatus 100. such as where the sensor 130 can be attached to the control circuitry 120.
- electrical communication can include the transfer of patient data sensed by the sensor 130, such as representing an indication of pressure evolution response, to the control circuitry 120, such as through the connector 140
- the connector 140 can include at least one of a wired connection, such as patient data can be transferred from the sensor 130 to the control circuitry 120 with a wire, or a wireless connection, such as electronic hardware utilizing a Wi-Fi or other wireless protocol to transfer data from the sensor 130 to the control circuitry 120 931 .
- the control circuitry 120 can include an input device 512, such as configured to allow a user to interact with the apparatu 100
- a user can include at least one of a patient, a patient caregiver, a health professional, or a non-person, such as a computing machine 500 or a data storage device
- the input device 512 can be configured to receive patient data.
- the input device 512 can include a graphical user interface (GUI), such as configured to receive patient data from the user including i nformation related to at least one of basic system functionality (e.g., start/stop of apparatus 100), an indication of user preference, such as a level of patient comfort during operation of the apparatus 100, or an indication of patient health history.
- GUI graphical user interface
- the input device 512 can include an electronic interface, such as to receive patient data from at least one of a sensor 130 configured to contemporaneously sense patient data from the patient and transfer the patient data to the input device 512, or a data storage device, such as configured to transfer patient data previously sensed from the patient and stored on the data storage device to the control circuitry 1 0.
- the control circuitry 120 can include a processing module, such as a programmable central processing unit (CPU).
- the CPU can execute an instruction, such as one or more instructions, to implement a method of using the apparatus 100, such as to compare patient data as describe elsewhere in this application.
- the CPU can be a component of a computing machine, such as a computing machine 500.
- the CPU can be configured to process received patient data, such as patient data received from the input device 512, to form an indication of a lung biomarker.
- An indication of a lung biomarker can include at least one of a Group 1 lung biomarker, such as an indication of patient health history, a Group 2 lung biomarker, such as an indication of a dynamic characteristic of the patient lung, or a Group 3 lung biomarker, such as an indication of a viscoelastic characteristic of the patient lung.
- the CPU can be configured to process an indication, such as an indication of a lung biomarker, or to generate an indication of an index, such as a king index configured to characterize the state or condition of the patient lung.
- a lung index can include a composite indicator of patient lung condition, such as described elsewhere in this application.
- the control circuitry 120 can include a storage device 522 to monitor and record patient data, such as an indication of at least one of a lung biomarker or a lung index.
- patient data can be monitored and recorded by the storage device 522 for a perio of time, such as for a period of seconds, minutes, hours, day s, years, or for the lifetime of the patient.
- the control circuitry 120 can include a power source, such as to supply electrical energy to the apparatus 100
- the power source can include at least one of a battery, such as a lithium ion battery, or a transformer, such as to receive power from a wall outlet for use in the apparatus 100 at a specified voltage and current.
- a biological marker can include an indicator, such as a subjecti ve or objective indication of patient health.
- a biomarker can include an indication of patient lung health, such as a lung biomarker selected as an indication of the health condition of a patient lung at a selected point in time.
- the lung biomarker can be processed from patient data sensed from a patient at periodic intervals, such as at least at one of daily, weekly, monthly, or yearly intervals.
- the lung biomarker can be compared, such as to monitor the patient lung condition or to enable a patient diagnosis based upon objective criteria.
- the patient lung biomarker can be compared to patient data, such as the patient lung biomarker can be compared to patient lung biomarker data collected previously from the same patient to monitor progression of a lung condition, or population data, such as the patient lung biomarker can be compared to lung biomarker data collected from others different than the patient, such as to provide an indication of at least one of patient prognosis for a treatment regimen or epidemiological data for public health assessment
- a lung biomarker can include a Group 1 lung biomarker, such as health history data of a patient. Health history data can inform a patient health assessment, such as to provide context for monitoring of patient lung health over an extended period of time.
- Health history can include an indication of an objective diagnostic measure, such as to characterize the patient condition.
- An objective diagnostic measure can include at least one of height, weight, blood-oxygen level, or systemic blood pressure including systolic and diastolic blood pressure.
- an objective diagnostic measure can also include an indication of one or more metrics associated with the use of spirometry and imaging, such as to stratify classes of patients including COPD patients, an indication of a Tiffeneau-PmelSi index (e.g., FEV1 ratio), an indication of positive end-expiratory pressure (e.g , PEEP), or an indication of patient respiratory tidal volume.
- a Tiffeneau-PmelSi index e.g., FEV1 ratio
- PEEP positive end-expiratory pressure
- Health history data can include an indication of a subjective diagnostic measure, such as to characterize the patient condition
- a subjective diagnostic measure can include at least one of a patient complaint, such as a patient statement regarding past or present general health condition or past or present lung condition.
- a statement of health condition can include an observation, such as “shortness of breath”, ‘ " persistent cough”, or “dizziness when I stand”.
- a subjective diagnostic measure can include the timing of the patient complaint, such as whether the patient complaint pertains to an acute event lasting hours or days, or a chronic event lasting days, weeks, months, or years.
- a subjective diagnostic measure can include an observation of the patient by another user, such as a medical professional.
- an observation can include a present-sense observation of a patient health condition, such as a user observation that an observed patient “is wheezing” or ‘ appeals to be in pain” during physical exertion.
- Health history data can be collected, such as with at least one of a written questionnaire answered by the patient or a verbal interview, such as with a health professional.
- Health history data can he processed, such as to prepare the data for further analysis.
