Predicting coronary no-reflow in patients with acute ST-segment elevation myocardial infarction using Bayesian approaches.

Abstract

ObjectiveThe no-reflow phenomenon is associated with a worse prognosis at follow-up for ST-segment elevation myocardial infarction (STEMI) patients with a primary percutaneous coronary intervention. To date, there is no effective method to predict no-reflow. The aim of this study was to establish a predictive system to evaluate the risk of no-reflow by integrating multiple types of information using Bayesian methods.Patients and methodsSTEMI patients undergoing primary percutaneous coronary intervention within 12 h from the symptom onset between January 2008 and May 2013 were initially screened from the registry database of Anzhen Hospital (Beijing, China). Baseline clinical data, laboratory studies, and procedural characteristics were recorded. The Bayesian Model and Ten-Factor Model were used and compared with the Single-Factor Models. A receiver operating characteristic curve was used to show the efficacy by presenting both sensitivity and specificity for different cutoff points.ResultsA total of 1059 consecutive STEMI patients were enrolled. Seventy-nine factors were collected to assess the confidence of the no-reflow phenomenon. The combined likelihood ratios were used to measure the reliability of the no-reflow phenomenon. The area under the curve (AUC) was 0.85 and 0.79 for the Bayesian Model and Ten-Factors Model, respectively, whereas the Single-Factor Model yielded a maximum AUC of 0.67.ConclusionThe Bayesian Model showed high sensitivity and good specificity in predicting true relations between multiple factors and the no-reflow outcome.