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September 23, 2025Open Access

Interpretable Machine Learning for Early Prediction of Acute Kidney Disease (AKD) in Sepsis-Associated Acute Kidney Injury (SA-AKI): A Multicenter Cohort Study with External Validation

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Authors

SCShuang ChenGLGuang LiQZQiyi Zeng

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Overview

Multicenter cohort study utilizes machine learning models to improve risk stratification in patients with sepsis-associated acute kidney injury.

Key Points

  • Gradient boosting model achieved 78.94% accuracy in predicting acute kidney disease progression.
  • External validation showed decreased performance, highlighting the risk of model overfitting.
  • Use of ACE inhibitors/ARBs was linked to reduced progression risk, while nephrotoxins increased it.
  • Prognostic scoring systems significantly correlated with outcomes, aiding in better risk management.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68d4724f31b076d99fa6ae7dhttps://doi.org/10.21203/rs.3.rs-7313497/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data2026
  2. 2Development of interpretable machine learning models for early diagnosis of sepsis-associated acute kidney injury2026
  3. 3Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study2025 · 12 citations
  4. 4Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury2024 · 18 citations
  5. 5Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care2024 · 9 citations