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
  • 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
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
  • A61B5/361—Detecting fibrillation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
  • A61B5/346—Analysis of electrocardiograms
  • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
  • A61B5/363—Detecting tachycardia or bradycardia
• • 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
  • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
  • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
  • A61B5/686—Permanently implanted devices, e.g. pacemakers, other stimulators, biochips
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N1/00—Electrotherapy; Circuits therefor
  • A61N1/18—Applying electric currents by contact electrodes
  • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
  • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
  • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
  • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
  • A61N1/36114—Cardiac control, e.g. by vagal stimulation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N1/00—Electrotherapy; Circuits therefor
  • A61N1/18—Applying electric currents by contact electrodes
  • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
  • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
  • A61N1/362—Heart stimulators
  • A61N1/3621—Heart stimulators for treating or preventing abnormally high heart rate
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N1/00—Electrotherapy; Circuits therefor
  • A61N1/18—Applying electric currents by contact electrodes
  • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
  • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
  • A61N1/362—Heart stimulators
  • A61N1/365—Heart stimulators controlled by a physiological parameter, e.g. heart potential
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N1/00—Electrotherapy; Circuits therefor
  • A61N1/18—Applying electric currents by contact electrodes
  • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
  • A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
  • A61N1/39—Heart defibrillators
  • A61N1/3987—Heart defibrillators characterised by the timing or triggering of the shock
• • 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
  • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
  • A61B2560/02—Operational features
  • A61B2560/0204—Operational features of power management
  • A61B2560/0209—Operational features of power management adapted for power saving
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/48—Other medical applications
  • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
  • A61B5/4839—Diagnosis combined with treatment in closed-loop systems or methods combined with drug delivery

Definitions

• Implantable medical devices and external medical devices (e.g., wearable devices, insertable cardiac monitors, implantable pacemakers, or implantable cardioverter-defibrillators) may record cardiac electrogram (EGM) and hemodynamic signals for sensing cardiac events such as P-waves and R-waves, impedance, heart sound, pressure, etc.
• IMDs may detect episodes of bradycardia, tachycardia, or fibrillation from the sensed cardiac events, and respond to the episodes as needed with pacing therapy or high-voltage anti -tachyarrhythmia shocks (e.g., cardioversion or defibrillation shocks).
• Some IMDs include, or are or part of a system that includes, sensors that generate other physiological signals, such as signals that vary based on patient movement or activity, cardiovascular pressure, blood oxygen saturation, edema, or thoracic impedance. Physiological parameters determined based on such signals may be used to assist in the detection of arrhythmia, as well as the detection or monitoring of other cardiac conditions, such as heart failure or infarction. Delivery of therapy in response to detection of a cardiac event, such as ventricular tachyarrhythmia, may negatively impact a patient’s quality of life, while delayed treatment may present risk to the patient.
• physiological signals such as signals that vary based on patient movement or activity, cardiovascular pressure, blood oxygen saturation, edema, or thoracic impedance.
• Physiological parameters determined based on such signals may be used to assist in the detection of arrhythmia, as well as the detection or monitoring of other cardiac conditions, such as heart failure or infarction. Delivery of therapy in response to detection of a cardiac event, such as ventricular
• Cardiac electrophysiology is the science of elucidating, diagnosing, and treating the electrical activities of the heart. It has been hypothesized that abnormality of cardiac cellular electrophysiology, such as repolarization alteration, is the direct cause for the occurrence.
• the alteration in cardiac cellular electrophysiology is built on the pathophysiological environment, such as surge of sympathetic activation or tissue underperfusion during heart failure. As such, the occurrence of a cardiac event may be predicted based on alterations in cardiac cellular electrophysiology.
• the amount of memory and processing resources that may be required to continuously assess alterations in cardiac cellular electrophysiology to predict the occurrence of a cardiac event may be prohibitive, especially on a device that has relatively limited resources.
• the alterations in cardiac cellular electrophysiology or specific risk markers can be assessed to predict the occurrence of a cardiac event. For example, if the heart rate of patient increases to meet or exceed a threshold, detection of t-wave alternans can be analyzed to predict the occurrence of a cardiac event. In another example, if a slow heart rate is detected with an increase in premature ventricular contraction, the short-long-short ventricular intervals of the patient can be analyzed to predict the occurrence of a cardiac event.
• the processing circuitry may predict the occurrence of a cardiac event. In some examples, the processing circuitry may responsively provide an alert indicating that the acute cardiac event is predicted and/or deliver a therapy configured to prevent the predicted cardiac event.
• FIGS. 4 A - 4C are a front- view, side-view, and top-view conceptual drawings, respectively, illustrating another example medical device system in conjunction with a patient.
• FIG. 7 is a functional block diagram illustrating an example configuration of an implantable medical device.
• FIG. 11 is a tabular representation of an example technique for periodically determining a score based on respective difference metrics for each of a plurality of physiological parameters.
• FIG. 19 is a flow diagram illustrating an example technique that may be implemented by a medical device system to modify a set of patient parameters or weightings applied to patient parameters used to determine whether an acute cardiac event is predicted.
• FIG. 20 is a flow diagram illustrating an example technique that may be implemented by a medical device system to provide an alert and/or preventative measure(s) in response to an acute cardiac event being predicted.
• this disclosure describes example techniques related to predicting an acute occurrence of a cardiac event or attack, such as a ventricular tachyarrhythmia, ventricular fibrillation, heart failure decompensation (may be referred to herein as “acute cardiac event”), and ischemia, and responsively providing an alert indicating that the acute cardiac event is predicted, and/or deliver a therapy configured to prevent the predicted cardiac event.
• acute cardiac event a ventricular tachyarrhythmia
• ventricular fibrillation ventricular fibrillation
• heart failure decompensation may be referred to herein as “acute cardiac event”
• ischemia ischemia
• External device 30A may be used to program commands or operating parameters into ICD 10A for controlling its functioning, e.g., when configured as a programmer for ICD 10 A.
• External device 30A may be used to interrogate ICD 10A to retrieve data, including device operational data as well as physiological data accumulated in IMD memory. The interrogation may be automatic, e.g., according to a schedule, or in response to a remote or local user command.
• Programmers, external monitors, and consumer devices are examples of external devices 30A that may be used to interrogate ICD 10 A.
• Examples of communication techniques used by ICD 10A and external device 30A include radiofrequency (RF) telemetry, which may be an RF link established via Bluetooth, Wi-Fi, or medical implant communication service (MICS).
• RF radiofrequency
• medical device system 8 A may also include a pressure-sensing IMD 50.
• pressure-sensing IMD 50 is implanted in the pulmonary artery of patient 14 A.
• one or more pressure-sensing IMDs 50 may additionally or alternatively be implanted within a chamber of heart 16 A, or generally at other locations in the circulatory system.
• pressure-sensing IMD 50 is configured to sense blood pressure of patient 14A.
• pressure-sensing IMD 50 may be arranged in the pulmonary artery and be configured to sense the pressure of blood flowing from the right ventricle outflow tract (RVOT) from the right ventricle through the pulmonary valve to the pulmonary artery.
• RVOT right ventricle outflow tract
• Pressure-sensing IMD 50 may therefore directly measure the pulmonary artery diastolic pressure (PAD) of patient 14A.
• the PAD value is a pressure value that can be employed in patient monitoring.
• PAD may be used as a basis for evaluating congestive heart failure in a patient.
• pressure-sensing IMD 50 includes a pressure sensor configured to respond to the absolute pressure inside the pulmonary artery of patient 14A.
• Pressure-sensing IMD 50 may be, in such examples, any of a number of different types of pressure sensors.
