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

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.

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Key information

Conditions

Frail Elderly Syndrome Behavior Behavioral Symptoms Depression Frailty Malnutrition Nutrition Disorders Nutritional and Metabolic Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

65 year and older

Sex eligibility

All sexes

Study type

Observational

About this study

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).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

PRESAE CARE

Device

PRESAGE CARE is a medical device CE marked based on artificial intelligence to prevent and reduce emergency department visits and unplanned hospitalization among frail older adults living at home.

These device is based on the use of a short 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 home care aides (HAs)or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 27 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level on emergency hospitalization risk and some health clinical situations and displayed it on a web-based secure medical device.

Primary outcomes

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).

Time frame: between one to six weeks

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.

Secondary outcomes

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).

Time frame: between one to six weeks

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.

Sponsors and collaborators

Lead sponsor

Presage

Industry

Collaborators

Assistance Publique - Hôpitaux de Paris
Assistance Publique Hopitaux De Marseille

Registry information

Official study title

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.

Important dates

Study start: 2016
Primary completion: 2016
Study completion: 2022
First posted: Aug 28, 2023
Registry last updated: Aug 28, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed