Akbar K Waljee1,2,3, Mohamed Noureldin2,4, Jeffrey A Berinstein2, Shirley A Cohen-Mekelburg1,2, Beth I Wallace1,5, Kelly C Cushing2, David A Hanauer5, Toby P Keeney-Bonthrone6, Brahmajee Nallamothu1,3,7, Peter D R Higgins2. 1. VA Center for Clinical Management Research, VA Ann Arbor Health Care System. 2. Department of Internal Medicine, Division of Gastroenterology and Hepatology, Michigan Medicine. 3. Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), Ann Arbor. 4. Department of Internal Medicine, Beaumont Hospital, Dearborn. 5. Department of Learning Health Sciences, University of Michigan. 6. Department of Surgery, St. Joseph Mercy Hospital. 7. Department of Internal Medicine, Division of Cardiology, Michigan Medicine, Ann Arbor, Michigan, USA.
Abstract
BACKGROUND: Massive amounts of patient data are captured daily in electronic medical records (EMR). Utilizing the power of such large data may help identify disease associations and generate hypotheses that can lead to a better understanding of disease associations and mechanisms. We aimed to comprehensively identify and validate associations between inflammatory bowel disease (IBD) and concurrent comorbid diagnoses. METHODS: We performed a cross-sectional study using EMR data collected between 1986 and 2009 at a large tertiary referral center to identify associations with a diagnosis of IBD. The resulting associations were externally validated using the Truven MarketScan database, a large nationwide dataset of private insurance claims. RESULTS: A total of 6225 IBD patients and 31 125 non-IBD controls identified using EMR data were used to abstract 41 comorbid diagnoses associated with an IBD diagnosis. The strongest associations included Clostridiodes difficile infection, pyoderma gangrenosum, parametritis, pernicious anemia, erythema nodosum, and cytomegalovirus infection. Two IBD association clusters were found, including diagnoses of nerve conduction abnormalities and nonspecific inflammatory conditions of organs outside the gut. These associations were validated in a national cohort of 80 907 patients with IBD and 404 535 age- and sex-matched controls. CONCLUSION: We leveraged a big data approach to identify several associations between IBD and concurrent comorbid diagnoses. EMR and big data provide the opportunity to explore disease associations with large sample sizes. Further studies are warranted to refine the characterization of these associations and evaluate their usefulness for increasing our understanding of disease associations and mechanisms.
BACKGROUND: Massive amounts of patient data are captured daily in electronic medical records (EMR). Utilizing the power of such large data may help identify disease associations and generate hypotheses that can lead to a better understanding of disease associations and mechanisms. We aimed to comprehensively identify and validate associations between inflammatory bowel disease (IBD) and concurrent comorbid diagnoses. METHODS: We performed a cross-sectional study using EMR data collected between 1986 and 2009 at a large tertiary referral center to identify associations with a diagnosis of IBD. The resulting associations were externally validated using the Truven MarketScan database, a large nationwide dataset of private insurance claims. RESULTS: A total of 6225 IBD patients and 31 125 non-IBD controls identified using EMR data were used to abstract 41 comorbid diagnoses associated with an IBD diagnosis. The strongest associations included Clostridiodes difficile infection, pyoderma gangrenosum, parametritis, pernicious anemia, erythema nodosum, and cytomegalovirus infection. Two IBD association clusters were found, including diagnoses of nerve conduction abnormalities and nonspecific inflammatory conditions of organs outside the gut. These associations were validated in a national cohort of 80 907 patients with IBD and 404 535 age- and sex-matched controls. CONCLUSION: We leveraged a big data approach to identify several associations between IBD and concurrent comorbid diagnoses. EMR and big data provide the opportunity to explore disease associations with large sample sizes. Further studies are warranted to refine the characterization of these associations and evaluate their usefulness for increasing our understanding of disease associations and mechanisms.