Determining likelihood of an adverse health event based on various physiological diagnostic states
Techniques for determining a likeliness that a patient may incur an adverse health event are described. An example technique may include utilizing a probability model that uses as evidence nodes various diagnostic states of physiological parameters, which may include one or more subcutaneous impedance parameters. The probability model may include a Bayesian Network that determines a posterior probability of the adverse health event occurring within a predetermined period of time.
1 . A system comprising:
an implantable medical device (IMD) comprising a housing and a plurality of electrodes on the housing, wherein the TNID, the housing, and the plurality of electrodes are configured for subcutaneous implantation in a patient, wherein the plurality of electrodes are positioned within 5 centimeters (cm) apart on the housing and the IMD is configured to determine one or more subcutaneous tissue impedance measurements of interstitial fluid in an interstitium of a subcutaneous space of the patient measured using only the electrodes on the housing; and processing circuitry coupled to one or more storage devices, and configured to:
receive the one or more subcutaneous tissue impedance measurement of the interstitial fluid determined via the electrodes;
determine a respective one or more values for each of a plurality of physiological parameters, the plurality of physiological parameters including:
a first period of time subcutaneous tissue impedance parameter determined from the one or more subcutaneous tissue impedance measurements of the interstitial fluid over the first period of time, and
a second period of time subcutaneous tissue impedance parameter determined from the one or more subcutaneous tissue impedance measurements of the interstitial fluid over the second period of time, the second period of time being longer in duration than the first period of time;
determine a diagnostic state for each of the physiological parameters based on the respective values, the diagnostic states defining a plurality of evidence nodes for a probability model; and
apply the plurality of evidence nodes to the probability model to determine a probability score indicating a likelihood that the patient (a) is experiencing an adverse health event or (b) is likely to experience the adverse health event within a predetermined amount of time,
wherein the first period of time is up to 7 days and the second period of time is up to 30 days.
2 . The system of claim 1 , wherein the determined values of the physiological parameters correspond to a preceding timeframe relative to when the probability score is determined.
3 . The system of claim 1 , wherein the physiological parameters include values corresponding to at least one of: heart rate variability (HRV), night heart rate (NHR), patient activity (ACT), atrial fibrillation (AF), R-wave amplitude, heart sounds, or ventricular rate.
4 . The system of claim 1 , wherein the processing circuitry is configured to:
determine, from the respective one or more values for each physiological parameter, a plurality of physiological parameter features that encode amplitude, out-of-normal range values, and temporal changes; and
determine the evidence nodes based at least in part on the plurality of physiological parameter features.
5 . The system of claim 1 , wherein the probability model is a Bayesian Network comprising at least two child nodes and a parent node.
6 . The system of claim 1 , wherein the processing circuitry is configured to:
determine an input to a first child node of the plurality of evidence nodes based on the respective one or more values of the first period of time subcutaneous tissue impedance parameter and the second period of time subcutaneous tissue impedance parameter; and
determine an input to a second child node of the plurality of evidence nodes based on a combination of one or more values indicating an extent of atrial fibrillation (AF) in the patient during a time period and one or more values indicating a ventricular rate during the time period.
7 . The system of claim 1 , wherein the processing circuitry is further configured to:
compare the probability score to at least one risk threshold; and
determine one of a plurality of discrete risk categorizations based on the comparison.
8 . The system of claim 1 , wherein the processing circuitry is further configured to:
determine an occurrence of missing data, the missing data corresponding to a particular physiological parameter;
determine an extent to which the data for the physiological parameter is missing; and
determine whether to use the physiological parameter when determining the probability score based on the extent to which the data is missing.
9 . A method comprising:
obtaining, via a plurality of electrodes on a housing of an implantable medical device (IMD) implanted subcutaneously in a patient, one or more subcutaneous tissue impedance measurements of interstitial fluid in an interstitium of a subcutaneous space of the patient wherein the plurality of electrodes are positioned within 5 centimeters (cm) apart on the housing;
determining, by processing circuitry of a computing device, a respective one or more values for each of a plurality of physiological parameters, the plurality of physiological parameters including:
a first period of time subcutaneous tissue impedance parameter determined from the one or more subcutaneous tissue impedance measurements of interstitial fluid measured over the first period of time, and
a second period of time subcutaneous tissue impedance parameter determined from the one or more subcutaneous tissue impedance measurements of the interstitial fluid measured over the second period of time, the first period of time is up to 7 days, and the second period of time is up to 30 days;
determining, by the processing circuitry, a diagnostic state for each of the physiological parameters based on the respective values, the diagnostic states defining a plurality of evidence nodes for a probability model; and
applying, by the processing circuitry, the plurality of evidence nodes to the probability model to determine a probability score indicating a likelihood that the patient (a) is experiencing an adverse health event or (b) is likely to experience the adverse health event within a predetermined amount of time.
10 . The method of claim 9 , wherein the determined values of the physiological parameters correspond to a preceding timeframe relative to when the probability score is determined.
