Identification of Nonresponse to Treatment Using Narrative Data in an Electronic Health Record Inflammatory Bowel Disease Cohort.
BACKGROUND
Electronic health records, increasingly a part of healthcare, provide a wealth of untapped narrative free text data that have the potential to accurately inform clinical outcomes.


METHODS
From a validated cohort of patients with Crohn's disease or ulcerative colitis, we identified patients with ≥1 coded or narrative mention of monoclonal antibodies to tumor necrosis factor α. Chart review by ascertained true use of therapy, time of initiation, and cessation of treatment, and also clinical response stratified as nonresponse, partial, or complete response at 1 year. Internal consistency was assessed in an independent validation cohort.


RESULTS
A total of 3087 patients had a mention of an antibodies to tumor necrosis factor α. Actual therapy initiation was within 60 days of the first coded mention in 74% of patients. In the derivation cohort, 18% of antibodies to tumor necrosis factor α starts were classified as nonresponse at 1 year, 21% as partial, and 56% as complete response. On multivariate analysis, the number of narrative mentions of diarrhea (odds ratio 1.08; 95% confidence interval, 1.02-1.14) and fatigue (odds ratio 1.16; 95% confidence interval, 1.02-1.32) was independently associated with nonresponse at 1 year (area under the curve 0.82). A likelihood of nonresponse score comprising a weighted sum of both demonstrated a good dose-response relationship across nonresponders (2.18), partial (1.20), and complete (0.50) responders (P < 0.0001) and correlated well with need for surgery or hospitalizations.


CONCLUSIONS
Narrative data in an electronic health record offer considerable potential to define temporally evolving disease outcomes such as nonresponse to treatment.
*Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts; †Harvard Medical School, Boston, Massachusetts; ‡Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts; §Research IS and Computing, Partners HealthCare, Charlestown, Massachusetts; ‖Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts; ¶Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts; **Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts; ††i2b2 National Center for Biomedical Computing, Brigham and Women's Hospital, Boston, Massachusetts; ‡‡Division of Rheumatology, Allergy and Immunology, Brigham and Women's Hospital, Boston, Massachusetts; §§Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts; and ‖‖Children's Hospital Boston, Boston, Massachusetts.
Ashwin N
AN
Ananthakrishnan
Andrew
A
Cagan
Tianxi
T
Cai
Vivian S
VS
Gainer
Stanley Y
SY
Shaw
Guergana
G
Savova
Susanne
S
Churchill
Elizabeth W
EW
Karlson
Shawn N
SN
Murphy
Katherine P
KP
Liao
Isaac
I
Kohane
D000328:Adult; D000465:Algorithms; D015331:Cohort Studies; D003093:Colitis, Ulcerative / Q000188:drug therapy*; D003424:Crohn Disease / Q000188:drug therapy*; D003627:Data Interpretation, Statistical; D057286:Electronic Health Records; D005260:Female; D005500:Follow-Up Studies; D005765:Gastrointestinal Agents / Q000627:therapeutic use*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors*; D055815:Young Adult
D005765:Gastrointestinal Agents; D014409:Tumor Necrosis Factor-alpha