Diagnostic Performance of Artificial Intelligence Algorithms in Prediction of Acute Coronary Syndrome Based on White Blood Cell Properties

Recruiting now

Conditions studied: Acute Coronary Syndrome, Angina Pectoris, NSTEMI - Non-ST Segment Elevation MI, STEMI - ST Elevation Myocardial Infarction, Unstable Angina (UA)

In brief

The goal of this observational study is to evaluate whether artificial intelligence (AI) algorithms can predict or exclude acute coronary syndrome (ACS) in adults using data generated by routine hematology testing. The main questions the study aims to answer are: * Can AI algorithms based on white blood cell (WBC) data predict or exclude ACS in subjects with suspected ACS? * Can erythrocyte (EC) and/or thrombocyte (TC) data, where available, improve or complement WBC-based AI prediction of ACS? * How does the diagnostic performance of the AI algorithms compare with high-sensitivity cardiac troponin (hs-cTn), and can the combination of AI algorithms and hs-cTn improve diagnostic performance? Participants will undergo clinical assessment and blood testing as part of usual clinical care. Their previously generated clinical information, hematology data, and hs-cTn results will be used to train and test the AI algorithms. Participation in the study does not determine the indication for coronary angiography or treatment, and no additional study-specific treatments are performed.

Key facts

Study ID
NCT06384846
Run by
RobotDreams GmbH
People needed
3350
Starts
2024-02-01
Expected to finish
2026-12-31
Last updated by the study team
2026-06-24

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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