Published online Jul 27, 2023. doi: 10.4240/wjgs.v15.i7.1485
Peer-review started: November 26, 2022
First decision: February 20, 2023
Revised: March 13, 2023
Accepted: May 22, 2023
Article in press: May 22, 2023
Published online: July 27, 2023
Oesophageal cancer is a major contributor to the worldwide cancer-related morbidity and mortality disease burden. Undertaking an oesophagectomy can offer a realistic curative option if the disease is detected in the early stages. The most significant drawback with respect to oesophagectomy is the considerable associated risk of major complications and even mortality throughout the perioperative period. Because of this, it is imperative to appropriately select surgical candidates and allocate resources closely to those at heightened risk. A vast number of multivariate risk prediction models have been constructed to assist in this decision-making which incorporate both preoperative and intraoperative factors, with some doubt existing as to which model is most reliable. This publication is the first systematic review to focus solely on models incorporating, at least in part, intraoperative factors with its ultimate goal being to determine which model most accurately forecasts perioperative outcomes.
The identification of the best risk prediction model which incorporates intraoperative data in isolation and in combination with preoperative factors would allow surgeons to utilise this model in clinical practice. Such a risk model could serve to augment clinical decision-making both in terms of prudently selecting surgical candidates and allocating resources in the postoperative period. It is expected that improved patient selection and more judicious resource allocation could lead to improved perioperative outcomes for patients with oesophageal cancer.
The objective of this research is to perform a systematic review assessing which multivariate risk model incorporating intraoperative variables, either in isolation or in conjunction with preoperative factors, best forecasts perioperative outcomes following oesophagectomy. The primary objective pertains to assessing predictive performance for mortality outcomes. The secondary objectives are to assess the predictive capacity of these models in forecasting major morbidity, overall morbidity and other key complications such as respiratory complications and anastomotic leak.
A systematic review incorporating the MEDLINE, Embase and Cochrane databases was performed from 2000-2020. The search terms were [(Oesophagectomy) AND (Risk OR predict OR score OR model) AND (Outcomes OR mortality OR morbidity OR complications OR anastomotic leak OR length of stay)]. Only multivariate based prediction models which utilised intraoperative factors, either in isolation or in combination with preoperative variables to predict perioperative outcomes following oesophagectomy were included. Articles were generated, collated then reported in accordance with preferred reporting items for systematic reviews and meta-analyses guidelines. All of the included risk models were appraised across five categories, namely clinical credibility, methodological quality, model performance, external validation and clinical effectiveness.
The initial search captured 8715 articles which was refined to 197 texts considered to be potentially relevant after deduplication, title and abstract screening. Following a detailed reading of these articles, 20 published studies were ultimately incorporated with these examining 11 multivariate risk prediction models. Eight of these combined preoperative and intraoperative data, with the other three models exclusively utilising intraoperative variables. The majority of these models were clinically credible and developed with sound methodological quality but many models had not been externally validated and none had been proven to be clinically effective in improving outcomes. Two models adequately predicted mortality, namely the Portsmouth physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) and POSSUM scores. A further two studies, the intraoperative factors and esophagectomy surgical Apgar score based nomograms were effective at predicting outcomes related to major morbidity. None of the included models were sufficiently accurate in predicting overall morbidity, respiratory complications or anastomotic leak rates.
There are a handful of credible and well-developed multivariate risk prediction models which demonstrate the capacity to discriminate perioperative mortality and major morbidity outcomes following oesophagectomy. However, there is a need to undertake more research in terms of external validation and demonstrating improved clinical outcomes by guiding patient selection and postoperative resource allocation with the use of these models.
There is an existing research gap to externally validate some of these models which are yet to be tested outside their development cohort. Further research should also take the form of a prospective randomised control trial in to compare the accuracy of clinical discretion against the results of the clinical risk prediction models in selecting appropriate surgical candidates and guiding postoperative resource allocation. Such a study could act as a catalyst to emphasise the importance of these tools which can augment decision-making and potentially lead to their widespread adoption in the care of patients undergoing oesophagectomy.