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<p>Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, careful consideration is needed due to patient-specific factors and potential side effects. This study aimed to develop a machine learning (ML) model to estimate the likelihood of achieving target LDL-C levels in patients hospitalized for coronary artery disease and treated with moderate-dose statins. The predictive performance of three ML models, including Extreme Gradient Boosting (XGBoost), Random Forest, and Logistic Regression, was evaluated using electronic medical records from the Asan Medical Center in Seoul across six performance metrics. Additionally, all three models achieved an average AUROC of 0.695 despite reducing features by over 43%. SHAP analysis was conducted to identify key features influencing model predictions, aiming insights into patient characteristics associated with achieving LDL-C targets. This study suggests that ML-based approaches may help identify patients likely to benefit from moderate-dose statins, potentially supporting personalized treatment strategies and clinical decision-making for LDL-C management.</p>

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Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月13日

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  • Jiye Han,
  • Yunha Kim,
  • Hee Jun Kang,
  • Jiahn Seo,
  • Heejung Choi,
  • Minkyoung Kim,
  • Gaeun Kee,
  • Seohyun Park,
  • Soyoung Ko,
  • HyoJe Jung,
  • Byeolhee Kim,
  • Tae Joon Jun,
  • Young-Hak Kim

摘要

Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, careful consideration is needed due to patient-specific factors and potential side effects. This study aimed to develop a machine learning (ML) model to estimate the likelihood of achieving target LDL-C levels in patients hospitalized for coronary artery disease and treated with moderate-dose statins. The predictive performance of three ML models, including Extreme Gradient Boosting (XGBoost), Random Forest, and Logistic Regression, was evaluated using electronic medical records from the Asan Medical Center in Seoul across six performance metrics. Additionally, all three models achieved an average AUROC of 0.695 despite reducing features by over 43%. SHAP analysis was conducted to identify key features influencing model predictions, aiming insights into patient characteristics associated with achieving LDL-C targets. This study suggests that ML-based approaches may help identify patients likely to benefit from moderate-dose statins, potentially supporting personalized treatment strategies and clinical decision-making for LDL-C management.