Immune cell profiling supports early prediction of sepsis-associated acute kidney disease using a decision tree algorithm

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 CD56^dim 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.

Keywords: Acute kidney disease; Decision tree; Immune cell profiling; Machine learning; Sepsis.

Fig. 1

Decision tree–based immune risk prediction for sepsis-associated acute kidney disease (SA-AKD). (A) Accuracy of SA-AKD predictions made using DT. (B) Sensitivity, specificity, and AUC of SA-AKD predictions made using DT. On the basis of the results of DT predictions, the expression of (C) naïve Treg cells and (D) CD56dim NK cells was measured in patients without SA-AKD and with SA-AKD. Correlation of the expression of these cells with renal function parameters, specifically with blood urea nitrogen (BUN) and creatinine (Cr) levels. Significant differences were assessed using the independent-samples Kruskal-Wallis test. ∗∗P < 0.01. ns, not significant