Identification of Nonresponse to Treatment Using Narrative Data in an Electronic Health Record Inflammatory Bowel Disease Cohort

Inflammatory Bowel Diseases

Volumn 22, Issue 1, 2016, Pages 151-158

  Ananthakrishnan, Ashwin N ^a,b   Cagan, Andrew ^c   Cai, Tianxi ^d   Gainer, Vivian S ^c   Shaw, Stanley Y ^a,b   Savova, Guergana ^e   Churchill, Susanne ^f   Karlson, Elizabeth W ^b,f   Murphy, Shawn N ^a,b,f   Liao, Katherine P ^b,f   Kohane, Isaac ^b,e,f  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c PARTNERS HEALTHCARE   (United States)

^d HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^e BOSTON CHILDREN S HOSPITAL   (United States)

^f BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

biologic; Crohn's disease; infliximab; treatment response; ulcerative colitis

Indexed keywords

ADALIMUMAB; INFLIXIMAB; TUMOR NECROSIS FACTOR ANTIBODY; GASTROINTESTINAL AGENT; TUMOR NECROSIS FACTOR;

ADULT; ARTICLE; COHORT ANALYSIS; DATA ANALYSIS; DIARRHEA; DRUG WITHDRAWAL; ELECTRONIC HEALTH RECORD; FATIGUE; FEMALE; HOSPITALIZATION; HUMAN; INFLAMMATORY BOWEL DISEASE; MAJOR CLINICAL STUDY; MALE; PRIORITY JOURNAL; TREATMENT RESPONSE; ULCERATIVE COLITIS; ALGORITHM; ANTAGONISTS AND INHIBITORS; COLITIS, ULCERATIVE; CROHN DISEASE; FOLLOW UP; MIDDLE AGED; PROGNOSIS; STATISTICAL ANALYSIS; YOUNG ADULT;

ADULT; ALGORITHMS; COHORT STUDIES; COLITIS, ULCERATIVE; CROHN DISEASE; DATA INTERPRETATION, STATISTICAL; ELECTRONIC HEALTH RECORDS; FEMALE; FOLLOW-UP STUDIES; GASTROINTESTINAL AGENTS; HUMANS; MALE; MIDDLE AGED; PROGNOSIS; TUMOR NECROSIS FACTOR-ALPHA; YOUNG ADULT;

EID: 84964584033     PISSN: 10780998     EISSN: 15364844     Source Type: Journal    
DOI: 10.1097/MIB.0000000000000580     Document Type: Article

References (33)

1. Jha AK, Burke MF, DesRoches C, et al. Progress toward meaningful use: hospitals' adoption of electronic health records. Am J Manag Care. 2011; 17:SP117-SP124.
2. Jha AK, DesRoches CM, Kralovec PD, et al. A progress report on electronic health records in U.S. hospitals. Health Aff (Millwood). 2010;29: 1951-1957.
3. Nakamura MM, Harper MB, Jha AK. Change in adoption of electronic health records by US children's hospitals. Pediatrics. 2013;131: e1563-e1575.
4. Charles D, King J, Patel V, et al. Adoption of Electronic Health Record Systems Among U.S. Non Federal Acute Care Hospitals: 2008-2012. ONC Data Brief, No 9. Washington, DC: Office of the National Coordinator for Health Information Technology; 2013.
5. Gottesman O, Kuivaniemi H, Tromp G, et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med. 2013;15:761-771.
6. Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011;12:417-428.
7. Mandl KD, Kohane IS. Escaping the EHR trap-The future of health IT. N Engl J Med. 2012;366:2240-2242.
8. Edwards ST, Neri PM, Volk LA, et al. Association of note quality and quality of care: A cross-sectional study. BMJ Qual Saf. 2014;23: 406-413.
9. Pollard SE, Neri PM, Wilcox AR, et al. How physicians document outpatient visit notes in an electronic health record. Int J Med Inform. 2013; 82:39-46.
10. Stetson PD, Bakken S, Wrenn JO, et al. Assessing electronic note quality using the physician documentation quality Instrument (PDQI-9). Appl Clin Inform. 2012;3:164-174.
11. Ananthakrishnan AN, Cai T, Savova G, et al. 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. 2013;19:1411-1420.
12. Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken). 2010;62: 1120-1127.
13. Xia Z, Secor E, Chibnik LB, et al. Modeling disease severity in multiple sclerosis using electronic health records. PLoS One. 2013;8:e78927.
14. Carroll RJ, Thompson WK, Eyler AE, et al. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc. 2012;19:e162-e169.
15. Cronin RM, Field JR, Bradford Y, et al. Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index. Front Genet. 2014;5:250.
16. Kho AN, Pacheco JA, Peissig PL, et al. Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med. 2011;3:79re1.
17. Kullo IJ, Ding K, Shameer K, et al. Complement receptor 1 gene variants are associated with erythrocyte sedimentation rate. Am J Hum Genet. 2011;89:131-138.
18. Ritchie MD, Denny JC, Crawford DC, et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet. 2010;86:560-572.
19. Ritchie MD, Denny JC, Zuvich RL, et al. Genome-and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk. Circulation. 2013;127:1377-1385.
20. Lin C, Karlson EW, Canhao H, et al. Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records. PLoS One. 2013;8:e69932.
21. Baumgart DC, Sandborn WJ. Crohn's disease. Lancet. 2012;380:1590-1605.
22. Ordas I, Eckmann L, Talamini M, et al. Ulcerative colitis. Lancet. 2012; 380:1606-1619.
23. D'Haens GR, Panaccione R, Higgins PD, et al. The London position Statement of the World Congress of Gastroenterology on Biological Therapy for IBD with the European Crohn's and Colitis Organization: when to start, when to stop, which drug to choose, and how to predict response? Am J Gastroenterol. 2011;106:199-212; quiz 213.
24. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010;17:507-513.
25. Duran-Garcia E, Fernandez-Llamazares CM, Calleja-Hernandez MA. Medication reconciliation: passing phase or real need? Int J Clin Pharm. 2012;34:797-802.
26. Lehnbom EC, Stewart MJ, Manias E, et al. Impact of medication reconciliation and review on clinical outcomes. Ann Pharmacother. 2014;48: 1298-1312.
27. Quelennec B, Beretz L, Paya D, et al. Potential clinical impact of medication discrepancies at hospital admission. Eur J Intern Med. 2013;24: 530-535.
28. Pellino G, Sciaudone G, Caserta V, et al. Fatigue in inflammatory bowel diseases: relationship with age and disease activity. Int J Surg. 2014;12 (suppl 2):S60-S63.
29. Graff LA, Clara I, Walker JR, et al. Changes in fatigue over 2 years are associated with activity of inflammatory bowel disease and psychological factors. Clin Gastroenterol Hepatol. 2013;11:1140-1146.
30. Yasmina A, Deneer VH, Maitland-van der Zee AH, et al. Application of routine electronic health record databases for pharmacogenetic research. J Intern Med. 2014;275:590-604.
31. Perlis RH, Iosifescu DV, Castro VM, et al. Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol Med. 2012;42:41-50.
32. Jones J, Loftus EV Jr, Panaccione R, et al. Relationships between disease activity and serum and fecal biomarkers in patients with Crohn's disease. Clin Gastroenterol Hepatol. 2008;6:1218-1224.
33. Solem CA, Loftus EV Jr, Tremaine WJ, et al. Correlation of C-reactive protein with clinical, endoscopic, histologic, and radiographic activity in inflammatory bowel disease. Inflamm Bowel Dis. 2005;11:707-712.