Development of a Diagnostic Nomogram to Predict CAP in Hospitalized Patients with AECOPD

Abstract

The purpose of this study was to establish a nomogram for predicting community-acquired pneumonia (CAP) in hospitalized patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). The retrospective cohort study included 1249 hospitalized patients with AECOPD between January 2012 and December 2019. The patients were divided into pneumonia-complicating AECOPD (pAECOPD) and non-pneumonic AECOPD (npAECOPD) groups. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to identify prognostic factors. A prognostic nomogram model was established, and the bootstrap method was used for internal validation. Discrimination and calibration of the nomogram model were evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Logistic and LASSO regression analysis showed that C-reactive protein (CRP) >10 mg/L, albumin (Alb) 50 U/L, fever, bronchiectasis, asthma, previous hospitalization for pAECOPD in the past year (Pre-H for pAECOPD), and age-adjusted Charlson score (aCCI) ≥6 were independent predictors of pAECOPD. The area under the ROC curve (AUC) of the nomogram model was 0.712 (95% CI: 0.682–0.741). The corrected AUC of internal validation was 0.700. The model had well-fitted calibration curves and good clinical usability DCA curve. A nomogram model was developed to assist clinicians in predicting the risk of pAECOPD. China Clinical Trials Registry: ChiCTR2000039959

Keywords

• chronic obstructive pulmonary disease
• exacerbations
• community-acquired pneumonia
• predictive model
• logistic regression