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Machine learning-based prediction model for 28-day mortality in acute kidney injury patients with liver cirrhosis: A MIMIC-IV database analysis

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  • Luyu Chai
  • Yuxiang Zhou
  • Nan Zhou
  • Yao Xiao
  • Renqi Pang

Abstract

Background: Acute kidney injury (AKI) in patients with liver cirrhosis represents a significant clinical challenge with high mortality rates. This study aimed to develop and validate a machine learning-based prediction model for 28-day mortality in AKI patients with liver cirrhosis using the MIMIC-IV database. Methods: This retrospective study analyzed data from 4,168 AKI patients, including 601 with concurrent liver cirrhosis, from the MIMIC-IV database. Patient selection followed strict inclusion and exclusion criteria. The study implemented comprehensive data preprocessing, including feature normalization and selection through Recursive Feature Elimination. Multiple machine learning algorithms were evaluated, with model performance assessed through ROC curves, calibration curves, and precision-recall analysis. SHAP analysis was conducted to interpret feature contributions to mortality prediction. Results: The liver cirrhosis group demonstrated distinct clinical characteristics, including significantly lower age (median 60 vs 70 years, p

Suggested Citation

  • Luyu Chai & Yuxiang Zhou & Nan Zhou & Yao Xiao & Renqi Pang, 2025. "Machine learning-based prediction model for 28-day mortality in acute kidney injury patients with liver cirrhosis: A MIMIC-IV database analysis," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0328662
    DOI: 10.1371/journal.pone.0328662
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