Dynamic prediction of adverse outcomes in discharged COPD patients using patient-reported outcomes.

backgroundTo develop dynamic prediction models based on longitudinal patient-reported outcomes (PROs) and evaluate the time-varying risk of adverse outcomes after hospital discharge in patients with chronic obstructive pulmonary disease (COPD), thereby improving the accuracy of risk prediction.

methodsHospitalized COPD patients who met the inclusion and exclusion criteria from five hospitals in Shanxi Province between December 2020 and April 2022 were included. The occurrence of readmission or death within three years after discharge was defined as the adverse outcome. PROs measured repeatedly during follow-up were treated as time-varying predictors. Three dynamic prediction approaches were constructed and compared: the Joint Latent Class Model (JLCM), Shared Random-Effects Model (SREM), and Landmark model. Model performance was evaluated and compared using the time-dependent area under the receiver operating characteristic curve (AUC), Brier score, and calibration curves. In addition, individual-level trajectories of PRO scores and their corresponding dynamic risk predictions were illustrated.

resultsA total of 344 COPD patients were included, among whom 111 patients (32.27%) experienced adverse outcomes during follow-up. Model comparison demonstrated that the SREM model consistently achieved higher AUC values and lower Brier scores across all landmark time points, and its calibration curves were close to the ideal diagonal line, indicating the best predictive performance. In the survival submodel, the association parameter for PRO scores was statistically significant (P < 0.001). Each one-unit increase in PRO score was associated with a 4.3% reduction in the risk of adverse outcomes (HR = 0.957, 95% CI: 0.933-0.980). In addition, educational level, procalcitonin, and C-reactive protein levels were significantly associated with the risk of adverse outcomes. In the longitudinal submodel, dyspnea severity (mMRC grade) was significantly negatively associated with PRO scores.

conclusionsLongitudinal PRO data effectively capture the dynamic changes in health status among COPD patients and can serve as important time-varying predictors for adverse outcomes. Among the three dynamic prediction approaches evaluated, the SREM model demonstrated the best predictive performance. These findings provide a scientific basis for risk stratification and individualized management strategies in COPD patients after hospital discharge. CLINICAL

trial registrationThe study was registered with the Chinese Clinical Trial Registry (Registration number: ChiCTR2200064900;Date of Registration:2022-10-21).