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

NCT ID: NCT05497830

Last Updated: 2024-11-26

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 1300 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-09-12
• Study Completion Date: 2024-11-01

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

Conditions

Acute Pain; Emergencies

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

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.

Group Type: NO_INTERVENTION

No interventions assigned to this group

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.

Group Type: EXPERIMENTAL

RISK-INDEX

Intervention Type: 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.

Interventions

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.

Intervention Type: OTHER

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).

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Maastricht University Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Steven Meex, PhD

Role: PRINCIPAL_INVESTIGATOR

Maastricht University Medical Center

Locations

Maastricht University Medical Centre

Maastricht, Limburg, Netherlands

Countries

Netherlands

References

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.

Reference Type: BACKGROUND

PMID: 38263188 (View on PubMed)

Other Identifiers

METC 21-068

Identifier Type: OTHER

Identifier Source: secondary_id

NL78478.068.21

Identifier Type: -

Identifier Source: org_study_id