Grantham James Paul, Hii Amanda, Shenfine Jonathan
Department of General Surgery, Modbury Hospital, Modbury 5092, South Australia, Australia.
Department of General Surgical Unit, Jersey General Hospital, Saint Helier JE1 3QS, Jersey, United Kingdom.
World J Gastrointest Surg. 2023 Jul 27;15(7):1485-1500. doi: 10.4240/wjgs.v15.i7.1485.
Oesophageal cancer is the eighth most common malignancy worldwide and is associated with a poor prognosis. Oesophagectomy remains the best prospect for a cure if diagnosed in the early disease stages. However, the procedure is associated with significant morbidity and mortality and is undertaken only after careful consideration. Appropriate patient selection, counselling and resource allocation is essential. Numerous risk models have been devised to guide surgeons in making these decisions.
To evaluate which multivariate risk models, using intraoperative information with or without preoperative information, best predict perioperative oesophagectomy outcomes.
A systematic review of the MEDLINE, EMBASE and Cochrane databases was undertaken from 2000-2020. The search terms used were [(Oesophagectomy) AND (Model OR Predict OR Risk OR score) AND (Mortality OR morbidity OR complications OR outcomes OR anastomotic leak OR length of stay)]. Articles were included if they assessed multivariate based tools incorporating preoperative and intraoperative variables to forecast patient outcomes after oesophagectomy. Articles were excluded if they only required preoperative or any post-operative data. Studies appraising univariate risk predictors such as preoperative sarcopenia, cardiopulmonary fitness and American Society of Anesthesiologists score were also excluded. The review was conducted following the preferred reporting items for systematic reviews and meta-analyses model. All captured risk models were appraised for clinical credibility, methodological quality, performance, validation and clinical effectiveness.
Twenty published studies were identified which examined eleven multivariate risk models. Eight of these combined preoperative and intraoperative data and the remaining three used only intraoperative values. Only two risk models were identified as promising in predicting 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, adequately forecasted major morbidity. The latter two models are yet to have external validation and none have been tested for clinical effectiveness.
Despite the presence of some promising models in forecasting perioperative oesophagectomy outcomes, there is more research required to externally validate these models and demonstrate clinical benefit with the adoption of these models guiding postoperative care and allocating resources.
食管癌是全球第八大常见恶性肿瘤，预后较差。如果在疾病早期确诊，食管切除术仍是治愈的最佳希望。然而，该手术会带来显著的发病率和死亡率，只有在经过仔细考虑后才会进行。合适的患者选择、咨询和资源分配至关重要。已经设计了许多风险模型来指导外科医生做出这些决策。
评估哪些多变量风险模型，使用术中信息（有无术前信息），能最好地预测食管切除围手术期结果。
对2000年至2020年的MEDLINE、EMBASE和Cochrane数据库进行系统综述。使用的检索词为[（食管切除术）AND（模型OR预测OR风险OR评分）AND（死亡率OR发病率OR并发症OR结果OR吻合口漏OR住院时间）]。如果文章评估了结合术前和术中变量以预测食管切除术后患者结果的多变量工具，则纳入。如果文章仅需要术前或任何术后数据，则排除。评估单变量风险预测指标（如术前肌肉减少症、心肺功能和美国麻醉医师协会评分）的研究也被排除。该综述遵循系统评价和Meta分析的首选报告项目模型进行。对所有捕获的风险模型进行临床可信度、方法学质量、性能、验证和临床有效性评估。
确定了20项已发表的研究，这些研究检验了11种多变量风险模型。其中8种结合了术前和术中数据，其余3种仅使用术中值。在预测死亡率方面，只有两种风险模型被认为有前景，即用于计算死亡率和发病率的朴茨茅斯生理和手术严重程度评分（POSSUM）和POSSUM评分。另外两项研究，即术中因素和基于食管切除手术阿普加评分的列线图，充分预测了主要发病率。后两种模型尚未进行外部验证，且均未进行临床有效性测试。
尽管在预测食管切除围手术期结果方面存在一些有前景的模型，但仍需要更多研究来对这些模型进行外部验证，并证明采用这些模型指导术后护理和分配资源具有临床益处。