- the health history data can be stored, such as in at least one of an analog format including paper records or a digital format including an electronic record.
- health history data can be organized to allow for an objective scale to be applied to the health history data for inclusion or use in another metric, such as a lung index metric.
- An objective scale can include a numerical scale, such as a numerical scale to quantify (or normalize) a patient response for comparison with another patient response.
- a numerical scale including delineations of‘T ⁇ “2”, “3”, “4”, and “5” can be applied to a patient response to the question, “how are you feeling today?”.
- a patient response of “feeling bad” can be assigned a value of “I such as to indicate a lower bound of patient condition
- a patient response of “feeling good” can be assigned a value of “5”, such as to indicate an upper bound of patient condition
- a patient response other than “feel ing bad” or “feeling good” can be assigned a value between “I” and “5”, such as to locate the response relative to the lower and upper patient condition bounds.
- a lung biomarker can include a Group 2 lung biomarker, such as a dynamic lung characteristic of the patient.
- a dynamic lung characteristic can include a dimensional measurement of the lung that can change over time, such as with patient respiration.
- a dynamic lung characteristic can include an indication of patient lung displacement, such as an indication of a change in displacement between two landmarks on the patient lung.
- a lung landmark can include any- selected location on the patient lung that can be monitored, such as located or “tracked”, over a period of time, such as with a sensor 130.
- the indication of a change in displacement can include at least one of an indication of a change in distance, an indication of a change in velocity, or an indication of a change in acceleration.
- a dynamic lung characteristic can include an indication of patient lung volume, such as an indication of a change in displacement between two or more landmarks on the patient lung.
- the indication of a change in volume can include at least one of an indication of a change iu distance between the two or more landmarks defining the volume, an indication of a change in velocity of the two or more landmarks, or an indication of a change in acceleration of the two or more landmarks.
- the dynamic lung characteristic can be collected or otherwise received from the patient, such as with a sensor 130 integrated into a sensor system,
- the sensor 130 can include a pressure sensor, such as a pressure sensor system in an example, the sensor can include a mouthpiece 210 with an integrated pressure sensor, such as described elsewhere in this application.
- the pressure sensor can be configured to sense an indication of the patient lung, such as an indication of pressure evo lution in the patient lung in an example, the indication of the patient l ung can include a pressure-time (or P-T) curve, such as related to pressure evolution in the lung associated with at least one of a dynamic (or flow ⁇ measurement of pressure, such as during patient respiration, or a .static ⁇ (or non-flow ⁇ measurement of pressure, such as related to a patient held breath for a period of time
- FIG, 2 shows an example sensor 130, such as a mouthpiece 210 including an optional volumetric inflator 215.
- the pressure sensor can be included in or attached to the mouthpiece 210, such as to sense pressure evolution in the patient mouth or respiratory tract.
- the mouthpiece 210 can be configured or shaped, such as to locate the pressure sensor at a selected location in the patient mouth, respiratory tract, or lung,
- the volumetric inflator 215 can optionally be attached to the mouthpiece 210, such as to introduce a selected volume of air into the patient respiratory tract, such as to sense an indication of pressure evolution in the patient lung subject to a known inflation volume.
- the volumetric inflator 215 can be used optionally with the mouthpiece 210, such as a surrogate ventilation device when patient volume inspiration effort is insufficient to sense an indication of pressure evolution in the patient lung.
- the volumetric inflator 215 can include at least one of a balloon 220, such as a closed membrane configured to separate a volume 230 enclosed by the membrane from the surrounding atmosphere, and a relief val ve 240 In an example, the volumetric inflator 215 can include a bellows device.
- the volumetric inflator 215 can be located in communication with the patient mouth, such as in communication with the mouthpiece 210, and compressed, such as to by force fluid front the volume 230 into the patient lung to provide positive pressure ventilation and expand the patient lung. Expansion of the patient lung can assist in sensing a patient data, such as an indication of pressure evolution in the patient lung.
- the fluid in the volume 230 can include a gaseous fluid, such as at least one of ambient air or a fluid with a composition other than ambient air, such as a composition selected to treat the patient lung or assist in sensing an indication of pressure evolution in the patient lung.
- the relief value 240 can be configured to close during compression of the volumetric inflator 215, such as to force fluid into the patient lung, and open during rarefaction of the volumetric inflator 215, such as to allow a fluid to flow into the volume 230 including from the surrounding atmosphere, such as to prevent negative pressure ventilation of the patient. 0950 . 1
- the sensor 130 can include at least one of an ultrasonic sensor, such as an ultrasonic sensor system associated with use in sonography, or an X-ray sensor, such as an X- ray sensor system associated with radiography.
- the ultrasound sensor or the X-ray sensor can be configured to sense pa tient data, such as an indication of lung displacement including a change of distance between two landmarks on the patient lung.
- the indication of displacement can be related to pressure evolution in the lung associated with at least one of a dynamic (or flow) measurement of pressure during patient inspiration or expiration or a static (or non-flow) measurement of pressure in an example, the indication of lung displacement can be combined with other information, such as an estimate of patient lung elasticity, to estimate a change in lung pressure with respect to time, such as to generate a P-T curve or similar metric
- the sensor 130 can include an MRI sensor, such as an MR] sensor system associated with use in medical imaging.
- the MRI sensor can be configured to sense an indication of the patient lung, such as an indication of displacement including a change of d istance between two landmarks on the patient lung.
- the indication of displacement can be related to pressure evolution in the Sung associated with at least one of a dynamic (or flow) measurement of pressure during patient inspiration or expiration or a static (or non-flow) measurement of pressure.
