Elsevier

Journal of Clinical Lipidology

Volume 10, Issue 5, September–October 2016, Pages 1230-1239
Journal of Clinical Lipidology

Original Article
Rapid identification of familial hypercholesterolemia from electronic health records: The SEARCH study

https://doi.org/10.1016/j.jacl.2016.08.001Get rights and content

Highlights

  • An EHR-based phenotyping algorithm identified FH cases with accuracy.

  • In a large primary-care practice in the United States, the prevalence of FH was 0.32% (1:310).

  • Among those with severe hypercholesterolemia FH phenotype was present in 1 in 14.

  • Only half of FH patients had a diagnosis code 272.0 relevant to hypercholesterolemia.

  • An LDL-C level ≤100 mg/dL on treatment was achieved in 47% of subjects with FH.

Background

Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States.

Objective

To address these knowledge gaps, we developed an ePhenotyping algorithm for rapid identification of FH in electronic health records (EHRs) and deployed it in the Screening Employees And Residents in the Community for Hypercholesterolemia (SEARCH) study.

Methods

We queried a database of 131,000 individuals seen between 1993 and 2014 in primary care practice to identify 5992 (mean age 52 ± 13 years, 42% men) patients with low-density lipoprotein cholesterol (LDL-C) ≥190 mg/dL, triglycerides <400 mg/dL and without secondary causes of hyperlipidemia.

Results

Our EHR-based algorithm ascertained the Dutch Lipid Clinic Network criteria for FH using structured data sets and natural language processing for family history and presence of FH stigmata on physical examination. Blinded expert review revealed positive and negative predictive values for the SEARCH algorithm at 94% and 97%, respectively. The algorithm identified 32 definite and 391 probable cases with an overall FH prevalence of 0.32% (1:310). Only 55% of the FH cases had a diagnosis code relevant to FH. Mean LDL-C at the time of FH ascertainment was 237 mg/dL; at follow-up, 70% (298 of 423) of patients were on lipid-lowering treatment with 80% achieving an LDL-C ≤100 mg/dL. Of treated FH patients with premature CHD, only 22% (48 of 221) achieved an LDL-C ≤70 mg/dL.

Conclusions

In a primary care setting, we found the prevalence of FH to be 1:310 with low awareness and control. Further studies are needed to assess whether automated detection of FH in EHR improves patient outcomes.

Introduction

An important paradigm of precision medicine is to screen individuals for disease before overt clinical manifestations particularly when treatment is available and beneficial. An example is familial hypercholesterolemia (FH) where major adverse events such as sudden cardiac death, myocardial infarction, and stroke can be prevented by timely initiation of lipid-lowering therapy. A relatively common genetic disorder, FH is associated with dramatically increased lifetime risk of premature atherosclerotic cardiovascular disease (ASCVD) due to elevated plasma low-density lipoprotein cholesterol (LDL-C) levels.1, 2 FH has been labeled a Tier 1 public health genomics condition3 and is one of the few genetic diseases that meets the World Health Organization criteria for population-based large-scale screening programs aimed at early disease detection and timely treatment.4 Increased attention has focused on FH recently as advances in genomics are providing important insights into the genetic architecture of lipid disorders,5, 6 and novel classes of lipid-lowering drugs are opening up new avenues of therapy in these high-risk patients.7, 8, 9, 10

However, substantial gaps in our knowledge of prevalence, awareness, and control of FH remain. Few studies have specifically addressed the prevalence of heterozygous FH. Almost half a century ago, the prevalence of heterozygous FH was estimated at 1:500 among relatives of survivors of myocardial infarction.11 Excluding specific populations with a “founder effect,” the reported estimates of prevalence of FH vary widely.12 In the Danish population, prevalence was reported to be 1:137 (0.7%),13 whereas in a study from neighboring Finland, the prevalence was 1:600 (0.2%).14 Based on genetic screening, the prevalence of FH in the Netherlands' population was 1:200.15 In the US National Health and Nutrition Examination Survey (NHANES), the prevalence of FH diagnosed using clinical criteria was estimated to be 1:250 (0.4%).16 This significant difference in reported prevalence rates motivates study of the prevalence of FH in a community-based setting in the United States. Furthermore, little is known about the extent to which FH is underdiagnosed and undertreated in the United States. Indeed, some have projected that <10% of prevalent cases are diagnosed and treated.17, 18

To address these knowledge gaps, we undertook the Mayo Screening Employees And Residents in the Community for Hypercholesterolemia (SEARCH) study in the Mayo Employee and Community Health (ECH) system that delivers primary care to residents of Olmsted County and southeastern Minnesota. A unified electronic data trust that includes comprehensive clinical records of ECH patients enabled the research described in this report. We developed an electronic phenotyping algorithm to mine electronic health records (EHR) to identify patients who met the Dutch Lipid Clinic Network (DLCN) criteria for FH with the long-term goal of addressing knowledge gaps in prevention, awareness, control of FH, and the prevention of premature ASCVD.

Section snippets

Study population and settings

This cross-sectional study was approved by the Institutional Review Board of Mayo Clinic, Rochester, Minnesota. Individuals in the Mayo ECH system who had given permission for their medical records to be used for research and had clinical data available in the EHR were considered eligible for the study. Lipid levels were extracted from structured laboratory databases from June 21, 1993, to December 31, 2014. The index date was defined as the date of the earliest LDL-C level ≥190 mg/dL. Race was

Patient characteristics

Figure 2 illustrates how we used an EHR-based approach to identify FH cases. The prevalence of severe hypercholesterolemia (any LDL-C level ≥190 mg/dL) in the ECH cohort (n = 131,000 patients with measured lipid profile and research authorization) was 5% (n = 6547). Clinical characteristics of these patients are summarized in Table 2. Mean age at the time of qualifying LDL-C measurement was 52 years, and 41% were men. The average time interval between the first LDL-C ≥190 mg/dL (index date) and

Discussion

To the best of our knowledge, the present study is the first to report the prevalence, detection, and control of FH in a large US primary care system using an automated ePhenotyping approach. In pursuit of a rapid and efficient method for ascertaining FH cases, we developed an EHR-based phenotyping algorithm that included NLP and had reasonable accuracy in identifying FH case status. We demonstrated the prevalence of clinical FH to be higher than the commonly reported projected estimate of

Conclusion

An EHR-based phenotyping algorithm had high accuracy in ascertaining FH cases among individuals with severe hypercholesterolemia. By implementing this algorithm in a large primary care practice cohort, we were able to estimate the proportion of patients with LDL-C ≥190 mg/dL who met the DLCN criteria for FH. We noted prevalence of FH to be higher than commonly presumed. Only half of the FH patients had a “pure hypercholesterolemia” diagnosis code or optimal LDL-C levels on follow-up. Our

Acknowledgment

We are indebted to the staff and patients of the Mayo Employee and Community Health program. We acknowledge the assistance of Saeed Mehrabi, Majid Rastegar Mojarad and Carin Y. Smith (Mayo Clinic, Rochester, MN) for assistance with deploying the ePhenotyping algorithm.

Author contributions: Drs Safarova and Kullo had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Kullo contributed to the supervision of

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  • Cited by (0)

    Dr. Safarova is supported by American Heart Association Postdoctoral Fellowship Award 16POST27280004. Dr. Kullo is funded by the National Human Genome Research Institute's electronic Medical Records and Genomics Network through grants HG04599 and HG006379 to Mayo Clinic. The National Human Genome Research Institute and American Heart Association had no role in the design and conduct of the work; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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