BPG is committed to discovery and dissemination of knowledge
Articles Published Processes
12/20/2024 3:53:47 AM | Browse: 118 | Download: 707
 |
Received |
|
2024-09-02 16:28 |
|
Peer-Review Started |
|
2024-09-02 16:28 |
|
First Decision by Editorial Office Director |
|
2024-09-24 08:12 |
|
Return for Revision |
|
2024-09-24 08:12 |
|
Revised |
|
2024-10-05 08:50 |
|
Publication Fee Transferred |
|
2024-11-29 06:50 |
|
Second Decision by Editor |
|
2024-11-25 02:34 |
 |
Second Decision by Editor-in-Chief |
|
|
|
Final Decision by Editorial Office Director |
|
2024-11-25 06:35 |
|
Articles in Press |
|
2024-11-25 06:35 |
|
Edit the Manuscript by Language Editor |
|
|
|
Typeset the Manuscript |
|
2024-12-13 02:42 |
|
Publish the Manuscript Online |
|
2024-12-20 03:53 |
| ISSN |
1007-9327 (print) and 2219-2840 (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: http://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
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Surgery |
| Manuscript Type |
Retrospective Study |
| Article Title |
Prediction and stratification for the surgical adverse events after minimally invasive esophagectomy: A two-center retrospective study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Qi-Hong Zhong, Jiang-Shan Huang, Fei-Long Guo, Jing-Yu Wu, Mao-Xiu Yuan, Jia-Fu Zhu, Wen-Wei Lin, Sui Chen, Zhen-Yang Zhang and Jiang-Bo Lin |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Joint Funds for the Innovation of Science and Technology, Fujian Province |
2023Y9187, 2021Y9057 |
|
| Corresponding Author |
Jiang-Bo Lin, PhD, Professor, Department of Thoracic Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou 350001, Fujian Province, China. jiangbolin99@163.com |
| Key Words |
Surgical adverse events; Minimally invasive esophagectomy; Esophageal cancer; Stratification model; Perioperative management |
| Core Tip |
In this study, a predictive model was developed to stratify the risk of surgical adverse events following minimally invasive esophagectomy for esophageal cancer. By identifying key risk factors, including chronic obstructive pulmonary disease, low forced expiratory volume in the first second, and hypoalbuminemia, the model enhances preoperative assessment, allowing for targeted interventions. The model’s high predictive accuracy underscores its potential for integration into clinical practice to improve patient outcomes and reduce postoperative complications after minimally invasive esophagectomy. This approach represents a significant advancement in personalized surgical management for esophageal cancer patients. |
| Publish Date |
2024-12-20 03:53 |
| Citation |
Zhong QH, Huang JS, Guo FL, Wu JY, Yuan MX, Zhu JF, Lin WW, Chen S, Zhang ZY, Lin JB. Prediction and stratification for the surgical adverse events after minimally invasive esophagectomy: A two-center retrospective study. World J Gastroenterol 2025; 31(3): 101041 |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i3/101041.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i3.101041 |
All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.
