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Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record

OBJECTIVE: To compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinic...

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Autores principales: Wei, Wei-Qi, Bastarache, Lisa A., Carroll, Robert J., Marlo, Joy E., Osterman, Travis J., Gamazon, Eric R., Cox, Nancy J., Roden, Dan M., Denny, Joshua C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501393/
https://www.ncbi.nlm.nih.gov/pubmed/28686612
http://dx.doi.org/10.1371/journal.pone.0175508
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author Wei, Wei-Qi
Bastarache, Lisa A.
Carroll, Robert J.
Marlo, Joy E.
Osterman, Travis J.
Gamazon, Eric R.
Cox, Nancy J.
Roden, Dan M.
Denny, Joshua C.
author_facet Wei, Wei-Qi
Bastarache, Lisa A.
Carroll, Robert J.
Marlo, Joy E.
Osterman, Travis J.
Gamazon, Eric R.
Cox, Nancy J.
Roden, Dan M.
Denny, Joshua C.
author_sort Wei, Wei-Qi
collection PubMed
description OBJECTIVE: To compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, the Agency for Healthcare Research and Quality Clinical Classification Software for ICD-9-CM (CCS), and manually curated “phecodes” designed to facilitate phenome-wide association studies (PheWAS) in EHRs. METHODS AND MATERIALS: We selected 100 disease phenotypes and compared the ability of each coding system to accurately represent them without performing additional groupings. The 100 phenotypes included 25 randomly-chosen clinical phenotypes pursued in prior genome-wide association studies (GWAS) and another 75 common disease phenotypes mentioned across free-text problem lists from 189,289 individuals. We then evaluated the performance of each coding system to replicate known associations for 440 SNP-phenotype pairs. RESULTS: Out of the 100 tested clinical phenotypes, phecodes exactly matched 83, compared to 53 for ICD-9-CM and 32 for CCS. ICD-9-CM codes were typically too detailed (requiring custom groupings) while CCS codes were often not granular enough. Among 440 tested known SNP-phenotype associations, use of phecodes replicated 153 SNP-phenotype pairs compared to 143 for ICD-9-CM and 139 for CCS. Phecodes also generally produced stronger odds ratios and lower p-values for known associations than ICD-9-CM and CCS. Finally, evaluation of several SNPs via PheWAS identified novel potential signals, some seen in only using the phecode approach. Among them, rs7318369 in PEPD was associated with gastrointestinal hemorrhage. CONCLUSION: Our results suggest that the phecode groupings better align with clinical diseases mentioned in clinical practice or for genomic studies. ICD-9-CM, CCS, and phecode groupings all worked for PheWAS-type studies, though the phecode groupings produced superior results.
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spelling pubmed-55013932017-07-25 Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record Wei, Wei-Qi Bastarache, Lisa A. Carroll, Robert J. Marlo, Joy E. Osterman, Travis J. Gamazon, Eric R. Cox, Nancy J. Roden, Dan M. Denny, Joshua C. PLoS One Research Article OBJECTIVE: To compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, the Agency for Healthcare Research and Quality Clinical Classification Software for ICD-9-CM (CCS), and manually curated “phecodes” designed to facilitate phenome-wide association studies (PheWAS) in EHRs. METHODS AND MATERIALS: We selected 100 disease phenotypes and compared the ability of each coding system to accurately represent them without performing additional groupings. The 100 phenotypes included 25 randomly-chosen clinical phenotypes pursued in prior genome-wide association studies (GWAS) and another 75 common disease phenotypes mentioned across free-text problem lists from 189,289 individuals. We then evaluated the performance of each coding system to replicate known associations for 440 SNP-phenotype pairs. RESULTS: Out of the 100 tested clinical phenotypes, phecodes exactly matched 83, compared to 53 for ICD-9-CM and 32 for CCS. ICD-9-CM codes were typically too detailed (requiring custom groupings) while CCS codes were often not granular enough. Among 440 tested known SNP-phenotype associations, use of phecodes replicated 153 SNP-phenotype pairs compared to 143 for ICD-9-CM and 139 for CCS. Phecodes also generally produced stronger odds ratios and lower p-values for known associations than ICD-9-CM and CCS. Finally, evaluation of several SNPs via PheWAS identified novel potential signals, some seen in only using the phecode approach. Among them, rs7318369 in PEPD was associated with gastrointestinal hemorrhage. CONCLUSION: Our results suggest that the phecode groupings better align with clinical diseases mentioned in clinical practice or for genomic studies. ICD-9-CM, CCS, and phecode groupings all worked for PheWAS-type studies, though the phecode groupings produced superior results. Public Library of Science 2017-07-07 /pmc/articles/PMC5501393/ /pubmed/28686612 http://dx.doi.org/10.1371/journal.pone.0175508 Text en © 2017 Wei et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wei, Wei-Qi
Bastarache, Lisa A.
Carroll, Robert J.
Marlo, Joy E.
Osterman, Travis J.
Gamazon, Eric R.
Cox, Nancy J.
Roden, Dan M.
Denny, Joshua C.
Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record
title Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record
title_full Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record
title_fullStr Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record
title_full_unstemmed Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record
title_short Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record
title_sort evaluating phecodes, clinical classification software, and icd-9-cm codes for phenome-wide association studies in the electronic health record
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501393/
https://www.ncbi.nlm.nih.gov/pubmed/28686612
http://dx.doi.org/10.1371/journal.pone.0175508
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