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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2019
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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. |
format | Online Article Text |
id | pubmed-6911227 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
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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