WO2020257609A1 - Diagnostics et traitements pour une maladie cardiaque - Google Patents

Diagnostics et traitements pour une maladie cardiaque Download PDF

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Publication number
WO2020257609A1
WO2020257609A1 PCT/US2020/038674 US2020038674W WO2020257609A1 WO 2020257609 A1 WO2020257609 A1 WO 2020257609A1 US 2020038674 W US2020038674 W US 2020038674W WO 2020257609 A1 WO2020257609 A1 WO 2020257609A1
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patient
plethysmography
cardiac disease
data processing
training
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PCT/US2020/038674
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English (en)
Inventor
Eric M. Green
Reinier VAN MOURIK
Charles WOLFUS
Onur DUR
Marc J. SEMIGRAN
Ching-Pin Chang
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MyoKardia, Inc.
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Publication of WO2020257609A1 publication Critical patent/WO2020257609A1/fr

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    • 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
    • A61B5/02028—Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
    • 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
    • A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
    • A61B5/02116—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics of pulse wave amplitude
    • 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
    • A61B5/026—Measuring blood flow
    • A61B5/0261—Measuring blood flow using optical means, e.g. infrared light
    • 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
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/681—Wristwatch-type devices
    • 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
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • 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
    • 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

Definitions

  • This disclosure relates to diagnosing and treating a patient who knowingly or unknowingly has an elevated pressure gradient in his/her cardiac outflow tract.
  • Diseases that are associated with an elevated pressure gradient in the cardiac outflow tract include obstructive hypertrophic cardiomyopathy (oHCM) and aortic stenosis (AS).
  • oHCM obstructive hypertrophic cardiomyopathy
  • AS aortic stenosis
  • the likelihood of oHCM or AS can be predicted with a machine learning model.
  • HCM Hypertrophic cardiomyopathy
  • oHCM outflow tract obstruction
  • Characteristic hemodynamic abnormalities in patients with outflow tract obstruction were noted in the earliest descriptions of HCM and have been observed using arterial pressure tracings, acoustic phonography and echocardiography.
  • Echocardiographic screening is a standard diagnostic for HCM.
  • ECG electrocardiography
  • echocardiography to screen asymptomatic individuals for HCM have been limited by test characteristics and cost.
  • the non specific symptoms of HCM exercise intolerance, dyspnea, or fatigue
  • aortic stenosis is now one of the most common valvular heart diseases. Early recognition and management of aortic stenosis are of paramount importance because untreated symptomatic severe disease is universally fatal. The progression of aortic valve disease and the need for eventual aortic valve replacement varies considerably.
  • One aspect of the disclosure provides a method for training a predictive model for predicting whether a patient suffers from a cardiac disease characterized by an elevation of a pressure gradient in the patient’s cardiac outflow tract.
  • the method includes receiving, at data processing hardware, a training dataset comprising a plurality of features extracted from training plethysmography signals. The training
  • plethysmography signals are obtained from a first group of subjects, each suffering from a cardiac disease characterized by an elevation of a pressure gradient in a cardiac outflow tract, and from a second group of subjects who are healthy.
  • the plurality of features are related to measurements of left ventricle outflow tract obstruction (LVOTO), diastolic function, and/or LV systolic function of the subjects.
  • the method further includes training, by the data processing hardware, a predictive model of the cardiac disease with the training dataset.
  • the predictive model of the cardiac disease is trained according to a multiple instance learning via embedded instance selection (MILES) approach with yet- another-radial-distance-based-similarity (YARDS) measure.
  • MILES embedded instance selection
  • YARDS yet- another-radial-distance-based-similarity
  • Another aspect of the disclosure provides a method for predicting whether a patient is at risk of or suffers from a cardiac disease characterized by an elevation of a pressure gradient in the patient’s cardiac outflow tract.
  • the method includes receiving, at data processing hardware, a plethysmography signal obtained from a patient.
  • the plethysmography signal comprises a plurality of features related to measurements of LVOTO, LV diastolic function, and/or LV systolic function of the patient.
  • the method further includes determining, by the data processing hardware, an indicator score indicating a likelihood of the patient is at risk of or suffering from the cardiac disease using a predictive model.
  • the predictive model is configured to receive the plurality of features as inputs and is trained on a corpus of training plethysmography signals according to a multiple instance learning via embedded instance selection (MILES) approach with yet-another-radial-distance-based-similarity (YARDS) measure.
  • MILES embedded instance selection
  • YARDS yet-another-radial-distance-based-similarity
  • Each training plethysmography signal comprises a corresponding plurality of features related to measurements of LVOTO, LV diastolic function, and/or LV systolic function.
  • the method further includes outputting, by the data processing hardware, the determined indicator score.
  • Yet another aspect of the disclosure provides a method of diagnosing and treating a patient suffering from a cardiac disease characterized by an elevation of a pressure gradient in the patient’s cardiac outflow tract.
  • the method includes diagnosing, by data processing hardware, the patient suffers from a cardiac disease characterized by an elevation of a pressure gradient in a cardiac outflow tract of the patient based on an indicator score.
  • the indicator score is determined by a predictive model.
