Predicting Outcomes after Hospitalisation for COPD Exacerbation Using Machine Learning

Abstract

BackgroundEarly readmission and death are critical adverse outcomes following hospitalisation due to exacerbation of chronic obstructive pulmonary disease (ECOPD). This study aimed to develop and validate machine learning models to enhance the prediction of these outcomes after ECOPD hospitalisation.MethodsUtilising a nationwide database, data from the index ECOPD hospitalisation and the preceding year were collected. Prediction models for 30-day readmission and death were developed using logistic lasso regression, random forest, extreme gradient boosting (XGBoost), and neural network, with the LACE index serving as a reference. Model performance was assessed with receiver operating characteristic (ROC) curves and calibration plots from the validation dataset. Key predictors were identified using SHapley Additive exPlanations.ResultsThe study included 101 011 hospitalisations in the development dataset and 17 565 in the validation dataset. The rates of 30-day readmission and death were 29.1% and 4.3%, respectively. AmoXGBoost outperformed other models, achieving an area under the ROC curve of 0.721 (95% CI, 0.713–0.729) for readmission and 0.809 (95% CI, 0.794–0.824) for death, both exceeding the corresponding values for the LACE index (0.651 and 0.641). All machine learning models demonstrated good calibration. The number of hospitalisations in the previous year and the lowest haemoglobin level during the index hospitalisation were the top predictors of readmission and death, respectively.ConclusionsApplying machine learning techniques to large-scale data effectively improves the prediction of early readmission and death following ECOPD hospitalisation. Identifying critical prognostic factors could enhance targeted post-discharge care for this high-risk patient group.