Establishment and validation of a predictive model for respiratory failure within 48 h following admission in patients with sepsis: a retrospective cohort study

Abstract

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Objective: The objective of this study is to identify patients with sepsis who are at a high risk of respiratory failure.Methods: Data of 1,738 patients with sepsis admitted to Dongyang People’s Hospital from June 2013 to May 2023 were collected, including the age at admission, blood indicators, and physiological indicators. Independent risk factors for respiratory failure during hospitalization in the modeling population were analyzed to establish a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative ability, the GiViTI calibration graph was used to evaluate the calibration, and the decline curve analysis (DCA) curve was used to evaluate and predict the clinical validity. The model was compared with the Sequential Organ Failure Assessment (SOFA) score, the National Early Warning Score (NEWS) system, and the ensemble model using the validation population.Results: Ten independent risk factors for respiratory failure in patients with sepsis were included in the final logistic model. The AUC values of the prediction model in the modeling population and validation population were 0.792 and 0.807, respectively, both with good fit between the predicted possibility and the observed event. The DCA curves were far away from the two extreme curves, indicating good clinical benefits. Based on the AUC values in the validation population, this model showed higher discrimination power than the SOFA score (AUC: 0.682; p < 0.001) and NEWS (AUC: 0.520; p < 0.001), and it was comparable to the ensemble model (AUC: 0.758; p = 0.180).Conclusion: Our model had good performance in predicting the risk of respiratory failure in patients with sepsis within 48 h following admission.

Keywords

• sepsis
• respiratory failure
• predictive models
• machine learning
• risk factors