Abstract
Background
Unplanned readmission among patients with Chronic Obstructive Pulmonary Disease (COPD) is increasingly prevalent and imposes significant clinical and economic burdens. The aim of this study was to investigate the influencing factors of unplanned readmission in patients with COPD within 1 year after discharge, construct a risk prediction model and evaluate its effect.
Methods
We conducted a prospective observational study on 719 individuals diagnosed with COPD at HePu People's Hospital from January 2023 to May 2024. Participants were randomly divided into a model group (n = 427) and a validation group (n = 180), with a ratio of 7:3. We employed LASSO regression to identify optimal predictors and developed a nomogram prediction model using multivariable logistic regression. The model's performance was assessed through ROC curves, calibration plots, and decision curve analysis.
Results
Of 607 patients included in the final analysis, the incidence of readmission within one year was 40.0%. Multivariate logistic regression analysis identified several independent risk factors for readmission: white blood cell count (WBC; OR = 1.07, 95% CI = 1.03–1.12, P = 0.002), disease duration over ten years (OR = 1.36, 95% CI = 0.75–2.462, P = 0.043), more than one acute exacerbation in the past year (OR = 1.12, 95% CI = 1.05–1.20, P = 0.001), and concurrent respiratory failure (OR = 1.50, 95% CI = 0.97–2.33, P = 0.047). A predictive nomogram model was developed based on these factors. The nomogram exhibited an AUC of 0.719 in the model group and 0.676 in the validation group, demonstrating good predictive performance. The calibration curve showed a good degree of fit, and the Hosmer-Lemeshow test confirmed no significant deviations in model fit (P > 0.05).The clinical decision curve demonstrated that both the model and the validation groups provided better net benefits than the treat-all tactics or the treat-none tactics with threshold probability values of 0.25–0.95 and 0.25–0.85.
Conclusion
The developed nomogram model, integrating WBC count, disease duration, number of acute exacerbations within the past year and concurrent respiratory failure, effectively predicts the risk of one-year unplanned readmission in patients with COPD, offering a valuable tool for clinical decision-making.