Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Apr 27, 2024; 16(4): 1097-1108
Published online Apr 27, 2024. doi: 10.4240/wjgs.v16.i4.1097
Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning
Hassam Ali, Faisal Inayat, Vishali Moond, Ahtshamullah Chaudhry, Arslan Afzal, Zauraiz Anjum, Hamza Tahir, Muhammad Sajeel Anwar, Dushyant Singh Dahiya, Muhammad Sohaib Afzal, Gul Nawaz, Amir H Sohail, Muhammad Aziz
Hassam Ali, Arslan Afzal, Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, NC 27834, United States
Faisal Inayat, Gul Nawaz, Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab 54550, Pakistan
Vishali Moond, Department of Internal Medicine, Saint Peter's University Hospital and Robert Wood Johnson Medical School, New Brunswick, NJ 08901, United States
Ahtshamullah Chaudhry, Department of Internal Medicine, St. Dominic's Hospital, Jackson, MS 39216, United States
Zauraiz Anjum, Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, United States
Hamza Tahir, Department of Internal Medicine, Jefferson Einstein Hospital, Philadelphia, PA 19141, United States
Muhammad Sajeel Anwar, Department of Internal Medicine, UHS Wilson Medical Center, Johnson, NY 13790, United States
Dushyant Singh Dahiya, Division of Gastroenterology, Hepatology and Motility, The University of Kansas School of Medicine, Kansas, KS 66160, United States
Muhammad Sohaib Afzal, Department of Internal Medicine, Louisiana State University Health, Shreveport, LA 71103, United States
Amir H Sohail, Department of Surgery, University of New Mexico School of Medicine, Albuquerque, NM 87106, United States
Muhammad Aziz, Department of Gastroenterology and Hepatology, The University of Toledo, Toledo, OH 43606, United States
Author contributions: Ali H, Inayat F, Moond V, Chaudhry A, Afzal A, and Anjum Z concepted and designed the study, participated in the acquisition of data, interpretation of results, writing of the original draft, and critical revisions of the important intellectual content of the final manuscript; Tahir H, Anwar MS, Dahiya DS, Afzal MS, Nawaz G, and Sohail AH contributed to the analysis and interpretation of results and drafting of the manuscript; Aziz M reviewed, revised, and improved the manuscript by suggesting pertinent modifications; and all authors critically assessed, edited, and approved the final manuscript and are accountable for all aspects of the work.
Institutional review board statement: The Metabolic and Bariatric Surgery Accreditation Quality Improvement Program database is based on de-identified aggregated data with accepted privacy standards. This database does not report patient identifiers, clinician information, or hospital locations. This study did not require institutional review board approval.
Informed consent statement: Participants were not required to give informed consent to this retrospective study since the analysis of baseline characteristics used anonymized clinical data.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Faisal Inayat, MBBS, Research Scientist, Department of Internal Medicine, Allama Iqbal Medical College, Allama Shabbir Ahmad Usmani Road, Faisal Town, Lahore, Punjab 54550, Pakistan.
Received: December 30, 2023
Peer-review started: December 30, 2023
First decision: January 16, 2024
Revised: February 7, 2024
Accepted: March 5, 2024
Article in press: March 5, 2024
Published online: April 27, 2024

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.


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


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.


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).


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.