Developing and Evaluating Mappings of ICD-10 and ICD-10-CM Codes to PheCodes

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) in the electronic health record (EHR). Objective Here, we present our work on the development and evaluation of maps from ICD-10 and 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 Unified Medical Language System (UMLS), Observational Health Data Sciences and Informatics (OHDSI), Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), and National Library of Medicine (NLM). 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-CM and ICD-10 codes to PheCodes. Of the unique codes observed in the VUMC (ICD-10-CM) and UKBB (ICD-10) cohorts, >90% were mapped to PheCodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease. A PheWAS with a lipoprotein(a) (LPA) genetic variant, rs10455872, using the ICD-9-CM and ICD-10-CM maps replicated two genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P < .001, OR = 1.60 vs. ICD-10-CM: P < .001, OR = 1.60) and with chronic ischemic heart disease (ICD-9-CM: P < .001, OR = 1.5 vs. ICD-10-CM: P < .001, OR = 1.47). Conclusions This study introduces the initial “beta” versions of ICD-10 and ICD-10-CM to PheCode maps that will enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for high-throughput PheWAS in the EHR. * EHR : electronic health record ICD : International Classification of Diseases AHRQ : Agency for Healthcare Research and Quality CCS : Clinical Classification Software PheWAS : phenome-wide association studies CM : Clinical Modification WHO : World Health Organization NCHS : National Center for Health Statistics UMLS : Unified Medical Language System GEM : General Equivalence Mapping SNOMED CT : Systematized Nomenclature of Medicine Clinical Terms CUI : Concept Unique Identifier OHDSI : Observational Health Data Sciences and Informatics CDM : Common Data Model NLM : National Library of Medicine VUMC : Vanderbilt University Medical Center UKBB : UK Biobank OR : odds ratio LPA : lipoprotein(a) SNP : single nucleotide polymorphism M:1 : many to one SD : standard deviation

Download data

• Downloaded 2,965 times
• Download rankings, all-time:
  • Site-wide: 3,413 out of 118,732
  • In bioinformatics: 391 out of 9,606
• Year to date:
  • Site-wide: 6,177 out of 118,732
• Since beginning of last month:
  • Site-wide: 15,809 out of 118,732

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide