Development and validation of a nomogram to predict pulmonary function and the presence of chronic obstructive pulmonary disease in a Korean population

Abstract

Abstract Background Early suspicion followed by assessing lung function with spirometry could decrease the underdiagnosis of chronic obstructive pulmonary disease (COPD) in primary care. We aimed to develop a nomogram to predict the FEV1/FVC ratio and the presence of COPD. Methods We retrospectively reviewed the data of 4241 adult patients who underwent spirometry between 2013 and 2019. By linear regression analysis, variables associated with FEV1/FVC were identified in the training cohort (n = 2969). Using the variables as predictors, a nomogram was created to predict the FEV1/FVC ratio and validated in the test cohort (n = 1272). Results Older age (β coefficient [95% CI], − 0.153 [− 0.183, − 0.122]), male sex (− 1.904 [− 2.749, − 1.056]), current or past smoking history (− 3.324 [− 4.200, − 2.453]), and the presence of dyspnea (− 2.453 [− 3.612, − 1.291]) or overweight (0.894 [0.191, 1.598]) were significantly associated with the FEV1/FVC ratio. In the final testing, the developed nomogram showed a mean absolute error of 8.2% between the predicted and actual FEV1/FVC ratios. The overall performance was best when FEV1/FVC < 70% was used as a diagnostic criterion for COPD; the sensitivity, specificity, and balanced accuracy were 82.3%, 68.6%, and 75.5%, respectively. Conclusion The developed nomogram could be used to identify potential patients at risk of COPD who may need further evaluation, especially in the primary care setting where spirometry is not available.

Keywords

• Chronic obstructive pulmonary disease
• Machine learning
• Spirometry
• Primary care