ArticleBMC infectious diseases2025
Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Association of the atherogenic index of plasma with in-hospital mortality in patients with sepsis-induced coagulopathy.Lipids in health and disease · 2025Article
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6 authors.
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Abstract
objectiveThe aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients.
methodsWe enrolled 10,321 patients with sepsis older than eighteen years from the MIMIC-IV (Medical Information Mart for Intensive Care) database. Sepsis is defined as the presence or suspected presence of infection, along with a SOFA (Sequential Organ Failure Assessment) score of ≥ 2. Four machine learning models, namely XGBoost (extreme gradient Boost), SVM (support vector machine), Logistic (logistic regression) and RF (random forest), were established for prediction, and the prediction model was constructed.
resultsA total of 10,321 sepsis patients were included, among whom 4,691 (45.45%) developed delirium. The 28-day mortality rate was markedly elevated in the delirium group (log-rank P < 0.001). The XGBoost model has the best performance. Finally, 5 variables were selected to draw a nomogram: hypertension, SOFA score, chlorine, Hb (hemoglobin), creatinine. The receiver operating characteristic (ROC) curve of the predictive delirium model showed better predictive efficiency, with an AUC of 0.767 (95%CI (confidence interval): 0.726-0.798).
conclusionThe nomogram built on the XGBoost model provides clinicians with an easy tool to quickly assess the risk of developing delirium in patients with sepsis. It provides a new idea and direction for the best model to predict delirium in patients with sepsis, so as to promote the development of delirium related research.
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