  • the predictive model is trained to predict the cardiac disease from a plurality of features related to measurements of left ventricle outflow tract obstruction (LVOTO), LV diastolic function, and/or LV systolic function of the patient.
  • the plurality of features are extracted from a plethysmography signal obtained from the patient.
  • the method further includes instructing, by the data processing hardware, treatment of the diagnosed cardiac disease.
  • Still yet another aspect of the disclosure provides a method of determining whether a patient is at risk for a cardiac disease that is characterized by an elevation of a pressure gradient in the patient’s cardiac outflow tract.
  • the method includes determining, by data processing hardware, a patient is at risk for the cardiac disease based on an indicator score determined by a predictive model trained to predict a risk of the cardiac disease from a plurality of features related to measurements of left ventricle outflow tract obstruction (LVOTO), LV diastolic function, and/or LV systolic function of the patient extracted from a plethysmography signal obtained from the patient.
  • the method further includes instructing, by the data processing hardware, the patient to seek guidance from a physician to address the determined risk for the cardiac disease.
  • Still another aspect of the disclosure provides a method for treating a patient suffering from a disease having an elevated pressure gradient of a cardiac outflow tract of the patient comprising: treating a patient with a drug to treat a disease causing an elevated pressure gradient of a cardiac outflow tract of the patient, wherein the patient has been diagnosed as having the elevated pressure gradient using a method of the present disclosure.
  • the predictive model can be used to monitor a patient who is risk for a cardiac disease such as aortic stenosis and obstructive hypertrophic cardiomyopathy (oHCM), which are characterized by an elevation of a pressure gradient in the patient’s cardiac outflow tract.
  • aortic stenosis and obstructive hypertrophic cardiomyopathy oHCM
  • oHCM obstructive hypertrophic cardiomyopathy
  • a patient to be monitored with a predictive aortic stenosis model of the present disclosure is an at risk patient for aortic stenosis such as a patient who has been diagnosed with a cardiac valve impairment (e.g., calicified valve, bicuspid valve, a unicuspid or quadricuspid aortic valve, aortic valve sclerosis) but has not yet developed or been diagnosed with aortic stenosis.
  • a patient to be monitored with a predictive oHCM model of the present disclosure is an at risk patient for oHCM such as a patient who carries mutations associated with the development of oHCM but who has not developed or been diagnosed with oHCM.
  • a patient to be monitored with the predictive oHCM model has been diagnosed with New York Heart Association (NYHA) Class I heart failure.
  • NYHA New York Heart Association
  • FIG. l is a schematic view of an example system for predicting whether a patient is at risk for or suffering from a cardiac disease based on a plethysmography signal obtained from the patient.
  • FIG. 2A is a plot view of an example arterial pulse waveform.
  • FIG. 2B is a perspective view of an example plethysmography sensor that is worn around a patient’s fingertip.
  • FIG. 2C is a diagram illustrating the technique of photoplethysmography.
  • FIGS. 2D and 2E are graph views of example photoplethysmographic traces obtained from a healthy patient and a patient with obstructive hypertrophic
  • FIG. 2F is a perspective view of an example wearable medical device having a photoplethysmography sensor.
  • FIG. 3 is a schematic view of an example pulse engine for training a cardiac di sease predi ctive model .
  • FIG. 4 is a graph view of example photoplethysmographic traces, including beats with aberrant waveforms.
  • FIG. 5 is a schematic view of an example system for predicting a cardiac disease from a photoplethysmography signal obtained from a patient.
  • FIG. 6 is a flow chart of an example method for predicting whether a patient is at risk for or suffering from cardiac disease based on a plethysmography signal obtained from a patient.
  • FIG. 7 is a flow chart of an example method for diagnosing and treating a patient for a cardiac disease.
  • FIGS. 8A-8D are graph views illustrating data from an experimental protocol.
  • FIG. 9 is a schematic view of an example computing device that may be used to implement the systems and methods described herein.
  • the present invention relates to diagnosing and treating a patient who knowingly or unknowingly has an elevated pressure gradient in his/her cardiac outflow tract.
  • Cardiac diseases that are associated with an elevated pressure gradient in the cardiac outflow tract include obstructive hypertrophic cardiomyopathy (oHCM) and aortic stenosis (AS).
  • oHCM obstructive hypertrophic cardiomyopathy
  • AS aortic stenosis
  • the patient who is suffering from oHCM has an elevated pressure gradient that is greater than 50 millimeters of mercury (mmHg).
  • the patient having a valve impairment who is suffering from AS has a pressure gradient that is greater than 16 mmHg, greater than 36 mmHg, greater than 40 mmHg, or greater than 50 mmHg.
  • HCM Hypertrophic cardiomyopathy
  • NYHA New York Heart Association
  • HCM is a progressive disease that can result in shortness of breath, chest pain, an inability to participate in normal activities, disabling heart failure, stroke or sudden death.
  • Obstructive HCM oHCM
  • LVOT left ventricular outflow tract
  • the identification of obstruction in HCM is important for both prognostic and therapeutic reasons.
  • the presence of obstruction portends a worse prognosis but also suggests potential established medical therapies (e.g., beta blocker, calcium channel blocker, disopyramide) and interventional therapies (e.g., septal reduction).
