WO2025010429A1 - Procédé et appareil d'évaluation de fonction cardiaque - Google Patents

Procédé et appareil d'évaluation de fonction cardiaque Download PDF

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Publication number
WO2025010429A1
WO2025010429A1 PCT/US2024/036959 US2024036959W WO2025010429A1 WO 2025010429 A1 WO2025010429 A1 WO 2025010429A1 US 2024036959 W US2024036959 W US 2024036959W WO 2025010429 A1 WO2025010429 A1 WO 2025010429A1
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Prior art keywords
tdp
medium
cardiac
probability
alarm
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PCT/US2024/036959
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English (en)
Inventor
Keith A. MARILL
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General Hospital Corp
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General Hospital Corp
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    • 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
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—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

Definitions

  • QT prolongation is one factor that can limit the development of pharmacologic agents.
  • prolonged myocardial repolarization is not specific for TdP and the majority of patients with a prolonged QT interval may not develop TdP.
  • a related risk factor associated with the development of TdP is the QT interval corrected for heart rate (i.e., the QTc).
  • QTc is an electrocardiographic manifestation of the myocardial depolarization to repolarization.
  • the QTc interval alone is an imprecise marker because QTc prolongation is sensitive but nonspecific to TdP.
  • the majority of patients with QTc prolongation do not suffer from TdP.
  • FIG. 3 is a flowchart setting forth steps for a process of determining a probability of TdP in electrocardiographic data, in accordance with the present disclosure.
  • FIG. 8 is a scatterplot of an example T peak to PVC R peak interval divided by preceding Q to T peak interval versus an example baseline Qt interval for PVC’s preceding TdP, in accordance with the present disclosure.
  • the sensor assembly 106 shown is intended to represent one or more sensors and adapted to receive signals from the patient 104.
  • the sensor assembly 106 can include ECG, EMG, ECoG, LFP, EEG, EOG, GSR, and SAO2 sensors, as well as respiration sensors, wearable devices, implanted devices, and other sensors used for other physiological recordings.
  • respiration sensors wearable devices, implanted devices, and other sensors used for other physiological recordings.
  • respiration sensors wearable devices, implanted devices, and other sensors used for other physiological recordings.
  • Various combinations of numbers and types of sensors, as mentioned, are suitable for use with the physiological monitoring system 100.
  • the system 200 may include an input 202, a preprocessor 204, a tracking engine 206, an analyzer 208, and an output 210. Some or all of the modules of the system 200 can be implemented by a physiological patient monitor as described above with respect to FIG. 1.
  • the input 202 may be configured to accept an indication from a user related to a particular subject profile such as a patient’s age, height, weight, gender, or the like, as well as drug administration information, such as timing, dose, rate, and the like.
  • the indication may also include information related to the physiological conditions or scenarios of a subject being monitored by system 200.
  • physiological conditions may include the subject being under pharmacological-induced states, such as general anesthesia or sedation, or while asleep, or while undergoing a medical procedure.
  • the pre-processor 204 may be designed to carry out any number of processing steps for operation of system 200.
  • the pre-processor 204 may be configured to receive physiological data obtained via input 202 and assemble the data into time-series.
  • the pre-processor 204 may be capable of performing steps for removing interfering and/or undesired signals associated with the data via signal rejection or filtering techniques.
  • the pre-processor 204 may also be configured to assemble raw or processed signals from acquired physiological data into time-frequency representations, such as telemetry traces.
  • the pre-processor 204 may process and assemble acquired ECG data, for example, to generate intervals representative of Q to T peaks, T peak to PVC R peaks, or other representations of electrocardiographic characteristics in the data.
  • the pre-processor 204 may also be configured to receive an indication from a user and perform pre-processing steps in accordance with the indication.
  • the T wave is a deflection that represents the electrical activity of ventricular repolarization in the heart.
  • the QTc interval extends from the start of the Q wave to the end of the T wave and is a measurement of the time it takes for the heart's electrical system to reset, corrected for heart rate.
  • JTc interval denotes the interval from the J point to the end of the T wave corrected for heart rate.
  • JTc may reflect the depolarized phase duration of the ventricular myocardium more precisely than QTc because it reduces confounding when more variable depolarization onset time is present.
  • Patients who display characteristics such as PVC’s occurring in association with QTc or JTc prolongation may have a heightened risk of TdP, and hence the tracking engine 206 may provide parameters associated with QTc or JTc intervals, or other intervals.
  • electrocardiographic features associated with a heightened risk of TdP may be determined, which may then be used, in addition to other determined indicators, by the analyzer 208 to identify parameters of a subject that could be used to trigger an alert or alarm.
  • the analyzer 208 may determine their location with respect to preceding T wave peaks as a proportion of a preceding QT (or Q to T) peak or JT (or J to T) interval.
