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

NCT06013709 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1478

Last updated 2023-08-28

No results posted yet for this study

Summary

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.

Conditions

  • Frail Elderly Syndrome

Interventions

DEVICE

PRESAE CARE

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.

Sponsors & Collaborators

  • Assistance Publique - Hôpitaux de Paris

    collaborator OTHER

  • Assistance Publique Hopitaux De Marseille

    collaborator OTHER

  • Presage

    lead INDUSTRY

Eligibility

Min Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2016-04-01
Primary Completion
2016-04-01
Completion
2022-12-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06013709 on ClinicalTrials.gov