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. 2019 Jun 28;25(24):2990–3008. doi: 10.3748/wjg.v25.i24.2990

Table 4.

Examples of studies on gastrointestinal bleeding and/or proton pump inhibitor research by utilization of large healthcare datasets

Gastrointestinal bleeding and/or proton pump inhibitors
Country/Region Database Area of research Sample size Design, statistical methods and 3V Application
Taiwan, China Taiwan National Health Insurance Database (NHID) PUD 403567 Nationwide retrospective cohort study Effect of H. pylori therapy and PPIs on PUD
Wu et al[58], 2009
Volume, Velocity and Variety
PUD 32235 Nationwide retrospective cohort study Risk of rebleeding from PUD in ESRD patients
Wu et al[95], 2011
Volume, Velocity and Variety
PPIs 6552 Nationwide retrospective cohort study Effect of clopidogrel and PPIs on ACS
Volume, Velocity and Variety
Wu et al[59], 2010
South Korea Korean Health Insurance Review and Assessment Service (HIRA) PPIs 59233 Nationwide retrospective cohort study Effect of PPIs on thrombotic risk
Kim et al[96], 2019
Volume, Velocity and Variety
Hong Kong, China Clinical Data Analysis and Reporting System (CDARS) Dabigatran 5041 Territory-wide retrospective cohort study Risk factors for dabigatran-associated gastrointestinal bleeding
Chan et al[62], 2015
Volume, Velocity and Variety

This list is not exhaustive, but serves to provide a few distinct examples of how Big Data analysis can generate high-quality research outputs in the field of gastroenterology and hepatology. 3V: Volume/velocity/variety; PUD: Peptic ulcer disease; H. pylori: Helicobacter pylori; PPIs: Proton pump inhibitors; ESRD: End-stage renal disease; ACS: Acute coronary syndrome.