title: Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients.
abstract: BACKGROUND
  Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients.
  
  
  METHODS
  We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed.
  
  
  RESULTS
  In training population the variables gender (men/women OR: 1.7, 95%CI: 1.237-2.5, P < 0.05), dyslipidemia (OR: 1.8, 95%CI: 1.2-2.5, P < 0.01) and smoking habit (OR: 1.5, 95%CI: 1.2-1.9, P < 0.001) were significantly associated with CAD in COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%CI: 77.78-84.71) and an AUC of 0.81 (95%CI: 0.78-0.85) for the validation set.
  
  
  CONCLUSIONS
  The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach.
authors.affiliation: Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. Electronic address: mario.cazzola@uniroma2.it.
authors.forename: Mario
authors.initials: M
authors.lastname: Cazzola
authors.affiliation: Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy.
authors.forename: Luigino
authors.initials: L
authors.lastname: Calzetta
authors.affiliation: Department of Experimental Medicine, Second University of Naples, Naples, Italy.
authors.forename: Maria Gabriella
authors.initials: MG
authors.lastname: Matera
authors.affiliation: Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy.
authors.forename: Saverio
authors.initials: S
authors.lastname: Muscoli
authors.affiliation: Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Rogliani
authors.affiliation: Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Romeo
mesh_terms: D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000175:diagnosis* / Q000453:epidemiology / Q000209:etiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D009017:Morbidity / Q000639:trends; D011237:Predictive Value of Tests; D011379:Prognosis; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications* / Q000175:diagnosis* / Q000453:epidemiology; D012372:ROC Curve; D012129:Respiratory Function Tests; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors
keywords: COPD; Comorbidities; Coronary artery disease; Predictive algorithm