# Personalized Statin Treatment Plan to Optimize Clinical Outcomes Using Big Data

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2020 · $758,900

## Abstract

PROJECT SUMMARY
An estimated 47% of Americans 65 years of age and older take statins, which are highly effective in lowering
low-density lipoprotein (LDL) 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 1 year of treatment initiation. There, Statin discontinuation has
been identified as a major public-health concern due to 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, physicians employ reactive strategies to manage
SAS concerns after they manifest, such as offering an alternative statin treatment plan (e.g., reducing dosage
or changing medication) or a `statin holiday'. However, with numerous statin treatment strategies available and
no means of optimizing their match to a given patient, physician decision-making is based on minimal patient
data elements. Moreover, using a single patient's data to identify the optimal statin regimen and treatment plan
is inadequate to ensure that the harms of statin use are minimized and the benefits are maximized. A decision-
support system, by contrast, can use a vast number of variables from a large number of patients (“big data”) to
match an optimal statin treatment plan to an individual patient prospectively. We propose to use complex
patient information to develop and test an effective predictive model and tool, the personalized statin treatment
plan (PSTP) platform, which could be used by physicians to optimize personalized statin treatment to minimize
harms (SAS and statin discontinuation) while at the same time maximizing benefits (LDL reduction). The
proposed study leverages data from the OptumLabs Data Warehouse (which includes more than 20 years of
insurance claims and electronic health records data from more than 150 million patients across the United
States), as well as clinical trial simulations, for model development, validation, and evaluation. We will address
the following specific aims: 1) Develop and validate a deep-learning model to predict both SAS and statin
discontinuation using linked insurance claims and EHR data; 2) Develop and validate the PSTP platform to
identify statin treatment plans that optimize both LDL reduction (benefit) and SAS and statin discontinuation
(harm) for a given patient profile; and 3) Evaluate the PSTP relative to current and guideline-driven practices
using CTS to assess clinical benefits and harms. The proposed study will produce a precision-medicine tool to
empower physicians to make proactive clinical decisions regarding statin treatment planning (i.e., selecting the
statin drug and dosage optimized for a particular patient to maximize LDL reduction and minimize statin
discontinuation and SAS) before any...

## Key facts

- **NIH application ID:** 9869932
- **Project number:** 5R01HL143390-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Chih-Lin Chi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $758,900
- **Award type:** 5
- **Project period:** 2019-02-15 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9869932

## Citation

> US National Institutes of Health, RePORTER application 9869932, Personalized Statin Treatment Plan to Optimize Clinical Outcomes Using Big Data (5R01HL143390-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9869932. Licensed CC0.

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