NCT05497830

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

Machine Learning for Risk Stratification in the Emergency Department: A Pilot Clinical Trial

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 1,300 (actual)
• Sponsor: Maastricht University Medical Center · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

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

Detailed description

See our protocol paper, PMID 38263188

Conditions

• Acute Pain
• Emergencies

Interventions

Type	Name	Description
OTHER	RISK-INDEX	Presentation of RISKINDEX to the physician after approximately 2 hours. The ML RISKINDEX is a prediction model based on laboratory data from the ED. It is based on date of birth, sex and at least four laboratory data which are sampled within the first two hours of the ED visit. Laboratory data that are used as input include samples that are commonly drawn in patients that require treatment from an internal medicine physician, such as urea, albumin, C-reactive protein (CRP), lactate and bilirubin.

Timeline

• Start date: 2022-09-12
• Primary completion: 2024-11-01
• Completion: 2024-11-01
• First posted: 2022-08-11
• Last updated: 2024-11-26

Locations

1 site across 1 country: Netherlands