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Development of a Nomogram to Predict the Risk of 30-Day Re-Exacerbation for Patients Hospitalized for Acute Exacerbation of Chronic Obstructive Pulmonary Disease.
COPD. 2019 May 16;:1-8
Authors: Hu WP, Lhamo T, Liu D, Hang JQ, Zhang FY, Zuo YH, Zeng YY, Zhang J
Abstract
Acute exacerbation (AE) is the main cause of increased disability and mortality for patients with Chronic Obstructive Pulmonary Disease (COPD). Short-term re-exacerbation after discharge is common for in-hospital patients with AECOPD. Thus, we aimed to design a scoring system to effectively predict the 30-day re-exacerbation using simple and easily accessible variables. We retrospectively enrolled 686 cases hospitalized for AECOPD in two Chinese hospitals from 2005 to 2017. A variety of parameters were collected like demographics, clinical manifestations and treatments in stable and AE period. The optimal subset of covariates in the multivariate logistic analysis was identified by the smallest Akaike Information Criterion (AIC) and was further used to develop a practical and reliable nomogram to predict the 30-day re-exacerbation. The efficacy of the nomogram was internally validated by concordance index (C-index) and a calibration plot. The incidence of 30-day re-exacerbation was 15.8%. Based on the smallest AIC, eight easily-accessible parameters were included in the nomogram, including sex, COPD assessment test (CAT) scores, AE with respiratory failure in the previous year, new purulent sputum, new cardiovascular events, combined antibiotic therapy, theophylline therapy for AE and ICU admission. Our nomogram revealed good discriminative ability with the C-index of 0.702. The calibration curve showed good agreement between nomogram-predicted probability and actual observation. Incorporating eight common variables, a nomogram for 30-day re-exacerbation after discharge with high predictive performance was constructed for patients with AECOPD, which was helpful in predicting individualized risk of re-exacerbation and offering individualized post-discharge support.
PMID: 31094228 [PubMed - as supplied by publisher]