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

NCT06013709 | Completed

• 1 other identifier: PRESAGE
• Study Type: observational
• Target: 1,478
• Locations: 0 countries
• Sites: N/A

Brief 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

Keywords

frailty; older adults; artificial intelligence; falls; undernutrition; depression; heart failure risk

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• 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).

  between one to six weeks

Interventions

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.

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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

Sponsors & Collaborators

• Presage: lead
• Assistance Publique - Hôpitaux de Paris: collaborator
• Assistance Publique Hopitaux De Marseille: collaborator

MeSH Terms

Conditions

Frailty; Malnutrition; Depression

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms; Nutrition Disorders; Nutritional and Metabolic Diseases; Behavioral Symptoms; Behavior

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 21, 2023
• First Posted: August 28, 2023
• Study Start: April 1, 2016
• Primary Completion: April 1, 2016
• Study Completion: December 1, 2022
• Last Updated: August 28, 2023

Record last verified: 2023-08