• One form of pressure sensor that may be useful for measuring blood pressure is a capacitive pressure sensor.
• Another example pressure sensor is an inductive sensor.
• pressure-sensing IMD 50 may also comprise a piezoelectric or piezoresistive pressure transducer.
• pressure-sensing IMD 50 may comprise a flow sensor.
• pressure-sensing IMD 50 comprises a leadless pressure sensor including capacitive pressure sensing elements configured to measure blood pressure within the pulmonary artery.
• Pressure-sensing IMD 50 may be in wireless communication with ICD 10A and/or external device 30A, e.g., in order to transmit blood pressure measurements to one or both of the devices.
• Pressure-sensing IMD 50 may employ, e.g., radio frequency (RF) or other telemetry techniques for communicating with ICD 10A and other devices, including, e.g., external device 30A.
• RF radio frequency
• Medical device system 8A is an example of a medical device system configured to determine whether an acute occurrence of a cardiac event, such as a ventricular tachyarrhythmia, is predicted to occur, and to responsively provide an alert indicating that the acute cardiac event is predicted, and/or deliver a therapy configured to prevent the predicted cardiac event.
• the techniques may be performed by processing circuitry of medical device system 8A, such as processing circuitry of one or both of ICD lOA and external device 30A, individually, or collectively.
• ICD 10A and pressure-sensing IMD 50 may include or be coupled to one or more other sensors that generate one or more other physiological signals, such as signals that vary based on patient motion and/or posture, blood flow, respiration, or edema.
• the processing circuitry may determine other patient parameters based on therapy delivered by ICD 10 A, such as patient parameters indicating the extent to which patient 14A is dependent on pacing, e.g., a percentage of time or other characterization of amount of pacing delivered to the patient.
• the processing circuitry of medical device system 8A indicates that the acute cardiac event is predicted if the cumulative degree of change across the patient parameters during the current period is significantly greater than the variation in the patient parameters during N recently preceding periods. For example, as will be described in greater detail below, the processing circuitry may determine, for each of a plurality of patient parameters that do not include parameters associated with cardiac electrophysiology of the patient, a difference metric for a current period based on a value of a patient parameter determined for the current period and a value of the patient parameter determined for an immediately preceding period.
• Patient parameters associated with physiological triggers for acute cardiac events may include or indicate the presence or extent of: autonomic changes, such as increase sympathetic and/or decreased parasympathetic drive; acute ischemia; physical exertion; hypoxia; drug effects; electrolyte abnormalities; myocardial toxin; heart failure, which may be autonomic, metabolic, due to supraventricular tachycardia (SVT), and/or cardiogenic shock; or the presence of other arrhythmias, such as PVCs, R-on-T events, non-sustained ventricular tachycardias, short-long-short rhythm, or the like.
• Patient parameters associated with cardiac electrophysiology may include certain patient parameters associated with morphological features of the cardiac electrogram, such as QRS width or duration, QT interval length, T-wave amplitude, R-R interval length, an interval between a peak and the end of the T-wave, a ratio between the T-wave peak to end interval and the QT interval lengths, or T-wave alternan.
• morphological features of the cardiac electrogram such as QRS width or duration, QT interval length, T-wave amplitude, R-R interval length, an interval between a peak and the end of the T-wave, a ratio between the T-wave peak to end interval and the QT interval lengths, or T-wave alternan.
• the processing circuitry determines a score for the current period based on a sum of the difference metrics for the current period for at least some of the plurality of patient parameters.
• the processing circuitry determines a threshold for the current period based on scores determined for N periods (e.g., hours to days) that precede the current period, and compares the score for the current period to the threshold for the current period to determine whether to assess alterations in cardiac cellular electrophysiology of the patient.
• the processing circuitry may determine that the actual cardiac event is predicted if the cumulative degree of change across the patient parameters that include parameters associated with the cardiac electrophysiology of the patient during the current period is significantly greater than the variation in the patient parameters during N recently preceding periods (e.g., hours to days). For example, the processing circuitry may determine, for each of a plurality of patient parameters, the plurality of parameters including parameters associated with the cardiac electrophysiology of the patient, a difference metric for a current period based on a value of a patient parameter determined for the current period and a value of the patient parameter determined for an immediately preceding period.
• the processing circuitry determines a score for the current period based on a sum of the difference metrics for the current period for at least some of the plurality of patient parameters.
• the processing circuitry determines a threshold for the current period based on scores determined for N periods that precede the current period, and compares the score for the current period to the threshold for the current period to determine whether the acute event is predicted. If the processing circuitry determines that the acute cardiac event is predicted, the processing circuitry may generate an alert and, in some examples, control delivery of one or more preventative measures configured to prevent the event, such as cardiac pacing, neuromodulation, or one or more therapeutic substances, e.g., drugs.
• Medical device system 8A is one example of a medical device system that may be configured to implement the techniques described herein for determining whether an acute cardiac event is predicted.
• Other example medical device systems that may be configured to implement the techniques are described with respect to FIGS. 2 - 6.
• a medical device system that implements the techniques described in this disclosure may additionally or alternatively include an external medical device, e.g., external cardiac monitor, and/or external pacemaker, cardioverter and/or defibrillator, configured to generate one or more of the physiological signals described herein, determine whether an acute cardiac event is predicted, provide an alert, and/or deliver one or more of the preventative therapies described herein.
• an external medical device e.g., external cardiac monitor, and/or external pacemaker, cardioverter and/or defibrillator
• IMD 10B is an insertable cardiac monitor (ICM) capable of sensing and recording cardiac EGM signals or hemodynamic signals like tissue blood perfusion by optical sensor from a position outside of heart 16B, and will be referred to as ICM 10B hereafter.
• ICM 10B includes or is coupled to one or more additional sensors that generate one or more other physiological signals, such as signals that vary based on patient motion and/or posture, blood flow, or respiration.
• ICM 10B may be implanted outside of the thorax of patient 14B, e.g., subcutaneously or submuscularly, such as the pectoral location illustrated in FIG. 2.
• ICM 10B may take the form of a Reveal LINQTM ICM, available from Medtronic pic, of Dublin, Ireland.
• External device 30B may be configured in a manner substantially similar to that described above with respect to external device 30A and FIG. 1.
• External device 30B may wirelessly communicate with ICM 10B, e.g., to program the functionality of the ICM, and to retrieve recorded physiological signals and/or patient parameter values or other data derived from such signals from the ICM.
• the processing circuitry may determine whether to assess alterations in cardiac cellular electrophysiology of the patient. If the processing circuitry determines to assess alterations in cardiac cellular electrophysiology of the patient, the processing circuitry may determine, for a period, parameter values that include parameter values associated with cardiac electrophysiology of the patient, determining difference metrics based on the patient parameter values, determining a score for the period based on the difference metrics, and comparing the score to a second determined threshold.
• One or more such devices may generate physiological signals, including physiological signals associated with cardiac electrophysiology, and may include processing circuitry configured to perform, in whole or in part, the techniques described herein for predicting an acute cardiac event.
• the implanted devices may communicate with each other and/or an external device 30, and one of the implanted or external devices may ultimately determine whether the acute cardiac event is predicted based on information received from the other device(s).
• ICM 10B is defined by a length /., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D.
• the geometry of the ICM 1 OB - in particular a width W greater than the depth D - is selected to allow ICM 10B to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion.
• the device shown in FIG. 3 includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion.
• the spacing between proximal electrode 64 and distal electrode 66 may range from 30 millimeters (mm) to 55mm, 35mm to 55mm, and from 40mm to 55mm and may be any range or individual spacing from 25mm to 60mm.
