Abstract: A system and associated method receives, by a database coupled to a communication network, patient medical data from multiple data sources including data retrieved from implantable medical devices implanted in patients. A processor accesses the database to generate a dataset from the medical data having at least one data characteristic matching a corresponding data characteristic of a patient group of at least one patient. At least one subset of the dataset is identified that had a therapy intervention subsequent to a time point that the subset had the matching data characteristic(s). An outcome of the subset is determined and a predictive outcome for the patient group is produced based on the outcome of at least one subset.

Abstract: The present disclosure is directed to an electrogram summary. In various examples, a subset of cardiac episodes are selected and displayed based on a set of summary rules. The subset of cardiac episodes includes at least one episode from each of a plurality of episode categories with at least one cardiac episode. In some examples, the order in which the cardiac episodes selected are displayed is based on the set of summary rules. The electrogram summary may include images or information regarding each of the selected cardiac episodes.

Abstract: A system and associated method receives, by a database coupled to a communication network, patient medical data from multiple data sources including data retrieved from implantable medical devices implanted in patients. A processor accesses the database to generate a dataset from the medical data having at least one data characteristic matching a corresponding data characteristic of a patient group of at least one patient. At least one subset of the dataset is identified that had a therapy intervention subsequent to a time point that the subset had the matching data characteristic(s). An outcome of the subset is determined and a predictive outcome for the patient group is produced based on the outcome of at least one subset.

Abstract: An implantable cardioverter defibrillator (ICD) senses ventricular depolarizations (R-waves) in an electrogram signal to detect a ventricular tachycardia or fibrillation episodes. The EGM signal is also monitored in real time for characteristics that uniquely identify instances of T-wave oversensing. The ICD determines whether detection of a tachycardia or fibrillation episode is appropriate based upon counts of each of the unique characteristics evidencing T-wave oversensing.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data to determine whether T-wave oversensing has occurred. The T-wave oversensing analysis may include, for example, identifying beat runs within the cardiac episode whether the beats within the run have at least one characteristic that alternates beat to be or clustering beats within the cardiac episode based on beat to beat interval length. The T-wave oversensing determination may be based on probabilistic analysis in some examples.

Abstract: A method for identifying oversensing in implantable medical devices (IMDs), such as implantable cardioverter defibrillators (ICDs), is described. A near-field electrogram signal and a far-field electrogram signal are obtained via a near-field electrode pair and a far-field electrode pair. The near-field electrogram signal is compared to the far-field electrogram signal and a determination of whether oversensing exists is made based on the comparison. In some instances, a scheduled therapy is withheld in response to determining that oversensing exists.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data from a near-field channel and a far-field channel. The episode classification algorithm classifies the cardiac episode based on the evaluation of the electrogram signal data for at least one of the near-field and far-field channels. In some examples, a cardiac episode being classified may be an episode that resulted in treatment being provided by an implantable medical device. Possible classifications of the cardiac episode may include, for example, unknown, inappropriate, appropriate, supraventricular tachycardia, ventricular tachycardia, ventricular fibrillation or ventricular over-sensing.

Abstract: The present disclosure is directed to generating and displaying an electrogram (EGM) summary for use by physicians or other clinicians. An implantable medical device (IMD) transmits EGM signal data for a number of cardiac episodes to an external computing device. The external computing device selects a subset of the cardiac episodes for which information or images are displayed to the user. In various examples, cardiac episodes may be selected for display based at least in part on a retrospective analysis classification of the cardiac episode.

Abstract: Techniques, systems, and devices, for generating a patient management report based on clinician input and patient data are described. For example, one or more processors may be configured to receive a clinician input selecting at least one reporting characteristic for each of a plurality of diagnostic metrics and organize the diagnostic metrics based on the selected reporting characteristic. In addition, the one or more processors may be configured to receive patient data for at least one patient, determine a value for at least a subset of the diagnostic metrics based on the patient data, and generate a patient management report comprising the diagnostic metrics having a value that exceeds a respective threshold. The diagnostic metrics may be ordered in the patient management report based on the organization.

