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Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation

BACKGROUND: The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). OBJECTIVE: The goal of this paper was to develop and perform an initial...

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Autores principales: Wu, Patrick, Gifford, Aliya, Meng, Xiangrui, Li, Xue, Campbell, Harry, Varley, Tim, Zhao, Juan, Carroll, Robert, Bastarache, Lisa, Denny, Joshua C, Theodoratou, Evropi, Wei, Wei-Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6911227/
https://www.ncbi.nlm.nih.gov/pubmed/31553307
http://dx.doi.org/10.2196/14325
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author Wu, Patrick
Gifford, Aliya
Meng, Xiangrui
Li, Xue
Campbell, Harry
Varley, Tim
Zhao, Juan
Carroll, Robert
Bastarache, Lisa
Denny, Joshua C
Theodoratou, Evropi
Wei, Wei-Qi
author_facet Wu, Patrick
Gifford, Aliya
Meng, Xiangrui
Li, Xue
Campbell, Harry
Varley, Tim
Zhao, Juan
Carroll, Robert
Bastarache, Lisa
Denny, Joshua C
Theodoratou, Evropi
Wei, Wei-Qi
author_sort Wu, Patrick
collection PubMed
description BACKGROUND: The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). OBJECTIVE: The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. METHODS: We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. RESULTS: We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). CONCLUSIONS: This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.
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spelling pubmed-69112272020-01-02 Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation Wu, Patrick Gifford, Aliya Meng, Xiangrui Li, Xue Campbell, Harry Varley, Tim Zhao, Juan Carroll, Robert Bastarache, Lisa Denny, Joshua C Theodoratou, Evropi Wei, Wei-Qi JMIR Med Inform Original Paper BACKGROUND: The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). OBJECTIVE: The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. METHODS: We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. RESULTS: We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). CONCLUSIONS: This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR. JMIR Publications 2019-11-29 /pmc/articles/PMC6911227/ /pubmed/31553307 http://dx.doi.org/10.2196/14325 Text en ©Patrick Wu, Aliya Gifford, Xiangrui Meng, Xue Li, Harry Campbell, Tim Varley, Juan Zhao, Robert Carroll, Lisa Bastarache, Joshua C Denny, Evropi Theodoratou, Wei-Qi Wei. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 29.11.2019. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Wu, Patrick
Gifford, Aliya
Meng, Xiangrui
Li, Xue
Campbell, Harry
Varley, Tim
Zhao, Juan
Carroll, Robert
Bastarache, Lisa
Denny, Joshua C
Theodoratou, Evropi
Wei, Wei-Qi
Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
title Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
title_full Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
title_fullStr Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
title_full_unstemmed Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
title_short Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
title_sort mapping icd-10 and icd-10-cm codes to phecodes: workflow development and initial evaluation
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6911227/
https://www.ncbi.nlm.nih.gov/pubmed/31553307
http://dx.doi.org/10.2196/14325
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