  • the process 300 may begin at process block 302, where electrocardiographic data, in accordance with the present disclosure, may be received.
  • the electrocardiographic data be acquired using an ECG system with a selected number of leads (e.g., a 12-lead ECG system, or a 9-lead ECG system, a single-lead ECG system, or other number).
  • the electrocardiographic data may include telemetry data, such as tracings, as well as other data pertaining to cardiac cycle parameters and measurements.
  • the electrocardiographic data may be received from a memory or other suitable data storage device or medium. Additionally, or alternatively, the electrocardiographic data may be received from a wearable device or a monitoring system (e.g., an ECG system), which could include as few as a single lead with two contacts to the patient.
  • a wearable device or a monitoring system e.g., an ECG system
  • the electrocardiographic data is analyzed to identify a predetermined characteristic.
  • the predetermined characteristic may be QTc or JTc prolongation.
  • QTc prolongation may be identified using a threshold interval.
  • the threshold QTc interval may be 500ms. That is, for example, a QTc interval exceeding a time of 500ms (or other duration) may be a characteristic of a prolonged QTc interval at process block 304.
  • the threshold interval may be automatically defined, or defined manually by a user, such as a physician or a medical professional.
  • cardiac parameters are extracted from the plurality of electrocardiographic data.
  • cardiac parameters can include wave dynamics or signatures in the wave dynamics, such as with respect to the T-wave.
  • these cardiac parameters can include changes in heart rate, ectopy, and T-wave metrics.
  • T-wave metrics may be extracted to identify dynamic changes in T-wave morphology, or an instability of T-wave repolarization heterogeneity.
  • a repolarization may be extracted or identified at process block 306.
  • the cardiac parameters are analyzed.
  • the analysis may include comparing the cardiac parameters to one or more threshold values, or otherwise analyzing such, for example, using a trained machine learning or artificial intelligence algorithm.
  • the one or more threshold values may be associated with a probabilistic algorithm that combines all cardiac parameters and additional metrics and information about the patient (e.g., sex, age, medical history, etc.).
  • the cardiac parameters being analyzed may be static or dynamic.
  • analysis may be performed using artificial intelligence, a deep learning algorithm, a machine learning model, etc. trained on identifying and analyzing parameters of interest within electrocardiographic data.
  • a patient monitor may implement the above-described process.
  • the monitor may be configured to collect or receive physiological or other patient data, may be able to generate and/or communicate reports or alarms. In some instances, the monitor may be able to determine or even deliver therapy.
  • Such system may be configured to provide one or more, or even all of these functions.
  • a report may include delivering more than one alarm corresponding to more than one risk level and/or delivering a therapy, including an electrical, magnetic, or chemical/drug therapies.
  • a first alarm may correspond to a high arrhythmogenic risk
  • a second alarm may correspond to a low arrhythmogenic risk.
  • the system 410 includes a patient monitoring device 412, such as a physiological monitoring device, illustrated in FIG. 4 as an ECG electrode array.
  • a patient monitoring device 412 such as a physiological monitoring device, illustrated in FIG. 4 as an ECG electrode array.
  • the patient monitoring device 412 may also include mechanisms for monitoring EMG, ECoG, LFP, EEG, EOG, GSR, SAO2, OMT and other physiological or behavioral data.
  • the patient monitoring device 412 is connected via a cable 414 to communicate with a monitoring system 416.
  • the cable 414 and similar connections can be replaced by wireless connections between components.
  • the monitoring system 416 may be further connected to a dedicated analysis system 418.
  • the analysis system 418 may be a computer, comprising computational hardware such as a processor, memory, etc.
  • the analysis system 418 may be a hospital monitor or monitoring network connected to one or more leads of a bedside monitor (such as monitoring system 416).
  • the analysis system 418 may be wirelessly connected to monitoring system 416.
  • the monitoring system 416 and analysis system 418 may be integrated.
  • the analysis system 418 may be a patch monitor wirelessly connected to a mobile device (e g., a cell phone, a tablet, a watch, a laptop, etc ).
  • a mobile device e g., a cell phone, a tablet, a watch, a laptop, etc .
  • the patch monitor may stick to the surface of a patient’s skin for monitoring via one or more leads.
  • the monitoring system 416 may be configured to receive raw signals acquired by the ECG, or other physiological, electrode array, and assemble, and even display, the raw signals as waveforms. Accordingly, the analysis system 418 may receive the physiological, or other, waveforms from the monitoring system 416 and, as will be described, process the data and generate a probability, for example, as indicated by an alarm. However, it is also contemplated that the functions of monitoring system 416 and analysis system 418 may be combined into a common system.
  • the monitoring system 416 may also be configured to determine or recommend, and/or deliver a therapy to the patient 412 based on the above-described analysis.
  • the monitoring system 416 may include a therapeutic delivery coupling 420 that can be coupled to the patient 412.
  • the monitoring system 416 may form a closed-loop system.
  • the therapeutic delivery coupling 420 may be configured to deliver at least one of electrical, chemical/drug, magnetic, or other therapeutic when indicated via the abovedescribed analysis.