- the indication of displacement can he combined with other information, such as an estimate of patient lung elasticity, to estimate a change in lung pressure with respect to time, such as to generate a P-T curve or similar metric, or a change in lung volume with respect to time,
- the dynamic lung characteristic data can be processed, such as to prepare the data for further analysis,
- dynamic lung characteristic data can he stored, such as in at least one of an analog format including paper records or a digital format including an electronic record, such as to a storage device 522.
- dynamic lung characteristic data can be correlated, such as the dynamic lung characteristic data ca be considere as an indication of a viscoelastic characteristic of the lung.
- an indication of displacement such as a change in displacement, between two landmarks on a patient lung due to pressure evolution, such as during a held breath, can be correlated to a characteristic of the patient lung, such as a viscoelastic characteristic of the patient lung.
- one or more dynamic lung characteristic can be organized for inclusion or use in another metric, such as a lung index metric.
- a lung biomarker can include a Group 3 lung biomarker, such as a viscoelastic characteristic of a patient lung.
- a viscoelastic characteristic can describe the property of tissue, such as at least one of elastic tissue behavior or viscous tissue behavior.
- a Group 3 lung biomarker can include a viscoelastic characteristic, such as a patient lung viscoelastic parameter (PL VP) including a signature viscoelastic feature.
- PL VP patient lung viscoelastic parameter
- Patient data can be collected from a patient, such as to characterize a patient physiological parameter.
- patent data can be collected by a survey, such as by asking a question of the patient and recording the patient response.
- patient dat can be collected with a sensor 130, such as with a sensor 130 integrated into a sensor system as described elsewhere in this application.
- the sensor 130 can include at least one of a pressure sensor system, an ultrasound system, an MRI system, or an X-ray system.
- Patient data collected with the sensor 130 can include an indication of a physiological parameter, such as an indication of a change in patient lung pressure related to a held breath sensed in a patient over a period of time.
- an indication of change in patent lung pressure over time can include a pressure versus time (or P-T) curve.
- FIG 3 shows an example P-T curve, such as representing pressure evolution in a patient respiratory tract.
- the horizontal axis can represent time and the vertical axis can represent pressure, such as lung pressure magnitude.
- the P-T curve can be characterized by an indication of a physiological parameter, such as at least one of an indication of peak pressure 310 (or Pp), an indication of asymptotic pressure 312, an indication of fractional relaxation 314, an indication of a time-constant 316, or an indication of degree of model non-linearity 318.
- Patient data can he “reduced” or curvefit with a mathematical (or math) model, such as to generate a value for one or more model parameter variable (MPV) to characterize the patient data.
- MPV model parameter variable
- a math model can be used to define or describe a lung biomarker, such as an MPV value to characterize at least one of a Group 2 lung biomarker or a Group 3 lung biooiarker
- An MPV can include a variable in a math model, such as the value of the variable that can define a curve to curvefit the patient data.
- a math model can include a rheological math model including at least one of a Fractional Standard Linear Solid model, a Maxwell model, or a Kelvin model.
- an indication of an MPV value can represent an indication of a PL VP, such as an indication of patient lung viscoelasticity,
- an exponential decay model such as a linear first-order ordinary differential equation defined by a time constant parameter, can be applied to patient data.
- the collected patient data can be processed * or otherwise curvefit to approximate a ’“best-fit” curve to identify a value for the time constant parameter, such as to characterize the collected patient data.
- a best-fit characterization can include identifying a value for an MPV, such as an MPV selected to minimize error between the mathematical model and the collected data, such as using a least squares error metric.
- the value of the time constant parameter, such as resulting from curve fitting the mathematical model to the collected patient data can represent a PL VP, such as an indication of patient lung viscoelasticity estimated from the exponential decay model.
- the PL VP such as estimated from the exponential decay model, can include a viscoelastic characteristic of the patient lung, such as to characterize the viscoelastic characteristic of the “bulk” or “whole organ”.
- a PL VP ca include an indication of peak pressure (Pp), such as an inspiratory peak pressure associated with a patient held breath in a P-T curve.
- Pp can be increased for a patient, such as with the use of the optional volumetric inflator 215,
- a PL VP can include an indication of fractional relaxation of the patient lung, such as an indication of fractional relaxation formed from information in a P-T curve
- the fractional relaxation can include a ratio, such as the ratio of peak pressure to a selected asymptotic value.
- the selected asymptotic value can include the sense pressure from the P-T curve, such as at a selected time after peak pressure. 962 . 1
- the indication of fractional relaxation can be influenced by the data examined, such as the value of an indication offractional relaxatio can be affected by the portion of the P-T curve examined during a curve-fit..
- a value of an indication of fractional relaxation can be estimated at a selected time, such as one or more selected times, associated with the P-T curve measurement, such as to obtain a value for an indication of fractional relaxation at the one or more selected times.
- a user can estimate a value for an indication of fractional relaxation at a se lected time of at least one of 1 second after peak pressure (Pp), at 5 seconds after Pp, at 10 seconds after Pp, or at 20 seconds after Pp, such as to characterize the patient lung for use as an indication of a lung biomarker.
- Pp peak pressure
- a PL VP can include an indication of percent relaxation of the patient lung, such as a percentage indication of fractional relaxation.
- a value for percent relaxation can be formed by multiplying fractional relaxation by 100, such as to generate a percentage level of peak pressure to the selected asymptotical value.
