Published online Mar 27, 2023. doi: 10.4240/wjgs.v15.i3.450
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
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.
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.
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.
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.
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.
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.
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.