Certain embodiments described herein related to methods, devices, and systems that provide improved communications between first and second IMDs remotely located relative to one another and capable of communicating using both conductive communication and RF communication. Such a method can include the first IMD using conductive communication to transmit message(s) intended for the second IMD, without using RF communication, during a first period of time that a first trigger event is not detected. The method can also include the first IMD detecting the first trigger event, and in response thereto, the first IMD using RF communication to transmit message(s) intended for the second IMD during a second period of time. Thereafter, in response to first IMD detecting a second trigger event, the first IMD uses conductive communication to transmit one or more messages intended for the second IMD, without using RF communication, during a third period of time.

A system comprises processing circuitry and memory comprising program instructions that, when executed by the processing circuitry, cause the processing circuitry to: apply a first set of rules to first patient parameter data for a first determination of whether sudden cardiac arrest of a patient is detected; determine that a one or more context criteria of the first determination are satisfied; and in response to satisfaction of the context criteria, apply a second set of rules to second patient parameter data for a second determination of whether sudden cardiac arrest of the patient is detected. At least the second set of rules comprises a machine learning model, and the second patient parameter data comprises at least one patient parameter that is not included in the first patient parameter data.

A subcutaneously implantable device includes a housing, a clip attached to a top side of the housing, a first prong with a proximal end attached to the housing and a distal end extending away from the housing, and a first electrode on the first prong. The clip is configured to anchor the device to a muscle, a bone, and/or a tissue. The first prong is configured to contact a heart. The first electrode is configured to contact the heart. Sensing circuitry in the housing that is configured to sense an electrical signal from the heart, and therapeutic circuitry in the housing is in electrical communication with the first electrode and is configured to deliver electrical stimulation to the heart through the first electrode.

An implantable medical device contains a hermetic battery. The hermetic battery contains a hermetically sealed battery housing defining an internal chamber, an electrochemical cell disposed within the internal chamber, and an electronic module disposed within the internal chamber. The electronic module is electrically conductively connected to the electrochemical cell, and the electronic module is arranged in the electrochemical cell.

This document describes implantable medical devices and methods of using such devices. The implantable medical devices include a cardiac lead sized for insertion in a cardiac cavity, the cardiac lead having a distal end and a proximal end and a lead body extending therebetween, a heat exchange module disposed at the distal end of the lead body, the heat exchange module comprising an enclosure having a first surface and a second surface, and one or more temperature sensors located within the enclosure.

Techniques are disclosed for monitoring a patient for the occurrence of a cardiac arrhythmia. A computing system generates sample probability values by applying a machine learning model to sample patient data. The machine learning model determines a respective probability value that indicates a probability that the cardiac arrhythmia occurred during each respective temporal window. The computing system outputs a user interface comprising graphical data based on the sample probability values and receives, via the user interface, an indication of user input to select a probability threshold for a patient. The computing system receives patient data for the patient and applies the machine learning model to the patient data to determine a current probability value. In response to the determination that the current probability exceeds the probability threshold for the patient, the computing system generates an alert indicating the patient has likely experienced the occurrence of the cardiac arrhythmia.

Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem to placement procedures for cardiac implantable electronic device (“CIED”). In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and/or tool-based data), and might generate one or more recommendations for guiding a medical professional in performing a CIED placement procedure in a heart of the patient, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the heart of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.

Systems, methods and implantable devices configured to provide cardiac resynchronization therapy and/or bradycardia pacing therapy. A first device located in the heart of the patient is configured to receive a communication from a second device and deliver a pacing therapy in response to or in accordance with the received communication. A second device located elsewhere is configured to determine an atrial event has occurred and communicate to the first device to trigger the pacing therapy. The second device may be configured for sensing the atrial event by the use of vector selection and atrial event windowing, among other enhancements. Exception cases are discussed and handled as well.

Systems and methods for monitoring and treating patients with heart failure (HF) are discussed. The system may sense cardiac signals, and receives information about patient physiological or functional conditions. A stimulation parameter table that includes recommended values of atrioventricular delay (AVD) or other timing parameters maybe created at a multitude of patient physiological or functional conditions. The system may periodically reassess patient physiological or functional conditions. A therapy programmer circuit may dynamically switch between left ventricular-only pacing and biventricular pacing, or switch between single site pacing and multisite pacing based on the patient condition. The therapy programmer circuit may adjust AVD and other timing parameters using the cardiac signal input and the stored stimulation parameter table. A HF therapy may be delivered according to the determined stimulation site, stimulation mode, and the stimulation timing.

A patient-worn arrhythmia monitoring and treatment device includes a pair of therapy electrodes and at least one pair of sensing electrodes disposed proximate to the skin and configured to continually sense at least one ECG signal of the patient over an extended period of time. The device includes a therapy delivery circuit coupled to the pair of therapy electrodes and configured to deliver one or more therapeutic pulses. A controller coupled to therapy delivery circuit is configured to analyze the at least one ECG signal and detect one or more treatable arrhythmias and cause the therapy delivery circuit to deliver the one or more therapeutic pulses to the patient. At least one of the one or more therapeutic pulses is formed as a biphasic waveform delivering within 15 percent of 360 J of energy to a patient body having a transthoracic impedance from about 20 to about 200 ohms.