Prediction of risk of cardiovascular events in patients with mild to moderate coronary artery lesions using naïve Bayesian networks

Background This prospective study integrated multiple clinical indexes and inflammatory markers associated with coronary atherosclerotic vulnerable plaque to establish a risk prediction model that can evaluate a patient with certain risk factors for the likelihood of the occurrence of a coronary heart disease event within one year. Methods This study enrolled in 2686 patients with mild to moderate coronary artery lesions. Eighty-five indexes were recorded, included baseline clinical data, laboratory studies, and procedural characteristics. During the 1-year follow-up, 233 events occurred, five patients died, four patients suffered a nonfatal myocardial infarction, four patients underwent revascularization, and 220 patients were readmitted for angina pectoris. The Risk Estimation Model and the Simplified Model were conducted using Bayesian networks and compared with the Single Factor Models. Results The area under the curve was 0.88 for the Bayesian Model and 0.85 for the Simplified Model, while the Single Factor Model had a maximum area under the curve of 0.65. Conclusion The new models can be used to assess the short-term risk of individual coronary heart disease events and may assist in guiding preventive care.