ArticleAmerican journal of translational research2026
A gradient boosting machine model for predicting prognosis in patients with acute respiratory distress syndrome.
Article in American journal of translational research, 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
objectivesTo develop a gradient boosting model for predicting the prognosis of patients with acute respiratory distress syndrome (ARDS), providing a data-driven reference for early identification of high-risk patients in clinical settings.
methodsThis retrospective study analyzed the 28-day mortality in 307 ARDS patients treated at Qinzhou First People's Hospital between July 2023 and June 2024. Patients were divided into a mortality group (n=92) and a survival group (n=215) based on in-hospital death. Demographic characteristics, clinical variables, and biochemical parameters were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors, which were subsequently used to construct a gradient boosting machine (GBM) model and a nomogram model. Model performance was evaluated with calibration curves and the area under the receiver operating characteristic (ROC) curve (AUC).
resultsLogistic regression identified age, oxygenation index (OI), neutrophil-to-lymphocyte ratio (NLR), interleukin-8 (IL-8), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) as independent prognostic factors for ARDS. In the GBM model, the relative importance of NT-proBNP, age, NLR, IL-8, and OI was ranked. The nomogram indicated that older age, lower OI, and higher levels of NLR, IL-8, and NT-proBNP were associated with poorer prognosis. The AUC values for the GBM model in the training and validation sets were 0.907 (95% CI: 0.866-0.947) and 0.887 (95% CI: 0.803-0.971), respectively, which surpassed the values of 0.866 (95% CI: 0.810-0.923) and 0.835 (95% CI: 0.733-0.937) for the Nomogram model.
conclusionThe 28-day mortality rate among ARDS patients was 29.97%, and was mainly associated with age, oxygenation index, NLR, IL-8, and NT-proBNP levels. A GBM model constructed using these factors showed good predictive performance, offering valuable data references for clinical identification of ARDS patients at a high-risk of poor prognosis.
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