• ICM 10B may have a length L that ranges from 30mm to about 70mm. In other examples, the length L may range from 40mm to 60mm, 45mm to 60mm and may be any length or range of lengths between about 30mm and about 70mm.
• the width W of major surface 68 may range from 3mm to 10mm and may be any single or range of widths between 3mm and 10mm.
• the thickness of depth D of ICM 10B may range from 2mm to 9mm.
• the depth D of ICM 10B may range from 2mm to 5mm and may be any single or range of depths from 2mm to 9mm.
• ICM 10B according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of ICM 10B described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic centimeters.
• the first major surface 68 faces outward, toward the skin of the patient while the second major surface 70 is located opposite the first major surface 68.
• proximal end 72 and distal end 74 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of the patient.
• ICM 10B including instrument and method for inserting ICM 10B is described, for example, in U.S. Patent Publication No. 2014/0276928.
• proximal electrode 64 is in close proximity to the proximal end 72 and distal electrode 66 is in close proximity to distal end 74.
• distal electrode 66 is not limited to a flattened, outward facing surface, but may extend from first major surface 68 around rounded edges 76 and/or end surface 78 and onto the second major surface 70 so that the electrode 66 has a three-dimensional curved configuration.
• proximal electrode 64 is located on first major surface 68 and is substantially flat, outward facing.
• proximal electrode 64 may utilize the three dimensional curved configuration of distal electrode 66, providing a three dimensional proximal electrode (not shown in this example).
• ICM 10B may include electrodes on both major surface 68 and 70 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on ICM 10B.
• Electrodes 64 and 66 may be formed of a plurality of different types of biocompatible conductive material, e.g. stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.
• lead 102 A may be implanted at other extracardiovascular locations.
• defibrillation lead 102 A may extend subcutaneously above the ribcage from ICD 10C toward a center of the torso of patient 14C, bend or turn near the center of the torso, and extend subcutaneously superior above the ribcage and/or sternum 110.
• Defibrillation lead 102A may be offset laterally to the left or the right of the sternum 110 or located over the sternum 110.
• Defibrillation lead 102A may extend substantially parallel to the sternum 110 or be angled lateral from the sternum 110 at either the proximal or distal end.
• Defibrillation lead 102 A includes an insulative lead body having a proximal end that includes a connector 104 configured to be connected to ICD 10C and a distal portion that includes one or more electrodes. Defibrillation lead 102A also includes one or more conductors that form an electrically conductive path within the lead body and interconnect the electrical connector and respective ones of the electrodes. [0078] Defibrillation lead 102 A includes a defibrillation electrode that includes two sections or segments 106 A and 106B, collectively (or alternatively) defibrillation electrode 106.
• the defibrillation electrode 106 is toward the distal portion of defibrillation lead 102 A, e.g., toward the portion of defibrillation lead 102 A extending along the sternum 110.
• Defibrillation lead 102A is placed below and/or along sternum 110 such that a therapy vector between defibrillation electrodes 106 A or 106B and a housing electrode formed by or on ICD 10C (or other second electrode of the therapy vector) is substantially across a ventricle of heart 16C.
• lead 102A may include more or fewer electrodes at various locations proximal and/or distal to defibrillation electrode 106.
• ICD 10C may include one or more electrodes on another lead (not shown).
• IPD 10D is configured to sense electrical activity of heart 16C and deliver pacing therapy, e.g., bradycardia pacing therapy, cardiac resynchronization therapy (CRT), antitachycardia pacing (ATP) therapy, and/or post-shock pacing, to heart 16C.
• IPD 10D may be attached to an interior wall of heart 16C via one or more fixation elements that penetrate the tissue. These fixation elements may secure IPD 10D to the cardiac tissue and retain an electrode (e.g., a cathode or an anode) in contact with the cardiac tissue.
• IPD 10D may be capable sensing electrical signals using the electrodes carried on the housing of IPD 10D. These electrical signals may be electrical signals generated by cardiac muscle and indicative of depolarizations and repolarizations of heart 16C at various times during the cardiac cycle. IPD 10D may analyze the sensed electrical signals to detect bradycardia and tachyarrhythmias, such as ventricular tachycardia or ventricular fibrillation. In response to detecting bradycardia, IPD 10D may deliver bradycardia pacing via the electrodes of IPD 10D.
• bradycardia and tachyarrhythmias such as ventricular tachycardia or ventricular fibrillation.
• IPD 10D may deliver bradycardia pacing via the electrodes of IPD 10D.
• IPD 10D may be configured to detect anti -tachyarrhythmia shocks delivered by ICD system 100 A, which may improve the coordination of therapy between subcutaneous ICD 10C and IPD 10D without requiring device-to-device communication. In this manner, IPD 10D may coordinate the delivery of cardiac stimulation therapy, including the termination of ATP and the initiation of the delivery of post-shock pacing, with the application of an anti-tachyarrhythmia shock merely through the detection of defibrillation pulses and without the need to communicate with the defibrillation device applying the anti-tachyarrhythmia shock.
• the user may interact with external device 30C to send an interrogation request and retrieve sensed physiological data or therapy delivery data stored by one or both of ICD 10C and IPD 10D, and program or update therapy parameters that define therapy, or perform any other activities with respect to ICD 10C and IPD 10D.
• the user is a physician, technician, surgeon, electrophysiologist, or other healthcare professional, the user may be patient 14C in some examples.
• external device 30C may allow a user to program any coefficients, weighting factors, or techniques for determining difference metrics, scores, and/or thresholds, or other data described herein as being used by a medical device system to determine whether an acute cardiac event is predicted.
• a pacing system may be implanted having a pacemaker and one or more leads connected to and extending from the pacemaker into one or more chambers of the heart or attached to the outside of the heart to provide pacing therapy to the one or more chambers.
• FIGS. 4A - 4C the example of FIGS. 4A - 4C is illustrated for example purposes only and should not be considered limiting of the techniques described herein.
• the techniques described herein may be used in conjunction or integrated with additional devices not described herein, such as wearable devices (e.g., smart watches), mobile computing devices (e.g., smart phones), and the like.
• Defibrillation lead 102B includes an insulative lead body having a proximal end that includes a connector 104 configured to be connected to ICD 10C and a distal portion that includes one or more electrodes. Defibrillation lead 102B also includes one or more conductors that form an electrically conductive path within the lead body and interconnect the electrical connector and respective ones of the electrodes. In the illustrated example, defibrillation lead 102B includes a single defibrillation electrode 106 toward the distal portion of defibrillation lead 102B, e.g., toward the portion of defibrillation lead 102B extending along sternum 110.
• Defibrillation lead 102B is placed along sternum 110 such that a therapy vector between defibrillation electrode 106 and a housing electrode formed by or on ICD 10C (or other second electrode of the therapy vector) is substantially across a ventricle of heart 16D.
• Defibrillation lead 102B may also include one or more sensing electrodes, such as sensing electrodes 108 A and 108B, located along the distal portion of defibrillation lead 102B. In the example illustrated in FIG. 5, sensing electrodes 108A and 108B are separated from one another by defibrillation electrode 106. In other examples, however, sensing electrodes 108 A and 108B may be both distal of defibrillation electrode 106 or both proximal of defibrillation electrode 106.
• lead 102B may include more or fewer electrodes at various locations proximal and/or distal to defibrillation electrode 106, and lead 102B may include multiple defibrillation electrodes, e.g., defibrillation electrodes 106A and 106B as illustrated in the example of FIGS. 4A - 4C.
• FIG. 6 is a conceptual drawing illustrating an example configuration of IPD 10D. As shown in FIG. 6, IPD 10D includes case 130, cap 138, electrode 140, electrode 132, fixation mechanisms 142, flange 134, and opening 136. Together, case 130 and cap 138 may be considered the housing of IPD 10D.