11 . The method of claim 9 , wherein the physiological parameters include values corresponding to at least one of: heart rate variability (HRV), night heart rate (NHR), patient activity (ACT), atrial fibrillation (AF), heart sounds, or ventricular rate.
12 . The method of claim 9 , further comprising:
determining, by the processing circuitry, based on the one or more subcutaneous tissue impedance measurements, a periodic variation in subcutaneous tissue impedance; and
determining, by the processing circuitry, based on the periodic variation in subcutaneous tissue impedance, a parameter value for at least one of the physiological parameters.
13 . The method of claim 9 , further comprising:
determining, by the processing circuitry, a plurality of physiological parameter features based on the respective one or more values for each physiological parameter, wherein the features are configured to, upon analysis, yield a same number of potential diagnostic states for each physiological parameter; and
determining, by the processing circuitry, from the potential diagnostic states, the diagnostic state for each of the physiological parameters.
14 . The method of claim 9 , wherein the probability model is a Bayesian Network comprising at least two child nodes and a parent node.
15 . The method of claim 9 , further comprising:
determining, by the processing circuitry, an input to a first child node of the plurality of evidence nodes based on the respective one or more values of the first period of time subcutaneous tissue impedance parameter and the second period of time subcutaneous tissue impedance parameter; and
determining, by the processing circuitry, an input to a second child node of the plurality of evidence nodes based on a combination of one or more values indicating an extent of atrial fibrillation (AF) in the patient during a time period and one or more values indicating a ventricular rate during the time period.
16 . The method of claim 9 , further comprising:
comparing, by the processing circuitry, the probability score to at least one risk threshold; and
determining, by the processing circuitry, one of a plurality of discrete risk categorizations based on the comparison.
17 . The method of claim 9 , further comprising:
determining, by the processing circuitry, for each of the plurality of physiological parameters, the respective one or more values determined at various frequencies;
determining, by the processing circuitry, the diagnostic states using the respective one or more values; and
storing, by the processing circuitry, to a memory device, at least one of: the respective one or more values or the probability score.
18 . A non-transitory computer-readable storage medium having stored thereon instructions that, when executed, cause one or more processors to at least:
obtain, via a plurality of electrodes on a housing of an implantable medical device (IMD) implanted subcutaneously in a patient, one or more subcutaneous tissue impedance measurements of interstitial fluid in an interstitium of a subcutaneous space of the patient, wherein the plurality of electrodes are positioned within 5 centimeters (cm) apart on the housing;
determine a respective one or more values for each of a plurality of physiological parameters, the plurality of physiological parameters including:
a first period of time subcutaneous tissue impedance parameter identified from the one or more subcutaneous tissue impedance measurements of interstitial fluid measured over the first period of time, and
a second period of time subcutaneous tissue impedance parameter determined from the one or more subcutaneous tissue impedance measurements of the interstitial fluid measured over the second period of time, the first period of time is up to 7 days, and the second period of time is up to 30 days;
determine a diagnostic state for each of the physiological parameters based on the respective values, the diagnostic states defining a plurality of evidence nodes for a probability model; and
apply the plurality of evidence nodes to the probability model to a probability score indicating a likelihood that the patient (a) is experiencing an adverse health event or (b) is likely to experience the adverse health event within a predetermined amount of time.
19 . The system of claim 1 , wherein the plurality of electrodes are positioned on the housing to face a skin layer of the patient when the IMD is subcutaneously implanted.
20 . The system of claim 1 , wherein at least two electrodes of the plurality of electrodes are separated by a fixed distance.
21 . The system of claim 1 , wherein the processing circuitry is configured to:
determine the probability score satisfies a health condition worsening threshold; and
generate a remediation action in response to the determination that the probability score satisfies the health condition worsening threshold.
22 . The system of claim 21 , wherein the remediation action includes modifying a therapy.
23 . The system of claim 1 , wherein the processing circuitry is configured to:
perform the one or more subcutaneous tissue impedance measurement of the interstitial fluid by delivering an electrical signal between at least two electrodes of the plurality of electrodes, determining a value of a resulting current amplitude or a value of a resulting voltage amplitude, and determining the subcutaneous tissue impedance measurement based on at least one of the value of the resulting current amplitude or the value of the resulting voltage amplitude.
24 . The system of claim 1 , wherein the processing circuitry is configured to:
perform the one or more subcutaneous tissue impedance measurement of the interstitial fluid by at least one of:
delivering a voltage pulse between at least two electrodes of the plurality of electrodes, determining a resulting current amplitude value, and determining the subcutaneous tissue impedance measurement based on the resulting current amplitude value; or
delivering a current pulse across at least two electrodes of the plurality of electrodes, determining an amplitude of a resulting voltage, and determining the subcutaneous tissue impedance measurement based on an amplitude of the current pulse and the amplitude of the resulting voltage.
25 . The system of claim 1 , wherein the first period of time is within the second period of time.