Machine Learning Model for Predicting the Risk of AKI in Early Hemodynamically Stable Sepsis Patients: A Study Based on the MIMIC IV Database - Report - MDSpire

Machine Learning Model for Predicting the Risk of AKI in Early Hemodynamically Stable Sepsis Patients: A Study Based on the MIMIC IV Database

  • By

  • He, Miao

  • Li, Xinran

  • Wu, Jiajing

  • Huang, Luyao

  • Wang, Nan

  • Chen, Limin

  • Jiang, Mingxin

  • Chen, Zhe

  • Wei, Lin

  • Zhang, Hong

  • May 18, 2026

  • 0 min

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Predictive Machine Learning Approach for Assessing AKI Risk in Sepsis Patients

Overview

This study developed a machine learning model to predict acute kidney injury (AKI) risk in hemodynamically stable sepsis patients using data from the MIMIC IV Database. The XGBoost model demonstrated performance in identifying patients at high risk of AKI.

Background

Sepsis and acute kidney injury (AKI) are critical conditions that impact patient survival in intensive care settings. Early identification of AKI risk factors in sepsis patients is important. This study leverages machine learning techniques to enhance risk stratification in this population.

Data Highlights

Total PatientsPatients with AKIRisk Factors Identified
8,2763,061 (37%)9

Key Findings

  • A total of 8,276 hemodynamically stable sepsis patients were analyzed.
  • 37% of the patients experienced acute kidney injury (AKI).
  • Nine risk factors were identified for model development.
  • The XGBoost model showed optimal performance based on various evaluation metrics.
  • External validation confirmed the model's predictive capability.

Clinical Implications

The XGBoost model can assist clinicians in identifying hemodynamically stable sepsis patients at high risk for AKI.

Conclusion

The study presents a machine learning approach for predicting AKI risk among sepsis patients, with the XGBoost model being a focus of the analysis.

Related Resources & Content

  1. Intensive Care Medicine, 2021 -- Updated Insights on Pathophysiology and Management of Acute Kidney Injury in Critically Ill Patients
  2. Intensive Care Medicine, 2010 -- The Relationship Between Sepsis and Acute Kidney Injury: Enhancing Care in Acute Renal Conditions
  3. Intensive Care Medicine, 2020 -- Impact of Transient versus Persistent Acute Kidney Injury on Mortality and Host Response in Critically Ill Sepsis Patients: A Prospective Cohort Analysis
  4. Nature Reviews Nephrology, 2023 -- Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup
  5. SCCM -- Surviving Sepsis Campaign Adult Guidelines
  6. Frontiers in Medicine — Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: A SHAP-interpretable approach
  7. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup
  8. Surviving Sepsis Campaign Adult Guidelines
  9. Timing of Initiation of Renal-Replacement Therapy in Acute Kidney Injury | New England Journal of Medicine

Original Source(s)

Machine Learning Model for Predicting the Risk of AKI in Early Hemodynamically Stable Sepsis Patients: A Study Based on the MIMIC IV Database

  • by He, Miao , Li, Xinran , Wu, Jiajing , Huang, Luyao , Wang, Nan , Chen, Limin , Jiang, Mingxin , Chen, Zhe , Wei, Lin , Zhang, Hong

  • May 18, 2026

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