• case 130 and cap 138 may enclose and protect the various electrical components, e.g., circuitry, within IPD 10D.
• Case 130 may enclose substantially all of the electrical components, and cap 138 may seal case 130 and create the hermetically sealed housing of IPD 10D.
• IPD 10D is generally described as including one or more electrodes, IPD 10D may typically include at least two electrodes (e.g., electrodes 132 and 140) to deliver an electrical signal (e.g., therapy such as cardiac pacing) and/or provide at least one sensing vector.
• an electrical signal e.g., therapy such as cardiac pacing
• ICD 10C and IPD 10D may include or be coupled to one or more other sensors that generate one or more other physiological signals, such as signals that vary based on patient motion and/or posture, blood flow, blood pressure (e.g., systems 8C and 8D may include pressure sensing IMD 50, described above with respect to FIG. 1), respiration, or edema.
• the processing circuitry may determine other patient parameters based on therapies delivered by ICD 10C and/or IPD 10D, such as patient parameters indicating the extent to which patient 14C or 14D is dependent on pacing, e.g., a percentage of time or other characterization of amount of pacing delivered to the patient, or the number of antitachyarrhythmia therapies delivered to the patient.
• Processing circuitry 160 may implement programmable counters. If IMD 10 is configured to generate and deliver pacing pulses to heart 26, such counters may control the basic time intervals associated with bradycardia pacing (e.g., DDD, VVI, DVI, VDD, AAI, DDI, DDDR, VVIR, DVIR, VDDR, AAIR, DDIR pacing) and other modes of pacing. Intervals defined by processing circuitry 160 may include atrial and ventricular pacing escape intervals, refractory periods during which sensed P-waves and R-waves are ineffective to restart timing of the escape intervals, and the pulse widths of the pacing pulses.
• bradycardia pacing e.g., DDD, VVI, DVI, VDD, AAI, DDI, DDDR, VVIR, DVIR, VDDR, AAIR, DDIR pacing
• Intervals defined by processing circuitry 160 may include atrial and ventricular
• Interval counters implemented by processing circuitry 160 may be reset upon sensing of R-waves and P-waves with detection channels of sensing circuitry 162, or upon the generation of pacing pulses by therapy delivery circuitry 164, and thereby control the basic timing of cardiac pacing functions, including bradycardia pacing, CRT, ATP, or post-shock pacing.
• processing circuitry 160 may determine that tachyarrhythmia has occurred by identification of shortened R-R (or P-P) interval lengths. Generally, processing circuitry 160 detects tachycardia when the interval length falls below 220 milliseconds and fibrillation when the interval length falls below 180 milliseconds. In other examples, processing circuitry 160 may detect ventricular tachycardia when the interval length falls between 330 milliseconds and ventricular fibrillation when the interval length falls below 240 milliseconds. These interval lengths are merely examples, and a user may define the interval lengths as desired, which may then be stored within memory 170.
• sensing circuitry 162 is configured to sense other physiological signals of patient.
• sensing circuitry 162 may be configured to sense signals that vary with changing thoracic impedance of patient 14. The thoracic impedance may vary based on fluid volume or edema in patient 14.
• Therapy delivery circuitry 164 may include charging circuitry, one or more charge storage devices, such as one or more capacitors, and switching circuitry that controls when the capacitor(s) are discharged to electrodes 190 and the widths of pulses. Charging of capacitors to a programmed pulse amplitude and discharging of the capacitors for a programmed pulse width may be performed by therapy delivery circuitry 164 according to control signals received from processing circuitry 160, which are provided by processing circuitry 160 according to parameters stored in memory 170. Processing circuitry 160 controls therapy delivery circuitry 164 to deliver the generated therapy to the heart via one or more combinations of electrodes 190, e.g., according to parameters stored in memory 170. Therapy delivery circuitry 164 may include switch circuitry to select which of the available electrodes 190 are used to deliver the therapy, e.g., as controlled by processing circuitry 160.
• IMD 10 may additionally or alternatively be configured to deliver other therapies configured to prevent the predicted acute cardiac event.
• processing circuitry 160 may control therapy delivery circuitry 164 to deliver cardiac pacing therapy configured to prevent a ventricular tachyarrhythmia, such as overdrive pacing therapy when one or more of the patient parameters 174 indicate that the heart rate is not fast or down-drive pacing therapy if one or more of the patient parameters 174 indicate that the heart rate is too fast.
• IMD 10 may additionally or alternatively be configured to deliver neuromodulation therapy to prevent an acute cardiac event, such as ventricular tachyarrhythmia, heart failure decompensation, or ischemia.
• processing circuitry 160 may be programmed, and therapy delivery circuitry 164 and electrodes 190 configured and placed, to generate and deliver the neuromodulation therapy.
• Example neuromodulation therapies include vagal nerve stimulation, spinal cord stimulation, peripheral nerve stimulation, cardiac intrinsic nerve modulation, and cardiac stellate ganglion stimulation.
• IMD 10 may additionally or alternatively be configured to deliver a therapeutic substance, e.g., infuse a drug.
• IMD 10 may include a pump to deliver the substance, and processing circuitry 160 may be configured to control the pump according to therapy parameters stored in memory 170.
• Examples of delivery of therapy substances to prevent an acute cardiac event include delivery of substances that modulate the cardiovascular or neurological systems of the patient.
• processing circuitry 160 periodically, i.e., for each of a plurality of periods, determines a respective value for each of a plurality of patient parameters.
• the determined patient parameter values are stored as patient parameter values 174 in memory 170.
• the length of each period is greater than one hour, such as a predetermined integer number of hours or days.
• the period length is between eight hours and three days, such as one day.
• Each of patient parameter values 174 may be the single value of a patient parameter determined during the period. In other examples, each of patient parameter values 174 is a representative value determined based on a plurality of values determined during the period. In some examples, patient parameter values 174 may include one or more means, medians, modes, sums, or other values determined based on a plurality of values of a patient parameter determined during the period.
• the plurality of patient parameters may include one or more parameters determined based on the cardiac electrogram, such as one or more heart rate parameters, and/or one or more tachyarrhythmia episode parameters.
• Example heart rate parameters include average heart rate during the period, average daytime heart rate during the period, average nighttime heartrate during the period, and one or more measures of heart rate variability during the period.
• Example tachyarrhythmia episode parameters include the number, frequency and/or duration (total, mean, or median) of tachyarrhythmia episodes during the period, such as atrial tachycardia episodes, atrial fibrillation episodes, or nonsustained tachyarrhythmia (NST) episodes.
• NST nonsustained tachyarrhythmia
• NST episodes may be a series of short R-R intervals greater than an NST threshold number of short R-R intervals, but fewer than a number of intervals to detect (NID) for ventricular tachyarrhythmia.
• NID intervals to detect
• Another example patient parameter that processing circuitry 160 may determine based on the cardiac electrogram is the ventricular rate during atrial tachyarrhythmia, e.g., atrial fibrillation, which may be a mean or median value during the period.
• the plurality of patient parameters may not include patient parameters associated with cardiac electrophysiology.
• Patient parameters associated with cardiac electrophysiology may include certain patient parameters associated with morphological features of the cardiac electrogram, such as QRS width or duration, QT interval length, T- wave amplitude, R-R interval length, an interval between a peak and the end of the T- wave, a ratio between the T-wave peak to end interval and the QT interval lengths, or T- wave alternan.
• the plurality of patient parameters may include patient parameters relating to arrhythmic substrate and/or relating to physiological triggers for acute cardiac events.