- a PL VP can include an indication of a time constant, such as a time constant associated with an exponential decay model, in an example, a math model, such as a fractional standard linear solid model, can be used to identify an indication of a lung biomarker, such as to characterize a patient P-T curve with at least one of a “solid-like” contribution metric and a “fluid- like” contribution metric.
- a time constant such as a time constant associated with an exponential decay model
- a math model such as a fractional standard linear solid model
- the contribution metrics can be characterized with a standard exponential model, such as a model described with a model parameter including at least one of a base, an exponent (e.g., a power of the base), or a coefficient (e.g., a gain applied to the base), where a value of the model parameter can serve as an indication of a lung biomarker
- a PL VP can include an indication of non-linearity, such as for patient data where least squares error associated with a linear math model can be reduced by applying a nonlinear math model.
- the example of the indication of fractional relaxation influenced by the data examined can be described by an exponential decay model characterized by a non-linear time constant, such as an indication of non-l inearity can include a metric to describe the non-linearity of the time constant.
- a Sung biomarker such as one or more lung biomarkers
- a lung index can include a composite indicator, such as a combination, at least in part, of one or more lung biomarkers that can form an improved monitoring or diagnostic tool as compared to the constituent lung biomarkers alone, such as to characterize the patient lung.
- One or more lung biomarkers can be collected, such as into a group of lung biomarkers that have a common characteristic. As such, a set of appropriately grouped hlomarkers can be used, such as by a medical professional to predict or diagnose a potential lung condition in a patient.
- a Group 1 lung biomarker such as describing a patient health history, can be considere at least one of a present or lagging indicator for a lung condition.
- an objective diagnostic measure such as blood-oxygen level
- a subjective diagnostic measure such as a patient statement of present health condition
- a Group 2 lung bioniarker such as describing a dynamic lung characteristic of a patient lung, can be considered a present or leading indicator for a lung condition.
- Changes in lung displacement such as between two lung landmarks, or changes in lung vol ume can, in some cases, signal the presence of a lung condition.
- a decrease in lung displacement or lung volume such as signaled by patient exercise intolerance or direct measurement of the patient with a sensor 130, can indicate the presence of a potential lung condition, such as in a sedentary patient.
- a Group 3 lung biomarker such as describing a viscoelastic character of a patient lung, can he considered a present or a leading indicator for a lung condition.
- Subtle changes in viscoelastic behavior of patient lung tissue at the molecular level can, in some cases, anticipate pathological progression of a lung condition. For example, a decrease in patient lung viscoelasticity, such as compared to the general population, can indicate the presence of a potential lung condition, such as in an asymptomatic patient.
- the lung index can include, at least in part, a lung biomarker, such as a lung biomarker from at least one of the Group 1 lung biomarker, the Group 2 lung bioniarker, or the Group 3 lung biomarker.
- the lung index can include, at least in part, a lung biomarker selected from each of the Group 1 lung biomarker and the Group 2 lung biomarker.
- the lung index can include, at least in part, a lung biomarker selected from each of the Group 1 lung biomarker and the Group 3 lung biomarker.
- the lung index can include, at least in part, a lung biomarker selected from each of the Group 2 lung biomarker and the Group 3 lung biomarker.
- the lung index can include, at least in part, a lung biomarker selected from each of the Group 1 lung biomarker, the Group 2 lung biomarker, and the Group 3 lung biomarker.
- FIG.4 shows an example method 300 for using an apparatus, such as the apparatus 100, to monitor a patient, such as to monitor a lung condition of the patient.
- the apparatus100 can include control circuitry 120, such as control circuitry configured to receive patient data related to a patient and process the received patient data, such as to form at least one of a lung biomarker or a lung index.
- a method, such as the example method 400 can be embodied in one or more data structures or instructions, such as implemented on a computing machine 500.
- patient data can include an indication of a physiological parameter, such as an indication of a lung biomarker from the patient, or an indication of patient health history.
- a patient can be received, such as by a medical professional to assess the patient lung.
- Receiving a patient can include at least one of examining the patient, such as to screen the patient for a lung condition, diagnosing the patient, such as to deliver a recommendation as to the probability of a lung condition based on data available to the medical professional, or monitoring the patient, such as to assess the progression of a previously diagnosed lung condition by comparison of present patient data, such as an indication of a present lung index score, to previous patient data, such as an indication of a lung index score from a previous encounter.
- patient data can be collected, such as for use as a lung biomarker.
- Collecting patient data can include as least one of receiving contemporaneous patient data, such as patient data collected from the patient upon receiving the patient, or receiving stored patient data, such as patient data collected prior to receiving the patient.
- Collecting patient data can include interviewing the patient, such as to collect health history data from the patient hi an example, collecting patient data can include collecting Group 1 lung biomarker data from the patient.
- Collecting patient data can include processing collected health history data, such as to form a lung biomarker.
- processing can include applying an objective scale to health history data., such as a numerical scale of 1 to 5 to form an indication of a lung biomarker.
- processing health history data can include forming a lung index, such as at least in part from an indication of the lung biomarker.
- Collecting patient dat can include sensing an indication of a dynamic lung characteristic from the patient, such as a dimensional measurement of the lung that can change over time.
- collecting patient data can include collecting Group 2 Sung biomarker data from the patient
- Collecting patient data can include processing an indication of a dynamic lung characteristic from the patient, such as to form a lung biomarker.
- processing patient data can include estimating the lung biomarker, such as from a dynamic lung characteristic in an example, processing an indication of a dynamic lung characteristic can Include correlating an indication of a dynamic lung characteristic, such as an indication of a change in distance between two landmarks on a patient lung due to pressure evolution during a held breath, with a characteristic of the patient lung, such as a viscoelastic characteristic of the patient lung.
