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.

Public Health Relevance

Familial hypercholesterolemia (FH) is a relatively common genetic disorder characterized by high cholesterol levels and increased risk of heart attack or sudden cardiac death. The proposed research will develop electronic health record (EHR)-based strategies to prevent adverse outcomes such as heart attack in FH patients. These include methods to rapidly identify FH patients, estimate prevalence of FH and develop clinical decision support to help care providers manage FH patients. The proposed work will have a significant impact on clinical management of FH patients.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL135879-01A1
Application #
9389934
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Hsu, Lucy L
Project Start
2017-07-01
Project End
2021-05-31
Budget Start
2017-07-01
Budget End
2018-05-31
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905