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

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

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
NL-OMON
Registry ID
NL-OMON54014
Enrollment
1300
Registered
2022-06-20
Start date
2022-09-12
Completion date
Unknown
Last updated
2025-08-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acute aandoeningen bij patiënten op de SEH n.v.t.

Interventions

Physicians will be presented with the ML risk score of the patients they are actively treating, directly after assessment of regular diagnostics has taken place.

Sponsors

Medisch Universitair Ziekenhuis Maastricht
Lead Sponsor

Eligibility

Age
18 Years to 99 Years

Inclusion criteria

Inclusion criteria: - Adult, defined as >= 18 years of age - Assessed and treated by an internal medicine specialist in the ED - Willing to give written consent, either directly or after deferred consent procedure

Exclusion criteria

Exclusion criteria: -

Design outcomes

Primary

MeasureTime frame
- Calculated ML risk scores and observed mortality, to evaluate discriminatory performance of ML risk score to predict 31-day mortality. - Physicians self-reported policy changes to evaluate whether presentation of the ML risk score causes changes in clinical decision making. Policy changes include treatment policy, requesting ancillary investigations, treatment restrictions (i.e., no intubation or resuscitation).

Secondary

MeasureTime frame
- Clinical endpoints such as 31-day mortality, ICU and MC admission and readmission will be compared between the control an intervention group to evaluate differences. - Diagnostic performance of other clinical risk scores and physicians will be compared to the ML score.

Countries

Netherlands

Outcome results

None listed

Source: NL-OMON (via WHO ICTRP)