EP-4736172-A1 - METHODS AND SYSTEMS FOR PREDICTING CHANGES IN PHYSIOLOGICAL PARAMETERS OF A PATIENT

Abstract

The present disclosure relates to methods and systems for predicting changes in a target physiological parameter of a target patient expected to result from a medication to be administered to the target patient. The methods and systems may comprise deriving one or more parameter-estimation functions based on historical data, wherein each parameter-estimation function models how a separate parameter of a prediction function varies in accordance with one or more starting physiological parameters. The methods and systems may further comprise receiving user input indicative of a value for one or more starting physiological parameters for the target patient, calculating a value for each parameter of the prediction function by applying the derived parameter-estimation functions to the one or more indicated values, and predicting changes in the target physiological parameter using the prediction function and the calculated parameter values. The predicted changes may then be displayed on a user-interface.

Inventors

• BLEICH, Kevin Christopher
• CARR, Laura Rose
• FU, Haoda
• HAY, Matthew Thomas
• HOWELL, Jefferson Patrick
• KROTCHEN, Ashley Lauren
• ROBERTS, Aubrey Lenora
• ROELEN, Kelsey Sparks
• WANG, WENJIE

Assignees

• Eli Lilly and Company

Dates

Publication Date

20260506

Application Date

20240626

Claims (20)

1. CLAIMS We claim: 1. A method for predicting changes in a target physiological parameter of a target patient expected to result from a medication to be administered to the target patient, the method comprising: accessing, at one or more computing devices, historical data indicative of changes observed over an observation period in the target physiological parameter of a plurality of patients resulting from administration of the medication; deriving, at the one or more computing devices, one or more parameter-estimation functions based on the historical data, wherein each parameter-estimation function models how a separate parameter of a prediction function for predicting changes in the target physiological parameter over time varies in accordance with one or more starting physiological parameters observed in the plurality of patients; receiving, at the one or more computing devices, user input indicative of a value for each of the one or more starting physiological parameters for the target patient; calculating, by the one or more computing devices, a value for each parameter of the prediction function by applying the one or more derived parameter-estimation functions to the one or more values indicated by the received user input; predicting, by the one or more computing devices, changes in the target physiological parameter of the target patient at a plurality of future time points using the prediction function and the calculated parameter values; and displaying, on a user interface of at least one of the computing devices, the predicted changes in the target physiological parameter of the target patient to assist at least one of the target patient and a medical professional in determining whether the medication should be administered to the target patient.
2. 2. The method of claim 1, wherein the medication is an anti-obesity medication and the target physiological parameter is body weight.
3. 3. The method of claim 1, wherein the medication is an anti-diabetes medication and the target physiological parameter is A1C level.
4. 4. The method of any one of claims 1-3, wherein the one or more starting physiological parameters comprise at least one of age, A1C level, height, weight, resting heart rate, and biological sex.
5. 5. The method of any one of claims 1-4, wherein the prediction function is a probability distribution function.
6. 6. The method of claim 5, wherein predicting changes in the target physiological parameter of the target patient comprises predicting, for each time point of the plurality of future time points, an expected change in the target physiological parameter and a prediction interval for the expected change.
7. 7. The method of any one of claims 5-6, wherein at least one of the parameter-estimation functions models how a parameter indicative of a measure of statistical variance varies in accordance with the one or more starting physiological parameters.
8. 8. The method of any one of claims 1-7, wherein: the plurality of patients in the historical data were administered different dose levels of the medication, such that the historical data is indicative of how changes in the target physiological parameter varies with dose level; at least one of the derived parameter-estimation functions models how one of the parameters of the prediction function varies in accordance with dose level; the user input received at the one or more computing devices is further indicative of a target dose level for the medication to be administered to the target patient; the value for at least one parameter of the prediction function is calculated, by the one or more computing devices, based at least in part on the target dose level; and the predicted changes in the target physiological parameter of the target patient displayed on the user interface are calculated based at least in part on the target dose level to assist at least one of the target patient and the medical professional in determining whether the target dose level of the medication should be administered to the target patient.
9. 9. The method of any one of claims 1-8, further comprising receiving, at the one or more computing devices, further user input indicative of actual changes to the target physiological parameter observed in the target patient in response to a course of the medication previously administered to the target patient, wherein the calculated parameter values of the prediction function are calculated based at least in part on the further user input.
10. 10. The method of any one of claims 1-9, wherein the parameter-estimation functions are derived by the one or more computing devices and saved into a memory accessible by the one or more computing devices before the user input is received.
11. 11. The method of any one of claims 1-10, wherein the accessing and deriving steps are implemented at a first computing device, and the receiving, calculating, predicting, and displaying steps are implemented at a second computing device.
12. 12. The method of any one of claims 1-12, wherein the one or more parameter-estimation functions are derived from the historical data using linear regression.
13. 13. A system for predicting changes in a target physiological parameter of a target patient expected to result from a medication to be administered to the target patient, the system comprising: a user interface; one or more memory systems storing computer-executable instructions; and one or more processors communicably coupled with the one or more memory systems and the user interface, and operable to execute the instructions to: access historical data indicative of changes observed over an observation period in the target physiological parameter of a plurality of patients resulting from administration of the medication; derive one or more parameter-estimation functions based on the historical data, wherein each parameter-estimation function models how a separate parameter of a prediction function for predicting changes in the target physiological parameter over time varies in accordance with one or more starting physiological parameters observed in the plurality of patients; receive user input indicative of a value for each of the one or more starting physiological parameters for the target patient; calculate a value for each parameter of the prediction function by applying the one or more derived parameter-estimation functions to the one or more values indicated by the received user input; predict changes in the target physiological parameter of the target patient at a plurality of future time points using the prediction function and the calculated parameter values; and display, on the user interface, the predicted changes in the target physiological parameter of the target patient to assist at least one of the target patient and a medical professional in determining whether the medication should be administered to the target patient.
14. 14. The system of claim 13, wherein the medication is an anti-obesity medication and the target physiological parameter is body weight.
15. 15. The system of claim 13, wherein the medication is an anti-diabetes medication and the target physiological parameter is A1C level.
16. 16. The system of any one of claims 13-15, wherein the one or more starting physiological parameters comprise at least one of age, A1C level, height, weight, resting heart rate, and biological sex.
17. 17. The system of any one of claims 13-16, wherein the prediction function is a probability distribution function.
18. 18. The system of claim 17, wherein predicting changes in the target physiological parameter of the target patient comprises predicting, for each time point of the plurality of future time points, an expected change in the target physiological parameter and a prediction interval for the expected change.
19. 19. The system of claim 17-18, wherein at least one of the parameter-estimation functions models how a parameter indicative of a measure of statistical variance varies in accordance with the one or more starting physiological parameters.
20. 20. The system of any one of claims 13-19, wherein: the plurality of patients in the historical data were administered different dose levels of the medication, such that the historical data is indicative of how changes in the target physiological parameter varies with dose level; at least one of the derived parameter-estimation functions models how one of the parameters of the prediction function varies in accordance with dose level; the received user input is further indicative of a target dose level for the medication to be administered to the target patient; the value for at least one parameter of the prediction function is calculated, by the one or more processors, based at least in part on the target dose level; and the predicted changes in the target physiological parameter of the target patient displayed on the user interface are calculated based at least in part on the target dose level to assist at least one of the target patient and the medical professional in determining whether the target dose level of the medication should be administered to the target patient.

