Abstract
BackgroundThe 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).ObjectiveThe 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.MethodsWe 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.ResultsWe 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]).ConclusionsThis 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.
Highlights
BackgroundElectronic health records (EHRs) have become a powerful resource for biomedical research in the last decade, and many studies based on electronic health record (EHR) data have used International Classification of Diseases (ICD) codes [1]
ICD-10 codes were mapped to phecodes in a similar manner to ICD-10-CM, but since a General Equivalence Mappings (GEMS) to translate ICD-10 to ICD-9-CM was not available, we used only string matching and previously manually reviewed resources from the Unified Medical Language System (UMLS) [24], National Library of Medicine (NLM) [23], and Observational Health Data Sciences and Informatics (OHDSI) [25,26]
To evaluate the phecode coverage of ICD-10 and ICD-10-CM source codes in UK Biobank (UKBB) and Vanderbilt University Medical Center (VUMC), respectively, we calculated the number of source codes in the 2018AA UMLS, the number of source codes mapped to phecodes, and the number of mapped and unmapped source codes that were used in the two EHRs (Figure 2)
Summary
Electronic health records (EHRs) have become a powerful resource for biomedical research in the last decade, and many studies based on EHR data have used International Classification of Diseases (ICD) codes [1]. The initial version of phecodes consisted of 733 custom groups of ICD Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes. Many health systems and international groups use the International Classification of Diseases, 10th Revision (ICD-10) or the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes [15], necessitating a new phecode map. 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)
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