- processing patient data can include forming a lung index, such as at least in part from an indication of a dynamic lung characteristic.
- Collecting patient data can include sensing an indication of a viscoelastic characteristic from a patient lung, such as to form a lung biomarker.
- collecting patient data can include collecting Group 3 lung biomarker data from the patient.
- jOOSOj Collecting patient data can include processing an indication of a viscoelastic characteristic from a patient lung, such as to form the lung biomarker.
- processing an indication of a viscoelastic characteristic can include generating a model parameter variable (MPV) from a math model, such as to estimate an indication of a lung biomarker.
- the MPV can Include a patient lung viscoelastic parameter (PL VP).
- processing patient data can include forming a lung index, such as at least in part from an Indication of a viscoelastic characteristic of the patient lung.
- patient data can be compared, such as to identify a difference between a first patient data set and a second patient data set. Comparing patient data can allow a user, such as a medical professional, to observe a change in one or more lung biomarkers, such as to indicate the presence of a lung condition in the patient,
- Comparing patient data can include forming a metric, such as a composite metric to characterize a patient lung condition based at least in part on one or more lung biomarkers.
- the composite metric can include a lung index, such as at least one of selected lung biomarkers or an arrangement of patient data configured to indicate a patient risk for a lung condition, such as indicative of an increased or decreased risk of the presence of a lung condition.
- Comparing patient data can include comparing data from the same patient, such as to form a first example of a lung index.
- a first patient data set such as a collected from a patient at a first time
- a second patient data set such as collected from the patient at a second time
- a user can compare a baseline biomarker value, such as collected and processed from a previous visit of the patient to the medical professional, with a subsequent biomarker value, such as collected and processe during a visit contemporaneous with the comparison to the baseline biomarker value, such as to indicate the presence of a lung condition in the patient,
- Comparing patient data can include comparing a patient data set to a “nominal” patient data set, such as to form a second example of a lung index.
- a nominal patient data set can include a composite patient data set, such as a data set formed from epidemiological data and configured to represent the characteristics of a nominal (or average) patient.
- a first patient data set such as collected from a patient
- a second patient data set such as a nominal patient data set
- Comparing patient data can include applying a mathematical operation to a biomarker, such as one or more biomarkers in a patient data set, such as to form a third example of a lung index, in an example, a mathematical operation can include at least one of addition, subtraction, multiplication, division, or a combination of operations.
- a mathematical operation can include at least one of addition, subtraction, multiplication, division, or a combination of operations.
- Inspection of individual lung biomarkers can result in an indefinite finding (e.g., weak signal ) of a lung condi tion, such as when the changes are of small magnitude as compared to the lung biomarker level.
- an indefinite finding e.g., weak signal
- a mathematical combination of individual lung biomarkers can magnify information contained within the one or more indication of present and leading indicators, such as to clarify a finding (e.g , strong signal) of a lung condition.
- dividing a Grou 3 lung biomarker value (leading indicator) by a Group 2 lung biomarker value (present indicator) can result in a ratio, such as an example of a fourth king index.
- a fourth lung index greater than I such as indicating a greater difference between a first and second Group 3 lung biomarker than between a first and second Group 2 lung biomarker, can indicate an increased risk for a lung condition, such as a lung condition in an asymptomatic patient.
- Comparing patient data can include diagnosing a patient lung condition, such as in an asymptomatic patient.
- a leading Indicator such as a Group 3 lung biomarker
- a present indicator such as a Group 2 lung biomarker or a Group 1 lung biomarker
- Correlating experimental data such as from a clinical trial with a selected combination of one or more lung biomarkers, such as forming a lung index:, can assist a medical profession in patient diagnosis, such as to distinguish a first suspected lung condition from a second suspected lung condition.
- diagnosis of a lung condition in an asymptomatic patient can afford options to the patient, such as to initiate a therapeutic regimen to treat the lung condition.
- FIG. 5 illustrates a block diagram of an example machine 500 upon which any one or more of the techniques (e.g., methodologies) discussed herein may perform.
- the apparatus 100 communicates with the machine 500 (e.g., a server machine) which may be used to receive patient data, such as front the sensor 130, process patient data, such as to form at least one of a lung biomarker or a lung index, and execute the trained models and provide the motion controls based on inferred intended movement, according to the contextual data.
- the machine 500 may be a local or remote computer, or processing node in an on-the-go (OTG) device such as a smartphone, tablet, or wearable device.
- OOG on-the-go
- the machine 500 may operate as a standalone device or may be connected (e.g., networked) to other machines. In an embodiment, the machine 500 may be directly coupled or be integrated with the apparatus 100. In a networked deployment, the machine 500 may operate in the capacity of a server machine, a client machine, or both in server-client network environments. In an example, the machine 500 may act as a peer machine in peer-to-peer (P2P) (or other distributed) network environment.
- P2P peer-to-peer
- the machine 500 may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine.
- PC personal computer
- PDA personal digital assistant
- machine shall also be taken to include any collection of machines that indi vidual ly or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.
- cloud computing software as a service
- SaaS software as a service
- Circuitry is a collection of circuits implemented in tangible entities that include hardware (e.g., simple circuits, gates, logic, etc.). Circuitry membership may be flexible over time and underlying hardware variability. Circuitries include members that may, alone or in combination, perform specified operations when operating. In an example, hardware of the circuitry may be immutably designed to carry out a specific operation (e.g , hardwired). In an example, the hardware of the circuitry may include variably connected physical components (e.g..
