MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
MALBEC
1 other identifier
observational
200,000
1 country
1
Brief 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Apr 2024
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
April 1, 2024
CompletedFirst Submitted
Initial submission to the registry
April 7, 2025
CompletedFirst Posted
Study publicly available on registry
April 15, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
March 1, 2027
ExpectedStudy Completion
Last participant's last visit for all outcomes
March 1, 2027
April 15, 2025
April 1, 2025
2.9 years
April 7, 2025
April 7, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
Developing a clinical decision support tool
Developing and implementing a clinical decision support tool that integrates and visualizes results of established diagnostic variables in a dashboard
During whole study duration of 3 years
Validate machine learning (ML) models
Derive and validate ML models that integrate cardiac biomarkers with key clinical information and the digital 12-lead ECG to rapidly inform the diagnostic probability for six acute life-threatening cardiovascular conditions in patients presenting with acute chest pain and/or acute dyspnoea to the Emergency Department
During whole study duration of 3 years
Study Arms (1)
Patients with acute chest pain and/or acute dyspnoea
Patients with acute chest pain and/or acute dyspnoea
Interventions
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
Eligibility Criteria
Dataset of about 200'000 extensively characterized patients enrolled in randomized controlled trials and observational studies are used
You may qualify if:
- Acute cardiovascular disease (ACVD)
You may not qualify if:
- age \< 18 years old
- patients presenting in cardiogenic shock
- chronic terminal kidney failure requiring dialysis
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- University Hospital, Basel, Switzerlandlead
- University of Baselcollaborator
Study Sites (1)
University Hospital Basel
Basel, 4031, Switzerland
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Christian Müller, Prof. Dr. med.
University Hospital, Basel, Switzerland
- PRINCIPAL INVESTIGATOR
Jasper Boeddinghaus, PD Dr. med.
University Hospital, Basel, Switzerland
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- OTHER
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
April 7, 2025
First Posted
April 15, 2025
Study Start
April 1, 2024
Primary Completion (Estimated)
March 1, 2027
Study Completion (Estimated)
March 1, 2027
Last Updated
April 15, 2025
Record last verified: 2025-04
Data Sharing
- IPD Sharing
- Will not share