MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions (MALBEC)

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.

Study Overview

• Status: Recruiting
• Conditions: NSTEMI - Non-ST Segment Elevation MI; ST-segment Elevation Myocardial Infarction (STEMI); Acute Cardiovascular Disease
• Intervention / Treatment: Other: Machine learning based development of a diagnostic tool for acute cardiovascular disease

Detailed Description

Current State of Research in the Field

Acute cardiovascular disease (ACVD) is the leading cause of death in Switzerland and Europe, responsible for 29% of deaths in Switzerland and 36% across Europe. The increasing prevalence of ACVD, including acute myocardial infarction (AMI), acute heart failure (AHF), pulmonary embolism (PE), and acute aortic syndromes (AAS), places a significant burden on healthcare systems. Diagnosing these conditions in emergency departments (EDs) is challenging due to overlapping symptoms and the need for rapid, accurate decision-making.

The introduction of cardiovascular biomarkers, including high-sensitivity cardiac troponin, B-type natriuretic peptide, and D-dimer has revolutionized early diagnosis. These biomarkers, alongside clinical assessments and electrocardiograms (ECGs), are now essential diagnostic tools. However, current diagnostic algorithms have still tremendous limitations.

Recent advances in machine learning (ML) and deep learning (DL) offer opportunities to improve diagnosis. ML-based ECG interpretation and deep transferable learning (DTL) techniques could enhance diagnostic accuracy by integrating complex ECG and biomarker data. AutoML approaches can further refine these models, reducing human error and improving clinical workflows.

The research team has conducted multiple large-scale studies leading to significant advancements in cardiovascular biomarker research and precision medicine. Their contributions include:

• Validation of the MI3 model, which uses ML to improve NSTEMI
• Introduction of the BASEL ECG Score, a quantitative tool that enhances NSTEMI diagnosis.
• Validation of CoDE-ACS, an ML-based clinical decision support-tool that predicts the probability of NSTEMI more effectively than standard cardiac troponin thresholds.

The team is now focussing on integrating ECG data with biomarkers using AI/ML to enhance accuracy and automate decision-making. Collaboration with international experts has enabled the successful application of neural networks to ECG interpretation. The next steps include:

• Refining ML-based ECG interpretation to incorporate non-additive effects.
• Expanding ML models to include multiple cardiovascular conditions beyond AMI.
• Integrating these AI-driven tools into clinical workflows and electronic health records.

This research aims to revolutionise cardiovascular diagnostics by leveraging AI and ML for more precise, faster, and clinically relevant decision-making.

Objectives:

1. Develop and implement a clinical decision support tool that visualizes key diagnostic data.
2. Train and validate ML models to diagnose acute cardiovascular diseases (ACVD).
3. Compare ML model performance with existing diagnostic algorithms.
4. Validate ML models in large international clinical trials.
5. Integrate ML models into the electronic patient record at the University Hospital Basel.

• Study Type: Observational
• Enrollment (Estimated): 200000

Contacts and Locations

• Study Contact: Name: Jasper Boeddinghaus, PD Dr. med.; Phone Number: +41 61 32 87897; Email: Jasper.Boeddinghaus@usb.ch
• Study Contact Backup: Name: Ivo Strebel, PhD; Email: ivo.strebel@usb.ch

Study Locations

• Switzerland
  • Basel, Switzerland, 4031
    • Recruiting
    • University Hospital Basel
    • Contact: Jasper Boeddinghaus, PD Dr. med; Phone Number: +41 61 32 87897; Email: jasper.boeddinghaus@usb.ch

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Dataset of about 200'000 extensively characterized patients enrolled in randomized controlled trials and observational studies are used

Description

Inclusion Criteria:

• Acute cardiovascular disease (ACVD)

Exclusion Criteria

• age < 18 years old
• patients presenting in cardiogenic shock
• chronic terminal kidney failure requiring dialysis

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Patients with acute chest pain and/or acute dyspnoea	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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: University Hospital, Basel, Switzerland
• Collaborators: University of Basel
• Investigators: Study Director: Christian Müller, Prof. Dr. med., University Hospital, Basel, Switzerland; Principal Investigator: Jasper Boeddinghaus, PD Dr. med., University Hospital, Basel, Switzerland

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2024
• Primary Completion (Estimated): March 1, 2027
• Study Completion (Estimated): March 1, 2027

Study Registration Dates

• First Submitted: April 7, 2025
• First Submitted That Met QC Criteria: April 7, 2025
• First Posted (Actual): April 15, 2025

Study Record Updates

• Last Update Posted (Actual): April 15, 2025
• Last Update Submitted That Met QC Criteria: April 7, 2025
• Last Verified: April 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Machine Learning; Cardiac Biomarkers; Clinical Decision Support Tool; Deep Transfer Learning (DTL); Acute cardiovascular conditions
• Additional Relevant MeSH Terms: Vascular Diseases; Pathologic Processes; Heart Diseases; Necrosis; Myocardial Ischemia; Ischemia; ST Elevation Myocardial Infarction; Cardiovascular Diseases; Myocardial Infarction; Infarction
• Other Study ID Numbers: kt25boeddinghaus

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No