  • FIG. 1 is a schematic view of an example diagnostic system 100 for predicting whether a patient 10 is at risk for or suffering from a cardiac disease that is characterized by an elevated pressure gradient in the patient’s cardiac outflow tract.
  • the diagnostic system 100 includes a plethysmography sensor 200, which in the example shown is worn by the patient 10 around their wrist.
  • the plethysmography sensor 200 generates a plethysmography signal 210.
  • a computing device 110 collects the plethysmography signal 210 generated by the plethysmography sensor 200 and transmits the
  • the remote system 140 may be a distributed system (e.g., a cloud or edge computing environment) having scalable/elastic computing resources 142 (e.g., data processing hardware) and/or storage resources 146 (e.g. memory hardware).
  • scalable/elastic computing resources 142 e.g., data processing hardware
  • storage resources 146 e.g. memory hardware.
  • the remote system 140 executes a pulse engine 300 configured to receive the plethysmography signal 210 from the patient 10 and to output an indicator score 330 back to the patient 10.
  • the indicator score 330 indicates a likelihood of the patient 10 is at risk for or is suffering from the cardiac disease.
  • the plethysmography sensor 200 obtains, in a non-invasive manner, information about how the volume of blood in a part of the patient’s body changes over time.
  • the information gathered by the plethysmography sensor 200 can be represented, graphically, as an arterial pulse waveform (or simply waveform) as shown in FIG. 2A.
  • an example of the plethysmography sensor 200 uses photoplethysmography and optically detects blood volume changes within the microvascular bed of the patient’s 10 fingertip.
  • Light emitted by the plethysmography sensor 200 e.g., by way of a LED
  • the plethysmography sensor 200 e.g., by way of a photodiode.
  • the plethysmography sensor 200 samples reflected light many times within a second to construct a
  • plethysmography signal 210 The absorption of light varies with the changes in pulsatile arterial blood flow and generates a time-varying pulse waveform (also known as arterial pulse waves) corresponding to the plethysmography signal 210.
  • a time-varying pulse waveform also known as arterial pulse waves
  • the plethysmography sensor 200 can perform blood volume measurements without penetrating the patient’s skin.
  • Other examples of the plethysmography sensor 200 may use tonometry, radar spectroscopy, inductance plethysmography or impedance plethysmography to
  • FIGS. 2D and 2E illustrate example photoplethysmographic traces obtained from a healthy individual (FIG. 2D) and from an individual with HCM and obstruction (FIG. 2E).
  • FIG. 2F shows another example of the plethysmography sensor 200.
  • the plethysmography sensor 200 is placed snugly on a patient’s arm above their wrist bone.
  • the plethysmography sensor 200 comes with a removable sensor carriage 202 and a plastic band 204.
  • the sensor carriage 202 contains one or more light emitting diodes 206 (LEDs) and an optical sensor 208 along with a battery (not shown) and an inductive charging coil (not shown).
  • LEDs light emitting diodes
  • the sensor carriage 202 contains two light emitting diodes (LEDs) 206 producing light of two wavelengths, e.g., green light, at about 520-530 nanometer (nm) and infrared light, at about 850-1000 nm.
  • the LEDs 206 fire at a rate configurable between 20 and 95 Hz driven by a submillisecond resolution low-jitter external clock signal.
  • a fully integrated analog front end receives and digitizes plethysmography 210.
  • the plethysmography sensor 200 digitizes plethysmography 210 with an amplitude resolution of 16 bits. Such resolution may be advantageous to predicting cardiac diseases, including oHCM and aortic stenosis, from plethysmography signals 210 according to the approaches described herein.
  • the plethysmography sensor 200 is also capable of collecting inertial motion data using a 3-axis accelerometer and 3-axis gyroscope built into the sensor carriage 202.
  • the sampling rate and duty cycle of the light and motion sensors are configurable.
  • the plethysmography sensor 200 collects light sensor data at a time resolution greater than 30 Hz (e.g., 86 Hz) and collects motion data at 10 Hz.
  • the pulse engine 300 includes a cardiac disease predictor 310 that uses a cardiac disease predictive model 320 to generate the indicator score 330.
  • the cardiac disease predictive model 320 may be trained by a cardiac disease trainer 340 based on a training dataset 350, which may be obtained from a data store 360.
  • the cardiac disease predictor 310 uses a cardiac disease predictive model 320 that is configured to receive a plurality of features 212, 212a-n associated with the plethysmography signal 210 as feature inputs 212.
  • the plurality of features 212a-n includes features 212 related to measurements of left ventricle (LV) diastolic function and/or LV systolic function of the patient 10.
  • LV left ventricle
  • the plurality of features 212a-n may include features 212 related to any one or any combination of following measurements: 1) a ratio of early filling (E) velocity to atrial filling (A) velocity; 2) a peak A wave velocity; 3) a peak E wave velocity; 4) an ejection fraction; 5) a LV global circumferential strain; 6) a LV global longitudinal strain; and 7) a LV outflow tract gradient.
  • the plurality of features 212a-n are wave features extracted from a plethysmography waveform corresponding to the plethysmography signal 210.
