Emergency admission parameters for predicting in-hospital mortality in patients with acute exacerbations of chronic obstructive pulmonary disease with hypercapnic respiratory failure

BMC Pulmonary Medicine

Saibin Wang¹  Sunying Wu²  Han Zheng²  Lijun Chen²  Lan Chen³ 

[1] Department of Respiratory Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang Province, China;Emergency Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang Province, China;Nursing Education Department, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua Municipal Central Hospital, Jinhua, Zhejiang Province, China;

关键词: Acute exacerbation of chronic obstructive pulmonary disease;    Hypercapnic respiratory failure;    Nomogram;    Mortality risk;   

DOI  :  10.1186/s12890-021-01624-1

来源: Springer

【 摘 要 】

BackgroundAcute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common presentation in emergency departments (ED) that can be fatal. This study aimed to develop a mortality risk assessment model for patients presenting to the ED with AECOPD and hypercapnic respiratory failure.MethodsWe analysed 601 participants who were presented to an ED of a tertiary hospital with AECOPD between 2018 and 2020. Patient demographics, vital signs, and altered mental status were assessed on admission; moreover, the initial laboratory findings and major comorbidities were assessed. We used least absolute shrinkage and selection operator (LASSO) regression to identify predictors for establishing a nomogram for in-hospital mortality. Predictive ability was assessed using the area under the receiver operating curve (AUC). A 500 bootstrap method was applied for internal validation; moreover, the model’s clinical utility was evaluated using decision curve analysis (DCA). Additionally, the nomogram was compared with other prognostic models, including CRB65, CURB65, BAP65, and NEWS.ResultsAmong the 601 patients, 19 (3.16%) died during hospitalization. LASSO regression analysis identified 7 variables, including respiratory rate, PCO2, lactic acid, blood urea nitrogen, haemoglobin, platelet distribution width, and platelet count. These 7 variables and the variable of concomitant pneumonia were used to establish a predictive model. The nomogram showed good calibration and discrimination for mortality (AUC 0.940; 95% CI 0.895–0.985), which was higher than that of previous models. The DCA showed that our nomogram had clinical utility.ConclusionsOur nomogram, which is based on clinical variables that can be easily obtained at presentation, showed favourable predictive accuracy for mortality in patients with AECOPD with hypercapnic respiratory failure.

【 授权许可】

CC BY