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CompletedNCT05497830MARS-EDUpdated Nov 26, 2024

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

An interventional study of RISK-INDEX in Acute Pain and Emergencies, sponsored by Maastricht University Medical Center. Completed at 1 site in Netherlands. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-11-26.

Sponsored by Maastricht University Medical Center · Not applicable, Interventional, and Diagnostic

Phase
Not applicable
Study type
Interventional
Enrollment
1,300
Allocation
Randomized
Ages
18 Years and older
Sex
All
01

Study 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
Read the detailed description

See our protocol paper, PMID 38263188

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Conditions studied

  • Acute Pain
  • Emergencies

Keywords

  • machine learning
  • risk stratification
  • pilot
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In context

Emergencies

1,692 studies on the registry are indexed under Emergencies; 333 are open to participants now.

This study's enrollment of 1,300 is above the median of 145 across 931 interventional studies indexed under Emergencies.

Browse Emergencies studies →

Lead sponsor

Maastricht University Medical Center is the lead sponsor of 835 studies on the registry; 122 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
Yes

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

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).
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Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Randomized
Intervention model
Parallel assignment
Masking
None (open label)
Enrollment
1,300 participants (actual)

Study arms

  • No intervention
    Standard care

    Routine clinical care. Physicians will actively be asked to self-report their clinical impression of each included patient and policy will be monitored.

  • Experimental
    RISKINDEX

    Routine clinical care. Physicians will actively be asked to self-report their clinical impression of each included patient and policy will be monitored. In the intervention group, physicians will be presented with the RISKINDEX. Subsequently, self-report will again be initiated to evaluate the physicians' response to the ML score and possible policy changes due to the intervention.

    Other: RISK-INDEX

Interventions

  • OtherRISK-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.

06

What researchers measure

Primary outcomes

  1. RISK-INDEX performance

    Discriminatory performance of ML risk score to predict 31-day mortality. This will be calculated using an area under the receiver operating characteristic curves (AUC).

    Time frame: 31 days

  2. Policy changes

    Policy changes after presentation of RISK-INDEX. This will be assessed by a filled out questionnaire by the physician where they state whether a policy change has been made as a result of the RISK-INDEX outcome.

    Time frame: As soon as RISK-INDEX score is presented

07

Study locations

1 site
  • Maastricht University Medical Centre
    Maastricht, Limburg 6229 HX, Netherlands
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References and documents

Publications

  • 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. PubMed 38263188 ↗

Individual participant data

Plan to share: No

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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Nov 26, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT05497830
Lead sponsor
Maastricht University Medical Center
Responsible party
Sponsor
First posted
Aug 11, 2022
Start date
Sep 12, 2022
Primary completion
Nov 1, 2024
Completion
Nov 1, 2024
Last update
Nov 26, 2024

Study contacts

Steven Meex, PhD
principal investigator · Maastricht University Medical Center

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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