  • the cardiac disease predictive model 320 is trained on the training dataset 350, which is obtained from a data store 360 residing on the storage resources 146 of the distributed system 140, or may reside at some other remote location in communication with the distributed system 140.
  • the training dataset 350 includes a corpus of training plethysmography signals 210T collected from a first group of subjects who suffer from a cardiac disease, such as oHCM and aortic stenosis, and from a second group of subjects who are healthy (i.e., who are not suffering from a cardiac disease).
  • Each training plethysmography signal 210T includes a corresponding plurality of features.
  • each training plethysmography signal 210T includes features related to measurements of left ventricle (LV) diastolic function and/or LV systolic function of each subject.
  • the corresponding plurality of features may include features 212 related to any one or any combination of following subject measurements:
  • the cardiac disease trainer 340 receives the training dataset 350 for training the cardiac disease predictive model 320. Based on the training dataset 350, the cardiac disease trainer 340 models cardiac disease score parameters 342 to train the cardiac disease predictive model 320.
  • the cardiac disease predictor 310 uses the trained the cardiac disease predictive model 320 during inference for determining the indicator scores 330 for corresponding plethysmography signals 210.
  • the cardiac disease predictive model 320 is trained to determine indicator scores 330 using the training dataset 350 associated with the corpus of training plethysmography signals 21 Or . each of which includes a corresponding plurality of features.
  • the cardiac disease predictive model 320 is trained according to a multiple instance learning via embedded instance selection (MILES) approach with yet-another-radial-distance-based-similarity (YARDS) measure.
  • MILES embedded instance selection
  • YARDS yet-another-radial-distance-based-similarity
  • the MILES approach with YARDS measure is described in the article“A review of multi instance learning assumptions”, the entirety of which is incorporated by reference herein. (Foulds, J., & Frank, E. (2010). A review of multi-instance learning assumptions. The Knowledge Engineering Review, 25(1), 1-25.) Briefly, training the cardiac disease predictive model 320 under this approach includes: 1) for each of the training
  • plethysmography signals 210T transforming feature vectors extracted from the training plethysmography signal 210T into a single vector for that training plethysmography signal and 2) fitting the resulting vectors with a support vector machine.
  • the cardiac disease predictive model 320 is evaluated using Leave-One-Group-Out cross-validation (LOGO CV) with nested hyperparameter tuning. Tuning of the hyperparameters may be done using a k-fold (e.g., 68) cross-validation with random selection of training and testing cohorts (e.g., 70% testing and 30% training).
  • the cardiac disease predictive model 320 is trained and tuned using all plethysmography signals except for plethysmography signals for that subject.
  • the LOGO CV runs N number of folds. In each fold, a validation sample corresponds to an excluded subject (i.e., the subject left out) and N-l number of test samples correspond to the N-l number of non-ex eluded subjects.
  • the hyperparameters in each fold of the N number of LOGO CV folds are selected by the k-fold random subject split cross-validation. That is, for each subject of the N number of subjects, the predictive model 320 trains on the plethysmography signals of the other N-l number of subjects and evaluates it subsequently on the excluded subject’s plethysmography signals. To ensure that the excluded subject (i.e., the validation sample) is entirely new to the predictive model 320, hyperparameter tuning excludes that subject. Thus, in each fold of the N number of LOGO CV folds, hyperparameter tuning by cross-validation is performed on a training dataset without the plethysmography signals from the excluded subject (viz., a N-l subject training set).
  • This nested cross-validation performs k number of folds (e.g., 68) of splitting of the N-l number of non-excluded subjects randomly into training (e.g., 70%) and testing (e.g., 30%) cohorts to select LOGO-fold-specific hyperparameters.
  • k number of folds e.g., 68
  • testing e.g., 30%
  • hyperparameters are then used during training of the predictive model 320 on the N-l number of non-excluded subjects, which in turn are used to evaluate the LOGO CV fold on the excluded subject’s plethysmography signals. Because plethysmography signals of the excluded subject are evaluated by an instance of the predictive model 320 that has not “seen” the excluded subject, the plethysmography signals of all N number of subjects are included in a final confusion matrix describing the performance of the predictive model 320.
  • the pulse engine 300 performs an initial processing step or steps on the plethysmography signal 210 prior to predicting a cardiac disease, including oHCM and aortic stenosis.
  • the pulse engine 300 combines digital signal processing and machine learning to generate an indicator score from sensor signals.
  • the pulse engine 300 applies an eighth- order Butterworth low-pass filter (e.g., with a 3 decibel point (dB) at 18 hertz (Hz)) to the plethysmography signal 210.
  • the low-pass filtering is designed to remove high frequency noise from the plethysmography signal 210, which may lead to more accurate prediction of a cardiac disease.
  • Zero-phase filtering can also be implemented, which involves filtering the plethysmography signal 210 in both forward and backward directions, to eliminate phase distortion.
  • Another example of initial processing (or digital signal processing) by the pulse engine 300 includes generating a plethysmography waveform (arterial pulse waveform) from the plethysmography signal 210 and separating out the plethysmography waveform into individual beats.
