Predicting in-hospital death in pneumonic COPD exacerbation via BAP-65, CURB-65 and machine learning.

Abstract

IntroductionThere is no established clinical prediction model for in-hospital death among patients with pneumonic COPD exacerbation. We aimed to externally validate BAP-65 and CURB-65 and to develop a new model based on the eXtreme Gradient Boosting (XGBoost) algorithm.MethodsThis multicentre cohort study included patients aged ≥40 years with pneumonic COPD exacerbation. The input data were age, sex, activities of daily living, mental status, systolic and diastolic blood pressure, respiratory rate, heart rate, peripheral blood eosinophil count and blood urea nitrogen. The primary outcome was in-hospital death. BAP-65 and CURB-65 underwent external validation using the area under the receiver operating characteristic curve (AUROC) in the whole dataset. We used XGBoost to develop a new prediction model. We compared the AUROCs of XGBoost with that of BAP-65 and CURB-65 in the test dataset using bootstrap sampling.ResultsWe included 1190 patients with pneumonic COPD exacerbation. The in-hospital mortality was 7% (88 out of 1190). In the external validation of BAP-65 and CURB-65, the AUROCs (95% confidence interval) of BAP-65 and CURB-65 were 0.69 (0.66–0.72) and 0.69 (0.66–0.72), respectively. XGBoost showed an AUROC of 0.71 (0.62–0.81) in the test dataset. There was no significant difference in the AUROCs of XGBoost versus BAP-65 (absolute difference 0.054; 95% CI −0.057–0.16) or versus CURB-65 (absolute difference 0.0021; 95% CI −0.091–0.088).ConclusionBAP-65, CURB-65 and XGBoost showed low predictive performance for in-hospital death in pneumonic COPD exacerbation. Further large-scale studies including more variables are warranted.