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MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions

NCT06927791 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 200000

Last updated 2025-04-15

No results posted yet for this study

Summary

The research project aims to develop clinical decision support tools integrating established diagnostic variables and machine learning (ML) models for rapid diagnosis of acute life-threatening cardiovascular conditions in emergency department (ED) patients with chest pain or dyspnea with the ultimate goal of Improved diagnostic accuracy, faster patient management, and reduced medical errors.

Conditions

  • Acute Cardiovascular Disease
  • ST-segment Elevation Myocardial Infarction (STEMI)
  • NSTEMI - Non-ST Segment Elevation MI

Interventions

OTHER

Machine learning based development of a diagnostic tool for acute cardiovascular disease

MALBEC will be delivered through five integrated work packages (WP) encompassing: (0) platform development and implementation, (1) data pooling, (2) model development, (3) performance comparison, (4) performance validation, and (5) platform plugin

Sponsors & Collaborators

  • University of Basel

    collaborator OTHER

  • University Hospital, Basel, Switzerland

    lead OTHER

Principal Investigators

  • Christian Müller, Prof. Dr. med. · University Hospital, Basel, Switzerland

  • Jasper Boeddinghaus, PD Dr. med. · University Hospital, Basel, Switzerland

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-04-01
Primary Completion
2027-03-31
Completion
2027-03-31

Countries

  • Switzerland

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06927791 on ClinicalTrials.gov