  • a plethysmography waveform arterial pulse waveform
  • the plethysmography signal 210 can be segmented into beats by: 1) detecting peaks in the plethysmography signal 210 using, for example, a wavelet transform-based peak detection algorithm; 2) from the peaks detected, selecting peaks representing systolic onsets using, for example, time and frequency domain heuristics; and 3) identifying segments of the plethysmography signal 210 from one systolic onset to the next as beats.
  • the location of the peaks in the plethysmography signal 210 can be refined using interpolation techniques to improve the signal time resolution.
  • FIG. 4 shows a plethysmography waveform 402 for patient 10, who is identified as“oHCM subject-1”.
  • the plethysmography waveform 402 is generated from a plethysmography signal acquired from patient 10 and represents or corresponds to a set of beats collected over a period of time from patient 10. It may be beneficial, however, to determine the indicator score 330 for patient 10 based on a subset of those beats. For example, any beat that fails to satisfy a signal quality metric is determined to have an “aberrant” waveform and is filtered out or removed from the prediction making process. Beats having aberrant waveforms are labeled in the figure with Xs.
  • Beats having a “normal” waveform i.e., satisfying the signal quality metric
  • the plethysmography waveform 402 includes more beats with aberrant waveforms than beats with normal waveforms.
  • the quality of the plethysmography signal used to generate the plethysmography waveform 402 is low or“unsatisfactory”.
  • the entire plethysmography signal may be not used to determine the indicator score 330 because of the lack of signal quality.
  • another plethysmography signal acquired from patient 10 is used instead. As such, the foregoing provides a quality control for the prediction making process.
  • Whether a beat has an aberrant waveform may be determined, e.g., by the data processing hardware 144 of FIG. 1, using an artifact motion associated with the beat, a short-time Fourier transformation of the beat, a correlation of the beat with other beats from the plethysmography signal, other heuristic, or a combination of the
  • the computing device 110 may discard beats that are acquired when a net acceleration amplitude is greater than a threshold motion (e.g., g/10, where g is the acceleration of gravity).
  • a threshold motion e.g., g/10, where g is the acceleration of gravity
  • a beat having an aberrant waveform is discarded when an average amplitude of the aberrant waveform over a threshold period of time (e.g., 60 seconds) is less than a threshold amplitude (e.g., 500 microvolts or 0.01% of a direct current signal component of a plethysmography signal corresponding to the beat).
  • a threshold period of time e.g. 60 seconds
  • a threshold amplitude e.g., 500 microvolts or 0.01% of a direct current signal component of a plethysmography signal corresponding to the beat.
  • a waveform shape or morphology of a beat can also be used to identify whether the beat has an aberrant waveform. For example, a beat without a clear systolic ramp up and diastolic ramp down with waves outside 0.3 Hz to 10Hz frequency domain is identified as having an aberrant waveform and is discarded. Comparing morphologies of a given beat to other beats in the same plethysmography signal can also be used to identify whether the given beat has an aberrant waveform. For example, a beat having frequency content over a waveform segment (e.g., two seconds) that is significantly different from frequency content of previous beats over previous waveform segments (e.g. having a threshold difference of 80% or more) is identified as having an aberrant waveform and is filtered out.
  • a waveform segment e.g., two seconds
  • Some implantations of the pulse engine 300 assess a patient’s health with respect to a cardiac disease based on the indicator score 330 and provide the assessment to the patient 10 or to their healthcare provider. For example, the pulse engine 300 compares the indicator score 330 against a threshold value. If the indicator score 330 is greater than the threshold value, then the pulse engine 300 informs the patient/healthcare provider that the patient 10 is predicted to have or be at risk of having a cardiac disease, such as oHCM or aortic stenosis.
  • a cardiac disease such as oHCM or aortic stenosis.
  • the pulse engine 300 notifies the patient/healthcare provider the likelihood of the patient 10 having or risk of having the cardiac disease as a probability (e.g., 25%, 50%, or 75%) or a rating (e.g.,“likely” or“not likely”). Such assessment can then be used by the patient’s healthcare provider, for example, to order additional tests (e.g., echocardiogram or ECG) or to prescribe medication.
  • a probability e.g. 25%, 50%, or 75%
  • a rating e.g.,“likely” or“not likely”.
  • Such assessment can then be used by the patient’s healthcare provider, for example, to order additional tests (e.g., echocardiogram or ECG) or to prescribe medication.
  • Other examples of the pulse engine 300 provide an action plan that is specific to a patient based on the indicator score 330 predicted for that patient.
  • Providing such action plan may include determining and/or causing the execution of a suitable medication dosing regimen, such as the administration of an oHCM drug (e.g., mavacamten) or a cardiac myosin inhibitor.
  • a suitable medication dosing regimen such as the administration of an oHCM drug (e.g., mavacamten) or a cardiac myosin inhibitor.
  • Developing the patient-specific medication dosing regimen as a function of plethysmography signals acquired from the patient (and from the information carried within those signals) may eliminate or reduce the trial-and- error aspect of conventional dosing regimen development.
  • the determined indicator score 330 can also be used to evaluate a patient’s response to a medication dosing regimen that is prescribed to them.
