# EHR-Based Strategies to Improve Outcomes in Familial Hypercholesterolemia

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2020 · $638,566

## Abstract

PROJECT SUMMARY/ABSTRACT
Familial hypercholesterolemia (FH) is a relatively common genetic disorder characterized by elevated plasma
low-density lipoprotein cholesterol (LDL-C) and a dramatically increased lifetime risk for premature
atherosclerotic cardiovascular disease (ASCVD). Available data suggest substantial under treatment of
individuals with FH, and it is estimated that <5% of prevalent FH cases in the US are diagnosed and treated.
The proposed research will develop electronic health record (EHR)-based strategies to reduce mortality and
morbidity from FH. We will develop and validate a phenotyping algorithm for rapid and efficient identification
of FH cases thereby enabling EHR-based surveillance of FH. We will deploy the phenotyping algorithm in the
population-based setting of Olmsted County, Minnesota, to estimate prevalence and provide hitherto
unavailable data on awareness, detection and control of FH. We will develop CDS to help care providers
manage FH patients and an FH-specific decision aid to facilitate shared decision making related to lipid-lowering therapy and screening of family members. To accomplish these goals, we will leverage the following
resources: a) the electronic phenotyping expertise available in the electronic Medical Records and Genomics
(eMERGE) network; b) the Rochester Epidemiology Project (REP), that links medical records of Olmsted
County MN residents thereby capturing nearly all health care delivered to residents of the community; and c)
expertise in developing and deploying CDS in the EHR and in creating decision aids for disclosing
cardiovascular risk and the benefits of lipid-lowering drugs. Our specific aims are: Aim 1. Develop and validate
an electronic phenotyping algorithm to rapidly identify FH cases from the EHR. Aim 2. Conduct an e-
epidemiology study to obtain hitherto unknown data regarding prevalence, awareness, detection, control of FH
in a population-based setting in the US. Aim 3. a) Develop EHR-based tools to help care providers manage FH
and facilitate shared decision making and cascade screening and b) assess outcomes after implementation of
CDS and decision aid. The proposed research will enable rapid identification of FH in EHRs, provide hitherto
unavailable data on the burden of FH in the community, facilitate EHR-based strategies for early detection,
increase awareness of FH among care providers, provide guidance for management of FH at point of care and
help both patients and providers make informed decisions about drug therapy and screening of family
members. These are critical steps for early detection and treatment of FH to reduce the burden of premature
ASCVD due to this condition.

## Key facts

- **NIH application ID:** 9938607
- **Project number:** 5R01HL135879-04
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Iftikhar J Kullo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $638,566
- **Award type:** 5
- **Project period:** 2017-07-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9938607

## Citation

> US National Institutes of Health, RePORTER application 9938607, EHR-Based Strategies to Improve Outcomes in Familial Hypercholesterolemia (5R01HL135879-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9938607. Licensed CC0.

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