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October 1, 2025Journal of Medical Internet ResearchOpen Access

Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study

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Authors

XGX. GeWCWeiwei ChenJSJianshan Shi

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Overview

Developed a dual-timepoint machine learning model for sepsis-associated acute kidney injury, validating in multiple regions and offering clinical decision support.

Key Points

  • The LightGBM model achieved an AUC of 0.839 in internal testing, demonstrating strong predictive performance for sepsis-associated acute kidney injury.
  • External validation showed AUCs of 0.770 and 0.793 across different cohorts, confirming the model's reliability in diverse populations.
  • SHAP analysis revealed key features like urine output and Sequential Organ Failure Assessment score as critical for predictions in sepsis-associated acute kidney injury.
  • The model has been deployed as a web-based tool for clinical use, aiding healthcare professionals in effective risk assessment for patients.

Cite This Study

Ge et al. (2025) studied this question.

synapsesocial.com/papers/68dd91d5fe798ba2fc498dfbhttps://doi.org/10.2196/73840
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Also Consider

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

  1. 1Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study (Preprint)2025
  2. 2Interpretable 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 2025
  3. 3Risk stratification for in-hospital mortality in sepsis-associated acute kidney injury patients receiving continuous renal replacement therapy: an interpretable, externally validated machine learning study2026
  4. 4Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review2025 · 7 citations
  5. 5Machine learning model for predicting the risk of AKI in early hemodynamically stable sepsis patients: a study based on the MIMIC IV database2026