  • the ability for these examples of the pulse engine 300 to accurately predict a cardiac disease or risk of a cardiac disease from plethysmography signals coupled with the ubiquitous and non- intrusive nature of wearable medical devices with plethysmography sensors provide a broadly available and inexpensive solution for screening and treating cardiac disease, such oHCM and aortic stenosis.
  • FIG. 5 shows an example diagnostic system 500 for predicting a cardiac disease from a photoplethysmography (PPG) signal.
  • the diagnostic system 500 obtains the PPG signal from a pulse oximetry sensor 502, which is worn around a patient’s finger 504, through a pulse oximeter front end 506.
  • a processor 508 obtains plethysmographic information carried in the PPG signal from the pulse oximeter front end 506 and generates an arterial pulse waveform from that information based on changes detected in the arterial blood flow over time.
  • the functionality of pulse oximeter front end 506 may be incorporated into the processor 508. While FIG.
  • the pulse oximetry sensor 502 communicating via a wired connection 505, in other examples the pulse oximetry sensor 502 can communicate with the processor 508 and the pulse oximeter front end 506 wirelessly (e.g., using Bluetooth® or WiFi® ). In some embodiments, other sensors are used to detect blood flow and/or blood flow parameters with which a possible LVOT obstruction can be diagnosed.
  • the processor 508 analyzes the arterial pulse waveform using the predicative approach described above to find characteristic shapes or signatures indicative of a cardiac obstruction. Such shapes or signatures may be symptoms of hypertrophic cardiomyopathy.
  • the analysis may include, for example, analysis of pressure segment slope, uniformity, amplitude, frequency, and/or pulse width.
  • the processor 508 may also compare the waveform generated from plethysmographic information with waveforms stored in memory 510 to identify shapes and/or signatures indicative of cardiac obstruction caused, for example, by hypertrophic cardiomyopathy.
  • the generated waveform may be identified as an obstruction signature if an obstruction is observed simultaneously via another means, such as echocardiography, thereby calibrating the device.
  • the example shown in FIG. 5 also includes an optional output 512, such as a display, for communicating the generated waveform and/or other results of the analysis to a patient or to the patient’s healthcare provider.
  • FIG. 6 is a flowchart of an example method 600 for providing an indicator score 330 to a patient.
  • the method 600 may be described with reference to FIGS. 1 and 3.
  • the method 600 includes, at operation 602, receiving, at data processing hardware 144, a plethysmography signal 210 acquired from patient 10.
  • the plethysmography signal 210 is associated with a plurality of features 212a-n related to measurements of outflow tract obstruction (LVOTO), LV diastolic function, and/or LV systolic function of the patient 10.
  • LVOTO outflow tract obstruction
  • the method 600 includes determining, by the data processing hardware 144, an indicator score 330 for the patient 10 using cardiac disease predictive model 320.
  • the cardiac disease predictive model 320 is configured to receive the plurality of features 212a-n as feature inputs 212.
  • the cardiac disease predictive model 320 is trained on a corpus of training plethysmography signals 210 T . Each of the training plethysmography signals 210 T includes a corresponding plurality of features.
  • the cardiac disease predictive model 320 may be trained according to the MILES approach with YARDS measure.
  • the method 600 at operation 606, further includes outputting, by the data processing hardware 144, the determined indicator score 330 to the patient 10.
  • the determined indicator score 330 indicates a likelihood that the patient 10 is at risk for or suffering from a cardiac disease like oHCM or aortic stenosis.
  • FIG. 7 is a flowchart of an example method 700 for treating a cardiac disease.
  • the method 700 may be described with reference to FIG. 1 and FIG. 3.
  • the method 700 includes, at operation 702, diagnosing, at data processing hardware 144, that a patient 10 suffers from a cardiac disease, such as oHCM or aortic stenosis, that is characterized by an elevation of a pressure gradient in the patient’s 10 cardiac outflow tract based on an indicator score 330.
  • the indicator score 330 is determined from a plurality of features 212a-n using cardiac disease predictive model 320.
  • the plurality of features 212a-n relate to measurements of the patient’s left ventricle outflow tract obstruction (LVOTO), LV diastolic function, and/or LV systolic function.
  • the plurality of features 212a-n are extracted from a plethysmography signal 210 obtained from the patient 10.
  • the cardiac disease predictive model 320 is trained on a corpus of training plethysmography signals 210T. Each of the training plethysmography signals 210T includes a corresponding plurality of features.
  • the cardiac disease predictive model 320 may be trained according to the MILES approach with YARDS measure.
  • the method 700 includes, at operation 704, instructing, by the data processing hardware 144, treatment of the diagnosed cardiac disease.
  • the method 700 provides instructions for administrating an oHCM drug, such as a myosin inhibitor, a MyBP-C inhibitor, a DNA methyltransferase inhibitor, a fatty acid beta-oxidation inhibitor, a MEF2 inhibitor, a mineralocorticoid receptor antagonist (MRA), a NPR-A/C agonist, a neprilysin inhibitor, mavacamten, CYK-274, CT-G20, Trimetazidine, Valsartan, Entresto, or Spironolactone.
