ArticleFrontiers in cellular and infection microbiology2025
Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.
Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic models for sepsis-associated encephalopathy: a comprehensive systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Integrating Environmental Exposure Profiles with Temporal Transcriptomics: An Explainable Machine Learning Model for Cardiovascular Disease Risk Prediction and Biological Validation.Cardiovascular drugs and therapy · 2026Article
- Research Topics and Trends in MIMIC-IV: A Large ICU Database Relevant for Critical Care Nursing.Nursing in critical care · 2026Review
- Clinical Significance of Serum Soluble Klotho Levels in Sepsis-Associated Encephalopathy: A Single-Center Prospective, Preliminary Study.Journal of clinical medicine · 2026Article
- Association between anion gap trajectories and mortality in patients with acute cholangitis: a cohort study.Scientific reports · 2026Article
- Development of a machine learning-based model for predicting postoperative survival in gastric cancer.World journal of gastrointestinal surgery · 2026Article
- Development and validation of an in-hospital cardiogenic shock prediction model for AMI patients based on machine learning.BMC cardiovascular disorders · 2026Article
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- Association between clinical characteristics within 6 h of ICU admission and 30-day mortality risk in immunocompromised sepsis patients: development and validation of a machine learning model based on the MIMIC-IV database.Frontiers in digital health · 2026Article
- Machine learning prediction models for mechanical ventilation requirement in patients with embolic stroke: a retrospective cohort study based on MIMIC-IV.Frontiers in medicine · 2026Article
- Mitochondrial dysfunction, neuroinflammation, and associated mechanisms in sepsis-associated encephalopathy: from pathogenesis to emerging therapeutics.Frontiers in neuroscience · 2026Review
- Identifying the clinical signature of anti-centromere antibody-positive Sjögren's syndrome: a machine learning-based analysis of a multicenter cohort.Frontiers in immunology · 2026Article
- Development and internal evaluation of an interpretable machine learning model based on clinical and radiomics features to differentiate lower extremity arterial embolism from atherothrombosis.Frontiers in medicine · 2026Article
- Association of the atherogenic index of plasma with in-hospital mortality in patients with sepsis-induced coagulopathy.Lipids in health and disease · 2025Article
- A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: a retrospective model development and validation study.Frontiers in immunology · 2025Article
- The relationship between improved elderly nutritional risk index and short-term all-cause mortality in patients with urinary sepsis: a retrospective cohort study.Frontiers in nutrition · 2025Article
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7 authors.
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Abstract
Background: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological disorders and the absence of specific biomarkers, early clinical diagnosis remains challenging. This study aimed to develop a predictive model for SAE in elderly ICU patients. Methods: The data of elderly sepsis patients were extracted from the MIMIC IV database (version 3.1) and divided into training and test sets in a 7:3 ratio. Feature variables were selected using the LASSO-Boruta combined algorithm, and five machine learning (ML) models, including Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost),Light Gradient Boosting Machine(LGBM), Multilayer Perceptron (MLP), and Support Vector Machines (SVM), were subsequently developed using these variables. A comprehensive set of performance metrics was used to assess the predictive accuracy, calibration, and clinical applicability of these models. For the machine learning model with the best performance, we employed the SHapley Additive Explanations(SHAP) method to visualize the model. Results: Based on strict inclusion and exclusion criteria, a total of 3,156 elderly sepsis patients were enrolled in the study, with an SAE incidence rate of 48.7%. The mortality rate of elderly sepsis patients who developed SAE was significantly higher than that of patients in the non-SAE group (28.78% vs. 12.59%, Conclusions: The XGBoost model effectively predicts SAE in elderly ICU patients and may serve as a reliable tool for clinicians to identify high-risk patients.
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