Abstract: An ambulatory medical device includes an energy storage component having a metal case and one output pin; a circuit board including a supply connection and a ground connection; a first conductive wire connected to the supply connection of the circuit board and the output pin of the energy storage component; and a second conductive wire connected to the ground connection of the circuit board and the metal case of the energy storage component.

Abstract: Systems and methods to couple electrical contacts of a header of a medical device to respective feedthrough pins of a connector block of a medical device housing using a preformed wire are disclosed. The preformed wire can include a proximate portion comprising a number of turns shaped to engage a feedthrough pin. The number of turns of the preformed wire, once engaged with the feedthrough pin, can physically separate a major portion of the preformed wire from the connector block and the housing. The major portion of the preformed wire can be shaped to route a distal portion of the preformed wire to a first electrical contact of the header when the proximate portion of the preformed wire engages the feedthrough pin.

Abstract: Systems and methods are disclosed herein, comprising a risk analysis module configured to determine a heart failure (HF) risk score for a subject using an S3 heart sound parameter of the subject and a control module configured to calculate a worsening heart failure (WHF) score for the subject using a HF parameter, wherein the control module is configured to enable a logistic regression detection of the WHF score if the determined HF risk score is in a first HF risk score range and to enable a neural network detection of the WHF score if the determined HF risk score is in a second HF risk score range.

Abstract: Various system embodiments comprise a stimulator adapted to deliver a stimulation signal for a heart failure therapy, a number of sensors adapted to provide at least a first measurement of a heart failure status and a second measurement of the heart failure status, and a controller. The controller is connected to the stimulator and to the number of sensors. The controller is adapted to use the first and second measurements to create a heart failure status index, and control the stimulator to modulate the signal using the index. Other aspects and embodiments are provided herein.

Abstract: A health monitoring (HM) computing device for monitoring patient vitals in real time is provided. The HM computing device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to receive patient data from a plurality of patient computer devices associated with a plurality of patients. The at least one processor is also programmed to, for each patient of the plurality of patients, compare the received patient data corresponding to the patient to a reference model tailored to the corresponding patient. The at least one processor is also programmed to determine, based upon the comparisons, at least one status category for each of the plurality of patients. Moreover, the at least one processor is further programmed to generate and transmit instructions for displaying a first graphical user interface to a healthcare provider computer device associated with the healthcare provider.

Abstract: Devices and methods for detecting events indicative of heart failure (HF) decompensation status are described. An ambulatory medical device can determine the present physiologic state as being either a drift state or a stable state, and applies an algorithm to detect HF decompensation event according to the physiologic state. In some embodiments, the ambulatory medical device uses the present physiologic state to estimate one ore more expected future signal characteristics, and to detect HF decompensation event using the one or more expected futures signal characteristics.

Abstract: This document discusses, among other things, systems and methods to receive physiologic information from a patient using an ambulatory medical device, and to determine an indication of cardiotoxicity using the received physiologic information.

Abstract: A health monitoring (HM) computing device for monitoring patient vitals in real time is provided. The HM computing device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to receive patient data from a plurality of patient computer devices associated with a plurality of patients. The at least one processor is also programmed to, for each patient of the plurality of patients, compare the received patient data corresponding to the patient to a reference model tailored to the corresponding patient. The at least one processor is also programmed to determine, based upon the comparisons, at least one status category for each of the plurality of patients. Moreover, the at least one processor is further programmed to generate and transmit instructions for displaying a first graphical user interface to a healthcare provider computer device associated with the healthcare provider.

Abstract: This document discusses, among other things, systems and methods to enable physician labels on a remote server and use labels to verify and improve algorithm results. A method comprises using patient data in an automated analysis to obtain a result; receiving a message from the user, wherein the message is related to the result; and using at least a portion of the message to automatically modify the analysis.

Abstract: Devices and methods for detecting physiological target event such as events indicative of HF decompensation status are described. A medical device is configured to receive at least a first and a second chronic condition indictors of a patient, receive one or more physiologic signals from the patient, and generate a plurality of signal metrics when the first and the second chronic condition indicators meet their respective criterion. The medical device can detect the target event or condition using one or more patient-specific signal metrics selected from a group including both the first and the second set of the signal metrics. The medical device and the methods can be configured to detect an event indicative of HF decompensation.

Abstract: Systems and methods for detecting a present, or predicting a future, target physiologic event such as worsening heart failure (HF) are described. A system can comprise a patient information receiver circuit, at least two categorical risk analyzer circuits, and a categorical fusion circuit. The patient information receiver circuit receives physiologic signals and generates signal trends. The categorical risk analyzer circuit receives a category-specific input selected from the signal trends according to an associative physiologic condition indicative or correlative of the target event. The categorical risk analyzer circuit produces a signal trend metric indicating relative change in signal strength over time. The categorical risk analyzer circuit calculates a categorical risk index that indicates likelihood of the patient developing or presenting the associative physiologic condition.

