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

dc.contributor.authorWu, Patrick
dc.contributor.authorGifford, Aliya
dc.contributor.authorMeng, Xiangrui
dc.contributor.authorLi, Xue
dc.contributor.authorCampbell, Harry
dc.contributor.authorVarley, Tim
dc.contributor.authorZhao, Jaun
dc.contributor.authorCarroll, Robert
dc.contributor.authorBastarache, Lisa
dc.contributor.authorDenny, Joshua C.
dc.contributor.authorTheodoratou, Evropi
dc.contributor.authorWei, Wei-Qi
dc.identifier.citationWu, P., Gifford, A., Meng, X., Li, X., Campbell, H., Varley, T., Zhao, J., Carroll, R., Bastarache, L., Denny, J. C., Theodoratou, E., & Wei, W. Q. (2019). Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation. JMIR medical informatics, 7(4), e14325.
dc.identifier.othereISSN: 2291-9694
dc.description.abstractBackground: 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.en_US
dc.description.sponsorshipThe project was supported by NIH grant R01 LM 010685, R01 HL133786, T32 GM007347, T15 LM007450, P50 GM115305, and AHA Scientist Development Grant 16SDG27490014. The dataset used in the analyses described were obtained from Vanderbilt University Medical Center's BioVU, which is supported by institutional funding and by the Vanderbilt CTSA grant ULTR000445 from NCATS/NIH. This research was also conducted using the UK Biobank Resource under Application Number 10775. The work conducted in Edinburgh was supported by funding for the infrastructure and staffing of the Edinburgh CRUK Cancer Research Centre. ET is supported by a CRUK Career Development Fellowship (C31250/A22804). XM and XL are supported by the China Scholarship Council Studentships. We thank those individuals who manually reviewed the various maps that we used in this study [18,22-25]. We also thank the peer-reviewers who provided feedback for this manuscript.en_US
dc.publisherJMIR Medical Informaticsen_US
dc.relation.ispartofseriesDBMI Technical Reports;#
dc.subjectelectronic health recorden_US
dc.subjectgenome-wide association studyen_US
dc.subjectphenome-wide association studyen_US
dc.subjectmedical informatics applicationsen_US
dc.subjectdata scienceen_US
dc.titleMapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluationen_US
dc.description.schoolSchool of Medicine
dc.description.departmentDepartment of Biomedical Informatics
CDType.cdtype.AnalyticFindingAid(, 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, as well as this copyright and license information must be included.

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