Zhao X, Li H, Jin L, Xue J, Yao Y, Pang W, Liu X, Wang W, Han Q, Zhang B, Zhao X, Zhang Q, Wu X, Tan Z, Zhang X, Su X, Zhang C
School of Medicine, Nankai University, Tianjin, China; Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China.
Department of Gastroenterology, Tianjin Union Medical Center, Tianjin, China.
Public Health. 2024 Sep;234:164-169. doi: 10.1016/j.puhe.2024.06.010. Epub 2024 Jul 15.
The present study aims to develop an effective risk-prediction score (RPS) to improve screening efficiency and contribute to secondary prevention of colorectal cancer (CRC).
Screening for colorectal lesions.
14,398 high-risk individuals aged 50-65 years were included. The baseline characteristics of participants with and without colorectal lesions (CL) were compared using a Chi-squared test. The overall population was randomly split into a training set and a test set in the ratio of 80% and 20%. One-factor and multifactor logistic regression analyses were performed in the training set to construct the RPS (scores of 0-9.62). Area under curve (AUC) was calculated as an estimate of predictive performance using the receiver-operating characteristic (ROC) curve in the test set.
In the study population, being male, advanced age, current or previous smoking, weekly alcohol consumption, high body mass index (BMI ≥24 kg/m), and previously detected colonic polyp were associated with higher risk of CL. Compared to the low-risk group (0-2.31 points), the ORs and 95% confidence intervals (CIs) for the moderate-risk group (2.31-3.85 points) and high-risk group (3.85-8.42 points) were 1.58 (1.44, 1.73) and 2.52 (2.30, 2.76), respectively. For every 1-point increase in score, participants had a 27% increased risk of CL (OR:1.27, 95% CI: 1.24, 1.30). For participants with CL predicted by RPS, the area under the working characteristic curve was 0.61 (P < 0.001).
Our RPS can quickly and efficiently identify multiple lesions of the colorectum. Combining RPS with existing screening strategies facilitates the identification of very high-risk individuals and may help to prevent CRC.
本研究旨在开发一种有效的风险预测评分（RPS），以提高筛查效率并促进结直肠癌（CRC）的二级预防。
结直肠病变筛查。
纳入14398名年龄在50 - 65岁的高危个体。采用卡方检验比较有和无结直肠病变（CL）参与者的基线特征。总体人群以80%和20%的比例随机分为训练集和测试集。在训练集中进行单因素和多因素逻辑回归分析以构建RPS（得分范围为0 - 9.62）。使用测试集中的受试者工作特征（ROC）曲线计算曲线下面积（AUC）作为预测性能的估计值。
在研究人群中，男性、高龄、当前或既往吸烟、每周饮酒、高体重指数（BMI≥24 kg/m²）以及既往检测到结肠息肉与CL风险较高相关。与低风险组（0 - 2.31分）相比，中风险组（2.31 - 3.85分）和高风险组（3.85 - 8.42分）的比值比（OR）及95%置信区间（CI）分别为1.58（1.44，1.73）和2.52（2.30，2.76）。评分每增加1分，参与者发生CL的风险增加27%（OR：1.27，95% CI：1.24，1.30）。对于RPS预测为CL的参与者，工作特征曲线下面积为0.61（P < 0.001）。
我们的RPS能够快速有效地识别结直肠的多种病变。将RPS与现有筛查策略相结合有助于识别极高风险个体，并可能有助于预防CRC。
