Development of a nomogram for predicting the presence of combined pulmonary fibrosis and emphysema

Abstract

Abstract Background In the clinical management of patients with combined pulmonary fibrosis and emphysema (CPFE), early recognition and appropriate treatment is essential. This study was designed to develop an accurate prognostic nomogram model to predict the presence of CPFE. Methods We retrospectively enrolled 85 patients with CPFE and 128 patients with idiopathic pulmonary fibrosis (IPF) between January 2015 and January 2020. Clinical characteristics were compared between groups. A multivariable logistic regression analysis was performed to identify risk factors for CPFE. Then, and a nomogram to predict the presence of CPFE was constructed for clinical use. Concordance index (C-index), area under the receiver operating characteristic curve (AUC), and calibration plot was used to evaluate the efficiency of the nomogram. Results Compared to the IPF group, the proportion of patients with male, smoking and allergies were significantly higher in the CPFE group. In terms of pulmonary function tests, patients with CPFE had lower FEV1/FVC%, DLCO/VA% pred, and higher RV, RV%pred, VC, VC%pred, TLC%pred, VA, TLC, TLC%pred, FVC, FVC%pred and FEV1 with significant difference than the other group. Positive correlation was found between DLCO and VA%, RV%, TLC% in patients with IPF but not in patients with CPFE. By multivariate analysis, male, smoking, allergies, FEV1/FVC% and DLCO/VA%pred were identified as independent predictors of the presence of CPFE. The nomogram was then developed using these five variables. After 1000 internal validations of bootstrap resampling, the C-index of the nomogram was 0.863 (95% CI 0.795–0.931) and the AUC was 0.839 (95% CI 0.764–0.913). Moreover, the calibration plot showed good concordance of incidence of CPFE between nomogram prediction and actual observation (Hosmer–Lemeshow test: P = 0.307). Conclusions Patients of CPFE have a characteristic lung function profile including relatively preserved lung volumes and ventilating function, contrasting with a disproportionate reduction of carbon monoxide transfer. By incorporating clinical risk factors, we created a nomogram to predict the presence of CPFE, which may serve as a potential tool to guide personalized treatment.

Keywords

• CPFE
• IPF
• Nomogram
• Risk factors
• Multivariable logistic regression analysis