title: Predicting coronary no-reflow in patients with acute ST-segment elevation myocardial infarction using Bayesian approaches.
abstract: OBJECTIVE
  The 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.
  
  
  METHODS
  STEMI 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.
  
  
  RESULTS
  A 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.
  
  
  CONCLUSIONS
  The Bayesian Model showed high sensitivity and good specificity in predicting true relations between multiple factors and the no-reflow outcome.
authors.affiliation: aDepartment of Cardiology, Capital Medical University affiliated Beijing Anzhen Hospital, Beijing Institute of Heart, Lung and Blood Vessel Disease bThe State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, China.
authors.forename: Dongfeng
authors.initials: D
authors.lastname: Zhang
authors.forename: Xiantao
authors.initials: X
authors.lastname: Song
authors.forename: Shuzheng
authors.initials: S
authors.lastname: Lv
authors.forename: Dong
authors.initials: D
authors.lastname: Li
authors.forename: Shuai
authors.initials: S
authors.lastname: Yan
authors.forename: Min
authors.initials: M
authors.lastname: Zhang
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D015331:Cohort Studies; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000628:therapy*; D054318:No-Reflow Phenomenon / Q000453:epidemiology*; D062645:Percutaneous Coronary Intervention; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies
references.citation: Am J Cardiol. 2013 Jan 15;111(2):178-84
references.pmid: 23111142
references.citation: Nat Biotechnol. 2004 Sep;22(9):1177-8
references.pmid: 15340486
references.citation: J Int Med Res. 2013 Apr;41(2):307-16
references.pmid: 23569011
references.citation: N Engl J Med. 1985 Oct 17;313(16):1018
references.pmid: 3930960
references.citation: Am J Cardiol. 2013 Jul 15;112(2):187-93
references.pmid: 23601576
references.citation: JACC Cardiovasc Interv. 2010 Jul;3(7):695-704
references.pmid: 20650430
references.citation: Bioinformatics. 2000 May;16(5):412-24
references.pmid: 10871264
references.citation: N Engl J Med. 2013 Apr 11;368(15):1446-7
references.pmid: 23473397
references.citation: J Cardiol. 2013 Aug;62(2):71-6
references.pmid: 23680004
references.citation: Catheter Cardiovasc Interv. 2008 Dec 1;72(7):950-7
references.pmid: 19021281
references.citation: Cardiology. 2013;124(3):153-60
references.pmid: 23485798
references.citation: J Am Coll Cardiol. 2009 Jul 21;54(4):281-92
references.pmid: 19608025