  • an oHCM drug such as a myosin inhibitor, a MyBP-C inhibitor, a DNA methyltransferase inhibitor, a fatty acid beta-oxidation inhibitor, a MEF2 inhibitor, a mineralocorticoid receptor antagonist (MRA), a NPR-A/C agonist, a neprilysin inhibitor, mavacamten, CYK-274, CT-G20, Trimetazidine, Val
  • the method 700 provides instructions for administrating an aortic stenosis drug, such as a beta-adrenergic receptor blocker, a cardiac glycoside, a diuretics, or an angiotensin-converting enzyme inhibitor.
  • an aortic stenosis drug such as a beta-adrenergic receptor blocker, a cardiac glycoside, a diuretics, or an angiotensin-converting enzyme inhibitor.
  • Other examples of the method 700 provide instructions for a surgical intervention to be performed, such as a valve replacement or a transaortic valve replacement.
  • FIGS. 8A-8D depicts data from an experimental protocol for predicting oHCM from a photoplethysmography (PPG) signal using an oHCM predicative model.
  • PPG photoplethysmography
  • Study subjects underwent resting echocardiography with standard two- dimensional, M-mode, and Doppler imaging by trained sonographers.
  • PPG signals were collected for five minutes (one to five recordings per participant) at rest using a wristworn biosensor (Wavelet Health, Mountain View, CA). PPG signals (traces) from all patients were acquired by a single investigator, who underwent training on a documented procedure that minimizes the impact of differences in environmental factors, including ambient light and temperature. All devices ran identical firmware and signal processing methods to obtain high quality signals. Signals were transmitted by
  • Bluetooth® to an Apple iPad® and uploaded to a cloud database for analysis.
  • PPG recordings from the remaining eighty-three subjects were segmented into beats and a multi-instance classifier was trained to assign each recording an oHCM score based on qualified beats (instances).
  • a set of morphometric pulse features was extracted into a feature vector for each beat.
  • the multiple-instance learning via embedded instance selection (MILES) method was used. The method consists of (i) transforming feature vectors from all beats in a recording into a single vector per recording and (ii) fitting the resulting vectors with a support vector machine.
  • hyperparameter tuning by cross-validation was performed on an eighty-one-patient training set. This nested cross-validation performed sixty-eight folds of splitting eighty- one patients randomly into training (70%) and testing (30%) cohorts solely to select LOGO-fold-specific hyperparameters. These hyperparameters were then used during training of the classifier on the eighty-one non-excluded patients, which in turn was used to evaluate the LOGO fold on the excluded patient’s recordings. Because all the patient’s recordings were evaluated by an instance of the classifier that never saw the patient, all recordings of the eighty-two patients were included in a final confusion matrix describing the performance of the classifier.
  • an oHCM predictive model 320 may distinguish recordings from oHCM patients and healthy volunteers. Although significant differences may exist in many morphometric pulse features, the substantial beat-to-beat variability within individual PPG traces (as illustrated in FIG. 8B) may limit the performance of predicative models based on averaging beats across a recording. To best account for this heterogeneity, the pulse engine 300 may employ a multi -instance classifier to calculate an oHCM score 330 for each recording. After training and cross-validation, the oHCM predictive model 320 may achieve a C-statistic for oHCM detection of 0.99 (95% Cl, 0.99-1.0).
  • the oHCM predictive model 320 may correctly classify 18/19 patients with oHCM and 62/63 healthy volunteers (98% accuracy) (FIG. 8D). The oHCM predictive model 320 thus achieves discrimination between patients with oHCM and healthy controls.
  • FIG. 9 is schematic view of an example computing device 900 that may be used to implement the systems and methods described in this document.
  • the computing device 900 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers.
  • the components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.
  • the computing device 900 includes a processor 910, memory 920, a storage device 930, a high-speed interface/controller 940 connecting to the memory 920 and high-speed expansion ports 950, and a low speed interface/controller 960 connecting to a low speed bus 970 and a storage device 930.
  • Each of the components 910, 920, 930, 940, 950, and 960 are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate.
  • the processor 910 can process instructions for execution within the computing device 900, including instructions stored in the memory 920 or on the storage device 930 to display graphical information, including, for example, indicator score 330 of FIG. 1 and FIG.
  • GUI graphical user interface
  • multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory.
  • multiple computing devices 900 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi -processor system).
  • the memory 920 stores information non-transitorily within the computing device 900.
  • the memory 920 may be a computer-readable medium, a volatile memory unit(s), or non-volatile memory unit(s).
  • the non-transitory memory 920 may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by the computing device 900.
  • non-volatile memory examples include, but are not limited to, flash memory and read-only memory (ROM) / programmable read-only memory (PROM) / erasable programmable read-only memory (EPROM) / electronically erasable programmable read only memory (EEPROM) (e.g., typically used for firmware, such as boot programs).
  • volatile memory examples include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.
  • RAM random access memory
  • DRAM dynamic random access memory
  • SRAM static random access memory
  • PCM phase change memory
  • the storage device 930 is capable of providing mass storage for the computing device 900. In some implementations, the storage device 930 is a computer- readable medium.
