Interpretable four-factor day-1 nomogram for predicting sepsis-associated encephalopathy in septic ICU patients with AKI: Development and internal validation in MIMIC-IV
Year: 2026
Type: article
Study design: Observational Study
Abstract: Sepsis-associated encephalopathy (SAE) is common in the intensive care unit (ICU) and portends worse short- and long-term outcomes. To enable real-time bedside use and multicenter deployment, we aimed to develop a parsimonious, transparent day-1 prediction model using routinely available variables while preserving discrimination, calibration, and clinical utility. Using MIMIC-IV (2008-2022), we conducted a single-center retrospective study of adult sepsis patients with KDIGO-defined AKI. Predictors were restricted to the first 24 hours after ICU admission; the endpoint was any in-ICU SAE ("ever" vs "never"). After multiple imputation (m = 5), 44 baseline variables were standardized and entered into LASSO with 20-fold cross-validation. A 3-rule clinical screen (24 hours availability; non-treatment; low collinearity) distilled LASSO-selected features to a four-predictor logistic model; performance was internally validated (bootstrap) and compared with an XGBoost benchmark. SHAP analyses supported interpretability. Among 6780 ICU stays (training n = 4746; validation n = 2034), SAE occurred in 69.8%. The final 4 predictors were age, SAPS II, serum sodium, and mean arterial pressure (MAP). Discrimination was stable (AUC 0.734 training; 0.739 validation) with excellent calibration (validation CITL = -0.045; slope = 0.996; Brier = 0.182). Decision-curve analysis showed greater net benefit than XGBoost across thresholds 0.15 to 0.55; although AUCs were similar, XGBoost calibrated worse (CITL = -0.289; slope = 0.729). SHAP ranked contributions as SAPS II, sodium, age, and MAP, indicating a near-linear sodium-risk rise within 138 to 144 mmol/L, age-related risk above ~70 years, and a U-shaped MAP effect with protection around 55 to 75 mm Hg. We developed and validated a four-factor nomogram that uses only routine day-1 data to stratify SAE risk rapidly and transparently, outperforming a complex learner in calibration and net benefit. This parsimonious, interpretable tool highlights modifiable targets (sodium, individualized MAP) and provides a pragmatic foundation for multicenter validation and EMR-embedded early warning and intervention strategies.
Source: Medicine
Institution: Hebei General Hospital
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,950
OpenAlex ID: W7128816837