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

Acute Pain Clinical Trial

— MARS-ED  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05497830
• Other study ID #: NL78478.068.21
• Secondary ID: METC 21-068
• Status: Recruiting
• Phase: N/A
• Start date: September 12, 2022
• Est. completion date: December 1, 2024

Study information

• Verified date: October 2023
• Source: Maastricht University Medical Center
• Contact: Steven Meex, PhD
• Phone: +31 (0)43 3874709
• Email: steven.meex[@]mumc.nl
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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

Description:

See our protocol paper, PMID 38263188

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1300
• Est. completion date: December 1, 2024
• Est. primary completion date: August 1, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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).

Related Conditions & MeSH terms

• Acute Pain
• Emergencies

Intervention

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.

Locations

Country	Name	City	State
Netherlands	Maastricht University Medical Centre	Maastricht	Limburg

Sponsors (1)

Lead Sponsor	Collaborator
Maastricht University Medical Center

Country where clinical trial is conducted

Netherlands, 

References & Publications (3)

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. — View Citation

van Doorn WPTM, Helmich F, van Dam PMEL, Jacobs LHJ, Stassen PM, Bekers O, Meex SJR. Explainable Machine Learning Models for Rapid Risk Stratification in the Emergency Department: A Multicenter Study. J Appl Lab Med. 2024 Mar 1;9(2):212-222. doi: 10.1093/jalm/jfad094. — View Citation

van Doorn WPTM, Stassen PM, Borggreve HF, Schalkwijk MJ, Stoffers J, Bekers O, Meex SJR. A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis. PLoS One. 2021 Jan 19;16(1):e0245157. doi: 10.1371/journal.pone.0245157. eCollection 2021. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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
Primary	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