Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK.
Department of Surveillance and Health Equity Science, American Cancer Society, Atlanta, GA, USA.
BMC Med. 2022 Sep 8;20(1):302. doi: 10.1186/s12916-022-02488-w.
In China, colorectal cancer (CRC) incidence and mortality have been steadily increasing over the last decades. Risk models to predict incident CRC have been developed in various populations, but they have not been systematically externally validated in a Chinese population. This study aimed to assess the performance of risk scores in predicting CRC using the China Kadoorie Biobank (CKB), one of the largest and geographically diverse prospective cohort studies in China.
Nine models were externally validated in 512,415 participants in CKB and included 2976 cases of CRC. Model discrimination was assessed, overall and by sex, age, site, and geographic location, using the area under the receiver operating characteristic curve (AUC). Model discrimination of these nine models was compared to a model using age alone. Calibration was assessed for five models, and they were re-calibrated in CKB.
The three models with the highest discrimination (Ma (Cox model) AUC 0.70 [95% CI 0.69-0.71]; Aleksandrova 0.70 [0.69-0.71]; Hong 0.69 [0.67-0.71]) included the variables age, smoking, and alcohol. These models performed significantly better than using a model based on age alone (AUC of 0.65 [95% CI 0.64-0.66]). Model discrimination was generally higher in younger participants, males, urban environments, and for colon cancer. The two models (Guo and Chen) developed in Chinese populations did not perform better than the others. Among the 10% of participants with the highest risk, the three best performing models identified 24-26% of participants that went on to develop CRC.
Several risk models based on easily obtainable demographic and modifiable lifestyle factor have good discrimination in a Chinese population. The three best performing models have a higher discrimination than using a model based on age alone.
在中国，过去几十年中，结直肠癌（CRC）的发病率和死亡率一直在稳步上升。已经在不同人群中开发了预测 CRC 发病的风险模型，但尚未在中国人群中进行系统的外部验证。本研究旨在使用中国慢性病前瞻性研究（CKB）评估风险评分预测 CRC 的性能，CKB 是中国最大和地理位置最多样化的前瞻性队列研究之一。
在 CKB 中，对 512415 名参与者进行了 9 种模型的外部验证，包括 2976 例 CRC 病例。使用受试者工作特征曲线下面积（AUC）评估了模型的整体和按性别、年龄、部位和地理位置的区分度。将这 9 种模型的区分度与仅使用年龄的模型进行了比较。评估了五个模型的校准度，并在 CKB 中对它们进行了重新校准。
三种具有最高区分度的模型（Ma（Cox 模型）AUC 0.70 [95% CI 0.69-0.71]；Aleksandrova 0.70 [0.69-0.71]；Hong 0.69 [0.67-0.71]）包括年龄、吸烟和饮酒等变量。这些模型的性能明显优于仅使用基于年龄的模型（AUC 为 0.65 [95% CI 0.64-0.66]）。在年轻参与者、男性、城市环境和结肠癌中，模型的区分度通常更高。两种（Guo 和 Chen）在中国人群中开发的模型的性能并不优于其他模型。在风险最高的 10%的参与者中，三种表现最好的模型确定了 24-26%的参与者会发展为 CRC。
基于易于获得的人口统计学和可改变的生活方式因素的几种风险模型在中国人群中具有良好的区分度。表现最好的三种模型的区分度高于仅使用基于年龄的模型。
