Bayesian predictors of very poor health related quality of life and mortality in patients with COPD

• Published in: BMC medical informatics and decision making 2013-03, Vol.13 (1), p.34-34, Article 34
• Main Authors: Ryynänen, Olli-Pekka, Soini, Erkki J, Lindqvist, Ari, Kilpeläinen, Maritta, Laitinen, Tarja
• Format: Article
• Language: English
• Subjects: Acute coronary syndromes; Age; Aged; Alcoholism; Angina pectoris; Atrial fibrillation; Bayes Theorem; Bayesian analysis; Bayesian statistical decision theory; Chronic illnesses; Chronic obstructive pulmonary disease; Clinical medicine; Comparative analysis; Development and progression; Disease; Family medical history; Female; Forecasts and trends; Health aspects; Health Behavior; Hospitalization; Hospitals; Humans; Logistic Models; Lung diseases, Obstructive; Male; Medical research; Medicine, Experimental; Middle Aged; Morbidity; Mortality; Patient outcomes; Prognosis; Pulmonary Disease, Chronic Obstructive - mortality; Pulmonary Disease, Chronic Obstructive - physiopathology; Quality of Life; Respiratory Function Tests; Studies; Survival Analysis
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/1472-6947-13-34

Chronic obstructive pulmonary disease (COPD) is associated with increased mortality and poor health-related quality of life (HRQoL) compared with the general population. The objective of this study was to identify clinical characteristics which predict mortality and very poor HRQoL among the COPD population and to develop a Bayesian prediction model. The data consisted of 738 patients with COPD who had visited the Pulmonary Clinic of the Helsinki and Turku University Hospitals during 1995-2006. The data set contained 49 potential predictor variables and two outcome variables: survival (dead/alive) and HRQoL measured with a 15D instrument (very poor HRQoL < 0.70 vs. typical HRQoL ≥ 0.70).In the first phase of model validation we randomly divided the material into a training set (n = 538), and a test set (n = 200). This procedure was repeated ten times in random fashion to obtain independently created training sets and corresponding test sets. Modeling was performed by using the training set, and each model was tested by using the corresponding test set, repeated in each training set. In the second phase the final model was created by using the total material and eighteen most predictive variables. The performance of six logistic regressions approaches were shown for comparison purposes. In the final model, the following variables were associated with mortality or very poor HRQoL: age at onset, cerebrovascular disease, diabetes, alcohol abuse, cancer, psychiatric disease, body mass index, Forced Expiratory Volume (FEV1) % of predicted, atrial fibrillation, and prolonged QT time in ECG. The prediction accuracy of the model was 77%, sensitivity 0.30, specificity 0.95, positive predictive value 0.68, negative predictive value 0.78, and area under the ROC curve 0.69. While the sensitivity of the model reminded limited, good specificity, moderate accuracy, comparable or better performance in classification and better performance in variable selection and data usage in comparison to the logistic regression approaches, and positive and negative predictive values indicate that the model has potential in predicting mortality and very poor HRQoL in COPD patients. We developed a Bayesian prediction model which is potentially useful in predicting mortality and very poor HRQoL in patients with COPD.