Background: Known coronary artery disease (CAD) risk scores (e.g., Framingham) estimate the CAD-related event risk rather than presence/absence of CAD. Artificial intelligence (AI) is rarely used in this context.
Aims: This study aims to evaluate the diagnostic power of AI (memetic pattern-based algorithm (MPA)) in CAD and to expand its applicability to a broader patient population.
Methods and results: Nine hundred eighty-seven patients of the Ludwigshafen Risk and Cardiovascular Health Study (LURIC) were divided into a training (n = 493) and a test population (n = 494). They were evaluated by the Basel MPA. The "training population" was further used to expand and optimize the Basel MPA, and after modifications, a final validation was carried out on the "test population." The results were compared with the Framingham Risk Score (FRS) using receiver operating curves (ROC; area-under-the-curve (AUC)). Of the 987 LURIC patients, 71% were male, age 62 ± 11 years and 68% had documented CAD. AUC of Framingham and BASEL MPA to diagnose CAD in "LURIC training" were 0.69 and 0.80, respectively. AUC of the optimized MPA in the training and test cohort were 0.88 and 0.87, respectively. The positive predictive values (PPV) of the optimized MPA for exclusion of CAD in "training" and "test" were 98 and 95%, respectively. The PPV of MPA for identification of CAD was 93 and 94%, respectively.
Conclusions: The successful use of the MPA approach has been demonstrated in a broad-risk spectrum of patients undergoing CAD evaluation, as an element of predictive, preventive, personalized medicine, and may be used instead of further non-invasive diagnostic procedures.
Keywords: Artificial intelligence-based prediction and decision-making; Coronary artery disease; Diagnosis; Memetic pattern-based algorithm; Non-invasive evaluation; PPPM; Risk scores.