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Related Experiment Video

Updated: Jul 4, 2025

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Decision rules for personalized statin treatment prescriptions over multi-objectives.

Pui Ying Yew1, Yue Liang1, Terrence J Adam1,2

  • 1Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA.

Experimental Biology and Medicine (Maywood, N.J.)
|January 28, 2024
PubMed
Summary

This study introduces Decision Rules for Statin Treatment (DRST), a transparent approach to personalized statin plans. DRST optimizes statin benefits while minimizing risks and discontinuation, offering a clinically usable alternative to complex models.

Keywords:
Clinical decision supportcardiovascularcholesterolstatin-associated symptomstreatment simulation

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Area of Science:

  • Cardiovascular Medicine
  • Health Informatics
  • Clinical Decision Support

Background:

  • Previous proactive statin prescription strategies, like personalized statin treatment plans (PSTP), utilized neural networks and big data but lacked transparency, limiting clinical adoption.
  • The need for interpretable and clinically usable decision-making tools in statin therapy is critical for optimizing patient outcomes.

Purpose of the Study:

  • To develop and evaluate a transparent, five-step pipeline approach called Decision Rules for Statin Treatment (DRST).
  • To improve the benefit-to-risk ratio of statin therapy through interpretable decision rules.
  • To assess the clinical efficacy and usability of DRST compared to standard care and previous complex models.

Main Methods:

  • A five-step pipeline was developed, refining a PSTP model and incorporating a decision tree for straightforward rules.
  • 107,739 de-identified patient records from the Optum Labs Database Warehouse were analyzed.
  • A clinical trial simulation was conducted to evaluate the efficacy of the DRST decision rules.

Main Results:

  • The DRST approach yielded a compact decision tree (max depth 3, 11 nodes) identifying age, LDL-C, and age-adjusted Charlson score as key factors.
  • DRST identified six subpopulations that could significantly benefit from the decision rules.
  • Clinical trial simulations showed DRST improved LDL-C reduction by 4.15 pp and reduced statin-associated symptoms and discontinuation by 11.71 pp and 3.96 pp, respectively, compared to standard care.

Conclusions:

  • The DRST pipeline offers a transparent and clinically usable method for initial statin treatment planning.
  • DRST achieves near-maximal statin treatment benefit-to-risk ratio, with results only 0.6 pp suboptimal to the non-transparent PSTP model.
  • This approach demonstrates the feasibility of creating interpretable clinical decision support tools for personalized medicine.