  • an alert or report may be generated by the monitoring system 416 and communicated to a clinician to prompt review and approval of a recommended therapy that is then delivered by the clinician via the therapeutic delivery coupling 420.
  • repolarization heterogeneity may not generally be greater in patients with QTc prolongation who suffer TdP and doesn’t appear mechanistically necessary or predictive of TdP.
  • repolarization instability causing altered or dynamically changing electrocardiographic T waves remains possible before TdP and may be usefully predictive.
  • the T wave may alternate on a beat to beat basis, and this is termed T wave alternans.
  • Decreasing heart rate can be measured as an increase in the electrocardiographic R to R interval. Increased R to R interval causes an increase in the QTc interval.
  • TdP may sometimes be associated with bradycardia.
  • the data suggested heartrate may actually increase prior to the occurrence of TdP. This could be due to increased adrenergic tone which may also promote PVC occurrence often necessary for TdP. Change in heartrate or other measures of adrenergic tone could be incorporated to predict imminent TdP.
  • QTc is a dynamic parameter and may also usefully change prior to TdP occurrence, particularly if it is increasing, such as in the setting of medication toxicity or overdose.
  • the research provided herein demonstrates that PVC’s prior to TdP occur with a temporal density centered on the period approximately 2/3 of the immediately preceding Q to T wave peak interval after the preceding T wave peak, as illustrated in FIG. 5 and FIG. 6. Because it is likely partly a stochastic process, PVCs occur with a distribution of durations after the preceding T waves. However, for each patient, this distribution has a small standard deviation generally less than 0.25, as illustrated in FIG. 7. The distribution may be modulated by the duration of the QT interval, as illustrated in FIG. 8.
  • the duration of the cardiac cycle preceding the PVC often differs from that of the underlying rhythm.
  • the preceding cycle often follows a pause as described by the “long-short” phenomenon, and consequently, the preceding cycle may be longer than that of the underlying rhythm. This is due to the phenomenon termed “restitution” where the cardiac cycle length or corrected QT interval increases as a function of the preceding R-R interval.
  • the preceding cycle length was used in the denominator of the ratio because using the underlying rhythm cycle length, which is generally shorter, could confound and inflate the ratio of interest.
  • the Q to Tpk may be the denominator metric because the QT interval, by definition, extends to the end of the T wave which may be obscured by the early onset of the PVC.
  • the delay from T peak to PVC R peak may vary within a patient’s telemetry strip.
  • some patients demonstrate at least one PVC in that early window centered on 2/3 times the preceding Q to T wave peak interval on these short strips. Therefore, the algorithm may elect to ignore PVC’s with longer delay because the patient will likely also have PVCs in the window of interest, or otherwise assess for a narrow distribution of delays.
  • a rolling temporal density map of PVC’s is created as a function of both PVCs per cardiac cycles and ratio of preceding T peak to PVC R wave peak interval divided by a measure of its preceding cardiac cycle length.
  • a feature of this device is the simultaneous precise cardiologic characteristics that may be present to cause an alarm. Once alarmed, staff can assess for reversible factors (contributing medicines) to decrease risk or treat with known agents (magnesium salts) to decrease risk of TdP and sudden cardiac death, rather than reacting to a cardiac event after it has already occurred. The wearer of such an alarm may be able to seek emergent assistance prior to unconsciousness. Alternatively, the device may automatically activate a smart phone or similar device or include communication capability itself to seek assistance, possibly in conjunction with an incorporated accelerometer to monitor for a sudden loss of posture in the event of partially compromised cardiac output. The device may also incorporate hemodynamic monitoring such as blood pressure to assess for associated compromised cardiac output. Current wearable or stationary monitors and previously described devices may record abnormal heart rhythm activity for later analysis, but this device is designed to provide automatically warning information contemporaneously and in advance of a life threatening TdP event.
  • a carrier wave can be employed to carry computer-readable electronic data such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN).
  • LAN local area network
  • the term “or” as used herein is intended to indicate exclusive alternatives only when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.”
  • a list preceded by “one or more” (and variations thereon) and including “or” to separate listed elements indicates options of one or more of any or all of the listed elements.
  • the phrases “one or more of A, B, or C” and “at least one of A, B, or C” indicate options of: one or more A; one or more B; one or more C; one or more A and one or more B; one or more B and one or more C; one or more A and one or more C; and one or more of each of A, B, and C.
  • the terms “about” and “approximately,” as used herein with respect to a reference value refer to variations from the reference value of ⁇ 15% or less (e g., ⁇ 10%, ⁇ 5%, etc.), inclusive of the endpoints of the range.
  • the term “substantially equal” (and the like) as used herein with respect to a reference value refers to variations from the reference value of less than ⁇ 30% (e.g., ⁇ 20%, ⁇ 10%, ⁇ 5%) inclusive.
  • “substantially” can indicate in particular a variation in one numerical direction relative to a reference value.