Abstract: Devices and methods for identifying patient at elevated risk of developing future heart failure (HF) events, such as events indicative of HF decompensation status, are described. The devices and methods can stratify the risk using sensor signals or signal metrics selected in accordance with patient chronic conditions. A medical device can receive a patient status input including at least a first and a second chronic condition indicators, sense one or more physiologic signals from the patient, and generate a plurality of signal metrics from the physiologic signals when the first chronic condition indicator and the second chronic condition indicator meets respective criterion. One or more patient-specific signal metrics can be selected from a group including the signal metrics selected for both the first and the second chronic conditions.

Abstract: Systems and methods to monitor an improvement of a subject's physiological condition using a goal improvement profile are described. In an example, a physiological condition of a subject can be compared to a goal improvement profile, where the profile includes at least one target condition indicative of an improved physiological condition detectable using a physiological sensor signal, and a target time when the subject is expected to reach the target condition, where the comparison can be used to indicate whether the subject has met the target condition by the target time.

Abstract: An emergency notification (EN) computing device includes at least one processor and a memory in communication with the processor. The processor is programmed to receive, from a user device, an emergency report that identifies an emergency situation and is associated with a report location, receive user input defining an impact zone within a geographical region based on the report location and the emergency situation, collect location data from a plurality of mobile user devices associated with a plurality of bystanders, detect a first bystander located within the impact zone based on the location data, and automatically notify the first bystander of the emergency situation.

Abstract: A system comprises a risk analysis module and a worsening heart failure (WHF) detection module. The risk analysis module measures at least one first physiological parameter of a subject using a physiological sensor of an ambulatory medical device, and determines a heart failure (HF) risk score for the subject according to the at least one measured first physiological parameter. The HF risk score indicates susceptibility of the subject to experiencing a HF event. The WHF detection module measures at least one second physiological parameter of the subject using the same or different physiological sensor, and generates an indication of prediction that the subject will experience a WHF event when the at least one second physiological parameter satisfies a WHF detection algorithm. The risk analysis module adjusts generation of the indication by the WHF detection algorithm according to the determined HF risk score.

Abstract: Systems and methods to indicate heart failure co-morbidity are described. In an example, one or more physiological signals can be analyzed for a specified time period to determine at least one of an indication of a heart failure event or a characteristic of heart failure co-morbidity, such as a characteristic of a non-heart failure physiological event. At least one of the different physiological signals can be compared to a specified criterion indicative of the heart failure event to provide a comparison value, where the comparison value is indicative of whether the at least one of the different physiological signals contributed towards the indication of the heart failure event. An indication of a non-heart failure physiological event can be determined using the heart failure event indication, the non-heart failure physiological event characteristic, and the comparison value indicative of whether the one or more physiological signals contributed towards the heart failure event.

Abstract: Various system embodiments comprise a stimulator adapted to deliver a stimulation signal for a heart failure therapy, a number of sensors adapted to provide at least a first measurement of a heart failure status and a second measurement of the heart failure status, and a controller. The controller is connected to the stimulator and to the number of sensors. The controller is adapted to use the first and second measurements to create a heart failure status index, and control the stimulator to modulate the signal using the index. Other aspects and embodiments are provided herein.

Abstract: A system comprises a risk analysis module and a worsening heart failure (WHF) detection module. The risk analysis module measures at least one first physiological parameter of a subject using a physiological sensor of an ambulatory medical device, and determines a heart failure (HF) risk score for the subject according to the at least one measured first physiological parameter. The HF risk score indicates susceptibility of the subject to experiencing a HF event. The WHF detection module measures at least one second physiological parameter of the subject using the same or different physiological sensor, and generates an indication of prediction that the subject will experience a WHF event when the at least one second physiological parameter satisfies a WHF detection algorithm. The risk analysis module adjusts generation of the indication by the WHF detection algorithm according to the determined HF risk score.

Abstract: Various system embodiments comprise a stimulator adapted to deliver a stimulation signal for a heart failure therapy, a number of sensors adapted to provide at least a first measurement of a heart failure status and a second measurement of the heart failure status, and a controller. The controller is connected to the stimulator and to the number of sensors. The controller is adapted to use the first and second measurements to create a heart failure status index, and control the stimulator to modulate the signal using the index. Other aspects and embodiments are provided herein.

Abstract: Systems and related methods for analyzing data sensed from a device implanted in a patient, such as a cardiac pacing system. The system detects and evaluates electric signals within the patient that share a common event marker. By using algorithms and graphical presentation of the collected signals having common event markers, deviations in signals over time can be identified and evaluated in consideration of taking further action related to the patient and the implanted device. The system can also be used in conjunction with an advanced patient management system that includes a programmer or repeater capable of gathering information from the implanted device and transmitting the data to a host via a communications network for evaluation at a remote location.