Evidence map›Paper›PMID 28686612›Full record

ArticlePloS one2017

Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.

Wei-Qi Wei, Lisa A Bastarache, Robert J Carroll, Joy E Marlo, Travis J Osterman, Eric R Gamazon, Nancy J Cox, Dan M Roden, Joshua C Denny

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 254 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
254citing papers in PubMed, 8 pooled it
18.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

254 citing papers in PubMed, 8 syntheses or guidelines pooled it, 418 citations in OpenAlex.

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194 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 2 countries.

Wei-Qi WeiDepartments of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America.ORCID http://orcid.org/0000-0003-4985-056X
Lisa A BastaracheDepartments of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America.
Robert J CarrollDepartments of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America.
Joy E MarloDepartments of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America.
Travis J OstermanDepartments of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America.
Eric R GamazonVanderbilt Genetic Institute and the Division of Genetic Medicine, Vanderbilt University, Nashville, TN, United States of America.
Nancy J CoxVanderbilt Genetic Institute and the Division of Genetic Medicine, Vanderbilt University, Nashville, TN, United States of America.
Dan M RodenDepartments of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America.
Joshua C DennyDepartments of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States of America.
Vanderbilt University Medical Center · USVanderbilt University · US

Funding

Understanding and preventing HLA-associated drug reactionsP50GM115305 · NIGMS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI PHILLIPS, ELIZABETH · 2015 to 2019
$13.0M
From GWAS to PheWAS: Scanning the EMR Phenome for Gene-disease AssociationsR01LM010685 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BASTARACHE, LISA · 2011 to 2022
$6.3M
National Infrastructure for Standardized and Portable EHR Phenotyping AlgorithmsR01GM105688 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI LUO, YUAN, PATHAK, JYOTISHMAN · 2013 to 2020
$5.7M
VGER, the Vanderbilt Genome-Electronic Records ProjectU01HG008672 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI DENNY, JOSHUA C., RODEN, DAN M · 2015 to 2019
$5.1M
Informatics Tools for Pharmacogenomic Discovery using Practice-based DataR01GM103859 · NIGMS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI DENNY, JOSHUA C., PATHAK, JYOTISHMAN · 2014 to 2017
$2.4M
Exploring Statin Pleiotropic Effects within a Very Large EHR Cohort - Diversity SupplementR01HL133786 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI WEI, WEI-QI · 2017 to 2020
$1.7M
NHGRI NIH HHS U01 HG008672NHLBI NIH HHS R01 HL133786NIGMS NIH HHS P50 GM115305NIGMS NIH HHS R01 GM103859NIGMS NIH HHS R01 GM105688NLM NIH HHS R01 LM010685
6 · The paper itself

Abstract

objectiveTo compare three groupings of Electronic Health Record (EHR) billing codes for their ability to represent clinically meaningful phenotypes and to replicate known genetic associations. The three tested coding systems were the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, the Agency for Healthcare Research and Quality Clinical Classification Software for ICD-9-CM (CCS), and manually curated "phecodes" designed to facilitate phenome-wide association studies (PheWAS) in EHRs. METHODS AND MATERIALS: We selected 100 disease phenotypes and compared the ability of each coding system to accurately represent them without performing additional groupings. The 100 phenotypes included 25 randomly-chosen clinical phenotypes pursued in prior genome-wide association studies (GWAS) and another 75 common disease phenotypes mentioned across free-text problem lists from 189,289 individuals. We then evaluated the performance of each coding system to replicate known associations for 440 SNP-phenotype pairs.

resultsOut of the 100 tested clinical phenotypes, phecodes exactly matched 83, compared to 53 for ICD-9-CM and 32 for CCS. ICD-9-CM codes were typically too detailed (requiring custom groupings) while CCS codes were often not granular enough. Among 440 tested known SNP-phenotype associations, use of phecodes replicated 153 SNP-phenotype pairs compared to 143 for ICD-9-CM and 139 for CCS. Phecodes also generally produced stronger odds ratios and lower p-values for known associations than ICD-9-CM and CCS. Finally, evaluation of several SNPs via PheWAS identified novel potential signals, some seen in only using the phecode approach. Among them, rs7318369 in PEPD was associated with gastrointestinal hemorrhage.

conclusionOur results suggest that the phecode groupings better align with clinical diseases mentioned in clinical practice or for genomic studies. ICD-9-CM, CCS, and phecode groupings all worked for PheWAS-type studies, though the phecode groupings produced superior results.

Indexed as

Electronic Health RecordsGenomicsComputational BiologyGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideSoftware

Identifiers

PMID28686612
PMCPMC5501393
OpenAlexW2728065598

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.