• the plurality of patient parameters may additionally or alternatively include at least one patient parameter indicative of edema, and processing circuitry 160 may determine values 174 of such patient parameters based on sensed thoracic impedance, as described above.
• a patient parameter value 174 may be a maximum, minimum, mean, or median thoracic impedance value during a period.
• a patient parameter value 174 may be a fluid index value during the period.
• Processing circuitry 160 may increment and decrement a fluid index value based on an accumulation of differences between a thoracic impedance value (or short-term average of impedance values) and a threshold determined based on a long-term average of thoracic impedance values.
• the plurality of patient parameters may additionally or alternatively include at least one patient parameter indicative of patient activity, e.g., gross patient body movement or motion.
• processing circuitry 160 determines a number of activity counts based on one or more accelerometer signals crossing (e.g., exceeding) one or more thresholds.
• a patient parameter value 174 during a period may be a total, mean, or median number of counts during the period.
• the plurality of patient parameters may additionally or alternatively include at least one patient parameter indicative of cardiovascular pressure, and processing circuitry 160 may determine values 174 of such patient parameters based on generated pressure waveform, e.g., generated by a sensor 166 or pressure-sensing IMD 50, as described above.
• the patient parameter values 174 for the period may include a maximum, minimum, mean, or median of systolic pressure and/or diastolic pressure, e.g., pulmonary artery diastolic pressure.
• the plurality of patient parameters may additionally or alternatively include at least one patient parameter determined based on patient respiration, and processing circuitry 160 may determine values 174 of such parameters based on a generated signal that varies based on respiration as described above, such as a signal that varies based on thoracic impedance.
• the patient parameter values 174 for the period may include a maximum, minimum, mean, or median of respiration rate, e.g., for a day, daytime, or nighttime.
• the patient parameter values 174 for the period may include an indication of the presence, a number, a frequency, or a duration (total, mean, or median) of respiration episodes, such as apneas or dyspneas.
• Processing circuitry 160 may additionally or alternatively determine values 174 of one or more patient parameters based on a generated signal that varies based on sound or other vibrations, which may indicate heart sounds, coughing, or rales.
• Patient parameter values may include morphological measurements of the SI and S2 heart sounds, the presence or frequency of occurrence of S3 and/or S4 heart sounds, or the presence, number, frequency, or duration (total, mean, or median) of episodes or coughing or rales.
• Other patient parameter values 174 that processing circuitry 160 may additionally or alternatively periodically determine based on signals generated by sensors 166 include maximum, minimum, mean, or median values of blood flow, blood oxygen saturation, or temperature.
• the plurality of patient parameters may additionally or alternatively include at least one patient parameter determined based on delivery of therapy to patient 14, e.g., by IMD 10.
• a patient parameter value 174 for a period indicates an amount of cardiac pacing delivered to the patient during the period, such as a total duration or percentage of the period during which atrial pacing, ventricular pacing, and/or CRT was delivered.
• the plurality of patient parameter values 174 determined for each period includes: a percentage of the period during which IMD 10 delivered ventricular pacing to patient 14; a percentage of the period during which IMD 10 delivered atrial pacing to patient 14; an average daytime ventricular heart rate; an average nighttime ventricular heart rate; a frequency or duration of atrial tachycardia event, atrial fibrillation events, and/or NSTs during the period; a total number of patient activity counts during the period; a measure of heart rate variability during the period; a daily thoracic impedance value; and a fluid index value.
• the plurality of patient parameter values 174 includes all or subset of the parameters included in Cardiac Compass® trends generated by IMDs available from Medtronic, pic, of Dublin Ireland. In some examples, the plurality of patient parameter values 174 additionally includes one or more cardiac electrogram morphology parameters.
• Processing circuitry 160 determines a difference metric 176 for each of the plurality patient parameters for the period. Processing circuitry 160 determines the difference metric 176 for each patient parameter based on a difference between a current value 174 of the patient parameter for the current period, and an immediately preceding value 174 of the patient parameter for the immediately preceding period. In some examples, processing circuitry 160 determines the difference metric 176 for each of the patient parameters according to the following equation:
• the difference metric may be referred to as “AVt” such as in Equation 1, or may be referred to as “Vt” such as in Equation 2 below.
• the difference metric may be indicative of daily changes in values of risk factors, for example.
• processing circuitry 160 determines the difference metric 176 for each of the plurality patient parameters for the period based on the difference between the current and preceding values, and a standard deviation (or other measure of variation) of values 174 of the patient parameter for N preceding periods.
• N is an integer constant, e.g., between 5 and 50, such as between 7 and 15 or, in one example, 15.
• the N preceding periods may be N preceding days.
• processing circuitry 160 determines the difference metric 176 for each of the patient parameters according to the following equation:
• Processing circuitry 160 determines a score 178 for the period based on the plurality of patient parameter-specific difference metrics 176 for the period. In some examples, processing circuitry 160 determines the score 178 for the period based on a sum of squares of the difference metrics 176 for the period or a sum of absolute values of the difference metrics 176. The difference metrics 176 may be positive or negative, and use of the sum of squares or absolute values may enable the score 178 to reflect the absolute magnitudes of change of the plurality of patient parameters during the period. In some examples, processing circuitry 160 determines the score 178 for the period using a sum of squares of difference metrics 176 according to the following equation, where n is the number of patient parameters for which difference metrics 176 are determined during the period (in this case 8):
• processing circuitry 160 applies coefficients or weights to one or more of difference metrics 176 when determining a score 178 for a period, such as in Equation 4 below.
• the weights may be determined and/or adjusted empirically based on an analysis of the sensitivity and specificity of the score 178 in predicting occurrence of acute cardiac events over time for patient 14 or population of patients, e.g., having similar characteristics to patient 14.
• the values of the weights may be adjusted over time, e.g., on a period-by-period or less frequent basis.
• the score of Equation 4 may be indicative of the weighted risk score based on pathophysiological changes.
• An example of a coefficient or weight, as described above, may include “an” as in Equation 4.
• an may be a wright constant, such that the moving window size, silence interval, threshold setting, and prediction window may be better optimized.
• a may be a value that is based on findings from previous research, event history from an individual or more than one individual, or other factors.
• T-wave alternans may be relevant to an arrhythmic event.
• the difference value of T wave alternans may be weighted up (e.g., 5pV weighted to 5x10).
• Processing circuitry 160 also determines a threshold 180 for the period based on scores 178 for N preceding periods, wherein N is the integer constant, e.g., 15. In some examples, processing circuitry 160 determines the threshold 180 based on a mean or median of the N preceding scores, e.g., by multiplying a median of the N scores and a coefficient. The coefficient may be, for example, between 1 and 3, and determined for a given patient 14 or patient population based on a receiver operator characteristic (ROC). [0150] Processing circuitry 160 compares the score for the period to the threshold for the period.
• ROC receiver operator characteristic
• processing circuitry 160 may determine to assess alterations in cardiac cellular electrophysiology of the patient to predict the occurrence of a cardiac event.
• processing circuitry 160 may periodically, i.e., for each of a plurality of periods, determines a respective value for each of a plurality of patient parameters, where the plurality of patient parameters include one or more patient parameters associated with cardiac electrophysiology together with one or more of the other patient parameters described above.
• the determined patient parameter values are stored as patient parameter values 175 in memory 170.
• the length of each period is greater than one hour, such as a predetermined integer number of hours or days. In some examples, the period length is between eight hours and three days, such as one day.
• Each of patient parameter values 175 may be the single value of a patient parameter determined during the period. In other examples, each of patient parameter values 175 is a representative value determined based on a plurality of values determined during the period. In some examples, patient parameter values 175 may include one or more means, medians, modes, sums, or other values determined based on a plurality of values of a patient parameter determined during the period.
