Predicting surgical site infections following laparoscopic bariatric surgery: development of the BariWound tool using the MBSAQIP database.

BACKGROUND

Although bariatric surgery is a safe procedure for severe obesity, incisional surgical site infections (SSI) remain a significant cause of morbidity. Bariatric surgery patients are at high risk due to obesity and diabetes. The objective of this study was to develop a predictive tool for incisional SSI within 30 days of bariatric surgery.

METHODS

Data were retrieved from the 2015 and 2016 MBSAQIP databases. This study included patients who underwent primary laparoscopic Roux-en-Y gastric bypass (LRYGB) or laparoscopic sleeve gastrectomy (LSG). The primary outcome of interest was incisional SSI occurring within 30 days. Surgeries performed in 2015 were used in a derivation cohort and the predictive tool was validated against the 2016 cohort. A forward selection algorithm was used to build a logistic regression model predicting probability of SSI.

RESULTS

A total of 274,187 patients were included with 71.7% being LSG and 28.3% LRYGB. 0.7% of patients had a SSI in which 71.0% had an incisional SSI, and 29.9% had an organ/space SSI. Of patients who had an incisional SSI, 88.7% were superficial, 10.9% were deep, and 0.4% were both. A prediction model to assess for risk of incisional SSI, BariWound, was derived and validated. BariWound consists of procedure type, chronic steroid or immunosuppressant use, gastroesophageal reflux disease, obstructive sleep apnea, sex, type 2 diabetes, hypertension, operative time, and body mass index. It stratifies individuals into very high (> 10%), high (5-10%), medium (1-5%), and low risk (< 1%) groups. This model accurately predicted events in the validation cohort with an area under the receiver operating characteristic curve of 0.73.

CONCLUSIONS

BariWound accurately predicted the risk of 30-day incisional SSI in individuals undergoing bariatric surgery. Stratifying low- and high-risk groups allows for customized SSI prophylactic measures for patients in various risk categories and potentially enables future research targeted at high-risk patients.