Detection of changes in patient health based on glucose data

Patent metadata

Field	Value
Publication number	US-12426811-B2
Application number	US-202217663657-A
Country	US
Kind code	B2
Filing date	May 16, 2022
Priority date	May 20, 2021
Publication date	Sep 30, 2025
Grant date	Sep 30, 2025

Abstract

This disclosure is directed to systems and techniques for detecting change in patient health based upon patient data. In one example, a medical system comprising processing circuitry communicably coupled to a glucose sensor and configured to generate continuous glucose sensor measurements of a patient. The processing circuitry is further configured to: extract at least one feature from the continuous glucose sensor measurements over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, a number of hyperglycemia events, or one or more statistical metrics corresponding to the continuous glucose sensor measurements; apply a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and generate output data based on the risk of the cardiovascular event.

First claim

What is claimed is:

1. A method comprising:
extracting at least one feature from continuous glucose sensor measurements of a patient over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, or a number of hyperglycemia events;
applying a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and
generating an output based on the risk of the cardiovascular event.

2. The method of claim 1 , wherein applying the machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event comprises applying the machine learning model to the at least one extracted feature to produce data indicative of a risk of at least one of cardiac inflammation, heart failure, an arrhythmia, or a stroke.

3. The method of claim 1 , wherein applying the machine learning model to the at least one extracted feature to produce data indicative of the risk of the cardiovascular event comprises applying the machine learning model to the at least one extracted feature to produce data indicative of a risk of hospitalization due to the cardiovascular event.

4. The method of claim 1 , wherein the amount of time within a pre-determined glucose level range comprises an amount of time corresponding to a portion of the continuous glucose sensor measurements in a first glucose range or a second glucose range.

5. The method of claim 1 , wherein the at least one feature further comprises at least one of a standard deviation, a coefficient of variation, an average, a median, an interquartile range, or a maximum rate of change of at least one dataset of the continuous glucose sensor measurements, wherein the at least one dataset comprises different time intervals of the continuous glucose sensor measurements.

6. The method of claim 1 , wherein the at least one feature comprises: the amount of time in the pre-determined glucose level range that is less than a first threshold or the number of hyperglycemic events that is greater than a second threshold.

7. The method of claim 1 , wherein the at least one feature comprises:
the amount of time in the pre-determined glucose level range that is greater than or equal to a first threshold,
the number of hyperglycemic events that is less than or equal to a second threshold, and
at least one of a standard deviation of a dataset of the continuous glucose sensor measurements that is a greater than a third threshold, the number of hypoglycemic events that is greater than a fourth threshold, or the amount of time in the pre-determined glucose level range that is greater than a fifth threshold.

8. The method of claim 1 , wherein applying the machine learning model comprises computing a likelihood probability of a glucose level of the patient causing the cardiovascular event, wherein the likelihood probability is incorporated into the machine learning model by at least one of including the likelihood probability in the at least one feature, including the likelihood probability as an independent prior probability, or adjusting at least one prior probability for the cardiovascular event.

9. The method of claim 1 , wherein the output comprises a first output, and wherein generating the output further comprises generating a second output indicative of the risk of the cardiovascular event based on the first output and data corresponding to at least one of impedance or cardiac electrogram metrics.

10. The method of claim 1 , wherein extracting at least one feature further comprises extracting at least one second feature from data corresponding to at least one of impedance or cardiac electrogram metrics, wherein the at least one second feature comprises at least one of impedance, respiratory rate, night heart rate, heart rate variability, activity, or atrial fibrillation (AF) parameters.

11. A method comprising:
extracting at least one feature from continuous glucose sensor measurements of a patient over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, a number of hyperglycemia events, or one or more statistical metrics corresponding to the continuous glucose sensor measurements;
applying a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardio-neurogenic event; and
generating an output based on the risk of a cardio-neurogenic event.

12. The method of claim 11 , wherein the cardio-neurogenic event comprises at least one of an ischemic stroke or a hemorrhagic stroke.

13. The method of claim 11 , wherein applying the machine learning model to the at least one extracted feature to produce data indicative of the risk of the cardio-neurogenic event comprises applying the machine learning model to the at least one extracted feature to produce data indicative of a risk of hospitalization due to the cardio-neurogenic event.

14. A medical system comprising:
processing circuitry communicably coupled to a glucose sensor and configured to generate continuous glucose sensor measurements of a patient, wherein the processing circuitry is further configured to:
extract at least one feature from the continuous glucose sensor measurements over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, or a number of hyperglycemia events;
apply a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and
generate output data based on the risk of the cardiovascular event.

15. The medical system of claim 14 , wherein one or more of a glucose monitor, a cardiac monitor, a neuro monitor, or a computing device in communication with at least one of the glucose monitor or the cardiac monitor comprises the processing circuitry.

16. The medical system of claim 15 , wherein the cardiac monitor or the glucose monitor comprises the glucose sensor, wherein the cardiac monitor or the neuro monitor is a wearable or an implant.

17. The medical system of claim 14 , wherein to apply the machine learning model, the processing circuitry is further configured to apply the machine learning model to the at least one extracted feature to produce data indicative of a risk of at least one of cardiac inflammation, heart failure, an arrhythmia, or a stroke.

18. The medical system of claim 14 , wherein to apply the machine learning model, the processing circuitry is configured to:
compute a likelihood probability that a glucose level of the patient causes the cardiovascular event; and
incorporate the likelihood probability into the machine learning model by at least one of including the likelihood probability in the at least one feature, including the likelihood probability as an independent prior probability, or adjusting at least one prior probability for the cardiovascular event.

19. The medical system of claim 14 , wherein to apply the machine learning model, the processing circuitry is configured to:
apply the machine learning model to the at least one extracted feature to produce data indicative of a risk of hospitalization due to the cardiovascular event.

20. The medical system of claim 14 , wherein the amount of time within a pre-determined glucose

Assignees

• Medtronic Inc

Inventors

• Mothilal Kamal Deep
• Eggen Michael D
• Yu Ning
• Keane John P
• Sarkar Shantanu
• Schulhauser Randal C
• Probst David L
• Boone Mark R
• Timmerman Kenneth A
• Taraszewski Stanley J
• Joyce Matthew A
• Dixit Amruta Paritosh
• Hilpisch Kathryn E
• Milbrandt Kathryn Ann
• Zimmerman Laura M
• Plante Matthew L

Classifications

• A61B5/7267

  involving training the classification device · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• A61B5/7282

  Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818) · CPC title

• A61B5/7275

  Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title