ArticleInternational journal of chronic obstructive pulmonary disease2026
Identification of Risk Factors for 30-Day and Frequent One-Year Readmissions in Chronic Obstructive Pulmonary Disease Patients Following Acute Exacerbation.
Article in International journal of chronic obstructive pulmonary disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Acute exacerbations of chronic obstructive pulmonary disease (COPD) are a major cause of hospitalization and readmission, severely impacting patients' quality of life and long-term prognosis. This study aimed to identify risk factors for both 30-day and frequent readmissions within one year following hospitalization for an acute exacerbation of COPD. Methods: We conducted a retrospective analysis of 1,208 patients hospitalized for acute COPD exacerbations. Patients were categorized based on readmission within 30 days or multiple readmissions in the subsequent year. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors. A predictive nomogram model was developed and internally validated using bootstrapping techniques. Results: For 30-day readmissions, age was identified as an independent risk factor (OR=1.044, 95% CI: 1.008-1.081). For frequent one-year readmissions, multivariate analysis revealed age (OR=1.025, 95% CI: 1.008-1.042) and Anthonisen classification Type I/II (OR=1.556, 95% CI: 1.021-2.369) as independent risk factors, while creatine kinase (CK) acted as a protective factor (OR=0.998, 95% CI: 0.996-1.000). The nomogram demonstrated good predictive performance, with a C-index of 0.730 and an AUC of 0.730 upon internal validation. Conclusion: This study identifies age and Anthonisen classification as key risk factors, and CK as a protective factor for readmissions in COPD patients following acute exacerbation. The validated nomogram provides a practical clinical tool for predicting readmission risks, enabling early identification of high-risk patients during hospitalization or at discharge. This risk stratification approach can facilitate implementation of personalized management strategies, such as intensified follow-up or targeted interventions, to reduce readmission rates and improve patient outcomes.
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