Objective: To explore risk factors for colorectal adenoma (CRA) in non-smoking women, develop a simplified and efficient predictive model, and evaluate its performances with existing risk evaluation tools and in different time periods. Methods: Clinical data were collected from non-smoking women between November 2021 and April 2023. The positive case group included patients with colorectal polyps confirmed as CRA by pathology. Candidate variables were identified through single-factor logistic analysis (P<0.2), and prelimiarily screened using multivariate logistic regression. To obtain the optimal model, a stepwise regression method based on the AIC was employed for predictor selection, and the final prediction model was constructed with the selected predictors. A nomogram and model list were developed to visually demonstrate the contribution of each factor. The model's discrimination and calibration were evaluated and compared with existing risk assessment tools, including the Asia-Pacific Colorectal Screening (APCS) score, its modified version (MAPCS), and the "Colorectal Cancer Screening and Early Diagnosis and Treatment Program (2024 Edition)" of China. A temporal external test set was used to further evaluate the model's stability and predictive performance in a real-world clinical setting. Results: After analyzing data from 1 155 non-smoking women, the final model based on age (5 age groups) and BMI (≥24.0 kg/m²) as the main predictive factors was constructed. The model achieved area under the curve (AUC) values of 0.705 (95%CI: 0.672-0.738) in the training set (n=927) and 0.695 (95%CI: 0.629-0.762) in the validation set (n=228), with calibration curves and Hosmer-Lemeshow tests showing good fitness (P>0.05). A risk threshold of 0.400 was applied, with predicted probabilities ≥0.400 indicating high-risk and <0.400 indicating non-high-risk. The model achieved stable performance in the temporal external test set (n=272) with an AUC of 0.783 (95%CI: 0.730-0.836), further confirming the model's temporal stability and clinical utility. Compared with the existing risk evaluation tools, the values of the model in terms of discrimination are slightly higher than those of the high-risk groups in APCS, MAPCS and "Colorectal Cancer Screening and Early Diagnosis and Treatment Program (2024 Edition)", and the values in terms of specificity and accuracy are also higher. Conclusions: The simplified prediction model based on age and BMI can effectively evaluate CRA risk in non-smoking women, demonstrating high discriminatory power and temporal stability. It can provide more precise risk stratification guidance for early CRA screening with improved efficiency. 目的： 分析不吸烟女性结直肠腺瘤（CRA）的危险因素，构建简洁高效的预测模型，并评估其与现有风险评估工具的比较优势和在不同时间段人群中的效能。 方法： 收集2021年11月至2023年4月不吸烟女性资料，阳性病例组病理结果证实为CRA者。通过单因素logistic回归分析确定候选变量（P<0.2），采用多因素logistic回归分析初步筛选自变量；为得到最优模型采用最小赤池信息准则的逐步回归法进行预测变量筛选，最终筛选出预测变量构建预测模型。绘制nomogram图及建立列表，评估模型区分度及校准度，并与亚太地区结直肠肿瘤筛查评估（APCS）、其修订版（MAPCS）及《结直肠癌筛查与早诊早治方案（2024年版）》等现有风险评估工具进行比较。同时采用时间外部测试集进一步评估模型在实际临床环境中的稳定性和预测性能。 结果： 分析1 155名不吸烟女性的相关数据后，最终模型纳入年龄（分5个年龄段））和BMI（≥24.0 kg/m²）2个主要预测因素。模型在训练集（n=927）和验证集（n=228）的曲线下面积（AUC）分别为0.705（95%CI：0.672~0.738）和0.695（95%CI：0.629~0.762），校准曲线和Hosmer-Lemeshow检验均显示良好拟合性（P>0.05）。以0.400为风险阈值，预测概率≥0.400为高危组，<0.400为非高危组。模型在时间外部验证集（n=272）中表现同样稳定，AUC为0.783（95%CI：0.730~0.836），提示模型具有时间稳定性和临床实用价值。与现有风险评估工具相比，所构建模型在区分度方面的数值稍高于APCS、MAPCS和《结直肠癌筛查与早诊早治方案（2024年版）》等的高危组，且在特异度和准确度方面数值较高。 结论： 基于年龄和BMI的预测模型能有效评估不吸烟女性CRA发病风险，具有较高的区分能力和时间稳定性，可为CRA早期筛查提供更精准的风险分层指导，提高筛查效率。.
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PubMed · 2026-03-10
PubMed · 2026-04-10
PubMed · 2026-04-10