• Example tachyarrhythmia episode parameters include the number, frequency and/or duration (total, mean, or median) of tachyarrhythmia episodes during the period, such as atrial tachycardia episodes, atrial fibrillation episodes, or non-sustained tachyarrhythmia (NST) episodes.
• NST episodes may be a series of short R-R intervals greater than an NST threshold number of short R-R intervals, but fewer than a number of intervals to detect (NID) for ventricular tachyarrhythmia.
• Another example patient parameter that processing circuitry 160 may determine based on the cardiac electrogram is the ventricular rate during atrial tachyarrhythmia, e.g., atrial fibrillation, which may be a mean or median value during the period.
• the plurality of patient parameters may additionally or alternatively include at least one patient parameter indicative of cardiovascular pressure, and processing circuitry 160 may determine values 175 of such patient parameters based on generated pressure waveform, e.g., generated by a sensor 166 or pressure-sensing IMD 50, as described above.
• the patient parameter values 175 for the period may include a maximum, minimum, mean, or median of systolic pressure and/or diastolic pressure, e.g., pulmonary artery diastolic pressure.
• the plurality of patient parameters may additionally or alternatively include at least one patient parameter determined based on patient respiration, and processing circuitry 160 may determine values 175 of such parameters based on a generated signal that varies based on respiration as described above, such as a signal that varies based on thoracic impedance.
• the patient parameter values 175 for the period may include a maximum, minimum, mean, or median of respiration rate, e.g., for a day, daytime, or nighttime.
• the patient parameter values 175 for the period may include an indication of the presence, a number, a frequency, or a duration (total, mean, or median) of respiration episodes, such as apneas or dyspneas.
• the plurality of patient parameter values 175 includes all or subset of the parameters included in Cardiac Compass® trends generated by IMDs available from Medtronic, pic, of Dublin Ireland. In some examples, the plurality of patient parameter values 175 additionally includes one or more cardiac electrogram morphology parameters.
• processing circuitry 160 determines the difference metric 177 for each of the patient parameters according to Equation 2 describe above.
• processing circuitry 160 applies coefficients or weights to one or more of difference metrics 177 when determining a score 179 for a period, such as in Equation 4.
• the weights may be determined and/or adjusted empirically based on an analysis of the sensitivity and specificity of the score 179 in predicting occurrence of acute cardiac events over time for patient 14 or population of patients, e.g., having similar characteristics to patient 14.
• the values of the weights may be adjusted over time, e.g., on a period-by-period or less frequent basis.
• a clinician or other user may retrieve data from IMD 10 using external device 30 or another local or networked computing device configured to communicate with processing circuitry 160 via communication circuitry 168.
• the clinician may also program parameters of IMD 10 using external device 30 or another local or networked computing device.
• the clinician may select patient parameters used to predict acute cardiac events, select values for a coefficient used to determine threshold 180 and/or threshold 181, select a value for the number of N preceding periods, and receive alerts that indicate that the acute cardiac event is predicted via communication circuitry 168 and external device 30 and/or another computing device.
• a user uses external device 30 to select or program any of the values for operational parameters of IMD 10, e.g., for patient parameter sensing, therapy delivery, and acute cardiac event prediction.
• a user uses external device 30 to receive data collected by IMD 10, such as patient parameter values 174 and/or parameter values 175, or other operational and performance data of IMD 10.
• the user may also receive alerts provided by IMD 10 that indicate that an acute cardiac event, e.g., ventricular tachyarrhythmia, is predicted.
• the user may interact with external device 30 via UI 204, which may include a display to present a graphical user interface to a user, and a keypad or another mechanism (such as a touch sensitive screen) for receiving input from a user.
• External device 30 may communicate wirelessly with IMD 10 using communication circuitry 206, which may be configured for RF communication with communication circuitry 168 of IMD 10.
• FIG. 9 is a functional block diagram 218 illustrating an example system that includes external computing devices, such as a server 224 and one or more other computing devices 230A-230N, that are coupled to IMD 10 and external device 30 via a network 222.
• IMD 10 may use its communication circuitry 168 to, e.g., at different times and/or in different locations or settings, communicate with external device 30 via a first wireless connection, and to communication with an access point 220 via a second wireless connection.
• access point 220, external device 30, server 224, and computing devices 230A-230N are interconnected, and able to communicate with each other, through network 222.
• Access point 220 may comprise a device that connects to network 222 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 220 may be coupled to network 222 through different forms of connections, including wired or wireless connections. In some examples, access point 220 may be co-located with patient 14. Access point 220 may interrogate IMD 10, e.g., periodically or in response to a command from patient 14 or network 222, to retrieve physiological signals, patient parameter values 174, difference metrics 176, scores 178, thresholds 180, alerts of acute cardiac events, and/or other operational or patient data from IMD 10. Access point 220 may provide the retrieved data to server 224 via network 222.
• server 224 may be configured to provide a secure storage site for data that has been collected from IMD 10 and/or external device 30.
• server 224 may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices 230A-230N.
• the illustrated system of FIG. 9 may be implemented, in some aspects, with general network technology and functionality similar to that provided by the Medtronic CareLink® Network developed by Medtronic pic, of Dublin, Ireland.
• one or more of access point 220, server 224, or computing devices 230A-230N may be configured to perform, e.g., may include processing circuitry configured to perform, some or all of the techniques described herein, e.g., with respect to processing circuitry 160 of IMD 10 and processing circuitry 200 of external device 30, relating to prediction of acute cardiac events, such as ventricular tachyarrhythmia.
• server 224 includes a memory 226 to store physiological signals or patient parameter values 174 received from IMD 10 and/or external device 30, and processing circuitry 228, which may be configured to provide some or all of the functionality ascribed to processing circuitry 160 of IMD 10 and processing circuitry 200 of external device 30 herein.
• processing circuitry 228 may determine values 174 of each of a plurality of patient parameters during each of a plurality of periods, and/or may receive patient parameter values 174 for the plurality of periods from one or more IMDs 10.
• Processing circuitry 228 may determine difference metrics 176, scores 178, and thresholds 180 based on the patient parameter values 174 in the manner described above with respect to processing circuitry 160 of IMD 10.
• Processing circuitry 228 may also compare scores 178 to thresholds 180 to determine whether to assess alterations in cardiac cellular electrophysiology of the patient to predict the occurrence of a cardiac event.
• FIG. 10 is a timing diagram illustrating values of a plurality of patient parameters of a patient over a plurality of time periods. More particularly, FIG. 10 illustrates a trend 250 of the scores of the plurality of periods, each of which in this example is one day. FIG. 10 also illustrates the score 178 and/or score 179 of patient parameter for the current period (Scoret) and the score 178 and/or score 179 of patient parameter for the immediately preceding period (Scoret-i).
• Example patient parameter specific criteria include: whether the difference metric for a percentage of pacing indicates a presence or increase of pacing during the period; whether the difference metric for a heart rate indicates an increase in heart rate during the period; whether the difference metric for a heart rate variability indicates a decrease in heart rate variability during the period; whether the difference metric for a patient activity parameter indicates a decrease in patient activity during the period; whether a difference metric for a thoracic impedance indicates a fluid index during the period indicates an increase in the fluid index during the period; whether a difference metric for a parameter relating to a number, frequency, or duration of tachyarrhythmia events, e.g., NSTs, indicates the occurrence of one or more tachyarrhythmia events during the period; or whether a difference metric for a cardiac electrogram morphology parameter indicates change during the period.