  • the storage device 930 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations.
  • a computer program product is tangibly embodied in an information carrier.
  • the computer program product contains instructions that, when executed, perform one or more methods, such as those described above.
  • the information carrier is a computer- or machine-readable medium, such as the memory 920, the storage device 930, or memory on processor 910.
  • the high speed controller 940 manages bandwidth-intensive operations for the computing device 900, while the low speed controller 960 manages lower bandwidth intensive operations. Such allocation of duties is exemplary only. In some
  • the high-speed controller 940 is coupled to the memory 920, the display 980 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 950, which may accept various expansion cards (not shown).
  • the memory 920 e.g., the RAM 920
  • the display 980 e.g., through a graphics processor or accelerator
  • the high-speed expansion ports 950 which may accept various expansion cards (not shown).
  • the low-speed controller 960 is coupled to the storage device 930 and a low-speed expansion port 990.
  • the low-speed expansion port 990 which may include various communication ports (e.g., USB, Bluetooth®, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
  • the computing device 900 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 900a or multiple times in a group of such servers 900a, as a laptop computer 900b, or as part of a rack server system 900c.
  • Various implementations of the systems and techniques described herein can be realized in digital electronic and/or optical circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or
  • a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • a programmable processor which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • the processes and logic flows described in this specification can be performed by one or more programmable processors, also referred to as data processing hardware, executing one or more computer programs to perform functions by operating on input data and generating output.
  • the processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
  • processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
  • a processor will receive instructions and data from a read only memory or a random access memory or both.
  • the essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.
  • a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • a computer need not have such devices.
  • Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.
  • semiconductor memory devices e.g., EPROM, EEPROM, and flash memory devices
  • magnetic disks e.g., internal hard disks or removable disks
  • magneto optical disks e.g., CD ROM and DVD-ROM disks.
  • the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
  • one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input

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Abstract

La présente demande concerne un procédé permettant de prédire si un patient souffre d'un gradient de pression élevé de la voie d'éjection cardiaque, par exemple, une cardiomyopathie hypertrophique obstructive (CMHO) et la sténose aortique (SA), qui comprend la réception d'un signal pléthysmographique obtenu à partir du patient. Le signal pléthysmographique comprend une pluralité de caractéristiques associées à des mesures de l'obstruction de la voie d'éjection ventriculaire gauche (LVOTO), de la fonction diastolique du ventricule gauche et/ou de la fonction systolique du ventricule gauche du patient. Le procédé comprend en outre la détermination d'un score indiquant une pression élevée dans la voie d'éjection cardiaque et une probabilité que le patient souffre d'une maladie associée à un tel événement ou soit susceptible de développer une maladie associée à un tel événement (comprenant, mais sans s'y limiter, la CMHO ou la SA), à l'aide d'un modèle prédictif. Le procédé comprend également l'émission du score d'indicateur déterminé (par exemple, le score CMHO ou le score SA).<i />
PCT/US2020/038674 2019-06-21 2020-06-19 Diagnostics et traitements pour une maladie cardiaque WO2020257609A1 (fr)

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WO2022047004A1 (fr) * 2020-08-28 2022-03-03 MyoKardia, Inc. Méthodes de traitement par modulateur de myosine
WO2024086821A1 (fr) * 2022-10-21 2024-04-25 Prolaio, Inc. Système et procédé de traitement de la cardiomyopathie hypertrophique
WO2024167522A1 (fr) * 2023-02-06 2024-08-15 Medici Technologies, LLC Système de détection d'anomalie valvulaire et structurale non invasive basé sur des aberrations d'écoulement

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US20170065190A1 (en) * 2015-09-09 2017-03-09 Eric M. Green Sensor for ventricular and outflow tract obstruction
US20170261520A1 (en) * 2006-06-07 2017-09-14 True Health Ip Llc Markers Associated With Arteriovascular Events And Methods Of Use Thereof
WO2019028360A1 (fr) * 2017-08-04 2019-02-07 MyoKardia, Inc. Mavacamten destiné à être utilisé dans le traitement de la cardiomyopathie hypertrophique

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US20170261520A1 (en) * 2006-06-07 2017-09-14 True Health Ip Llc Markers Associated With Arteriovascular Events And Methods Of Use Thereof
US20170065190A1 (en) * 2015-09-09 2017-03-09 Eric M. Green Sensor for ventricular and outflow tract obstruction
WO2019028360A1 (fr) * 2017-08-04 2019-02-07 MyoKardia, Inc. Mavacamten destiné à être utilisé dans le traitement de la cardiomyopathie hypertrophique

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Publication number Priority date Publication date Assignee Title
WO2022047004A1 (fr) * 2020-08-28 2022-03-03 MyoKardia, Inc. Méthodes de traitement par modulateur de myosine
WO2024086821A1 (fr) * 2022-10-21 2024-04-25 Prolaio, Inc. Système et procédé de traitement de la cardiomyopathie hypertrophique
WO2024167522A1 (fr) * 2023-02-06 2024-08-15 Medici Technologies, LLC Système de détection d'anomalie valvulaire et structurale non invasive basé sur des aberrations d'écoulement

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