- execution units transistors, simple circuits, etc.
- a computer readable medium physically modified (e.g., magnetically, electrically, moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation in connecting the physical components, the underlying electrical properties of a hardware constituent are changed, for example, from an insulator to a conductor or vice versa
- the instructions enable embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuitry ⁇ ' in hardware via the variable connections to carry out portions of the specific operation when in operation.
- the computer readable medium is communicatively coupled to the other components of the circuitry when the device is operating in an example, any of the physical components may he used in more than one member of more than one circuitry'.
- execution units may be used in a first circuit of a first circuitry at one point in time and reused by a second circuit in the first circuitry, or by a third circuit in a second circuitry at a different time.
- Machine 500 may include a hardware processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 504 and a static memory 506, some or ail of which may communicate with each other via an interlink (e.g., bus) 530.
- the machine 500 may further include a display unit 510, an input device 512, such as at least one of a keyboard, a graphical user interface (GUI), or an electronic interface, such as to receive a signal front a sensor, and a user interface (III) navigation device 514 (e.g., a mouse).
- a hardware processor 502 e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof
- main memory 504 e.g., main memory 504
- static memory 506 e.g., static memory
- the machine 500 may further include a display unit 510, an input
- the display unit 510, input device 512 and UI navigation device 514 may be a touch screen display.
- the machine 500 may additionally include a storage device (e.g , drive unit) 522 » a signal generation device 518 (e.g., a speaker), a network interface device 520, and one or more sensors 516, such as a sensor 130, a global positioning system (GPS) sensor, compass, accelerometer, or oilier sensor.
- sensors 516 including sensor 130 may include wearable or non-wearable sensors, such as described elsewhere in this application.
- the machine 500 may include an output controller 528, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).
- a serial e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).
- USB universal serial bus
- IR infrared
- NFC near field communication
- the storage device 522 may include a machine readable medium 508 on which is stored one or more sets of data structures or instructions 524 (e.g., software) embody ing or utilized by any one or more of the techniques or functions described herein.
- the instructions 524 may also reside, completely or at least partially, within the main memory 504, within static memory 506, or within the hardware processor 502 during execution thereof by the machine 500.
- one or any combination of the hardware processor 502, the main memory 504, the static memory 506, or the storage device 516 may constitute machine readable media,
- machine readable medium 508 is illustrated as a single medium, the term “machine readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) configured to store the one or more instructions 524
- machine readable medium may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine 500 and that cause the machine 500 to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data struc tures used by or a ssociated with such instructions.
- Non-limiting machine-readable medium examples may include solid-state memories, and optical and magnetic media.
- a massed machine-readable medium comprises a machine readable medium with a plurality of particles having invariant (e.g., rest) mass. Accordingly, massed machine-readable media are not transitory propagating signals.
- non-volatile memory such as semiconductor memory devices (e.g.. Electrically Programmable Read- Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks: magneto-optica! disks; and CD-ROM and DVD-ROM disks.
- semiconductor memory devices e.g.. Electrically Programmable Read- Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)
- EPROM Electrically Programmable Read- Only Memory
- EEPROM Electrically Erasable Programmable Read-Only Memory
- the instructions 524 may further be transmitted or received over a communications network including an interlink 530 using a transmission medium via the network interface device 520 utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.).
- Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g,, cellular networks).
- Plain Old Telephone (POT ’ S) networks and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®, IEE E 802.16 family of standards known as WiMax®), IEEE 802,15 X family of standards, peer-to-peer (P2P) networks, among others.
- IEEE Institute of Electrical and Electronics Engineers 802.11 family of standards known as Wi-Fi®, IEE E 802.16 family of standards known as WiMax®
- IEEE 802,15 X family of standards e.g., Wi-Fi®
- WiMax® Institute of Electrical and Electronics Engineers 802.11 family of standards known as Wi-Fi®
- WiMax® Institute of Electrical and Electronics Engineers 802.11 family of standards known as Wi-Fi®
- WiMax® Institute of Electrical and Electronics Engineers
- IEEE 802,15 X family of standards e.g., Wi-Fi®
- P2P peer-to-peer
- the network interface device 520 may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network 526.
- the network interface device 520 may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple- output (S1MO), multiple-input multiple-output (MEMO), or multiple-input single-output (MI SO) techniques.
- S1MO single-input multiple- output
- MEMO multiple-input multiple-output
- MI SO multiple-input single-output
- transmission medium shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 500, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
- program code may represent hardware using a hardware description language or another functional description language which essentially provides a model of how designed hardware is expected to perform.
- Program code may be assembly or machine language, or data that may be complied or Interpreted.
- Each program may be implemented in a high-level procedural, declarative, or object-oriented programming language to communicate with a processing system.
- programs may be implemented in assembly or machine language, if desired in any case, the language may he compiled or interpreted.
- Program instructions may be use to cause a general -purpose or special-purpose processing system that is programmed with the instructions to perform the operations described herein. Alternatively, the operations may be performed by specific hardware components that contain hardwired logic for performing the operations, or by any combination of programmed computer components and custom hardware components.
- the methods described herein may be provided as a computer program product, also described as computer or machine accessible or readable medium that may include one or more machine accessible storage media having stored thereo instructions that may be used to program a processing system or other electronic device to perform the methods.
- Program code may be stored in, for example, volatile or non volatile memory, such as storage devices or an associated machine readable or machine accessible medium including solid-state memory, hard-drives, floppy-disks, optical storage, tapes, flash memory, memory sticks, digital video disks, digital versatile discs (DVDs), etc., as well as more exotic mediums such as machine-accessible biological state preserving storage.
