Skip to main content

ORIGINAL RESEARCH article

Front. Med.
Sec. Nephrology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1483710

Machine Learning Prediction Models for Mortality Risk in Sepsis-Associated Acute Kidney Injury: Evaluating Early versus Late CRRT Initiation

Provisionally accepted
Chuanren Zhuang Chuanren Zhuang 1Ruomeng Hu Ruomeng Hu 2Ke Li Ke Li 3Zhengshuang Liu Zhengshuang Liu 1Songjie Bai Songjie Bai 4*Sheng Zhang Sheng Zhang 5Xuehuan Wen Xuehuan Wen 3*
  • 1 Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, Jiangsu Province, China
  • 2 Zhejiang University, Hangzhou, Zhejiang Province, China
  • 3 Wenzhou Medical University, Wenzhou, China
  • 4 Nanchang University, Nanchang, Jiangxi Province, China
  • 5 Zhejiang Taizhou Hospital, Taizhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    Background: Sepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRRT) being a crucial intervention. However, the optimal timing for CRRT initiation remains controversial.Using the MIMIC-IV database for model development and the eICU database for external validation, we analyzed patients with S-AKI to compare survival rates between early and late CRRT initiation groups. Propensity score matching was performed to address potential selection bias. Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤ 10, medium 11-15, high >15) and creatinine levels (low ≤3 mg/dL, medium 3-5 mg/dL, high >5 mg/dL). Multiple machine learning models were developed and evaluated to predict patient prognosis, with Shapley Additive exPlanations (SHAP) analysis identifying key prognostic factors.Results: After propensity score matching, late CRRT initiation was associated with improved survival probability, though at the cost of increased hospital and ICU stays.Subgroup analyses showed consistent trends favoring late CRRT across all SOFA categories, with the most pronounced effect in high SOFA scores (>15, p=0.058). The GBM model demonstrated robust predictive performance (average C-index 0.694 in validation and test sets). SHAP analysis identified maximum lactate levels, age, and minimum SpO2 as the strongest predictors of mortality, while CRRT timing showed minimal impact on outcome prediction.While later initiation of CRRT in S-AKI patients was associated with improved survival, this benefit comes with increased healthcare resource utilization.Our analysis reveals that systemic illness markers, rather than CRRT timing, are the primary determinants of patient outcomes, suggesting the need for a more personalized approach to CRRT initiation based on overall illness severity.

    Keywords: Sepsis-associated acute kidney injury, continuous renal replacement therapy, machine learning, Mortality prediction, CRRT timing

    Received: 20 Aug 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Zhuang, Hu, Li, Liu, Bai, Zhang and Wen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Songjie Bai, Nanchang University, Nanchang, 330031, Jiangxi Province, China
    Xuehuan Wen, Wenzhou Medical University, Wenzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.