Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning

Abstract

BACKGROUND

Roux-en-Y gastric bypass (RYGB) is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity. It aids in significant weight loss and improves obesity-related medical conditions. Despite its effectiveness, postoperative care still has challenges. Clinical evidence shows that venous thromboembolism (VTE) is a leading cause of 30-d morbidity and mortality after RYGB. Therefore, a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates.

AIM

To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB.

METHODS

Using the 2016–2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program, data from 6526 patients (body mass index ≥ 40 kg/m²) who underwent RYGB were analyzed. A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB. The resultant risk scores were derived from the coefficients of statistically significant variables. The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation.

RESULTS

Of the 26 initial variables, six predictors were identified. These included a history of chronic obstructive pulmonary disease with a regression coefficient (Coef) of 2.54 (P < 0.001), length of stay (Coef 0.08, P < 0.001), prior deep venous thrombosis (Coef 1.61, P < 0.001), hemoglobin A1c > 7% (Coef 1.19, P < 0.001), venous stasis history (Coef 1.43, P < 0.001), and preoperative anticoagulation use (Coef 1.24, P < 0.001). These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB. The risk model's area under the curve (AUC) was 0.79 [95% confidence interval (CI): 0.63-0.81], showing good discriminatory power, achieving a sensitivity of 0.60 and a specificity of 0.91. Without training, the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63 (95%CI: 0.62-0.64) and endoscopic sleeve gastroplasty with an AUC of 0.76 (95%CI: 0.75-0.78).

CONCLUSION

This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients, offering insights into factors that heighten the risk of VTE events.

Keywords: Roux-en-Y gastric bypass, Venous thromboembolism, Machine learning, Bariatric surgery, Predictive modeling

Core Tip: Venous thromboembolism (VTE) is an uncommon but important cause of morbidity and mortality following Roux-en-Y gastric bypass (RYGB). Clinical evidence regarding VTE risk stratification after RYGB remains limited. Using a multicenter database, this is the first retrospective cross-sectional study that used supervised machine learning to develop and internally validate a scoring system to assess the 30-d individualized risk of VTE post-RYGB. Our model uses only six preoperative variables, including a history of chronic obstructive pulmonary disease, length of stay, previous deep venous thrombosis, hemoglobin A1c > 7%, prior venous stasis, and preoperative anticoagulation use. Our findings may help to improve clinical outcomes and procedural safety for patients undergoing RYGB.