# Advancing Personalized Hypertension Care through Big Data Science

> **NIH NIH K01** · UNIVERSITY OF FLORIDA · 2020 · $142,789

## Abstract

PROJECT SUMMARY. The current hypertension (HTN) treatment paradigm of trial-and-error drug selection
has remained essentially unchanged for nearly half a century. Personalizing care has been challenging
because patients and clinicians too often lack adequate evidence to inform individual care decisions. But,
broad electronic health record (EHR) adoption has created opportunities for using routinely-collected clinical
data to inform evidence. Applying principles of causal inference, such data can be used to identify clinical
factors that influence observed variation in treatment response and, in turn, incorporate these factors into
statistical models for predicting future treatment response for individuals. The unifying theme of this NHLBI K01
proposal is the mentored career development of Dr. Steven M. Smith. This proposal will accelerate his
transition to an independent researcher and establish the foundation for achieving his long-term goal of using
routinely-collected clinical data to substantially improve the health and wellbeing of patients by personalizing
care. Dr. Smith's objective with this project is to better understand real world use of antihypertensive drugs and
factors that influence response to such drugs, with the goal of creating prediction models for use in clinical
decision support tools to make personalized HTN management recommendations. The specific research aims
include characterizing real world antihypertensive drug prescribing patterns and their determinants (Aim 1),
identifying treatment effect modifiers for both effectiveness and safety of two common antihypertensive
classes, angiotensin-converting enzyme inhibitors (ACE-Is) and thiazide diuretics (Aim 2) and, developing
models for predicting response to ACE-Is and thiazide diuretics to maximize antihypertensive efficacy (Aim 3).
This work will leverage observational research methodologies with the OneFlorida Data Trust, a statewide
repository of longitudinal EHR data on >8 million Floridians. Dr. Smith's training and experience in clinical
pharmacy, public/population health, and HTN care ensure the clinical relevance of the project. His previous
clinical HTN research experience and background in applied biostatistics, combined with the proposed training
incorporating biomedical informatics, pharmacoepidemiology, multilevel modeling, and leadership, ensure the
feasibility of this proposed work and his further development. University of Florida resources and infrastructure,
including the UF CTSI, the Biomedical Informatics Program, and the OneFlorida Research Consortium, provide
an ideal environment for achieving the proposed objectives and Dr. Smith's long-term goals. Dr. Rhonda
Cooper-DeHoff will lead a multidisciplinary mentorship team composed of experts in pharmacoepidemiology
(Dr. Almut Winterstein), biostatistics (Dr. Matthew Gurka), biomedical informatics (Dr. Bill Hogan), clinical HTN
(Dr. Carl Pepine), and leadership (Dr. Anne Libby). The integrated mentored re...

## Key facts

- **NIH application ID:** 9963044
- **Project number:** 5K01HL138172-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Steven Michael Smith
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $142,789
- **Award type:** 5
- **Project period:** 2018-07-15 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963044

## Citation

> US National Institutes of Health, RePORTER application 9963044, Advancing Personalized Hypertension Care through Big Data Science (5K01HL138172-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9963044. Licensed CC0.

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