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

PLoS ONE

Volumn 12, Issue 7, 2017, Pages

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

^a VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^b VANDERBILT UNIVERSITY   (United States)

^c UNIVERSITY OF AMSTERDAM   (Netherlands)

^d VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGENCY FOR HEALTHCARE RESEARCH AND QUALITY CLINICAL CLASSIFICATION SOFTWARE FOR ICD-9-CM; ARTICLE; CODING; CONTROLLED STUDY; DATA BASE; ELECTRONIC HEALTH RECORD; GENETIC ASSOCIATION; GENETIC PROCEDURES; GENOME-WIDE ASSOCIATION STUDY; HOSPITAL BILLING; ICD-9-CM; PHENOME-WIDE ASSOCIATION STUDY; PHENOTYPE; QUALITY CLINICAL CLASSIFICATION SOFTWARE FOR ICD-9-CM; SINGLE NUCLEOTIDE POLYMORPHISM; SOFTWARE; BIOLOGY; GENOMICS; HUMAN; PROCEDURES;

COMPUTATIONAL BIOLOGY; ELECTRONIC HEALTH RECORDS; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE;

EID: 85022176529     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0175508     Document Type: Article

References (49)

1. Roden DM, Xu H, Denny JC, Wilke RA. Electronic medical records as a tool in clinical pharmacology: opportunities and challenges. Clinical pharmacology and therapeutics. 2012; 91(6):1083–86. Epub 2012/04/27. https://doi.org/10.1038/clpt.2012.42 PMID: 22534870; PubMed Central PMCID: PMC3819803.
2. Wilke RA, Xu H, Denny JC, Roden DM, Krauss RM, McCarty CA, et al. The emerging role of electronic medical records in pharmacogenomics. Clinical pharmacology and therapeutics. 2011; 89(3):379–86. Epub 2011/01/21. https://doi.org/10.1038/clpt.2010.260 PMID: 21248726; PubMed Central PMCID: PMC3204342.
3. Bowton E, Field JR, Wang S, Schildcrout JS, Van Driest SL, Delaney JT, et al. Biobanks and electronic medical records: enabling cost-effective research. Science translational medicine. 2014; 6 (234):234cm3. Epub 2014/05/03. https://doi.org/10.1126/scitranslmed.3008604 PMID: 24786321.
4. Kohane IS. Using electronic health records to drive discovery in disease genomics. Nature reviews Genetics. 2011; 12(6):417–28. Epub 2011/05/19. https://doi.org/10.1038/nrg2999 PMID: 21587298.
5. Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV, et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Medical care. 2013; 51 (8 Suppl 3):S30–7. Epub 2013/06/19. https://doi.org/10.1097/MLR.0b013e31829b1dbd PMID: 23774517; PubMed Central PMCID: PMC3748381.
6. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association: JAMIA. 2013; 20(1):117–21. Epub 2012/09/08. https://doi.org/10.1136/amiajnl-2012-001145 PMID: 22955496; PubMed Central PMCID: PMC3555337.
7. Wei WQ, Leibson CL, Ransom JE, Kho AN, Chute CG. The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects. International journal of medical informatics. 2013; 82(4):239–47. Epub 2012/07/06. https://doi.org/10.1016/j.ijmedinf.2012.05.015 PMID: 22762862; PubMed Central PMCID: PMC3478423.
8. Denny JC, Crawford DC, Ritchie MD, Bielinski SJ, Basford MA, Bradford Y, et al. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. American journal of human genetics. 2011; 89(4):529–42. Epub 2011/10/11. https://doi.org/10.1016/j.ajhg.2011.09.008 PMID: 21981779; PubMed Central PMCID: PMC3188836.
9. Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, Weston N, et al. Electronic medical records for genetic research: results of the eMERGE consortium. Science translational medicine. 2011; 3 (79):79re1. Epub 2011/04/22. https://doi.org/10.1126/scitranslmed.3001807 PMID: 21508311; PubMed Central PMCID: PMC3690272.
10. Wei WQ, Denny JC. Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome medicine. 2015; 7(1):41. Epub 2015/05/06. https://doi.org/10.1186/ s13073-015-0166-y PMID: 25937834; PubMed Central PMCID: PMC4416392.
11. Elixhauser A SC, Palmer L. Clinical Classifications Software (CCS): Agency for Healthcare Research and Quality; 2014 [cited 2015]. Available from: http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp.
12. Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA, et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med. 2013; 15(10):761–71. https://doi.org/10.1038/gim.2013.72 PMID: 23743551; PubMed Central PMCID: PMCPMC3795928.
13. Denny JC. Chapter 13: Mining electronic health records in the genomics era. PLoS computational biology. 2012; 8(12):e1002823. Epub 2013/01/10. https://doi.org/10.1371/journal.pcbi.1002823 PMID: 23300414; PubMed Central PMCID: PMC3531280.
14. Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. Journal of the American Medical Informatics Association: JAMIA. 2013; 20(e1):e147–54. Epub 2013/ 03/28. https://doi.org/10.1136/amiajnl-2012-000896 PMID: 23531748; PubMed Central PMCID: PMC3715338.
15. Ritchie MD, Denny JC, Crawford DC, Ramirez AH, Weiner JB, Pulley JM, et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. American journal of human genetics. 2010; 86(4):560–72. Epub 2010/04/07. https://doi.org/10.1016/j.ajhg.2010.03.003 PMID: 20362271; PubMed Central PMCID: PMC2850440.
16. Kho AN, Hayes MG, Rasmussen-Torvik L, Pacheco JA, Thompson WK, Armstrong LL, et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. Journal of the American Medical Informatics Association: JAMIA. 2012; 19 (2):212–8. Epub 2011/11/22. https://doi.org/10.1136/amiajnl-2011-000439 PMID: 22101970; PubMed Central PMCID: PMC3277617.
17. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010; 26(9):1205–10. Epub 2010/03/26. https://doi.org/10.1093/bioinformatics/btq126 PMID: 20335276; PubMed Central PMCID: PMC2859132.
18. Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature biotechnology. 2013; 31(12):1102–10. Epub 2013/11/26. https://doi.org/10.1038/nbt.2749PMID: 24270849; PubMed Central PMCID: PMC3969265.
19. Rastegar-Mojarad M, Ye Z, Kolesar JM, Hebbring SJ, Lin SM. Opportunities for drug repositioning from phenome-wide association studies. Nature biotechnology. 2015; 33(4):342–5. https://doi.org/10.1038/nbt.3183 PMID: 25850054.
20. Doshi-Velez F, Ge Y, Kohane I. Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics. 2014; 133(1):e54–63. Epub 2013/12/11. https://doi.org/10.1542/peds.2013-0819 PMID: 24323995; PubMed Central PMCID: PMC3876178.
21. Neuraz A, Chouchana L, Malamut G, Le Beller C, Roche D, Beaune P, et al. Phenome-wide association studies on a quantitative trait: application to TPMT enzyme activity and thiopurine therapy in pharmacogenomics. PLoS computational biology. 2013; 9(12):e1003405. https://doi.org/10.1371/journal.pcbi. 1003405 PMID: 24385893; PubMed Central PMCID: PMCPMC3873228.
22. Hall MA, Verma A, Brown-Gentry KD, Goodloe R, Boston J, Wilson S, et al. Detection of pleiotropy through a Phenome-wide association study (PheWAS) of epidemiologic data as part of the Environmental Architecture for Genes Linked to Environment (EAGLE) study. PLoS genetics. 2014; 10(12): e1004678. Epub 2014/12/05. https://doi.org/10.1371/journal.pgen.1004678 PMID: 25474351; PubMed Central PMCID: PMC4256091.
23. Pendergrass SA, Brown-Gentry K, Dudek S, Frase A, Torstenson ES, Goodloe R, et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS genetics. 2013; 9(1):e1003087. Epub 2013/02/06. https://doi.org/10.1371/journal.pgen.1003087 PMID: 23382687; PubMed Central PMCID: PMC3561060.
24. Namjou B, Marsolo K, Caroll RJ, Denny JC, Ritchie MD, Verma SS, et al. Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis. Frontiers in genetics. 2014; 5:401. Epub 2014/12/06. https://doi.org/10.3389/fgene.2014.00401 PMID: 25477900; PubMed Central PMCID: PMC4235428.
25. Hebbring SJ, Schrodi SJ, Ye Z, Zhou Z, Page D, Brilliant MH. A PheWAS approach in studying HLA-DRB1*1501. Genes and immunity. 2013; 14(3):187–91. Epub 2013/02/09. https://doi.org/10.1038/gene.2013.2 PMID: 23392276; PubMed Central PMCID: PMC3637423.
26. Warner JL, Denny JC, Kreda DA, Alterovitz G. Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization. Journal of the American Medical Informatics Association: JAMIA. 2015; 22(2):324–9. Epub 2014/10/23. https://doi.org/10.1136/amiajnl-2014-002965 PMID: 25336590.
27. Leader JB, Pendergrass SA, Verma A, Carey DJ, Hartzel DN, Ritchie MD, et al. Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes. AMIA Annual Symposium proceedings / AMIA Symposium AMIA Symposium. 2015; 2015:824–32. Epub 2015/01/01. PMID: 26958218; PubMed Central PMCID: PMC4765620.
28. Hindorff LA, MacArthur J, Morales J, Junkins HA, Hall PN, Klemm AK, et al. A Catalog of Published Genome-Wide Association Studies 2015 [cited 2015 09/01/2015]. Available from: www.genome.gov/gwastudies.
29. Denny JC, Smithers JD, Miller RA, Spickard A 3rd. "Understanding" medical school curriculum content using KnowledgeMap. Journal of the American Medical Informatics Association: JAMIA. 2003; 10 (4):351–62. Epub 2003/04/02. https://doi.org/10.1197/jamia.M1176 PMID: 12668688; PubMed Central PMCID: PMC181986.
