Clinical trial NCT06927791: MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
Official title: MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Acute Cardiovascular Disease
ST-segment Elevation Myocardial Infarction (STEMI)
NSTEMI - Non-ST Segment Elevation MI
Keywords: Machine Learning
Clinical Decision Support Tool
Cardiac Biomarkers
Deep Transfer Learning (DTL)
Artificial Intelligence
Acute cardiovascular conditions
Lead sponsor: University Hospital, Basel, Switzerland
Sponsor class: OTHER
Collaborators: name: University of Basel
First posted: 2025-04-15
Enrollment: 200000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: PROBABILITY_SAMPLE
Study population: Dataset of about 200'000 extensively characterized patients enrolled in randomized controlled trials and observational studies are used

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.

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.

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

Primary outcomes:
measure: Developing a clinical decision support tool
timeFrame: During whole study duration of 3 years
measure: Validate machine learning (ML) models
timeFrame: During whole study duration of 3 years
Secondary outcomes:
Other outcomes:
Locations:
city: Basel
country: Switzerland
facility: University Hospital Basel
Study officials:
affiliation: University Hospital, Basel, Switzerland
name: Christian Müller, Prof. Dr. med.
role: STUDY_DIRECTOR
affiliation: University Hospital, Basel, Switzerland
name: Jasper Boeddinghaus, PD Dr. med.
role: PRINCIPAL_INVESTIGATOR
References: