Clinical trial NCT04678986: ER2 and Deep Learning for Prediction of Adverse Health Outcomes
Official title: Emergency Room Evaluation for Older Users of Emergency Departments: Predicting Adverse Health Outcomes With Deep Learning Algorithms
Study type: OBSERVATIONAL
Overall status: WITHDRAWN
Conditions: Emergencies
Keywords: Geriatrics
Emergency rooms
adverse health events prevention
Deep learning
Lead sponsor: Jewish General Hospital
Sponsor class: OTHER
First posted: 2020-12-22
Enrollment: 0
Sex: ALL
Minimum age (years): 75
Standard ages: OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Between September 2017 and July 2020, 47 000 Emergency Department visits met the selection criteria. Training DL models on tabular data has been shown to be less effective than on unstructured sources such as images or sound. An appropriate mitigation strategy is to increase the quantity of data. Hence, all participants of the ER2 database will be included in the analysis. All visits will be included in the analysis.

Brief summary: An Emergency Department (ED) visit for an older adult is a high-risk medical intervention. Known adverse events (AE) include delirium, prolonged ED or hospital stay, hospitalization, recurrent ED visits and hospital death. These happen in a growing proportion in ED visitors over age 65 are over who are represented in ED visits.

Tools predicting AEs in the ED are of paramount importance to help decision-making on patient triage and disposition. They can help identify areas of unmet needs for seniors in order to develop targeted actions. Multiple scoring systems including "Programme de recherche sur l'intégration des services de maintien de l'autonomie" (PRISMA-7), Identification of Seniors at Risk (ISAR), Clinical Frailty Scale (CFS), Brief Geriatric Assessment (BGA) have extensively been studied in the ED and other settings for various outcomes. These tools rely on a simple scoring system that minimally trained staff can reliably and quickly administer. Doing otherwise is unlikely to be applicable to daily clinical practice.

As prediction accuracy has not significantly improved in the past decade, perhaps new analysis strategies are necessary. The current hype surrounding deep learning comes from better and cheaper hardware and the availability of simple and open-source libraries supported by large companies and a broad community of users. Hence, implementing deep learning (DL) algorithms is now open to a wide range of settings, including medical care in a standard clinical practice. DL has been shown to be more accurate than the average board-certified specialist on very specific tasks. Prediction of various clinical outcomes has produced less dramatic results, perhaps as traditional (non-DL) models already outperformed clinicians for many disease states. Published DL approaches applied to outcome prediction in the ED have focused on acutely ill adults in general, specific conditions or administrative issues such as admitting department or ED overcrowding. None have targeted a specific age group like older ED visitors.

An important caveat to many DL approaches is interpretation of results. To develop interventions based on targeted features associated with AEs in a given model, it has to be somewhat transparent. If multiple layers of NNs improve prediction compared to linear regression, they often provide no clinically relevant insight on how and which variables interact to yield that result.


Eligibility criteria: Inclusion Criteria:

* Age above 75 years old
* Unplanned Emergency department visit

Exclusion Criteria:

* Do not meet inclusion criteria

Primary outcomes:
measure: ED length of stay
timeFrame: through database constitution, from September 2017 to July 2020
Secondary outcomes:
measure: Prolonged hospital stay
timeFrame: through database constitution, from September 2017 to July 2020
measure: Number of partciipants with at least one hospitalizations
timeFrame: through database constitution, from September 2017 to July 2020
measure: recurrent ED visits
timeFrame: through database constitution, from September 2017 to July 2020
measure: Number of partciipants with diagnosis of delirium
timeFrame: through database constitution, from September 2017 to July 2020
measure: Number of partciipants with hospital death
timeFrame: through database constitution, from September 2017 to July 2020
Other outcomes:
Locations:
city: Montréal
country: Canada
facility: Jewish General Hospital
state: Quebec
Study officials:
affiliation: McGill University
name: Olivier Beauchet, MD
role: PRINCIPAL_INVESTIGATOR
References: