Outcomes after bariatric surgery according to large databases: a systematic review

Area of Science:

• Medical Informatics
• Surgical Outcomes Research
• Big Data Analytics in Healthcare

Background:

• Technological advancements enable the creation of large datasets ('big data') for research.
• Major US surgical outcome databases include ACS-NSQIP, HCUP NIS, and MBSAQIP.
• Bariatric surgery research increasingly relies on these extensive data repositories.

Purpose of the Study:

• To systematically review and evaluate the clinical impact of studies utilizing major US surgical databases.
• Focus on bariatric surgery outcomes research.
• Assess the contribution of big data to the understanding of bariatric surgery.

Main Methods:

• Systematic review adhering to Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines.
• Literature search conducted on the PubMed database.
• Analysis of all identified outcomes related to bariatric surgery.

Main Results:

• Fifty-four studies published between 2005 and February 2017 were included.
• Studies covered surgical techniques, morbidity/mortality, readmission, disease-specific outcomes, training, and socio-economic factors.
• The majority of studies (42) utilized ACS-NSQIP data, followed by NIS (9) and MBSAQIP (3).

Conclusions:

• Large databases offer valuable complementary information for bariatric surgery research in the USA.
• These databases serve as external validation for evidence-based medicine, especially when randomized trials are infeasible.
• Big data enhances surgical knowledge by providing insights into infrequent situations and improving outcome quality.