ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
This article is part of the Research TopicApplication of Multimodal Data and Artificial Intelligence in Pulmonary DiseasesView all 14 articles
Early prediction of ARDS caused by non-pulmonary sepsis based on machine learning algorithms of inflammatory indicators and blood gas parameters
Provisionally accepted
- Suining Central Hospital, Suining, China
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Objective: Acute respiratory distress syndrome (ARDS) is a common complication in patients with non-pulmonary sepsis. Early identification and prediction of the occurrence of ARDS in non-pulmonary sepsis patients are of vital importance for timely intervention and improving the prognosis of these patients. Materials and Methods: 482 patients were included in this study. The Recursive Feature Elimination (RFE) method was employed to identify the key variables related to the prognosis of sepsis. The selected variables were used to construct nine different machine learning prediction models. To evaluate the performance of the model, we employed the Receiver Operating Characteristic (ROC) Curve, calibration curve, and Decision Curve Analysis (DCA). The clinical significance of the model was further analyzed through Shapley Additive Explanations (SHAP) analysis. Results: Through the RFE method, the final selected 11 variables. In the training set and test set, the AUC of the LightGBM model was 0.954 (95% CI: 0.933-0.973) and 0.923 (95% CI: 0.864-0.967) respectively. In this study, the calibration curve of the LightGBM model was close to the diagonal, indicating that its probability predictions were relatively reliable. In the DCA curves, the LightGBM model consistently maintained the highest net gain within the threshold range of 0 – 0.4, indicating LightGBM has greater clinical practical value. Through SHAP analysis, it was found that the SOFA score, PaO2/FiO2 ratio, lactate level, creatinine, and SAPS II score were the five most important features in the model prediction. Conclusions: In this study, a machine learning model based on inflammatory indicators and blood gas parameters was successfully developed and validated to predict the risk of ARDS in patients with non-pulmonary sepsis.
Keywords: Acuterespiratorydistresssyndrome(ARDS), bloodgasparameters, Inflammatory indicators, machine learning, Non-pulmonary sepsis
Received: 11 Oct 2025; Accepted: 27 Nov 2025.
Copyright: © 2025 Deng, Jia, Wu and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaojuan Wu
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