Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
June 20, 2026Renal FailureOpen Access

Risk stratification for in-hospital mortality in sepsis-associated acute kidney injury patients receiving continuous renal replacement therapy: an interpretable, externally validated machine learning study

View Full Paper
Ask AI
Bookmark
Share

Authors

YZYao ZhengJGJian GaoTHTianfeng Hua

Discussion

Loading...

Member takes

Overview

Retrospective study develops a prognostic model for AKI patients on CRRT, supporting clinical decision-making.

Key Points

  • The aim is to develop a prognostic model for in-hospital mortality in sepsis-associated acute kidney injury patients receiving continuous renal replacement therapy.
  • Retrospective analysis of 1,217 patients from two databases and 332 from an independent cohort.
  • Variables were selected using least absolute shrinkage and selection operator (LASSO) and Boruta algorithms.
  • Eight machine learning models were constructed and compared, with gradient boosting machine (GBM) chosen for validation.
  • GBM achieved AUCs of 0.890, 0.756, and 0.752 in respective cohorts.
  • Key predictors included urine output, serum creatinine, and age identified through SHAP analysis.
  • Performance of GBM was comparable to other methods (XGBoost, LightGBM) and exceeded conventional scores like SOFA and SAPS II.

Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/6a362e62db0793dc1a53624ahttps://doi.org/10.1080/0886022x.2026.2677246
View Full Paper
Ask AI
Bookmark
Share

Also Consider

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

  1. 1Interpretable 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
  2. 2Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study2025
  3. 3Machine learning model for predicting the risk of AKI in early hemodynamically stable sepsis patients: a study based on the MIMIC IV database2026
  4. 4Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review2025 · 7 citations
  5. 5Prediction 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