Clinical trial NCT05497830: Machine Learning for Risk Stratification in the Emergency Department (MARS-ED)
Official title: Machine Learning for Risk Stratification in the Emergency Department: a Pilot Clinical Trial
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Acute Pain
Emergencies
Keywords: machine learning
risk stratification
pilot
Lead sponsor: Maastricht University Medical Center
Sponsor class: OTHER
First posted: 2022-08-11
Enrollment: 1300
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

Brief 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

Eligibility criteria: Inclusion Criteria:

* Adult, defined as ≥ 18 years of age
* Assessed and treated by an internal medicine specialist (gastroenterologists included) in the ED
* Willing to give written consent, either directly or after deferred consent procedure (see section 11.2).

Exclusion Criteria:

* \<4 different laboratory results available (hematology or clinical chemistry) within the first two hours of the ED visit (calculation ML prediction score otherwise not possible)
* Unwilling to provide written consent, either directly or after deferred consent procedure (see section 11.2).

Primary outcomes:
measure: RISK-INDEX performance
timeFrame: 31 days
measure: Policy changes
timeFrame: As soon as RISK-INDEX score is presented
Secondary outcomes:
Other outcomes:
Locations:
city: Maastricht
country: Netherlands
facility: Maastricht University Medical Centre
state: Limburg
Study officials:
affiliation: Maastricht University Medical Center
name: Steven Meex, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: van Dam PMEL, van Doorn WPTM, van Gils F, Sevenich L, Lambriks L, Meex SJR, Cals JWL, Stassen PM. 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.