- a machine readable medium may include any mechanism for storing, transmitting, or receiving information in a form readable by a machine, and the medium may include a tangible medium through which electrical, optical, acoustical or other form of propagate signals or carrier wave encoding the program code may pass, such as antennas, optical libers, communications interfaces, etc.
- Program code may be transmitted in the form of packets, serial data, parallel data, propagated signals, etc., and may be used in a compressed or encrypted format.
- Program code may be implemented in programs executing on programmable machines such as mobile or stationary computers, personal digital assistants, smart phones, mobile Internet devices, set top boxes, cellular telephones and pagers, consumer electronics devices (including DVD players, personal video recorders, personal video players, satellite receivers, stereo receivers, cable TV receivers), and other electronic devices, each including a processor, volatile or non-volatile memory readable by the processor, at least one input device or one or more output devices.
- Program code may be applied to the data entered using the input device to perform the describe embodiments and to generate output information. The output information may be applied to one or more output devices.
- embodiments of the disclosed subject matter may be practice with various computer system configurations, including multiprocessor or multiple-core processor systems, minicomputers, mainframe computers, as well as pervasive or miniature computers or processors that may be embedded into virtually any device.
- Embodiments of the disclosed subject matter may also be practiced in distributed computing environments, cloud environments, peer-to-peer or networked microservices, where tasks or portions thereof may be performed by remote processing devices that are finked through a communications network.
- a processor subsystem may be used to execute the instruction on the machine- readable or machine accessible media.
- the processor subsystem may include one or more processors, each with one or more cores. Additionally, the processor subsystem may be disposed on one or more physical devices.
- the processor subsystem may include one or more specialized processors, such as a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), or a fixed function processor.
- GPU graphics processing unit
- DSP digital signal processor
- FPGA field programmable gate array
- Examples, as described herein, may include, or may operate on, circuitry, logic or a number of components, modules, or mechanisms.
- Modules may be hardware, software, or firmware communicatively coupled to one or more processors in order to carry out the operations described herein. It will be understoo that the modules or logic may be implemented in a hardware component or device, software or firmware running on one or more processors, or a combination.
- the modules may be distinct and independent components integrated by sharing or passing data, or the modules may be subcomponents of a single module, or be split among several modules.
- modules may be hardware modules, and as such modules maybe considered tangible entities capable of performing specified operations and may he configured or arranged in a certain maimer in an example, circuits may be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner as a module.
- the whole or part of one or more computer systems may he configured by firmware or software (e.g., instructions, an application portion, or an application) as a module that operates to perform specified operations.
- the software may reside on a machine-readable medium.
- the software when executed by the underlying hardware of the module, causes the hardware to perform the specified operations.
- the term hardware module is understood to encompass a tangible entity, be that an entity that is physically constructed, specifically configured (e.g., hardwired), or temporarily (e.g , transitorily) configured (e.g , programmed) to operate in a specified manner or to perform part or all of any operation described herein.
- each of the modules need not be instantiated at any one moment in time.
- the modules comprise a general-purpose hardware processor configured, arranged or adapted by using software; the general-purpose hardware processor may be configured as respective different modules at different times.
- Software may accordingly configure a hardware processor, for example, to constitute a particular module at one instance of time and to constitute a different module at a different instance of time.
- Modules may also be software or firmware modules, which operate to perform the methodologies described herein.
- Geometric terms such as “parallel”, “perpendicular”, “round”, or “square”, are not intended to require absolute mathematical precision, unless the context indicates otherwise. Instead, such geometric terms allow for variations due to manufacturing or equivalent functions. For example, i.f an element is described as “round” or “generally round,” a component that is not precisely circular (e.g., one that is slightly oblong or is a many-sided polygon) is still encompassed by this description,
- Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples.
- An implementation of such methods can include code, such as microcode, assembly language code, a higher- level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non volatile tangible computer-readable media, such as during execution or at other times.
- Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.
