ArticleBiomarker research2025
Immune cell profiling supports early prediction of sepsis-associated acute kidney disease using a decision tree algorithm.
Article in Biomarker research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Sepsis is a major cause of acute kidney injury, progressing to sepsis-associated acute kidney disease (SA-AKD). This study explores SA-AKD prediction by combining immune cell profiling. Peripheral immune cell expression and phenotypes were analyzed in sepsis patients without (n = 97) and with (n = 41) SA-AKD, admitted to a hospital (2020–2022). Blood urea nitrogen and creatinine levels were measured, and a decision tree (DT)-based model was used to evaluate their predictive power in the training (n = 106) and validation (n = 32) cohorts. The DT model, incorporating naïve Treg and CD56dim NK cells along with clinical parameters, showed high accuracy in predicting SA-AKD. The model using blood urea nitrogen as the first node reached 89.62% accuracy (sensitivity: 94.4% and specificity: 87.14%; area under the curve = 0.91). The model starting with creatinine showed 89.62% accuracy. Validation results confirmed an 81.25% accuracy. Profiling specific immune cells may enable pre-evaluation of SA-AKD.
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