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

## TL;DR

This study uses immune cell profiles and a decision tree model to predict sepsis-related kidney disease early, achieving high accuracy.

## Contribution

A novel decision tree model combining immune cell data and clinical parameters for early prediction of sepsis-associated acute kidney disease.

## Key findings

- A decision tree model using immune cell data and blood urea nitrogen achieved 89.62% accuracy in predicting SA-AKD.
- The model's sensitivity was 94.4% and specificity was 87.14% when using blood urea nitrogen as the first node.
- Validation confirmed the model's accuracy at 81.25%.

## 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.

The online version contains supplementary material available at 10.1186/s40364-025-00870-3.