Abstract
Description
Claims
Priority Applications (7)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CA3176990A CA3176990A1 (en) | 2020-05-08 | 2021-05-07 | Apparatus and methods for pulmonary monitoring |
US17/922,478 US20230197262A1 (en) | 2020-05-08 | 2021-05-07 | Apparatus and methods for pulmonary monitoring |
AU2021269051A AU2021269051A1 (en) | 2020-05-08 | 2021-05-07 | Apparatus and methods for pulmonary monitoring |
EP21800242.6A EP4147250A1 (en) | 2020-05-08 | 2021-05-07 | Apparatus and methods for pulmonary monitoring |
BR112022021560A BR112022021560A2 (en) | 2020-05-08 | 2021-05-07 | APPARATUS AND METHODS FOR LUNG MONITORING |
JP2022567582A JP2023524816A (en) | 2020-05-08 | 2021-05-07 | Apparatus and method for lung monitoring |
CN202180048419.2A CN115917667A (en) | 2020-05-08 | 2021-05-07 | Device and method for pulmonary monitoring |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US202063022221P | 2020-05-08 | 2020-05-08 | |
US63/022,221 | 2020-05-08 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2021226464A1 true WO2021226464A1 (en) | 2021-11-11 |
Family
ID=78468461
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2021/031306 WO2021226464A1 (en) | 2020-05-08 | 2021-05-07 | Apparatus and methods for pulmonary monitoring |
Country Status (8)
Country | Link |
---|---|
US (1) | US20230197262A1 (en) |
EP (1) | EP4147250A1 (en) |
JP (1) | JP2023524816A (en) |
CN (1) | CN115917667A (en) |
AU (1) | AU2021269051A1 (en) |
BR (1) | BR112022021560A2 (en) |
CA (1) | CA3176990A1 (en) |
WO (1) | WO2021226464A1 (en) |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050085801A1 (en) * | 1999-08-05 | 2005-04-21 | Broncus Technologies, Inc. | Methods of treating chronic obstructive pulmonary disease |
US20080160546A1 (en) * | 2005-12-22 | 2008-07-03 | Tracey Colpitts | Methods and Marker Combinations for Screening For Predisposition to Lung Cancer |
US20140316266A1 (en) * | 2013-03-15 | 2014-10-23 | The Regents Of The University Of Michigan | Lung ventillation measurements using ultrasound |
US20170100059A1 (en) * | 2015-10-09 | 2017-04-13 | General Electric Company | Lung function monitoring |
-
2021
- 2021-05-07 AU AU2021269051A patent/AU2021269051A1/en active Pending
- 2021-05-07 CA CA3176990A patent/CA3176990A1/en active Pending
- 2021-05-07 EP EP21800242.6A patent/EP4147250A1/en active Pending
- 2021-05-07 CN CN202180048419.2A patent/CN115917667A/en active Pending
- 2021-05-07 JP JP2022567582A patent/JP2023524816A/en active Pending
- 2021-05-07 BR BR112022021560A patent/BR112022021560A2/en unknown
- 2021-05-07 US US17/922,478 patent/US20230197262A1/en active Pending
- 2021-05-07 WO PCT/US2021/031306 patent/WO2021226464A1/en unknown
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050085801A1 (en) * | 1999-08-05 | 2005-04-21 | Broncus Technologies, Inc. | Methods of treating chronic obstructive pulmonary disease |
US20080160546A1 (en) * | 2005-12-22 | 2008-07-03 | Tracey Colpitts | Methods and Marker Combinations for Screening For Predisposition to Lung Cancer |
US20140316266A1 (en) * | 2013-03-15 | 2014-10-23 | The Regents Of The University Of Michigan | Lung ventillation measurements using ultrasound |
US20170100059A1 (en) * | 2015-10-09 | 2017-04-13 | General Electric Company | Lung function monitoring |
Also Published As
Publication number | Publication date |
---|---|
JP2023524816A (en) | 2023-06-13 |
EP4147250A1 (en) | 2023-03-15 |
CA3176990A1 (en) | 2021-11-11 |
AU2021269051A1 (en) | 2022-11-24 |
US20230197262A1 (en) | 2023-06-22 |
CN115917667A (en) | 2023-04-04 |
BR112022021560A2 (en) | 2022-12-06 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Stockley et al. | Small airways disease: time for a revisit? | |
JP5519778B2 (en) | Body sound inspection apparatus and body sound inspection method | |
US20180296092A1 (en) | System and method for monitoring and determining a medical condition of a user | |
JP2016526466A (en) | Determination of respiratory parameters | |
Brown et al. | Reference equations for respiratory system resistance and reactance in adults | |
Zubaydi et al. | MobSpiro: Mobile based spirometry for detecting COPD | |
Grzelewski et al. | Diagnostic value of lung function parameters and FeNO for asthma in schoolchildren in large, real‐life population | |
Kassem et al. | A smart spirometry device for asthma diagnosis | |
Saadeh et al. | Retrospective observations on the ability to diagnose and manage patients with asthma through the use of impulse oscillometry: comparison with spirometry and overview of the literature | |
Zubaydi et al. | Using mobiles to monitor respiratory diseases | |
US20230197262A1 (en) | Apparatus and methods for pulmonary monitoring | |
Lange et al. | Spirometry: don't blow it! | |
JP2013514850A (en) | BODE index measurement | |
Agarwal et al. | Design and development of a low-cost spirometer with an embedded web server | |
Hasan et al. | Diagnosing COPD using mobile phones | |
Grigoriadis et al. | Handgrip force and maximum inspiratory and expiratory pressures in critically ill patients with a tracheostomy | |
Sümbül et al. | Estimating the value of the volume from acceleration on the diaphragm movements during breathing | |
Hegewald et al. | Long-term intersession variability for single-breath diffusing capacity | |
Mishra et al. | Peak Expiratory Flow Rate Measure among Community Dwelling Elderly Rural Population | |
Sudan et al. | A comparative study to evaluate the effect of crook lying position versus sitting position on forced vital capacity (FVC) in healthy individuals | |
US20230225695A1 (en) | Analyzing a patient's breathing based on one or more audio signals | |
Zubaydi | A mobile based platform for monitoring respiratory diseases | |
Chandel et al. | Vocal Capacity as a Surrogate for Vital Capacity in Patients with Interstitial Lung Disease | |
Alrumuh | The development of normative values of LCI in healthy school-children using the SF6 (Innocor) Multiple Breath Washout technique | |
Raguindin et al. | Predictive COPD Monitoring Device (PCMD) |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21800242 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 3176990 Country of ref document: CA |
|
REG | Reference to national code |
Ref country code: BR Ref legal event code: B01A Ref document number: 112022021560 Country of ref document: BR |
|
ENP | Entry into the national phase |
Ref document number: 2022567582 Country of ref document: JP Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 2021269051 Country of ref document: AU Date of ref document: 20210507 Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 112022021560 Country of ref document: BR Kind code of ref document: A2 Effective date: 20221024 |
|
ENP | Entry into the national phase |
Ref document number: 2021800242 Country of ref document: EP Effective date: 20221208 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |