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.
Patients recruited at LKH-Universitätsklinikum Graz / Medical University of Graz, including subjects presenting to the emergency department or facilities of the Department of Cardiology, including the cardiac catheter laboratory and outpatient facilities. The study population includes adults with suspected acute coronary syndrome as well as subjects with no or stable angina pectoris depending on cohort assignment.
Case-control
Prospective
Not listed
Training of AI models
Diagnostic performance of AI models in predicting ACS, evaluated by area under curve (AUC) under the receiver operating characteristic (ROC) curve
Testing of AI models
Diagnostic performance of AI models in predicting ACS, evaluated by AUC under ROC curve ; Specificity and sensitivity of AI models to predict ACS in subjects with suspected ACS, calculated from AUC under ROC curve
Training of AI models
Sensitivity of AI models to predict ACS ; Specificity of AI models to predict ACS
Testing of AI models:
* Sensitivity of AI models to predict ACS * Specificity of AI models to predict ACS * Sensitivity of hs-cTn to predict ACS * Specificity of hs-cTn to predict ACS * Combined sensitivity of AI models and hs-cTn to predict ACS * Combined specificity of AI models and hs-cTn to predict ACS * AUC under ROC curve of hs-cTn predicting ACS * AUC under ROC curve of AI models and hs-cTn predicting ACS * Difference in predicting ACS between hs-cTn and AI models using AUC under ROC curve
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