Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a serious condition with a poor prognosis. No clinical study has reported an individual-level mortality risk curve for patients with COPD. As such, the present study aimed to construct a prognostic model for predicting individual mortality risk among patients with COPD, and to provide an online predictive tool to more easily predict individual mortality risk in this patient population. PATIENTS AND METHODS: The current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. A prognostic model for predicting mortality risk was constructed using eight risk factors. RESULTS: Cox proportional hazards regression analysis identified eight independent risk factors among COPD patients: B-type natriuretic peptide (hazard ratio [HR] 1.248 [95% confidence interval (CI) 1.155-1.348]); albumin (HR 0.952 [95% CI 0.931-0.974); age (HR 1.033 [95% CI 1.022-1.044]); globulin (HR 1.057 [95% CI 1.038-1.077]); smoking years (HR 1.011 [95% CI 1.006-1.015]); partial pressure of arterial carbon dioxide (HR 1.012 [95% CI 1.007-1.017]); granulocyte ratio (HR 1.018 [95% CI 1.010-1.026]); and blood urea nitrogen (HR 1.041 [95% CI 1.017-1.066]). A prognostic model for predicting risk for death was constructed using these eight risk factors. The areas under the time-dependent receiver operating characteristic curves for 1, 3, and 5 years were 0.784, 0.801, and 0.806 in the model cohort, respectively. Furthermore, an online predictive tool, the "Survival Curve Prediction System for COPD patients", was developed, providing an individual mortality risk predictive curve, and predicted mortality rate and 95% CI at a specific time. CONCLUSION: The current study constructed a prognostic model for predicting an individual mortality risk curve for COPD patients after discharge and provides a convenient online predictive tool for this patient population. This predictive tool may provide valuable prognostic information for clinical treatment decision making during hospitalization and health management after discharge (https://zhangzhiqiao15.shinyapps.io/Smart_survival_predictive_system_for_COPD/).