Using machine learning to improve early diagnosis of acute cardiovascular conditions
MAchine Learning to Boost the Early Diagnosis of Acute Cardiovascular Conditions
University Hospital, Basel, Switzerland · NCT06927791
This study is testing a new tool that uses advanced computer technology to help doctors quickly and accurately diagnose serious heart problems in patients who come to the emergency room with chest pain or trouble breathing.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 200000 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | University Hospital, Basel, Switzerland (other) |
| Locations | 1 site (Basel) |
| Trial ID | NCT06927791 on ClinicalTrials.gov |
What this trial studies
This project aims to develop a clinical decision support tool that integrates established diagnostic variables with machine learning models to enhance the rapid diagnosis of acute cardiovascular diseases in emergency department patients presenting with chest pain or dyspnea. By utilizing advanced techniques such as deep transfer learning and automated machine learning, the study seeks to improve diagnostic accuracy, expedite patient management, and minimize medical errors. The research builds on previous large-scale studies in cardiovascular biomarker research and precision medicine, addressing the challenges of diagnosing life-threatening conditions in a timely manner.
Who should consider this trial
Good fit: Ideal candidates for this study are patients presenting with symptoms of acute cardiovascular disease, such as chest pain or dyspnea.
Not a fit: Patients under 18 years old, those in cardiogenic shock, or individuals with chronic terminal kidney failure requiring dialysis may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could significantly improve the speed and accuracy of diagnosing acute cardiovascular conditions, potentially saving lives.
How similar studies have performed: Other studies have shown promise in using machine learning for diagnostic purposes in cardiovascular conditions, indicating a potential for success in this novel approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: • Acute cardiovascular disease (ACVD) Exclusion Criteria * age \< 18 years old * patients presenting in cardiogenic shock * chronic terminal kidney failure requiring dialysis
Where this trial is running
Basel
- University Hospital Basel — Basel, Switzerland (RECRUITING)
Study contacts
- Principal investigator: Jasper Boeddinghaus, PD Dr. med. — University Hospital, Basel, Switzerland
- Study coordinator: Jasper Boeddinghaus, PD Dr. med.
- Email: jasper.boeddinghaus@usb.ch
- Phone: +41 61 32 87897
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Acute Cardiovascular Disease, ST-segment Elevation Myocardial Infarction, NSTEMI - Non-ST Segment Elevation MI, Machine Learning, Clinical Decision Support Tool, Cardiac Biomarkers, Deep Transfer Learning, Artificial Intelligence