IDDF2022-ABS-0034 Construction and validation of a colorectal cancer risk prediction model in colorectal cancer screening population of China. (2nd September 2022)

• Record Type: Journal Article
• Authors: Guo, Lanwei; Zheng, Liyang; Chen, Qiong; Zhang, Shaokai
• Is Part Of: Gut. Volume 71(2022)Supplement 2
• Page Start: A114
• Page End: A114
• Publication Date: 2022-09-02
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2022-IDDF.149
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Background: Colorectal cancer (CRC) is a common and preventable disease for which screening rates by colonoscopy remain unacceptably low. Our primary objective was to develop and validate a clinical risk score predictive of risk for colorectal advanced neoplasia (CAN) in China. Methods: A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). The subjects comprise 7454 asymptomatic patients undergoing screening colonoscopy. From a training set of 3727 asymptomatic subjects undergoing screening colonoscopy, multiple logistic regression was applied to identify significant risk factors for CAN defined as invasive carcinoma or advanced adenoma. Risk scores were created by dividing beta coefficients by the absolute value of the smallest coefficient in the model and rounding up to the nearest integer. A receiver operating characteristic (ROC) analysis was performed to calculate the area under the curve (AUC) and 95% confidence interval (95% CI) according to the CRC risk score for each subject. The optimal cut-off value was established according to the ROC curve, and then the subjects were categorized as being at low-risk, medium-risk and high-risk according to the risk of CAN. Results: The baseline prevalence of CAN was 1.59% and 1.42% in the training and validation set, respectively. After variable screening and model optimization, the CRC risk prediction model established in the training set consisted of five variables: Age, race, heavy-grease diet, smoking packyears and first-degree family history of CRC. The distinguishing ability is moderate (AUC=0.77, 95% CI=0.72–0.82). Compared with those in the low-risk group (0–4 points), those in the high-risk group (8–15 points) had a 12.13-fold (95% CI=5.72–25.72) higher risk of CAN in the training set and 5.54-fold (95% CI=2.66–11.52) in the validation set. When we define 5 points as the cutoff, the sensitivity and specificity of the scoring system for CAN were 81.36% and 58.75%, respectively. Conclusions: The colorectal cancer risk prediction model based on CAN is useful in selecting asymptomatic Chinese subjects for the priority of colorectal screening.