• processing circuitry may include difference metrics 176 in the score 178 based on satisfaction of these example criteria, e.g., NSTs
• Patient parameters 324 relating to physiological triggers for acute cardiac events may include or indicate the presence or extent of: autonomic changes, such as increase sympathetic and/or decreased parasympathetic drive; acute ischemia; physical exertion; hypoxia; drug effects; electrolyte abnormalities; myocardial toxin; heart failure, which may be autonomic, metabolic, due to supraventricular tachycardia (SVT), and/or cardiogenic shock; or the presence of other arrhythmias, such as PVCs, R-on-T events, non-sustained ventricular tachycardias, short- long-short rhythm, or the like.
• Patient parameters 322 and 324 may be examples of parameter values 174.
• any of the above patient parameters, or any patient parameters related to these conditions, may be used to predict acute cardiac events according to the techniques of this disclosure. These parameters may be detected by processing circuitry 160 based on device-derived physiological parameters and/or indications from a clinician, e.g., via an external device 30 or other computing device. For example, changes in heart rate, heart rate variability, and the occurrence of PVCs may indicate changes in sy mpatheti c/parasy mpatheti c drive .
• a sudden oscillation in any of the patient parameters described herein may be considered a risk of an acute cardiac event.
• a sum of oscillations of several parameters may be considered as a combined risk score with the largest oscillation contributing to the combined score the most. This concept may provide weighted score or contributor.
• the techniques described herein e.g., including determining a score for a period based on a sum of difference metrics for a plurality of patient parameters and comparing the score to a longer term mean or median of the scores, may indicate the sum of the oscillations and allow identification of the most significant patient parameters that contribute to the occurrence cardiac events for a particular patient.
• the patient parameters used to predict acute cardiac events for a particular patient may be pre-defined (such as use only T-wave alternans and/or the frequency of non-sustained ventricular tachycardia). In some examples, many patient parameters are monitored to predict acute cardiac events and any (or some) of the parameters that show undesired changes will be weighted in the prediction score.
• the parameters that show significant oscillations prior to an acute cardiac event may vary from patient-to-patient, or from event-to-event for a particular patient. For example, one event may be predicted based on significant oscillations in T-wave alternans and the frequency of non-sustained ventricular tachycardia, while another is predicted based on significant oscillations in T- wave alternans and heart rate variability. In this manner, the prediction may be tailored to a particular patient, e.g., individual-based prediction.
• processing circuitry 160 determines patient parameter values 175 associated with cardiac electrophysiology for each of a plurality of patient parameters during the period (410).
• the patient parameters may include any of the patient parameters described herein associated with cardiac electrography of the patient such as one or more of: a QRS width or duration, a QT interval length, a T-wave amplitude, an R-R interval length, an interval between a peak and the end of the T-wave, a ratio between the T-wave peak to end interval and the QT interval lengths, a T-wave morphology alteration, a QT interval alteration, a prolonged QRS duration, a QRS morphology change, and an R-R interval oscillation.
• patient parameter values 175 may also include a portion or all of patient parameter values 174.
• Processing circuitry 160 may determine at least some of the values 175 based on physiological signals generated by sensing circuitry 162 and/or sensors
• Processing circuitry 160 determines a respective difference metric 177 for each of the plurality patient parameters for the period (412). In some examples, processing circuitry 160 determines the respective difference metrics 177 based on differences between the current and immediately preceding values 175 of the patient parameter, e.g., using equation 1 or 2.
• Processing circuitry 160 determines a score 179 for the period based on the difference metrics 176 for the period, e.g., based on a sum of the difference metrics (414). In some examples, processing circuitry 160 determines the score 179 based on a sum of squares of the difference metrics, e.g., according to equation 4. In some examples, processing circuitry 160 applies a weight to one or more of the difference metrics when determining the score.
• Processing circuitry 160 also determines a threshold 181 for the period based on the scores 179 of N preceding periods (416). In some examples, processing circuitry determines the score by applying a coefficient to the median of the scores 179 for the N preceding periods. Processing circuitry 160 determines whether the score 179 for the period is greater than (or greater than or equal to) the threshold 181 for the period (418). If the score 179 is greater than the threshold 181 (YES of 418), processing circuitry may provide an alert indicating that the acute cardiac event is predicted and/or control delivery of one or more preventative therapies (420).
• processing circuitry 160 also determines whether the acute cardiac event was in fact detected (rather than predicted) during the period. If the acute cardiac event is predicted, processing circuitry may exclude one or more periods, including the current period, from the window of N preceding periods using during subsequent periods.
• FIG. 17 is a flow diagram illustrating an example technique that may be implemented by a medical device system 8 to determine a score based on a plurality of difference metrics associated with respective patient parameters.
• the example technique described in FIG. 17 may be used, for example, by processing circuitry 160 of IMD 10 between blocks 402 and 404 and/or between blocks 412 and 414 of FIG. 16.
• processing circuitry 160 determines a difference metric for a particular patient parameter and for the current period (421).
• Processing circuitry 160 compares the difference metric to a patient parameterspecific criterion, e.g., as described above with respect to FIG. 12 (422).
• Processing circuitry 160 determines whether the difference metric 176 or difference metric 177 for the period satisfies the patient parameter-specific criterion (424).
• processing circuitry 160 includes the difference metric 176 or difference metric 177 in the score 178 or in the score 179, e.g., sum of difference metrics, for the period (426).
• processing circuitry 160 excludes the difference metric 176 or difference metric 177 from the score 178 or from the score 179 for the period (428). Processing circuitry 160 determines whether there are additional difference metrics 176 or additional difference metrics 177 for additional patient parameters to which parameter-specific criteria are to be applied during the period (430).
• processing circuitry 160 determines that the score 179 for the current period exceeds the threshold 181 for the period (440, e.g., YES of 418 of FIG. 16). Processing circuitry 160 determines which one or more patient parameters exhibited the greatest, or most significant, change during the period (442). For example, processing circuitry 160 may identify the one or more difference metrics 176 or one or more difference metrics 177 for the current having the greatest absolute value, or the greatest absolute value relative to a mean, median, or standard deviation of difference metrics for the parameter during the N preceding periods, e.g., determined as a percentage, ratio, or other normalized value.
• the associations of therapies and patient parameters may be programmed by a clinician and/or determined based on an analysis of historical efficacy of a particular therapy in preventing an acute cardiac event, for patient 14 and/or a population of patients anatomically, physiologically, and or clinically similar to patient 14.
• Processing circuitry 160 controls IMD 10 or another medical device to deliver the selected preventative measure(s) (446). For example, if processing circuitry 160 determines that a patient parameter 174 associated with heart rate is consistently too fast at the time a patient parameter 174 associated with patient activity indicates no increase in physical activity, a vagal stimulation can be triggered to slow down the heart rate or down-driving pacing can be triggered. On the other hand, if processing circuitry 160 determines that a patient parameter 174 associated with heart rate is slow in combination with an occurrence of more PVCs, then overdrive pacing can be triggered.
• FIG. 19 is a flow diagram illustrating an example technique that may be implemented by a medical device system to determine and/or modify a set of patient parameters or weightings applied to patient parameters used to determine whether an acute cardiac event is predicted.
• a medical device system determines and/or modifies a set of patient parameters or weightings applied to patient parameters used to determine whether an acute cardiac event is predicted.
• FIG. 19 is described as being performed by processing circuitry 160 of IMD 10.
• processing circuitry 160 may include or exclude certain patient parameters from use in the techniques to predict the acute cardiac event, or modify patient parameter-specific weighting parameters applied to the difference metrics to determine a score (e.g., sum- based) for a period, which may emphasize or de-emphasize the importance of certain patient parameters (454).
