ER2 and Deep Learning for Prediction of Adverse Health Outcomes

Study Overview
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Brief Summary
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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.

Official Title
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Emergency Room Evaluation for Older Users of Emergency Departments: Predicting Adverse Health Outcomes With Deep Learning Algorithms

Conditions
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Emergencies

Intervention / Treatment
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* Other: ER2


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age above 75 years old Unplanned Emergency department visit Exclusion Criteria: Do not meet inclusion criteria

Ages Eligible for Study
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Minimum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| ER2 participants<br>all participants of ER2 database will be included in the analysis | Other: ER2<br>* No intervention, data analysis only<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Prolonged hospital stay | The prolonged length of hospital stay is defined as a stay above the average number of days that patients spend in hospital | through database constitution, from September 2017 to July 2020 | 
| Number of partciipants with at least one hospitalizations | Defined as the admission in hospital after an admission in Emergency department | through database constitution, from September 2017 to July 2020 | 
| recurrent ED visits | Defined as all the Emergency department recurrent visit within 30 days | through database constitution, from September 2017 to July 2020 | 
| Number of partciipants with diagnosis of delirium | Defined as a diagnosis of delirium in teh medical chart of the patient | through database constitution, from September 2017 to July 2020 | 
| Number of partciipants with hospital death | Defined as a reported death during hospitalization | through database constitution, from September 2017 to July 2020 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Geriatrics, Emergency rooms, adverse health events prevention, Deep learning