title: Prediction of risk of cardiovascular events in patients with mild to moderate coronary artery lesions using naïve Bayesian networks.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Xian-Tao
authors.initials: XT
authors.lastname: Song
authors.affiliation: Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
authors.forename: Yun-Dai
authors.initials: YD
authors.lastname: Chen
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Xing-Sheng
authors.initials: XS
authors.lastname: Yang
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Xu
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Min
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Tan
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Yuan
authors.affiliation: The State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Li
authors.affiliation: Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
authors.forename: Shu-Zheng
authors.initials: SZ
authors.lastname: Lyu
keywords: Bayesian networks; Cardiovascular events; Prediction