Gastrointestinal Endoscopy ( IF 7.7 ) Pub Date : 2018-10-25 , DOI: 10.1016/j.gie.2018.10.025 Qiao-Li Wang , Jesper Lagergren , Shao-Hua Xie
Background and Aims
This study aimed to develop a prediction model for identifying individuals at high absolute risk of esophageal squamous cell carcinoma (ESCC) for endoscopic screening at a curable stage based on readily identifiable risk factors.
Methods
This was a nationwide Swedish population–based, case-control study, including 167 new cases of ESCC and 820 randomly selected control participants. Odds ratios with 95% confidence intervals (CI) were assessed by using multivariable unconditional logistic regression. The discriminative accuracy of the model was assessed by the area under the receiver operating characteristic curve (AUC) with leave-1-out cross validation. Models for projecting individuals’ absolute 5-year risk of ESCC were developed by incorporating the age-specific and sex-specific incidence rates and competing risk of death from other causes.
Results
A model including the risk factors age, sex, tobacco smoking, alcohol overconsumption, education, duration of living with a partner, and place of residence during childhood generated an AUC of 0.81 (95% CI, 0.77-0.84). A model based only on age, sex, tobacco smoking, and alcohol overconsumption obtained a similar AUC (0.79; 95% CI, 0.75-0.82). A 5-year follow-up of 355 men aged 70 to 74 years with over 35 years’ smoking and alcohol overconsumption history is needed to detect 1 ESCC case. The estimated individuals’ absolute 5-year risk of ESCC varied according to the combinations of risk factors.
Conclusion
This easy-to-use risk prediction model showed a good discriminative accuracy and had the potential to identify individuals at high absolute risk of ESCC who might benefit from tailored endoscopic screening and surveillance.
中文翻译:
食管鳞状细胞癌绝对危险高的个体预测
背景和目标
这项研究的目的是建立一个预测模型,该模型可基于易识别的危险因素,识别处于食管鳞状细胞癌(ESCC)绝对高风险的个体,以便在可治愈的阶段进行内窥镜检查。
方法
这是一项基于瑞典人口的全国性病例对照研究,包括167例ESCC新病例和820个随机选择的对照参与者。通过使用多变量无条件逻辑回归来评估具有95%置信区间(CI)的赔率。通过具有留一法交叉验证的接收器工作特性曲线(AUC)下的面积来评估模型的判别准确性。通过结合特定年龄和特定性别的发病率以及其他原因导致的死亡竞争风险,建立了预测个人绝对5年ESCC风险的模型。
结果
一个模型包括年龄,性别,吸烟,酗酒,教育,与伴侣生活在一起的时间以及儿童时期的居住地等危险因素,其AUC为0.81(95%CI,0.77-0.84)。仅基于年龄,性别,吸烟和过度饮酒的模型获得了相似的AUC(0.79; 95%CI,0.75-0.82)。需要对5名355名年龄在70至74岁,有超过35年吸烟和酗酒史的男性进行5年随访,以发现1例ESCC病例。根据风险因素的组合,估计的个人绝对5年ESCC风险有所不同。
结论
这种易于使用的风险预测模型显示出良好的判别准确性,并且有可能识别出可能从定制内窥镜筛查和监测中受益的ESCC绝对风险较高的个体。