A Machine Learning Algorithm to Predict Health Clinical Situations in Primary Healthcare for Frail Older Adults.

A Machine Learning Algorithm to Predict Health Clinical Situations (Fall, Undernutrition Risk, Depression Risk, Heart Failure Risk) and Improv Decision-support Tools in Primary Healthcare for Older Adults Living at Home.

Introduction: We developed a machine learning algorithm to predict the risk of emergency hospitalization within the new 7 to 14 days with a good predictive performance (AUC=0.85). Data recorded by home aides were send in real time to a secure server to be analyzed by our machine learning algorithm, which predicted risk level and displayed it on a secure web-based medical device. This study aims to implement and to evaluate the sensitivity and specificity's predictions of Presage system for four clinical situations with a high impact on unscheduled hospitalization of older adults living at home: falls, risk of depression (is sadder), risk of undernutrition (eat less well) and risk of heart failure (swollen leg).

Methods This is a retrospective observational multicenter study. To gain insight on both short-and middle-term predictions and how the risk factors evolve through different periods of observation, we developed a series of models which predict the risk of future clinical symptoms.

Study Overview

• Status: Completed
• Conditions: Frail Elderly Syndrome
• Intervention / Treatment: Device: PRESAE CARE

Detailed Description

This is a retrospective observational multicenter study. This study was conducted on two distinct cohorts.

Data between January 2020 - February 2023 from 50 home care facilities using PRESAEGE CARE medical device on a daily basis were analyzed. 740 853 data from 27 439 visits by home aides for 1 478 patients. The patients' mean age was 84,89 years (SD = 8.9 years) with a moderate dependency level and the sample included 1 038 women (70%).

PRESAGE CARE is a medical device CE marked to predict emergency hospitalizations. This e-health system is based on a questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the HA or their surroundings.

Based on these data, some others risks are evaluated and predict by the artificial intelligence algorithm.

This study aims to evaluate the sensitivity and specificity's predictions of PRESAGE CARE system for four clinical situations with a high impact on unscheduled hospitalization of olders adults living at home: falls, risk of depression (is sadder), risk if (eat less well) and risk of heart failure (swollen leg).

The principal objective was the sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events (when events no appear in the observation window).

Secondary objective was the sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for tautological events (when events appear in the observation window).

• Study Type: Observational
• Enrollment (Actual): 1478

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Frail older adults receiving the help of a home care aide using PRESAGE CARE device in France.

Description

Inclusion Criteria:

• frail older adults aged 65 years old and over
• Receive the help of a home care aide using PRESAGE CARE
• All eligible persons were invited to participate and were included if they provided consent

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for non-tautological events ((when events no appear in the observation window).	To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.	between one to six weeks

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity and specificity of four events' prediction: falls, "is sadder", "eat less well" and "swollen leg" for tautological events ((when events no appear in the observation window).	To evaluate the predictive performance of the models, we examined out-of-sample performance metrics, including area under the receiver operator characteristic curve (AUC), 5% and 15% AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) based on confusion matric which was created as followed: At each point of the ROC curve we calculated weighted average between Sensitivity and Specificity.	between one to six weeks

Collaborators and Investigators

• Sponsor: Presage
• Collaborators: Assistance Publique - Hôpitaux de Paris; Assistance Publique Hopitaux De Marseille

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2016
• Primary Completion (Actual): April 1, 2016
• Study Completion (Actual): December 1, 2022

Study Registration Dates

• First Submitted: August 21, 2023
• First Submitted That Met QC Criteria: August 24, 2023
• First Posted (Actual): August 28, 2023

Study Record Updates

• Last Update Posted (Actual): August 28, 2023
• Last Update Submitted That Met QC Criteria: August 24, 2023
• Last Verified: August 1, 2023

More Information

Terms related to this study

• Keywords: older adults; frailty; depression; falls; artificial intelligence; undernutrition; heart failure risk
• Other Study ID Numbers: PRESAGE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No