Clinical Trial Details
— Status: Completed
Administrative data
NCT number |
NCT06013709 |
Other study ID # |
PRESAGE |
Secondary ID |
|
Status |
Completed |
Phase |
|
First received |
|
Last updated |
|
Start date |
April 1, 2016 |
Est. completion date |
December 1, 2022 |
Study information
Verified date |
August 2023 |
Source |
Presage |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial 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.
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).