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  • Medical Informatics (AREA)
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  • General Health & Medical Sciences (AREA)
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  • Bioinformatics & Cheminformatics (AREA)
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  • General Business, Economics & Management (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention concerne un système et des procédés de surveillance de la fonction cardiaque. Des données électrocardiographiques sont reçues et analysées pour identifier une indication de prolongation d'une phase dépolarisée ou d'un retard en repolarisation. Les données électrocardiographiques sont analysées à l'aide de l'indication de prolongation pour déterminer une probabilité de torsades de pointes (TdP). La probabilité de TdP est comparée à un seuil. Lors de la détermination qu'une probabilité de TdP est supérieure au seuil, une alarme est déclenchée pour indiquer la probabilité de TdP.
PCT/US2024/036959 2023-07-06 2024-07-05 Procédé et appareil d'évaluation de fonction cardiaque Ceased WO2025010429A1 (fr)

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US202363512259P 2023-07-06 2023-07-06
US63/512,259 2023-07-06

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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010007948A1 (en) * 1998-10-26 2001-07-12 Stoop Gustaaf A.P. Placemaker system for preventing ventricular tachycardia
US20080097537A1 (en) * 2004-10-13 2008-04-24 Jeng-Ren Duann Method And System For Cardiac Signal Decomposition
US20100004255A1 (en) * 2002-04-04 2010-01-07 Luiz Belardinelli Method of treating arrhythmias
US20100191130A1 (en) * 2006-07-07 2010-07-29 The Royal Institution For The Advancement Of Learning/Mcgill University Method for detecting pathologies using cardiac activity data
US20150105268A1 (en) * 2013-10-10 2015-04-16 Severe Adverse Event (Sae) Consortium Rare biomarkers for increased risk of drug-induced elongated qt interval and torsades de pointes from exome sequencing studies
US20160045130A1 (en) * 2014-08-13 2016-02-18 Cameron Health, Inc. Methods and implantable devices for detecting arrhythmia
US20190059764A1 (en) * 2016-04-13 2019-02-28 Assistance Publique - Hopitaux De Paris Method for determining the likelihood of torsades de pointes being induced
US20220230758A1 (en) * 2019-06-05 2022-07-21 Assistance Publique - Hôpitaux De Paris Method for detecting risk of torsades de pointes

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010007948A1 (en) * 1998-10-26 2001-07-12 Stoop Gustaaf A.P. Placemaker system for preventing ventricular tachycardia
US20100004255A1 (en) * 2002-04-04 2010-01-07 Luiz Belardinelli Method of treating arrhythmias
US20080097537A1 (en) * 2004-10-13 2008-04-24 Jeng-Ren Duann Method And System For Cardiac Signal Decomposition
US20100191130A1 (en) * 2006-07-07 2010-07-29 The Royal Institution For The Advancement Of Learning/Mcgill University Method for detecting pathologies using cardiac activity data
US20150105268A1 (en) * 2013-10-10 2015-04-16 Severe Adverse Event (Sae) Consortium Rare biomarkers for increased risk of drug-induced elongated qt interval and torsades de pointes from exome sequencing studies
US20160045130A1 (en) * 2014-08-13 2016-02-18 Cameron Health, Inc. Methods and implantable devices for detecting arrhythmia
US20190059764A1 (en) * 2016-04-13 2019-02-28 Assistance Publique - Hopitaux De Paris Method for determining the likelihood of torsades de pointes being induced
US20220230758A1 (en) * 2019-06-05 2022-07-21 Assistance Publique - Hôpitaux De Paris Method for detecting risk of torsades de pointes

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