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| ID | Type | Description | Link |
|---|---|---|---|
| METC 21-068 | Other Identifier | METC |
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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
See our protocol paper, PMID 38263188
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Standard care | No Intervention | Routine clinical care. Physicians will actively be asked to self-report their clinical impression of each included patient and policy will be monitored. | |
| RISKINDEX | Experimental | 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| RISK-INDEX | Other | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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). | 31 days |
| 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. | As soon as RISK-INDEX score is presented |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Steven Meex, PhD | Maastricht University Medical Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Maastricht University Medical Centre | Maastricht | Limburg | 6229 HX | Netherlands |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38263188 | Result | 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. | |
| 41326390 |
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| ID | Term |
|---|---|
| D059787 | Acute Pain |
| D004630 | Emergencies |
| ID | Term |
|---|---|
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| Derived |
| van Dam PMEL, van Doorn WPTM, Sevenich L, Lambriks L, Cals JWL, Bekers O, Stassen PM, Meex SJR. Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial. Nat Commun. 2025 Dec 1;17(1):242. doi: 10.1038/s41467-025-66947-7. |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |