Waljee Akbar K, Noureldin Mohamed, Berinstein Jeffrey A, Cohen-Mekelburg Shirley A, Wallace Beth I, Cushing Kelly C, Hanauer David A, Keeney-Bonthrone Toby P, Nallamothu Brahmajee, Higgins Peter D R
VA Center for Clinical Management Research, VA Ann Arbor Health Care System.
Department of Internal Medicine, Division of Gastroenterology and Hepatology, Michigan Medicine.
Eur J Gastroenterol Hepatol. 2020 Oct;32(10):1341-1347. doi: 10.1097/MEG.0000000000001869.
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.
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.
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.
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.
电子病历（EMR）每天都会记录大量患者数据。利用这些大数据的力量可能有助于识别疾病关联并生成假设，从而更好地理解疾病关联和机制。我们旨在全面识别并验证炎症性肠病（IBD）与并发共病诊断之间的关联。
我们进行了一项横断面研究，使用1986年至2009年在一家大型三级转诊中心收集的EMR数据来识别与IBD诊断相关的关联。使用Truven MarketScan数据库（一个全国性的大型私人保险索赔数据集）对所得关联进行外部验证。
使用EMR数据识别出的6225例IBD患者和31125例非IBD对照被用于提取与IBD诊断相关的41种共病诊断。最强的关联包括艰难梭菌感染、坏疽性脓皮病、子宫旁炎、恶性贫血、结节性红斑和巨细胞病毒感染。发现了两个IBD关联簇，包括神经传导异常诊断和肠道外器官的非特异性炎症性疾病。这些关联在一个由80907例IBD患者和404535例年龄及性别匹配对照组成的全国队列中得到了验证。
我们利用大数据方法识别了IBD与并发共病诊断之间的几种关联。EMR和大数据为探索大样本量的疾病关联提供了机会。有必要进行进一步研究以完善这些关联的特征描述，并评估它们对于增进我们对疾病关联和机制理解的有用性。
