Objective Acute respiratory failure (ARF) is a major adverse event affecting the prognosis of patients with chronic obstructive pulmonary disease (COPD). This study aimed to identify risk factors for ARF in COPD patients using a LASSO-Logistic regression model. Methods This single-center retrospective cohort study included 360 hospitalized COPD patients from January 1, 2019 to December 31, 2021. Based on admission time, patients were divided into a derivation cohort (n=280) and a validation cohort (n=80). Thirty baseline variables—covering demographic features, laboratory tests, arterial blood gas parameters, and pulmonary function indices—were collected. Candidate predictors were first screened by univariate analysis, followed by LASSO-Logistic regression with 10-fold cross-validation, and subsequently incorporated into multivariable logistic regression to construct the final prediction model. Model discrimination and calibration were evaluated using ROC curves, the C-index, and calibration plots, with internal validation performed using 1, 000-time bootstrap resampling. Results Among the 280 patients in the derivation cohort, 89 developed ARF within one year. Baseline characteristics were comparable between the derivation and validation cohorts (all P>0.05). LASSO regression initially identified 10 potential predictors, and multivariable Logistic regression ultimately determined age, GOLD stage, FVC, cTnI, and NT-proBNP as independent predictors. The model yielded a pseudo R2 of 0.28(likelihood ratio test P<0.01). The AUC was 0.861 in the derivation cohort and 0.847(95% CI: 0.809-0.905) in the validation cohort, with a sensitivity of 0.704 and specificity of 0.782. The C-index was consistent with the AUC, and calibration curves demonstrated good agreement between predicted and observed outcomes. Bootstrap validation confirmed the stability and robustness of the model. Conclusion The LASSO-Logistic regression model developed in this study can effectively predict the risk of ARF in COPD patients. The model is simple, reproducible, and demonstrates good predictive performance, indicating promising clinical utility for early risk stratification.
Key words
COPD /
acute respiratory failure /
predictive model /
LASSO regression /
Logistic regression
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