Systematic Reviews
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Aug 15, 2025; 17(8): 109405
Published online Aug 15, 2025. doi: 10.4251/wjgo.v17.i8.109405
Predictive model for early postoperative stomal complications in colorectal cancer: A systematic review
Payal Kaw, Ashok Kumar
Payal Kaw, Ashok Kumar, Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
Co-first authors: Payal Kaw and Ashok Kumar.
Author contributions: Kaw P did the literature search and wrote the manuscript; Kumar A designed the concept, revised and edited the manuscript; Kaw P and Kumar A made equal contributions as co-first authors and approved the final version to publish.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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/
Corresponding author: Ashok Kumar, FACS, FASCRS, FRCS, FRCS (Ed), Full Professor, Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raebareli Road, Lucknow 226014, Uttar Pradesh, India. doc.ashokgupta@gmail.com
Received: May 12, 2025
Revised: May 22, 2025
Accepted: July 17, 2025
Published online: August 15, 2025
Processing time: 96 Days and 18.2 Hours
Abstract
BACKGROUND

Stomal complications though small in early postoperative period, but poses significant morbidity, therapeutic challenge, delay in adjuvant treatment and sometimes even leads to mortality. Predictive model for early detection of stomal complications is important to improve the outcome. A model including patients and disease related factors, intraoperative surgical techniques and biochemical markers would be a better determinant to anticipate early stomal complications. Incorporation of emerging tools and technology such as artificial intelligence (AI), will further improve the prediction.

AIM

To identify various risk factors and models for prediction of early post operative stomal complications in colorectal cancer (CRC) surgery.

METHODS

Published literatures on early postoperative stomal complications in CRC surgery were systematically reviewed between 1995 and 2024 from online search engines PubMed and MEDLINE.

RESULTS

Twenty-four observational studies focused on identifying various risk factors for early post operative stomal complications in CRC surgery were analyzed. Stomal complications in CRC are influenced by several factors such as disease factors, patient-specific characteristics, and surgical techniques. There are some biomarkers and tools loke AI which may play significant roles in early detection.

CONCLUSION

Careful analysis of these factors, changes in biochemical parameters, and application of AI, a predictive model for stomal complications can be generated, to help in early detection, prompt action to achieve better outcomes.

Keywords: Stomal complications; Colorectal cancer; Predictive model; Artificial intelligence; Patient’ factors; Surgeon factor; Disease factors; Biochemical markers

Core Tip: Early stomal complications following colorectal cancer surgery, increases the morbidity, hospital stay and delays the adjuvant treatment. Therefore, it is important to detect it before it is clinically become apparent. Prediction of stomal complications and early corrective measure is essential to improve the outcome. A predictive model utilizing patient factors, disease factors, surgeon factors, and biochemical parameters utilizing artificial intelligence may serve a determinant to anticipate early stomal complications.