Systematic Reviews
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Mar 27, 2023; 15(3): 450-470
Published online Mar 27, 2023. doi: 10.4240/wjgs.v15.i3.450
Preoperative risk modelling for oesophagectomy: A systematic review
James Paul Grantham, Amanda Hii, Jonathan Shenfine
James Paul Grantham, Department of General Surgery, Modbury Hospital, Adelaide 5092, South Australia, Australia
Amanda Hii, Department of General Surgery, Modbury Hospital, Modbury 5092, South Australia, Australia
Jonathan Shenfine, General Surgical Unit, Jersey General Hospital, Saint Helier JE1 3QS, Jersey, United Kingdom
Author contributions: Grantham JP and Shenfine J designed the research; Grantham JP and Hii A performed the research and analysed the data; Grantham JP, Hii A and Shenfine J all contributed to writing and reviewing the paper.
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: James Paul Grantham, MBBS, MSc, Doctor, Department of General Surgery, Modbury Hospital, Smart Road, Adelaide 5092, South Australia, Australia. jamespgrantham91@gmail.com
Received: November 21, 2022
Peer-review started: November 21, 2022
First decision: December 26, 2022
Revised: January 9, 2023
Accepted: February 22, 2023
Article in press: February 22, 2023
Published online: March 27, 2023
Processing time: 126 Days and 1.6 Hours
ARTICLE HIGHLIGHTS
Research background

Oesophageal cancer is the eighth most common type of cancer and sixth leading cause of cancer-related death worldwide. If it is detected in the early stages, an oesophagectomy can be undertaken with realistic curative intent. Unfortunately, this surgery comes with a significant morbidity burden and can result in fatal outcomes, making appropriate selection of surgical candidates imperative. Numerous multivariate risk prediction models have been devised to augment this decision-making with ongoing conjecture as to which risk prediction tool is most reliable. This publication is the first systematic review in seven years to attempt to resolve which model most accurately predicts perioperative outcomes following oesophagectomy.

Research motivation

The identification of the best preoperative risk prediction model would allow surgeons apply this to clinical practice. Such a tool may assist in augmenting clinical decision making to better identify and counsel appropriate surgical candidates for oesophagectomy. It is expected that improved patient selection would lead to overall improved perioperative outcomes for patients suffering from oesophageal cancer.

Research objectives

The objective of this research is to conduct a contemporary systematic review assessing which preoperative multivariate risk model best predicts perioperative oesophagectomy outcomes. The primary objective relates to appraising predictive performance for mortality outcomes. The secondary objectives are to assess the ability of the multivariate models in forecasting major morbidity, overall morbidity and specific key complications such as respiratory complications and anastomotic leak.

Research methods

A systematic review incorporating the MEDLINE, Embase and Cochrane databases was conducted from 2000-2020. Applied search terms were ((Oesophagectomy) AND (Risk OR predict OR model OR score) AND (Outcomes OR complications OR morbidity OR mortality OR length of stay OR anastomotic leak)). Only multivariate based tools which utilised exclusively data available preoperatively to predict perioperative outcomes following oesophagecotmy were included with articles generated, collated and then reported in accordance with PRISMA guidelines. All risk models were appraised across the five domains of clinical credibility, methodological quality, model performance, external validation and clinical effectiveness.

Research results

The initial search yielded 8715 articles which was reduced to 197 potentially relevant texts after deduplication, title and abstract screening. Following detailed assessment of these articles, 27 published studies were ultimately included with these examining 21 multivariate preoperative risk prediction models. The majority of models were clinically credible with sound methodological quality but many models still require external validation and none had yet proven clinical effectiveness with their adoption. Three models adequately predicted perioperative mortality (National Surgical Quality Improvement Program surgical risk calculator, revised Society of Thoracic Surgeons oesophagectomy composite score and Takeuchi model) whilst two (predicting postoperative complications score and prognostic nutritional index-multivariate model) predicted major morbidity sufficiently.

Research conclusions

There are a few well-constructed and credible multivariate risk prediction models that demonstrate promise in forecasting perioperative mortality and major morbidity outcomes. However, more research is required in the sphere of external validation and to demonstrate improved clinical outcomes with the adoption of these models in preoperative surgical patient selection.

Research perspectives

There is a research gap in externally validating some of these models which have yet to be assessed outside of their development cohort. Ultimately, the direction of future research should involve the development of a prospective randomised controlled trial in which one group would utilise clinical discretion with the other applying one of the promising preoperative risk prediction models in determining appropriate surgical candidates. In such a trial, clinical effectiveness with the adoption of a risk prediction model could be demonstrated if improved patient outcomes were observed. This would provide compelling evidence for the broader application of such a risk prediction model in patient selection for oesophagectomy.