Risk assessment of chronic obstructive pulmonary disease using a Bayesian network based on a provincial survey.

The diagnosis of chronic obstructive pulmonary disease (COPD) is based on spirometry tests that are difficult to perform in some populations. We aimed to construct a risk assessment model using a Bayesian Network (BN) that would enable screening high-risk populations. A provincial survey of COPD was performed with face-to-face interviews and spirometry tests among the population aged ≥40 years in Liaoning Province, northeastern China. The potential risk factors were initially identified by multivariable logistic regression, and then a BN was built. To validate its performance, cross-validation and external dataset validation were performed, and area under the curve (AUC) and accuracy of the BN were calculated. The estimated age-adjusted prevalence of COPD in the entire population was 21.23% (95% confidence interval [CI]: 18.35%-24.11%). The logistic regression revealed that low education level (OR=2.35, p<0.001), elderly age (OR=4.19, p<0.001), ever smoking (OR=1.49, p=0.03), lower air quality satisfaction (OR=1.55, p=0.03) were associated with COPD. For the BN, frequent cough was the strongest single risk indicator of COPD (risk=0.374). The risks increased as more factors were specified, and the top risk was 0.738, which included the combination of elderly age, smoking, wheezing during sickness, and frequent cough. The cross-validation indicated that BN performed better than logistic regression, with a mean AUC of 0.85 and the optimum accuracy of 0.87 (cutoff=0.473). The BN had a favorable performance in predicting COPD risks based on questionnaires. The risks associated with the combination of several risk factors should be noted.