ER2 and Deep Learning for Prediction of Adverse Health Outcomes

NCT04678986 | Withdrawn

• 1 other identifier: 2021-2699
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

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.

Conditions

Emergencies

Keywords

Geriatrics; Emergency rooms; adverse health events prevention; Deep learning

Outcome Measures

Primary Outcomes (1)

• ED length of stay

  The length of emergencey department stay is defined as the average number of hours that patients spend in Emergency department.

  through database constitution, from September 2017 to July 2020

Secondary Outcomes (5)

• Prolonged hospital stay

  through database constitution, from September 2017 to July 2020

• Number of partciipants with at least one hospitalizations

  through database constitution, from September 2017 to July 2020

• recurrent ED visits

  through database constitution, from September 2017 to July 2020

• Number of partciipants with diagnosis of delirium

  through database constitution, from September 2017 to July 2020

• Number of partciipants with hospital death

  through database constitution, from September 2017 to July 2020

Study Arms (1)

ER2 participants

all participants of ER2 database will be included in the analysis

Other: ER2

Interventions

ER2 OTHER

No intervention, data analysis only

ER2 participants

Eligibility Criteria

• Age: 75 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• 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.

You may qualify if:

• Age above 75 years old
• Unplanned Emergency department visit

Sponsors & Collaborators

• Jewish General Hospital: lead

Study Sites (1)

Jewish General Hospital

Montreal, Quebec, H3T 1E2, Canada

Location

MeSH Terms

Conditions

Emergencies

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Olivier Beauchet, MD

  McGill University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Geriatrics

Study Record Dates

• First Submitted: December 14, 2020
• First Posted: December 22, 2020
• Study Start: February 24, 2023
• Primary Completion: February 24, 2023
• Study Completion: February 24, 2023
• Last Updated: July 23, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)