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
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.