The large and growing size of the healthcare system makes it imperative to understand what is happening to us, the recipients of healthcare, to be able to efficiently conduct research to improve healthcare delivery and to improve the state of biomedicine by advancing its science. i2b2, """"""""Informatics for Integrating Biology and the Bedside"""""""" seeks to provide this instrumentation using the informational by products of healthcare and the biological materials accumulated through the delivery of healthcare. This complements existing efforts to create prospective cohort studies or trials outside the delivery of routine healthcare. In the first round of i2b2, we demonstrated that we could identify known adverse events and phenotypically select and then genotype patients for genetic association at approximately 1/10* of the price and less than l/10 of the time usually entailed to develop such populations for study. The challenge we have set ourselves for the next methodological challenge in i2b2 is the development of Virtual Cohort Studies (VCS) encompassing the population of a healthcare system as study subjects and asking questions of comparative effectiveness, unforeseen adverse events and identification of clinically relevant subpopulations including both clinical and genome-scale measures. We will be comparing the results of the VCS to those of carefully planned and executed cohort studies such as the Framingham Heart Study. VCS will require multiple methodological advances and tools development including in the disciplines of natural language processing, temporal reasoning, predictive modeling, biostatistics and machine learning. VCS methods will be tested by two driving biology projects, the first studying a collection of autoimmune diseases and the second type 2 diabetes. In both projects, VCS methods will be applied to investigate the components of cardiovascular risk from the genetic to the epigenetic and including the full range of clinical history including medications exposure. A systems/integrative approach will be taken to identify commonalities in these risk profiles across these disparate disease domains. VCS methods will be shared with i2b2 user community under open source governance while i2b2 user community contributions are folded into the i2b2 toolkit.

Public Health Relevance

The i2b2 model of using existing clinical data for high throughput, cost effective, and timely research promises to rapidly leverage our nation's ability to better understand the existing disease burden and how best to achieve cost-effective clinical effectiveness, including new drug targets, new uses of existing drugs and improved management of existing disease.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54LM008748-10
Application #
8519533
Study Section
Special Emphasis Panel (ZRG1-BST-K (52))
Program Officer
Florance, Valerie
Project Start
2004-09-15
Project End
2014-09-14
Budget Start
2013-09-15
Budget End
2014-09-14
Support Year
10
Fiscal Year
2013
Total Cost
$3,468,437
Indirect Cost
$764,183
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Manrai, Arjun K; Funke, Birgit H; Rehm, Heidi L et al. (2016) Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med 375:655-65
Ananthakrishnan, Ashwin N; Cagan, Andrew; Cai, Tianxi et al. (2016) Statin Use Is Associated With Reduced Risk of Colorectal Cancer in Patients With Inflammatory Bowel Diseases. Clin Gastroenterol Hepatol 14:973-9
Castro, V M; Kong, S W; Clements, C C et al. (2016) Absence of evidence for increase in risk for autism or attention-deficit hyperactivity disorder following antidepressant exposure during pregnancy: a replication study. Transl Psychiatry 6:e708
Savova, Virginia; Chun, Sung; Sohail, Mashaal et al. (2016) Genes with monoallelic expression contribute disproportionately to genetic diversity in humans. Nat Genet 48:231-7
Nazeen, Sumaiya; Palmer, Nathan P; Berger, Bonnie et al. (2016) Integrative analysis of genetic data sets reveals a shared innate immune component in autism spectrum disorder and its co-morbidities. Genome Biol 17:228
Lerin, Carles; Goldfine, Allison B; Boes, Tanner et al. (2016) Defects in muscle branched-chain amino acid oxidation contribute to impaired lipid metabolism. Mol Metab 5:926-36
Manrai, Arjun K; Wang, Brice L; Patel, Chirag J et al. (2016) REPRODUCIBLE AND SHAREABLE QUANTIFICATIONS OF PATHOGENICITY. Pac Symp Biocomput 21:231-42
Lin, Chen; Dligach, Dmitriy; Miller, Timothy A et al. (2016) Multilayered temporal modeling for the clinical domain. J Am Med Inform Assoc 23:387-95
Ananthakrishnan, Ashwin N; Cagan, Andrew; Cai, Tianxi et al. (2016) Comparative Effectiveness of Infliximab and Adalimumab in Crohn's Disease and Ulcerative Colitis. Inflamm Bowel Dis 22:880-5
Ananthakrishnan, Ashwin N; Cagan, Andrew; Cai, Tianxi et al. (2016) Identification of Nonresponse to Treatment Using Narrative Data in an Electronic Health Record Inflammatory Bowel Disease Cohort. Inflamm Bowel Dis 22:151-8

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