30. Denny JC, Peterson JF. Identifying QT prolongation from ECG impressions using natural language processing and negation detection. Studies in health technology and informatics. 2007; 129(Pt 2):1283–8. Epub 2007/10/04. PMID: 17911921.
31. Roden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR, et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clinical pharmacology and therapeutics. 2008; 84(3):362–9. https://doi.org/10.1038/clpt.2008.89 PMID: 18500243; PubMed Central PMCID: PMCPMC3763939.
32. Duffy DL, Zhao ZZ, Sturm RA, Hayward NK, Martin NG, Montgomery GW. Multiple pigmentation gene polymorphisms account for a substantial proportion of risk of cutaneous malignant melanoma. The Journal of investigative dermatology. 2010; 130(2):520–8. Epub 2009/08/28. https://doi.org/10.1038/jid.2009.258 PMID: 19710684; PubMed Central PMCID: PMC3672059.
33. Han J, Kraft P, Nan H, Guo Q, Chen C, Qureshi A, et al. A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation. PLoS genetics. 2008; 4(5):e1000074. Epub 2008/05/17. https://doi.org/10.1371/journal.pgen.1000074 PMID: 18483556; PubMed Central PMCID: PMC2367449.
34. Gibson G. Human genetics. GTEx detects genetic effects. Science. 2015; 348(6235):640–1. Epub 2015/05/09. https://doi.org/10.1126/science.aab3002 PMID: 25953996.
35. The Genotype-Tissue Expression (GTEx) project. Nature genetics. 2013; 45(6):580–5. Epub 2013/05/ 30. https://doi.org/10.1038/ng.2653 PMID: 23715323; PubMed Central PMCID: PMC4010069.
36. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nature genetics. 2013; 45(11):1274–83. Epub 2013/10/08. https://doi.org/10.1038/ng.2797 PMID: 24097068; PubMed Central PMCID: PMC3838666.
37. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 26(9):1205–10. Epub 2010/03/26. btq126 [pii] https://doi.org/10.1093/bioinformatics/btq126 PMID: 20335276.
38. Carroll RJ, Bastarache L, Denny JC. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics. 2014; 30(16):2375–6. https://doi.org/10.1093/bioinformatics/btu197 PMID: 24733291; PubMed Central PMCID: PMCPMC4133579.
39. Turer RW, Zuckowsky TD, Causey HJ, Rosenbloom ST. ICD-10-CM Crosswalks in the primary care setting: assessing reliability of the GEMs and reimbursement mappings. Journal of the American Medical Informatics Association: JAMIA. 2015; 22(2):417–25. Epub 2015/02/11. https://doi.org/10.1093/jamia/ocu028 PMID: 25665703.
40. Scott SA, Sangkuhl K, Stein CM, Hulot JS, Mega JL, Roden DM, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clinical pharmacology and therapeutics. 2013; 94(3):317–23. https://doi.org/10.1038/clpt.2013.105PMID: 23698643; PubMed Central PMCID: PMCPMC3748366.
41. Mosley JD, Shaffer CM, Van Driest SL, Weeke PE, Wells QS, Karnes JH, et al. A genome-wide association study identifies variants in KCNIP4 associated with ACE inhibitor-induced cough. The pharmacogenomics journal. 2016; 16(3):231–7. https://doi.org/10.1038/tpj.2015.51 PMID: 26169577; PubMed Central PMCID: PMCPMC4713364.
42. Kohler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I, et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic acids research. 2014; 42(Database issue):D966–74. Epub 2013/11/13. https://doi.org/10.1093/nar/gkt1026PMID: 24217912; PubMed Central PMCID: PMC3965098.
43. Winnenburg R, Bodenreider O, editors. Coverage of Phenotypes in Standard Terminologies2014.
44. Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, et al. Disease Ontology: a backbone for disease semantic integration. Nucleic acids research. 2012; 40(Database issue):D940–6. Epub 2011/11/15. https://doi.org/10.1093/nar/gkr972 PMID: 22080554; PubMed Central PMCID: PMC3245088.
45. Orphanet [cited 2015 09/01/2015]. Available from: www.orpha.net/.
46. DECIPHER [09/01/2015]. Available from: https://decipher.sanger.ac.uk/.
47. OMIM—Online Mendelian Inheritance in Man [09/01/2015]. Available from: www.omim.org.
48. Wei WQ, Teixeira PL, Mo H, Cronin RM, Warner JL, Denny JC. Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. Journal of the American Medical Informatics Association: JAMIA. 2015. Epub 2015/09/05. https://doi.org/10.1093/jamia/ocv130 PMID: 26338219.
49. O’Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Serv Res. 2005; 40(5 Pt 2):1620–39. Epub 2005/09/24. https://doi.org/10.1111/j.1475-6773.2005.00444.x PMID: 16178999; PubMed Central PMCID: PMC1361216.