• a score e.g., sum- based
• FIG. 20 is a flow diagram illustrating an example technique that may be implemented by a medical device system 8, e.g., processing circuitry of the medical device system, to provide an alert and/or preventative measure(s) in response to an acute cardiac event being predicted.
• a medical device system e.g., processing circuitry of the medical device system
• sensing circuitry 162 of IMD 10 may generate one or more physiological signals of a patient (460).
• Processing circuitry 160 of IMD 10 may, for each of a plurality of periods, determine first values associated with a first plurality of patient parameters based on a first one or more of the physiological signals generated during the period, wherein the first plurality of patient parameters include at least one of patient parameters associated with arrhythmic substrate or patient parameters associated with physiological triggers for acute cardiac events (462), determine, based at least in part on the first values associated with the first plurality of patient parameters, whether to assess alterations in cardiac cellular electrophysiology of the patient (464), in response to determining to assess the alterations in cardiac cellular electrophysiology of the patient, determine second values associated with a second plurality of patient parameters based on a second one or more of the physiological signals generated during the period, wherein the second plurality of patient parameters include patient parameters relating to cardiac electrophysiology (466), and determine whether to generate an alert indicating that an acute cardiac event of the
• processing circuitry 160 may further determine, for each of the second plurality of patient parameters, a difference between the value of the patient parameter determined for the current period and the value of the patient parameter determined for the immediately preceding period as the second difference metric for the current period.
• FIG. 21 is a table of experimental results illustrating the performance of the example techniques of this disclosure in predicting ventricular tachyarrhythmia.
• each row of table 500 may correspond to a different patient identification number. In general, each row may correspond to a different patient. Twenty -four ICD-indicated patients were prospectively enrolled. After ICD implantation, each patient underwent weekly data collections for six-month follow-up and appropriate VTVF events were determined. A VTVF event could be a single discrete VT or VF episode or a VTVF storm with inter-episode interval less than 24 hours.
• the six parameters were used to predict VTVF events in each of the twenty four ICD patients in a twenty four hour window, in a forty eight hour window, and in a seventy two hour window.
• the six parameters include the percentage of ventricular pacing during the day, the ventricular heart rate during the day (e.g., between 08:00 a.m -08:00 p.m.), the average ventricular heart rate during the night (e.g., 12:00 p.m -04:00 a.m.), variability in the patient’s atrial and ventricular heart rates, the daily measured thoracic impedance, and the accumulated difference between the measured daily thoracic impedance and the reference impedance.
• the prediction performance of the techniques of this disclosure may include 96% sensitivity and 71.7% specificity in the seventy two hour window.
• the experimental results as illustrated in FIG. 21, demonstrate that VTVF events can be predicted up to three days (e.g., 72 hours) in advance according to the techniques of this disclosure, which may provide a time window for executing appropriate measures to prevent VTVF occurrence, especially for VTVF storms.
• processors including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components, embodied in programmers, such as physician or patient programmers, electrical stimulators, or other devices.
• processors including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components, embodied in programmers, such as physician or patient programmers, electrical stimulators, or other devices.
• processor or “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry.
• the functions described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit.
• Computer- readable media may include computer-readable storage media forming a tangible, non- transitory medium. Instructions may be executed by one or more processors, such as one or more DSPs, ASICs, FPGAs, general purpose microprocessors, or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to one or more of any of the foregoing structure or any other structure suitable for implementation of the techniques described herein.
• the functionality described herein may be provided within dedicated hardware and/or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements.
• the techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs, and/or discrete electrical circuitry, residing in an IMD and/or external programmer.
• IMD an intracranial pressure
• external programmer a combination of an IMD and external programmer
• IC integrated circuit
• set of ICs a set of ICs
• discrete electrical circuitry residing in an IMD and/or external programmer.
• a medical device system may comprise means for performing any of the methods or techniques described herein.
• a non-transitory computer-readable storage medium may comprise instructions, that when executed by processing circuitry of a medical device system, cause the medical device system to perform any of the methods or techniques described herein.
• a medical device system includes sensing circuitry configured to generate physiological signals of a patient; and processing circuitry that, for each of a plurality of periods, is configured to: determine first values associated with a first plurality of patient parameters based on a first one or more of the physiological signals generated during the period, wherein the first plurality of patient parameters include at least one of patient parameters associated with arrhythmic substrate or patient parameters associated with physiological triggers for acute cardiac events; determine, based at least in part on the first values associated with the first plurality of patient parameters, whether to assess alterations in cardiac cellular electrophysiology of the patient; in response to determining to assess the alterations in cardiac cellular electrophysiology of the patient, determine second values associated with a second plurality of patient parameters based on a second one or more of the physiological signals generated during the period, wherein the second plurality of patient parameters include patient parameters relating to cardiac electrophysiology; and determine whether to generate an alert indicating that an acute cardiac event of the patient is predicted based at least in part on the second values associated with the
• Example 2 The system of example 1, wherein: to determine the first values associated with the first plurality of patient parameters, the processing circuitry is further configured, for each of the plurality of periods, to determine a respective value for each of the first plurality of patient parameters; and to determine whether to assess alterations in cardiac cellular electrophysiology of the patient, the processing circuitry is further configured, for each of the plurality of periods, to: for each of the first plurality of patient parameters, determine a first difference metric for a current period for each of the plurality of periods based on a value of the patient parameter determined for the current period and a value of the patient parameter determined for an immediately preceding period of the plurality of periods; determine a first score for the current period based on a sum of the difference metrics for the current period for the first plurality of patient parameters; determine a first threshold for the current period based on first scores determined for N periods of the plurality of periods that precede the current period, wherein N is an integer constant; compare the first score for the current period to the first threshold for the current
• Example 3 The system of any of examples 1 and 2, wherein: to determine the second values associated with the second plurality of patient parameters, the processing circuitry is further configured, for each of the plurality of periods, to determine a respective value for each of the second plurality of patient parameters; and to determine whether to generate the alert, the processing circuitry is further configured, for each of the plurality of periods, to: for each of the second plurality of patient parameters, determine a second difference metric for a current period for each of the plurality of periods based on a value of the patient parameter determined for the current period and a value of the patient parameter determined for an immediately preceding period of the plurality of periods; determine a second score for the current period based on a sum of the difference metrics for the current period for the first plurality of patient parameters; determine a second threshold for the current period based on second scores determined for N periods of the plurality of periods that precede the current period, wherein N is an integer constant; compare the second score for the current period to the second threshold for the current period; and determine whether to
• Example 4 The system of example 3, wherein the processing circuitry is configured to determine, for each of the second plurality of patient parameters, a difference between the value of the patient parameter determined for the current period and the value of the patient parameter determined for the immediately preceding period as the second difference metric for the current period.
• Example 5 The system of any of examples 3 and 4, wherein the processing circuitry is configured to determine the second difference metric for the current period as a ratio between: a difference between the value of the patient parameter determined for the current period and the value of the patient parameter determined for the immediately preceding period; and a measure of variation of values of the patient parameter determined for the N periods of the plurality of periods that precede the current period.
• Example 6 The system of example 5, wherein the measure of variation comprises a standard deviation of the values of the patient parameter determined for the N periods of the plurality of periods that precede the current period.
• Example 8 The system of any of examples 3-7, wherein the processing circuitry is configured to determine the second threshold based on a median of the second scores determined for the N periods preceding the current period.
• Example 11 The system of any of examples 3-10, further comprising therapy delivery circuitry configured to deliver a therapy to the patient to prevent the predicted acute cardiac event, wherein the processing circuitry is configured to determine whether to control the therapy delivery circuitry to deliver the therapy based on the comparison of the second score for the current period to the second threshold for the current period.

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