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.
Male or female, aged 18 years or above.
Participant is willing and able to give informed consent for participation in the study.
Collection of WBC and hs-cTn data must be possible.
Criteria for timing of blood sampling for collection of WBC and hs-cTn data must be fulfilled, where applicable.
Age below 18 years.
Subject refuses informed consent.
Collection of WBC and hs-cTn data is not possible.
Criteria for timing of blood sampling for collection of WBC and hs-cTn data cannot be fulfilled.