NCT05497830: MARS-ED

Machine Learning for Risk Stratification in the Emergency Department (MARS-ED)

Completed NA Last updated 26 November 2024

What this trial tests

NA trial testing RISK-INDEX in Acute Pain in 1,300 participants. Completed in 1 November 2024.

Timeline

12 September 2022

Primary endpoint
1 November 2024

1 November 2024

Quick facts

Lead sponsor	Maastricht University Medical Center
Phase	NA
Status	Completed
Study type	INTERVENTIONAL
Allocation	randomized
Design	parallel
Masking	none
Primary purpose	diagnostic
Enrollment	1,300
Start date	12 September 2022
Primary completion	1 November 2024
Estimated completion	1 November 2024
Sites	1 location across Netherlands

Drugs / interventions tested

• RISK-INDEX

Conditions studied

• Acute Pain — all drugs for Acute Pain →
• Emergencies — all drugs for Emergencies →

Sponsor

Maastricht University Medical Center

Who can join

18 and older, any sex, with Acute Pain or Emergencies. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Rationale

Identifying emergency department (ED) patients at high and low risk shortly after admission could help decision-making regarding patient care. Several clinical risk scores and triage systems for stratification of patients have been developed, but often underperform in clinical practice. Moreover, most of these risk scores only have been diagnostically validated in an observational cohort, but never have been evaluated for their actual clinical impact. In a recent retrospective study that was conducted in the Maastricht University Medical Center (MUMC+), a novel clinical risk score, the RISKINDEX, was introduced that predicted 31-day mortality of sepsis patients presenting to an ED. The RISKINDEX hereby also outperformed internal medicine specialists. Observational follow-up studies underlined the potential of the risk score. However, it remains unknown to what extent these models have any beneficial value when it is actually implemented in clinical practice.

Objective

To determine the diagnostic accuracy, policy changes and clinical impact of the RISKINDEX as basis to conduct a large scale, multi-center randomised trial.

Study design

The MARS-ED study is designed as a multi-center, randomized, open-label, non-inferiority pilot clinical trial.

Study population

Adult patients who are assessed and treated by an internal medicine specialist in the ED of whom a minimum of 4 different laboratory results (hematology or clinical chemistry, required for calculation of ML risk score) are available within the first two hours of the ED visit.

Intervention

Physicians will be presented with the ML risk score (the RISKINDEX) of the patients they are actively treating, directly after assessment of regular diagnostics has taken place.

Main study parameters

Primary

\- Diagnostic accuracy, policy changes and clinical impact of a novel clinical risk score (the RISKINDEX)

Secondary

* Policy changes due to presentation of ML score (treatment policy, requesting ancillary investigations, treatment restrictions (i.e., no intubation or resuscitation)
* Intensive care (ICU) and medium care (MC) admission
* Length of admission
* Mortality within 31 days
* Readmission
* Patient preference
* Feasibility of novel clinical risk score

Publications & conference data

2 peer-reviewed publications reference this trial (live from Europe PMC):

1. 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.
   van Dam PMEL, van Doorn WPTM, van Gils F, Sevenich L, et al · · 2024 · cited 5× · PMID 38263188 · DOI 10.1186/s13049-024-01177-2
2. Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial.
   van Dam PMEL, van Doorn WPTM, Sevenich L, Lambriks L, et al · · 2025 · PMID 41326390 · DOI 10.1038/s41467-025-66947-7