ArticleBMC pulmonary medicine2024
Development and validation of a model for predicting the early occurrence of RF in ICU-admitted AECOPD patients: a retrospective analysis based on the MIMIC-IV database.
Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning identifies immune-perinatal predictors of infantile hemangioma.Frontiers in pediatrics · 2025Article
- Construction and Comparison of Multiple Serum-Based Prognostic Models for Predicting the Prognosis of Acute Exacerbations of Chronic Obstructive Pulmonary Disease.Journal of inflammation research · 2024Article
- Explainable machine learning model for predicting acute exacerbations of COPD combining sarcopenia index and traditional risk factors: A retrospective single-center exploratory study.Chronic respiratory diseaseArticle
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Authors and funding
5 authors.
Funding
Abstract
backgroundThis study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission.
methodsThis 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.
resultEight 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).
conclusionThe 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.
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