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Independent and combined predictive value of nutritional; electrolyte; inflammatory; and insulin resistance indices for post-stroke infection in acute ischemic stroke: a retrospective study
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DOI：10.3389/fnut.2026.1863416.png)
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Abstract
En 中文
BackgroundPost-stroke infection (PSI) is a major cause of poor prognosis in acute ischemic stroke (AIS). The independent and combined predictive value of nutritional; electrolyte; inflammatory; and insulin resistance indices for PSI requires further validation.MethodsWe retrospectively analyzed 6; 149 AIS patients from the Second Affiliated Hospital of Nanchang University (June 2017–April 2025). Multivariate logistic regression; restricted cubic spline (RCS); and ROC curves assessed the predictive value of nutritional (GNRI; PNI); electrolyte (serum sodium; potassium); inflammatory (NLR; PLR); and insulin resistance (TyG; TyG-BMI) indices. Hierarchical clustering selected clinical features; five ML algorithms (XGBoost; RF; LightGBM; Extra Trees; CatBoost) with 5-fold cross-validation built predictive models. SHapley Additive exPlanations (SHAP) identified key predictors.ResultsNLR; PLR and TyG were elevated in PSI patients; while GNRI; PNI; and serum sodium were decreased (all p < 0.05). RCS identified malnutrition (GNRI ≤98.799; PNI ≤ 46.898); a U-shaped sodium–PSI relationship (inflection: 139.611 mmol/L); elevated NLR (>2.703); and elevated TyG as risk factors; After adjusting for confounding factors such as comorbidities; the statistical significance of potassium metabolic disturbances disappeared (p = 0.151); suggesting that their impact on PSI may be due to confounding by underlying diseases rather than an independent causal association. The non-linear potassium–PSI association (inflection: 3.529 mmol/L) was not significant in the fully adjusted model. TyG-BMI was not independently associated with PSI. The CatBoost model integrating all four biomarker categories with clinical features performed best (AUC = 0.750; 95% CI: 0.722–0.779). SHAP identified TyG (|SHAP| = 0.409); GNRI (0.358); NLR (0.293); invasive procedure (0.194); coronary heart disease (0.179); and atrial fibrillation (0.137) as top features.ConclusionNutritional; electrolyte; inflammatory; and insulin resistance indices are significantly associated with PSI; and their integration with clinical features produces a predictive model with good discriminative performance.
Keywords:
prediction model
inflammatory index
nutritional index
post-stroke infection
electrolyte index
insulin resistance index
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