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

ARTICLE HIGHLIGHTS

Research background

The escalating global prevalence of obesity has prompted the advancement of various therapeutic interventions. Roux-en-Y gastric bypass (RYGB) has established efficacy, particularly for class III obesity. However, despite its benefits, postoperative complications like venous thromboembolism (VTE) remain a significant concern due to their contribution to morbidity and mortality within 30 d post-surgery. This study addresses the critical gap in clinical risk stratification and predictive modeling for VTE post-RYGB.

Research motivation

This research is driven by the need to develop a simple and reliable RYBG-specific predictive model for VTE. The goal is to mitigate the 30-d morbidity and mortality associated with VTE by enabling clinicians to identify high-risk individuals through a validated scoring system, thereby guiding preventive strategies and optimizing patient management post-RYGB.

Research objectives

The primary objective of this study was to construct and internally validate a scoring system for the prediction of individualized VTE risk within 30 d after RYGB. By focusing on preoperative variables, the study aimed to deliver a practical tool for clinicians to enhance preoperative risk stratification and improve overall patient outcomes.

Research methods

Utilizing data from the Metabolic and Bariatric Surgery Accreditation Quality Improvement Program database, this research used a backward elimination multivariate analysis to determine the predictors of VTE. The performance of the model was validated using receiver operating curves and 5-fold cross-validation.

Research results

Our study based on multivariate analysis identified six significant predictors: A history of chronic obstructive pulmonary disease, length of stay, prior deep venous thrombosis, hemoglobin A1c, a history of venous stasis, and preoperative anticoagulation use, each quantified by robust regression coefficients. The derived risk model exhibited commendable predictive performance with an area under the curve of 0.79, sensitivity of 0.60, and specificity of 0.91. This model also demonstrated satisfactory predictive capability in laparoscopic sleeve gastrectomy and endoscopic sleeve gastroplasty populations.

Research conclusions

Our study concludes that the devised risk model, underpinned by supervised machine learning, constitutes a significant step forward in preoperative risk stratification for VTE. It provides a clinically relevant, evidence-based tool that simplifies the assessment process without compromising accuracy through backward elimination multivariate analysis. This approach distills a comprehensive variable set down to six critical predictors, advancing the precision of risk stratification for VTE post-RYGB. The innovation of this study lies in its machine learning-based algorithm, which demonstrates a significant improvement in the predictive accuracy of short-term thromboembolic complications when compared to traditional statistical models.

Research perspectives

Our model stands out for its simplicity and clinical applicability, potentially aiding in the preoperative assessment of VTE risk and the tailoring of prophylactic measures. Future research should focus on external validation of the scoring system across diverse populations and healthcare settings. Moreover, incorporating additional variables, such as perioperative data, may further refine the predictive capability of the model. Expansion to include other surgical procedures may also be considered, broadening the scope and impact of the research findings.