Abstract
BACKGROUND: Exacerbations of chronic obstructive pulmonary disease (COPD) deteriorate patient outcomes and impose substantial burdens on healthcare systems and families. Given this, the present study aimed to develop a prediction model for in-hospital mortality in patients with acute exacerbation of COPD (AECOPD) using readily accessible clinical variables, with the goal of guiding clinical interventions. METHODS: A total of 878 consecutive AECOPD patients (807 non-death cases, 71 in-hospital death cases) were prospectively enrolled. Patients were randomly divided into a training set (n = 616) and a validation set (n = 262) at a 7:3 ratio. Logistic regression analysis was performed on the training set to identify factors influencing in-hospital mortality, followed by construction of a nomogram. The model's discrimination, calibration, and clinical applicability were evaluated using the area under the receiver-operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: The in-hospital mortality rate of AECOPD patients was 8.1%. Independent risk factors for in-hospital mortality included non-invasive mechanical ventilation, invasive mechanical ventilation, disease duration > 10 years, severe condition at admission, and comorbid heart failure (all P < 0.05). The nomogram showed an AUC of 0.909 (95% CI: 0.875-0.942) in the training set and 0.841 (95% CI: 0.742-0.939) in the validation set. Calibration curves and Hosmer-Lemeshow test (training set: χ(2) = 14.13, P = 0.12; validation set: χ(2) = 7.83, P = 0.55) confirmed good fit. DCA demonstrated higher net benefits of the model than "treat-all" or "treat-none" strategies within threshold probabilities of 5.0%-42.5% (training set) and 5.0%-42.0% (validation set). At the optimal threshold (0.082), the model's sensitivity, specificity, and accuracy were 0.885, 0.789, 0.797 (training set) and 0.632, 0.753, 0.744 (validation set), respectively. CONCLUSION: The nomogram based on mechanical ventilation, disease duration, admission condition, cor pulmonale, and heart failure exhibits excellent predictive performance for in-hospital mortality in AECOPD patients, providing a valuable tool for clinical decision-making.