Description

METHODS AND SYSTEMS FOR PREDICTING CHANGES IN PHYSIOLOGICAL PARAMETERS OF A PATIENT FIELD OF THE INVENTION [0001] The present disclosure relates to methods and systems for predicting changes in one or more physiological parameters of a patient. More specifically, the present disclosure relates to methods and systems for predicting changes in one or more target physiological parameters of a patient expected to result from a course of medication to be administered to the patient. BACKGROUND OF THE INVENTION [0002] Patients are generally administered medications in order to effect changes in one or more target physiological parameters of the patient. This is especially the case for patients suffering from chronic conditions that require long-term treatment. As a result, when a patient is administered a medication or a course of medication, patients and their caregivers (e.g. healthcare providers or HCPs) generally expect to see some measurable change in the one or more target physiological parameters of the patient. The changes in the physiological parameter(s) of the patient may be considered an outcome or a result of the administered medication or course of medication. However, the outcomes resulting from a medication or course of medication may differ from patient to patient. SUMMARY OF THE INVENTION [0003] In one aspect, the present disclosure is directed at a method for predicting changes in a target physiological parameter of a target patient expected to result from a medication to be administered to the target patient. The method may comprise: accessing, at one or more computing devices, historical data indicative of changes observed over an observation period in the target physiological parameter of a plurality of patients resulting from administration of the medication; deriving, at the one or more computing devices, one or more parameter- estimation functions based on the historical data, wherein each parameter-estimation function models how a separate parameter of a prediction function for predicting changes in the target physiological parameter over time varies in accordance with one or more starting physiological parameters observed in the plurality of patients; receiving, at the one or more computing devices, user input indicative of a value for each of the one or more starting physiological parameters for the target patient; calculating, by the one or more computing devices, a value for each parameter of the prediction function by applying the one or more derived parameter-estimation functions to the one or more values indicated by the received user input; predicting, by the one or more computing devices, changes in the target physiological parameter of the target patient at a plurality of future time points using the prediction function and the calculated parameter values; and displaying, on a user interface of at least one of the computing devices, the predicted changes in the target physiological parameter of the target patient to assist at least one of the target patient and a medical professional in determining whether the medication should be administered to the target patient. [0004] In some embodiments, the medication is an anti-obesity medication and the target physiological parameter is the patient’s body weight. [0005] In some embodiments, the medication is an anti-diabetes medication and the target physiological parameter is the patient’s A1C level. [0006] In some embodiment, the one or more starting physiological parameters comprise at least one of age, A1C level, height, weight, resting heart rate, and biological sex. [0007] In some embodiments, the prediction function is a probability distribution function. [0008] In some embodiments, predicting changes in the target physiological parameter of the target patient comprises predicting, for each time point of the plurality of future time points, an expected change in the target physiological parameter and a prediction interval for the expected change. [0009] In some embodiments, at least one of the parameter-estimation functions models how a parameter indicative of a measure of statistical variance varies in accordance with the one or more starting physiological parameters. [0010] In some embodiments, the plurality of patients in the historical data were administered different dose levels of the medication, such that the historical data is indicative of how changes in the target physiological parameter varies with dose level. In such embodiments, at least one of the derived parameter-estimation functions may model how one of the parameters of the prediction function varies in accordance with dose level; the user input received at the one or more computing devices may be further indicative of a target dose level for the medication to be administered to the target patient; the value for at least one parameter of the prediction function may be calculated, by the one or more computing devices, based at least in part on the target dos