Modeling disease severity in multiple sclerosis using electronic health records

PLoS ONE

Volumn 8, Issue 11, 2013, Pages

  Xia, Zongqi ^a,b,c   Secor, Elizabeth ^a   Chibnik, Lori B ^a,b,c   Bove, Riley M ^a,b,c   Cheng, Suchun ^d   Chitnis, Tanuja ^a,b   Cagan, Andrew ^e   Gainer, Vivian S ^e   Chen, Pei J ^f   Liao, Katherine P ^b,g   Shaw, Stanley Y ^a,b   Ananthakrishnan, Ashwin N ^a,b   Szolovits, Peter ^h   Weiner, Howard L ^a,b   Karlson, Elizabeth W ^b,g   Murphy, Shawn N ^a,b,e   Savova, Guergana K ^b,f   Cai, Tianxi ⁱ   Churchill, Susanne E ^e   Plenge, Robert M ^b,c,g   Kohane, Isaac S ^b,f,g   De Jager, Philip L ^a,b,c   more..

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^d DANA FARBER CANCER INSTITUTE   (United States)

^e PARTNERS HEALTHCARE   (United States)

^f BOSTON CHILDREN S HOSPITAL   (United States)

^g BRIGHAM AND WOMEN S HOSPITAL   (United States)

^h MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

ⁱ HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; BRAIN SIZE; CODING; CROSS-SECTIONAL STUDY; DISEASE DURATION; DISEASE MODEL; DISEASE SEVERITY; ELECTRONIC MEDICAL RECORD; FEMALE; GENDER; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL RESEARCH; MULTIPLE SCLEROSIS; ONSET AGE; PATIENT CARE; PREDICTIVE VALUE; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ELECTRONIC HEALTH RECORDS; FEMALE; HUMANS; MALE; MODELS, BIOLOGICAL; MULTIPLE SCLEROSIS; SEVERITY OF ILLNESS INDEX;

EID: 84892994525     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0078927     Document Type: Article

References (32)

1. Kohane IS (2011) Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 12: 417-428.
2. Jensen PB, Jensen LJ, Brunak S (2012) Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 13: 395-405.
3. Quan H, Parsons GA, Ghali WA (2002) Validity of information on comorbidity derived rom ICD-9-CCM administrative data. Med Care 40: 675-685.
4. Murphy SN, Dubey A, Embi PJ, Harris PA, Richter BG, et al. (2012) Current state of information technologies for the clinical research enterprise across academic medical centers. Clin Transl Sci 5: 281-284.
5. Kohane IS, Churchill SE, Murphy SN (2012) A translational engine at the national scale: informatics for integrating biology and the bedside. Journal of the American Medical Informatics Association: JAMIA 19: 181-185.
6. Murphy S, Churchill S, Bry L, Chueh H, Weiss S, et al. (2009) Instrumenting the health care enterprise for discovery research in the genomic era. Genome Res 19: 1675-1681.
7. Murphy SN, Weber G, Mendis M, Gainer V, Chueh HC, et al. (2010) Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association 17: 124-130.
8. Himes BE, Dai Y, Kohane IS, Weiss ST, Ramoni MF (2009) Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records. Journal of the American Medical Informatics Association: JAMIA 16: 371-379.
9. Perlis RH, Iosifescu DV, Castro VM, Murphy SN, Gainer VS, et al. (2012) Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol Med 42: 41-50.
10. Ananthakrishnan AN, Cai T, Savova G, Cheng SC, Perez RG, et al. (2013) Improving Case Definition of Crohn's Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing: A Novel Informatics Approach. Inflamm Bowel Dis.
11. Carroll RJ, Thompson WK, Eyler AE, Mandelin AM, Cai T, et al. (2012) Portability of an algorithm to identify rheumatoid arthritis in electronic health records. Journal of the American Medical Informatics Association: JAMIA 19: e162-169.
12. Kurreeman F, Liao K, Chibnik L, Hickey B, Stahl E, et al. (2011) Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records. Am J Hum Genet 88: 57-69.
13. Liao KP, Cai T, Gainer V, Goryachev S, Zeng-treitler Q, et al. (2010) Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res 62: 1120-1127.
14. Liao KP, Kurreeman F, Li G, Duclos G, Murphy S, et al. (2013) Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non-rheumatoid arthritis controls. Arthritis Rheum 65: 571-581.
15. Ritchie MD, Denny JC, Crawford DC, Ramirez AH, Weiner JB, et al. (2010) Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet 86: 560-572.
16. Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, et al. (2013) Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. Journal of the American Medical Informatics Association: JAMIA.
17. Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, et al. (2011) Electronic medical records for genetic research: results of the eMERGE consortium. Science Transl Med 3: 79re71.
18. Compston A, Coles A (2008) Multiple sclerosis. Lancet 372: 1502-1517.
19. Bermel RA, Bakshi R (2006) The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol 5: 158-170. (Pubitemid 43107851)
20. Gauthier SA, Glanz BI, Mandel M, Weiner HL (2006) A model for the comprehensive investigation of a chronic autoimmune disease: the multiple sclerosis CLIMB study. Autoimmun Rev 5: 532-536. (Pubitemid 44488756)
21. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, et al. (2010) Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Informatics Association: JAMIA 17: 507-513.
22. Zou H (2006) The adaptive Lasso and its oracle properties. Journal of the American Statistical Association 101: 1418-1429.
23. Efron B, Tibshirani R (1994) An introduction to the bootstrap: Chapman and Hall/CRC. 456 p.
24. Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, et al. (2011) Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 69: 292-302.
25. Wei X, Warfield SK, Zou KH, Wu Y, Li X, et al. (2002) Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy. Journal of magnetic resonance imaging : JMRI 15: 203-209. (Pubitemid 34112912)
26. Sampat MP, Healy BC, Meier DS, Dell'Oglio E, Liguori M, et al. (2010) Disease modeling in multiple sclerosis: assessment and quantification of sources of variability in brain parenchymal fraction measurements. NeuroImage 52: 1367-1373.
27. Xia Z, Chibnik LB, Glanz BI, Liguori M, Shulman JM, et al. (2010) A putative Alzheimer's disease risk allele in PCK1 influences brain atrophy in multiple sclerosis. PLoS One 5: e14169.
28. Roxburgh RH, Seaman SR, Masterman T, Hensiek AE, Sawcer SJ, et al. (2005) Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity. Neurology 64: 1144-1151.
29. Cree BA, Khan O, Bourdette D, Goodin DS, Cohen JA, et al. (2004) Clinical characteristics of African Americans vs Caucasian Americans with multiple sclerosis. Neurology 63: 2039-2045. (Pubitemid 39636249)
30. Sanfilipo MP, Benedict RH, Sharma J, Weinstock-Guttman B, Bakshi R (2005) The relationship between whole brain volume and disability in multiple sclerosis: a comparison of normalized gray vs. white matter with misclassification correction. NeuroImage 26: 1068-1077. (Pubitemid 40806485)
31. Rudick RA, Fisher E, Lee JC, Duda JT, Simon J (2000) Brain atrophy in relapsing multiple sclerosis: relationship to relapses, EDSS, and treatment with interferon beta-1a. Mult Scler 6: 365-372. (Pubitemid 32106795)
32. Kister I, Chamot E, Salter AR, Cutter GR, Bacon TE, et al. (2013) Disability in multiple sclerosis: A reference for patients and clinicians. Neurology 80: 1018-1024.