Abstract: In general, the disclosure relates to techniques for calculating mean impedance values and impedance variability values to detect a possible condition with a lead or device-lead pathway or connection. In one example, a device may be configured to determine an impedance value for an electrical path based on a plurality of measured impedance values for the electrical path, wherein the electrical path comprises a plurality of electrodes, and to determine an impedance variability value based on at least one of the plurality of measured impedance values. The device may be further configured to determine a threshold value based on the determined impedance value and the impedance variability value, compare a newly measured impedance value for the electrical path to the threshold value, and indicate a possible condition of the electrical path based on the comparison.

Abstract: A medical device system senses cardiac signals and generates and stores sensing data including sensed cardiac events. A processor receiving the sensing data is configured to detect undersensed and oversensed events. The processor generates an episode display comprising event identifying codes in response to the received sensing data and produces an adjusted episode display in response to an event being identified as an undersensed event or an oversensed event.

Abstract: A medical device system senses cardiac signals and generates and stores sensing data including sensed cardiac events. A processor receiving the sensing data is configured to detect undersensed and oversensed events. The processor generates an episode display comprising event identifying codes in response to the received sensing data and produces an adjusted episode display in response to an event being identified as an undersensed event or an oversensed event.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data using a probabilistic ventricular oversensing algorithm. The algorithm may look at a plurality of factors weighing for and against a determination of ventricular oversensing. In some examples, the algorithm may also determine whether the cardiac episode includes atrial sensing issues.

Abstract: A system including a communication module, a processor and a medical device configured to sense cardiac signals and detect cardiac rhythm episodes is configured to retrieve stored episode data accumulated by the medical device and generate truthed episode classifications from the retrieved episode data. The processor is configured to perform a detection simulation for detecting and classifying cardiac rhythm episodes included in the retrieved episode data to obtain simulated episode classifications. Sensitivity and specificity data is generated in response to the detection simulation, and recommended detection parameter settings are identified in response to the sensitivity and specificity data.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data collected by an implantable medical device. The episode classification algorithm may classify may include a sinus template and a comparison of the electrogram signal to the sinus template. Possible classifications of the cardiac episode may include, for example, unknown, inappropriate, appropriate, supraventricular tachycardia, ventricular tachycardia, ventricular fibrillation or ventricular over-sensing.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data using a probabilistic ventricular oversensing algorithm. The algorithm may look at a plurality of factors weighing for and against a determination of ventricular oversensing. In some examples, the algorithm may also determine whether the cardiac episode includes atrial sensing issues.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data to determine whether T-wave oversensing has occurred. The T-wave oversensing analysis may include, for example, identifying beat runs within the cardiac episode whether the beats within the run have at least one characteristic that alternates beat to be or clustering beats within the cardiac episode based on beat to beat interval length. The T-wave oversensing determination may be based on probabilistic analysis in some examples.

Abstract: The present disclosure is directed to an electrogram summary. In various examples, a subset of cardiac episodes are selected and displayed based on a set of summary rules. The subset of cardiac episodes includes at least one episode from each of a plurality of episode categories with at least one cardiac episode. In some examples, the order in which the cardiac episodes selected are displayed is based on the set of summary rules. The electrogram summary may include images or information regarding each of the selected cardiac episodes.

Abstract: The present disclosure is directed to generating and displaying an electrogram (EGM) summary for use by physicians or other clinicians. An implantable medical device (IMD) transmits EGM signal data for a number of cardiac episodes to an external computing device. The external computing device selects a subset of the cardiac episodes for which information or images are displayed to the user. In various examples, cardiac episodes may be selected for display based at least in part on a retrospective analysis classification of the cardiac episode.

Abstract: The present disclosure is directed to the classification of cardiac episodes using an algorithm. In various examples, an episode classification algorithm evaluates electrogram signal data collected by an implantable medical device. The episode classification algorithm may classify may include a sinus template and a comparison of the electrogram signal to the sinus template. Possible classifications of the cardiac episode may include, for example, unknown, inappropriate, appropriate, supraventricular tachycardia, ventricular tachycardia, ventricular fibrillation or ventricular over-sensing.