Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department

Paul M E L van Dam; William P T M van Doorn; Floor van Gils; Lotte Sevenich; Lars Lambriks; Steven J R Meex; Jochen W L Cals; Patricia M Stassen

doi:10.1186/s13049-024-01177-2

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department

Scand J Trauma Resusc Emerg Med. 2024 Jan 23;32(1):5. doi: 10.1186/s13049-024-01177-2.

Authors

Paul M E L van Dam¹, William P T M van Doorn², Floor van Gils³, Lotte Sevenich³, Lars Lambriks³, Steven J R Meex², Jochen W L Cals⁴, Patricia M Stassen^{3

5}

Affiliations

¹ Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +, PO Box 5800, Maastricht, 6202 AZ, The Netherlands. paul.van.dam@mumc.nl.
² Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center +, Maastricht, The Netherlands.
³ Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +, PO Box 5800, Maastricht, 6202 AZ, The Netherlands.
⁴ Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands.
⁵ School for Cardiovascular Diseases (CARIM), Maastricht University, Maastricht, The Netherlands.

Abstract

Background: Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.

Methods: The study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISK^INDEX, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISK^INDEX was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISK^INDEX to the attending physician in order to assess the extent to which clinical treatment is influenced by the results.

Discussion: This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISK^INDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models.

Trial registration: ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830 .

Keywords: Artificial intelligence; Emergency department; Implementation; Machine learning; Mortality; Prediction; Prognosis; Risk stratification.

Publication types

Clinical Trial Protocol

MeSH terms

Adult
Emergency Service, Hospital*
Humans
Machine Learning*
Pilot Projects
Prospective Studies
Randomized Controlled Trials as Topic
Risk Assessment
Technology

Associated data

ClinicalTrials.gov/NCT05497830