| ISSN |
1948-9366 (online) |
| 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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
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 and Muhammad Aziz |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Faisal Inayat, MBBS, Research Scientist, Department of Internal Medicine, Allama Iqbal Medical College, Allama Shabbir Ahmad Usmani Road, Faisal Town, Lahore 54550, Punjab, Pakistan. faisalinayat@hotmail.com |
| Key Words |
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. |
| Publish Date |
2024-04-22 11:03 |
| Citation |
Ali H, Inayat F, Moond V, Chaudhry A, Afzal A, Anjum Z, Tahir H, Anwar MS, Dahiya DS, Afzal MS, Nawaz G, Sohail AH, Aziz M. Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning. World J Gastrointest Surg 2024; 16(4): 1097-1108 |
| URL |
https://www.wjgnet.com/1948-9366/full/v16/i4/1097.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v16.i4.1097 |
