ArticleJournal of thoracic disease2026
Establishment of a prediction model for hospital mortality in critically ill patients with chronic obstructive pulmonary disease.
Article in Journal of thoracic 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
Background: Chronic obstructive pulmonary disease (COPD) patients admitted to the intensive care unit (ICU) are usually in serious conditions and have a high hospital mortality rate. Our study aimed to explore simple clinical parameters associated with hospital mortality and to establish a novel prediction model. Methods: Patients were retrospectively screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU collaborative research database (eICU-CRD). Logistic regression analysis was used to screen clinical parameters associated with hospital mortality in critically ill patients with COPD, and these parameters were used to construct the prediction model presented in the form of a nomogram. The concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were utilized to evaluate the model. Results: There were 1,174 patients included in the MIMIC-IV database and 3,778 patients available for validation in eICU-CRD. Logistic regression analysis indicated that age (A), leukocyte (L), platelet (P), and red blood cell distribution width-albumin ratio (R) were independent predictors of hospital mortality in critically ill patients with COPD, and the above four predictors were used to construct the ALPR nomogram model. In the ALPR model, the C-index of the establishing set and validation set was 0.759 [95% confidence interval (CI): 0.723-0.794] and 0.717 (95% CI: 0.692-0.742), respectively. ROC and calibration curves also showed good accuracy and discrimination. To a certain extent, the clinical value of the ALPR model was superior to that of the age-adjusted Charlson comorbidity index (CCI) and Sequential Organ Failure Assessment (SOFA) score. Conclusions: The ALPR model can help clinicians predict hospital mortality in critically ill patients with COPD, potentially facilitating personalized treatment.
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