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Abstract Background This study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission. Methods This study retrospectively extracted data from MIMIC-IV database, ultimately including 3776 AECOPD patients. The patients were randomly divided into a training set ( n = 2643) and a validation set ( n = 1133) in a 7:3 ratio. First, LASSO regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Subsequently, a multifactorial Cox regression analysis was employed to establish a predictive model. Thirdly, the model was validated using ROC curves, Harrell’s C-index, calibration plots, DCA, and K-M curve. Result Eight predictive indicators were selected, including blood urea nitrogen, prothrombin time, white blood cell count, heart rate, the presence of comorbid interstitial lung disease, heart failure, and the use of antibiotics and bronchodilators. The model constructed with these 8 predictors demonstrated good predictive capabilities, with ROC curve areas under the curve (AUC) of 0.858 (0.836–0.881), 0.773 (0.746–0.799), 0.736 (0.701–0.771) within 3, 7, and 14 days in the training set, respectively and the C-index was 0.743 (0.723–0.763). Additionally, calibration plots indicated strong consistency between predicted and observed values. DCA analysis demonstrated favorable clinical utility. The K-M curve indicated the model’s good reliability, revealed a significantly higher RF occurrence probability in the high-risk group than that in the low-risk group ( P < 0.0001). Conclusion The nomogram can provide valuable guidance for clinical practitioners to early predict the probability of RF occurrence in AECOPD patients, take relevant measures, prevent RF, and improve patient outcomes.
Hu et al. (Wed,) studied this question.