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Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver

Yasin Fatemi1, Mohsen Nikfar1, Amir Oladazimi1

  • 1Department of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA.

Healthcare (Basel, Switzerland)
|June 26, 2024
PubMed
Summary

Cardiovascular disease is a major risk for liver transplant recipients with nonalcoholic steatohepatitis (NASH). Machine learning models identified key risk factors and predicted cardiovascular death, aiding prevention strategies.

Keywords:
NASHUNOScardiovascularliver transplantmachine learning

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Area of Science:

  • Transplant medicine
  • Cardiology
  • Data science

Background:

  • Cardiovascular disease (CVD) is the primary cause of death in patients with nonalcoholic steatohepatitis (NASH) undergoing liver transplantation.
  • Effective risk stratification and prediction models are crucial for managing post-transplant outcomes.

Purpose of the Study:

  • To identify critical risk factors for cardiovascular death in NASH patients post-liver transplant.
  • To develop and validate a machine learning-based prediction model for cardiovascular mortality.

Main Methods:

  • Utilized the Standard Transplant Analysis and Research (STARE) dataset from the Organ Procurement and Transplantation Network (OPTN).
  • Applied Recursive Feature Elimination (RFE) and SelectFromModel (SFM) for feature selection.
  • Developed prediction models using algorithms including logistic regression, random forest, XGBoost, support vector machine, Gaussian naïve Bayes, and K-nearest neighbors.
  • Employed SHapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

  • RFE with a random forest estimator was the optimal feature selection method.
  • Key predictors identified include recipient/donor blood type, body mass index, recipient/donor state of residence, serum creatinine, and year of transplantation.
  • The XGBoost model demonstrated the highest performance, achieving an accuracy of 0.6909 and an AUC of 0.86.

Conclusions:

  • A significant predictive relationship exists between identified features and cardiovascular death in NASH liver transplant recipients.
  • The developed XGBoost model offers a valuable tool for predicting cardiovascular mortality.
  • Findings can inform clinical decision-making to mitigate cardiovascular complications in this patient population.