Abstract
Background
Bariatric surgery is an effective treatment for severe obesity; however, postoperative venous thromboembolism (VTE) remains a leading cause of morbidity and mortality. The objective of this study is to develop a tool to stratify individuals undergoing laparoscopic bariatric surgery according to their 30-day VTE risk.
Methods
This is a retrospective cohort study of the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database. This registry collects data specific for metabolic or bariatric surgery with 30-day outcomes from 791 centers. Individuals undergoing primary laparoscopic Roux-en-Y gastric bypass (LRYGB) or laparoscopic sleeve gastrectomy (LSG) were included. Characteristics associated with 30-day VTE were identified using univariate and multivariable analyses. A predictive model, BariClot, was derived from a randomly-generated derivation cohort using a forward selection algorithm. BariClot's robustness was tested against a validation cohort of subjects not included in the derivation cohort. The calibration and discrimination of two previously published VTE risk tools were assessed in the MBSAQIP population and compared to BariClot.
Results
A total of 274,221 patients underwent LRYGB or LSG. Overall, 1106 (0.4%) patients developed VTE, 452 (0.2%) developed pulmonary embolism, and 43 (0.02%) died due to VTE. VTE was the most commonly identified cause of 30-day mortality. A prediction model to assess for risk of VTE, BariClot, was derived and validated. BariClot consists of history of VTE, operative time, race, and functional status. It stratifies individuals into very high (> 2%), high (1–2%), medium (0.3–1%), and low risk groups (< 0.3%). This model accurately predicted events in the validation cohort and outperformed previously published scoring systems.
Conclusions
BariClot is a predictive tool that stratifies individuals undergoing bariatric surgery based on 30-day VTE risk. Stratifying low- and high-risk populations for VTE allows for informed clinical decision-making and potentially enables further research on customized prophylactic measures for low- and high-risk populations.
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