Zhao Liyan, Chen Binbin, Lagergren Jesper, Xie Shao-Hua
Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.
Upper Gastrointestinal Surgery, Department of Molecular medicine and Surgery, Karolinska Institute, Stockholm, Sweden.
Gastro Hep Adv. 2025 Jun 21;4(10):100737. doi: 10.1016/j.gastha.2025.100737. eCollection 2025.
Risk prediction models can identify individuals at high risk of esophageal adenocarcinoma. This systematic review aimed to critically appraise the available models for projecting absolute risk of esophageal adenocarcinoma in the general population.
We searched Medline, Embase, and Cochrane Library databases for studies of risk prediction models for esophageal adenocarcinoma. Data were extracted from eligible studies according to the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies. Risk of bias and applicability were assessed using the prediction model risk of bias assessment tool.
We identified 7 studies. Age, sex, gastroesophageal reflux disease, body mass index, and tobacco smoking were the most common predictors. The area under the receiver operating characteristic curve ranged between 0.76 and 0.88 in the derivation datasets. The models based on 2 cohort studies showed good agreement between observed and predicted risks. All studies had at least 1 domain with high risk of bias, primarily attributable to methodological shortcomings in the data analysis.
Most risk prediction models showed good performance in identifying individuals at high risk of esophageal adenocarcinoma. Validation in external populations and cost-effectiveness evaluation are needed before these models can be applied in public health and clinical practice.
风险预测模型能够识别出食管腺癌高风险个体。本系统评价旨在严格评估用于预测普通人群食管腺癌绝对风险的现有模型。
我们检索了Medline、Embase和Cochrane图书馆数据库，以查找关于食管腺癌风险预测模型的研究。根据预测模型研究系统评价的严格评价和数据提取清单，从符合条件的研究中提取数据。使用预测模型偏倚风险评估工具评估偏倚风险和适用性。
我们纳入了7项研究。年龄、性别、胃食管反流病、体重指数和吸烟是最常见的预测因素。在推导数据集中，受试者工作特征曲线下面积在0.76至0.88之间。基于2项队列研究的模型显示观察到的风险与预测风险之间具有良好的一致性。所有研究至少有1个领域存在高偏倚风险，主要归因于数据分析中的方法学缺陷。
大多数风险预测模型在识别食管腺癌高风险个体方面表现良好。在这些模型能够应用于公共卫生和临床实践之前，需要在外部人群中进行验证并进行成本效益评估。
