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

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

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Published in:	Journal of clinical lipidology Vol. 10; no. 5; pp. 1230 - 1239
Main Authors:	Safarova, Maya S., Liu, Hongfang, Kullo, Iftikhar J.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.09.2016
Subjects:	Adult Aged Algorithms Awareness Cardiovascular Cholesterol, LDL - blood Control Databases, Factual eEpidemiology Electronic Health Records Electronic phenotyping Familial hypercholesterolemia Female Humans Hypercholesterolemia Hyperlipoproteinemia Type II - diagnosis Hyperlipoproteinemia Type II - epidemiology Informatics Male Middle Aged Phenotype Prevalence Screening Triglycerides - blood Screening Control Prevalence Hypercholesterolemia Awareness Electronic phenotyping Familial hypercholesterolemia eEpidemiology Electronic health records Informatics
ISSN:	1933-2874, 1876-4789
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Abstract	Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States. 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. 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. 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. 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. •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.
AbstractList	Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States. 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. 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. 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. 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. Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States. 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. 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. 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. 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. •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. Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States.BACKGROUNDLittle is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States.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.OBJECTIVETo 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.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.METHODSWe 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.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.RESULTSOur 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.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.CONCLUSIONSIn 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. 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.
Author	Safarova, Maya S. Liu, Hongfang Kullo, Iftikhar J.
Author_xml	– sequence: 1 givenname: Maya S. surname: Safarova fullname: Safarova, Maya S. organization: Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA – sequence: 2 givenname: Hongfang surname: Liu fullname: Liu, Hongfang organization: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA – sequence: 3 givenname: Iftikhar J. surname: Kullo fullname: Kullo, Iftikhar J. email: Kullo.Iftikhar@mayo.edu organization: Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27678441$$D View this record in MEDLINE/PubMed
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Keywords	Screening Control Prevalence Hypercholesterolemia Awareness Electronic phenotyping Familial hypercholesterolemia eEpidemiology Electronic health records Informatics
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 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 the project. All authors contributed to the design and conduct of the study. All authors performed the collection, management, analysis, and interpretation of the data. Dr Safarova performed the statistical analysis. Drs Safarova and Kullo drafted the manuscript, and Drs Kullo and Liu reviewed the manuscript for important intellectual content. All authors prepared, reviewed, or approved the manuscript. All authors decided to submit the manuscript for publication.
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Snippet	Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States. To address these knowledge gaps, we... Background Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States. Objective To address these... Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States.BACKGROUNDLittle is known about prevalence,...
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SubjectTerms	Adult Aged Algorithms Awareness Cardiovascular Cholesterol, LDL - blood Control Databases, Factual eEpidemiology Electronic Health Records Electronic phenotyping Familial hypercholesterolemia Female Humans Hypercholesterolemia Hyperlipoproteinemia Type II - diagnosis Hyperlipoproteinemia Type II - epidemiology Informatics Male Middle Aged Phenotype Prevalence Screening Triglycerides - blood
Title	Rapid identification of familial hypercholesterolemia from electronic health records: The SEARCH study
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