# SURPASS: (Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in minority Subgroups)

> **NIH NIH K01** · STANFORD UNIVERSITY · 2021 · $171,290

## Abstract

PROJECT SUMMARY
Despite advances in technology, cardiovascular disease (CVD) remains the leading cause of death, disability,
and healthcare costs in the U.S. Yet, there is a tremendous gap in accurate cardiovascular risk prediction and
prevention, particularly in racial/ethnic minorities. Furthermore, there is significant heterogeneity in CVD risks
and outcomes for disaggregated Hispanic and Asian subgroups. The current cardiovascular risk assessment
tools have not been well-validated in these diverse populations, and it remains largely unknown why minority
patients are less likely to start and more likely to stop life-saving therapies. The overall goal of Dr. Rodriguez’s
K01 application is to address gaps in knowledge about CVD prediction and treatment in understudied
racial/ethnic minority populations. The proposed study will utilize the electronic health record (EHR) data from
an established NHLBI-funded cohort enriched with disaggregated Hispanic and Asian patients. Using this
cohort, Dr. Rodriguez will first test the ACC/AHA Pooled Cohort Equations in disaggregated Asian and
Hispanic subgroups using a large diverse mixed-payer cohort of 1,234,751 patients from two large healthcare
systems in Northern California and Hawaii. Secondly, she will build new CVD risk prediction models for diverse
patient subgroups using machine learning techniques. Finally, she will identify reasons for statin underuse and
discontinuation using natural language processing in the EHR. This study, which will evaluate existing data
from real-world clinical practice in a stable population, will inform future risk prediction models and cholesterol
treatment guidelines for diverse racial/ethnic groups. The proposal is aligned with the NHBLI’s strategic goals
to eliminate health disparities and inequities by leveraging epidemiology and data science to understand and
solve complex health problems. This proposal will also prepare Dr. Rodriguez to meet her long-term goal of
becoming a national leader and independent investigator in CVD prevention and minority health. The proposed
didactic and applied data science experiences, including training in advanced epidemiological methods and
machine learning, will prepare Dr. Rodriguez to apply her research to other areas of CVD prevention and
populations. This training program builds on the strengths of Stanford University in health services research,
epidemiology, and biomedical informatics. Her mentorship team, led by Dr. Latha Palaniappan, includes
experts in cardiovascular prevention and health services research (Dr. Heidenreich, co-mentor), applied
statistical analyses (Dr. Robert Tibshirani, advisor), machine learning in the EHR (Dr. Nigam Shah, advisor),
and chronic disease prediction and medical decision making (Dr. Michael Pignone, advisor). Dr. Rodriguez’s
team is committed to ensuring the success of the proposal as well as overseeing her advanced training in their
respective areas of expertise. The research and training ...

## Key facts

- **NIH application ID:** 10080751
- **Project number:** 5K01HL144607-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Fatima Rodriguez
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $171,290
- **Award type:** 5
- **Project period:** 2019-02-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10080751

## Citation

> US National Institutes of Health, RePORTER application 10080751, SURPASS: (Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in minority Subgroups) (5K01HL144607-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10080751. Licensed CC0.

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