MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Machine learning based development of a diagnostic tool for acute cardiovascular disease.
- Who it may be relevant to
- Registry conditions: Acute Cardiovascular Disease, ST-segment Elevation Myocardial Infarction (STEMI), NSTEMI - Non-ST Segment Elevation MI. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Switzerland
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
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.
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
Primary outcome measures
- Developing a clinical decision support tool [Time frame: During whole study duration of 3 years]
- Validate machine learning (ML) models [Time frame: During whole study duration of 3 years]
Eligibility criteria
Inclusion criteria
- Acute cardiovascular disease (ACVD)
Exclusion criteria
- age < 18 years old
- patients presenting in cardiogenic shock
- chronic terminal kidney failure requiring dialysis
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Switzerland · 1 center
- University Hospital Basel — Basel
Identifiers
NCT: NCT06927791 · kt25boeddinghaus