title: Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts.
abstract: BACKGROUND
  Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study.
  
  
  RESULTS
  We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors.
  
  
  CONCLUSIONS
  We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.
authors: Division of Rheumatology, Brigham and Women's Hospital, Boston, Massachusetts, 02115, United States of America; Harvard Medical School, Boston, Massachusetts, 02115, United States of America.
authors: Katherine P
authors: KP
authors: Liao
authors: Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, 02114, United States of America.
authors: Ashwin N
authors: AN
authors: Ananthakrishnan
authors: Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, 02114, United States of America.
authors: Vishesh
authors: V
authors: Kumar
authors: Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, 02115, United States of America.
authors: Zongqi
authors: Z
authors: Xia
authors: Research Computing, Partners Healthcare, Charlestown, Massachusetts, 02125, United States of America.
authors: Andrew
authors: A
authors: Cagan
authors: Research Computing, Partners Healthcare, Charlestown, Massachusetts, 02125, United States of America.
authors: Vivian S
authors: VS
authors: Gainer
authors: Research Computing, Partners Healthcare, Charlestown, Massachusetts, 02125, United States of America.
authors: Sergey
authors: S
authors: Goryachev
authors: Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, 02115, United States of America.
authors: Pei
authors: P
authors: Chen
authors: Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, 02115, United States of America.
authors: Guergana K
authors: GK
authors: Savova
authors: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, United States of America.
authors: Denis
authors: D
authors: Agniel
authors: Partners Healthcare Information Systems, Boston, Massachusetts, 02115, United States of America.
authors: Susanne
authors: S
authors: Churchill
authors: Tufts University School of Medicine, Boston, Massachusetts, 02111, United States of America.
authors: Jaeyoung
authors: J
authors: Lee
authors: Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, 02114, United States of America.
authors: Shawn N
authors: SN
authors: Murphy
authors: Division of Rheumatology, Brigham and Women's Hospital, Boston, Massachusetts, 02115, United States of America; Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts, 02115, United States of America.
authors: Robert M
authors: RM
authors: Plenge
authors: Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States of America.
authors: Peter
authors: P
authors: Szolovits
authors: Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, 02115, United States of America.
authors: Isaac
authors: I
authors: Kohane
authors: Harvard Medical School, Boston, Massachusetts, 02115, United States of America; Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, 02114, United States of America.
authors: Stanley Y
authors: SY
authors: Shaw
authors: Division of Rheumatology, Brigham and Women's Hospital, Boston, Massachusetts, 02115, United States of America; Harvard Medical School, Boston, Massachusetts, 02115, United States of America.
authors: Elizabeth W
authors: EW
authors: Karlson
authors: Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, 02115, United States of America.
authors: Tianxi
authors: T
authors: Cai
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000150:complications / Q000453:epidemiology / Q000503:physiopathology; D003324:Coronary Artery Disease / Q000150:complications / Q000453:epidemiology* / Q000503:physiopathology; D003920:Diabetes Mellitus / Q000453:epidemiology* / Q000503:physiopathology; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D006949:Hyperlipidemias / Q000150:complications / Q000453:epidemiology / Q000503:physiopathology; D015212:Inflammatory Bowel Diseases / Q000150:complications / Q000453:epidemiology / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D010641:Phenotype; D012307:Risk Factors