Mario Cazzola1, Luigino Calzetta2, Maria Gabriella Matera3, Saverio Muscoli2, Paola Rogliani2, Francesco Romeo2. 1. Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. Electronic address: mario.cazzola@uniroma2.it. 2. Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy. 3. Department of Experimental Medicine, Second University of Naples, Naples, Italy.
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.
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 COPDpatients. 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 (COPDpatients undergoing coronary arteriography), control population (non-COPDpatients undergoing coronary arteriography), test population (COPDpatients 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 COPDpatients, 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 COPDpatients via a rapid, inexpensive and non-invasive approach.
Authors: Jesús Recio Iglesias; Jesús Díez-Manglano; Francisco López García; José Antonio Díaz Peromingo; Pere Almagro; José Manuel Varela Aguilar Journal: Int J Chron Obstruct Pulmon Dis Date: 2020-05-07