Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning

Chih-Lin Chi, Jin Wang, Pui Ying Yew, Tatiana Lenskaia, Matt Loth, Prajwal Mani Pradhan, Yue Liang, Prashanth Kurella, Rishabh Mehta, Jennifer G. Robinson, Peter J. Tonellato, Terrence J. Adam

Research output: Contribution to journalComment/debatepeer-review

7 Scopus citations


Almost half of Americans 65 years of age and older take statins, which are highly effective in lowering low-density lipoprotein cholesterol, preventing atherosclerotic cardiovascular disease (ASCVD), and reducing all-cause mortality. Unfortunately, ∼50% of patients prescribed statins do not obtain these critical benefits because they discontinue use within one year of treatment initiation. Therefore, statin discontinuation has been identified as a major public health concern due to the increased morbidity, mortality, and healthcare costs associated with ASCVD. In clinical practice, statin-associated symptoms (SAS) often result in dose reduction or discontinuation of these life-saving medications. Currently, physician decision-making in statin prescribing typically relies on only a few patient data elements. Physicians then employ reactive strategies to manage SAS concerns after they manifest (e.g., offering an alternative statin treatment plan or a statin holiday). A preferred approach would be a proactive strategy to identify the optimal treatment plan (statin agent + dosage) to prevent/minimize SAS and statin discontinuation risks for a particular individual prior to initiating treatment. Given that using a single patient's data to identify the optimal statin regimen is inadequate to ensure that the harms of statin use are minimized, alternative tactics must be used to address this problem. In this proof-of-concept study, we explore the use of a machine-learning personalized statin treatment plan (PSTP) platform to assess the numerous statin treatment plans available and identify the optimal treatment plan to prevent/minimize harms (SAS and statin discontinuation) for an individual. Our study leveraged de-identified administrative insurance claims data from the OptumLabs® Data Warehouse, which includes medical and pharmacy claims, laboratory results, and enrollment records for more than 130 million commercial and Medicare Advantage (MA) enrollees, to successfully develop the PSTP platform. In this study, we found three results: (1) the PSTP platform recommends statin prescription with significantly lower risks of SAS and discontinuation compared with standard-practice, (2) because machine learning can consider many more dimensions of data, the performance of the proactive prescription strategy with machine-learning support is better, especially the artificial neural network approach, and (3) we demonstrate a method of incorporating optimization constraints for individualized patient-centered medicine and shared decision making. However, more research into its clinical use is needed. These promising results show the feasibility of using machine learning and big data approaches to produce personalized healthcare treatment plans and support the precision-health agenda.

Original languageEnglish (US)
Article number104029
JournalJournal of Biomedical Informatics
StatePublished - Apr 2022

Bibliographical note

Funding Information:
We thank the support from 1R01HL143390-01A1 and help from OptumLabs staff for the data extraction.

Publisher Copyright:
© 2022 Elsevier Inc.


  • Big data
  • Drug adverse reaction
  • Machine learning
  • Statin therapy
  • Translational research
  • Medicare
  • United States
  • Humans
  • Big Data
  • Machine Learning
  • Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use
  • Aged
  • Cardiovascular Diseases/diagnosis

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural


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