aceDevelopment and Validation of a Machine Learning Model for Predicting 28-Day Mortality in Patients with Sepsis-Associated Encephalopathy and Heart Failure
IntroductionPatients with sepsis-associated encephalopathy (SAE) and heart failure (HF) represent a clinically relevant but understudied subgroup in the intensive care unit, and early risk stratification may be important for guiding management. This study aimed to develop and temporally validate machine-learning models for predicting 28-day mortality in patients with SAE and HF.MethodsIn this retrospective cohort study, patients with SAE and HF were identified from the Medical Information Mart for Intensive Care…
Models built on six routine variables predicted 28-day mortality in this ICU subgroup more accurately than SOFA, SAPS II and LODS, with logistic regression offering the most balanced performance.
The study is retrospective and based on MIMIC database records, and the source reports no testing of whether using the models changes clinical management or outcomes.
Evidence
- Peer-reviewedJournal of Intensive Care Medicine2026-10-09
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Truvace Impact Record TRV-2026-1600, v1: “Development and Validation of a Machine Learning Model for Predicting 28-Day Mortality in Patients with Sepsis-Associated Encephalopathy and Heart Failure.” Truvace, 2026-10-16. /record/TRV-2026-1600 (accessed at citation time). sha256 e11fc49eaac15d55…
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