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中华结直肠疾病电子杂志

中华结直肠疾病电子杂志 ›› 2022, Vol. 11 ›› Issue (05) : 409 -414. doi: 10.3877/cma.j.issn.2095-3224.2022.05.009

论著

结直肠息肉发病风险预测模型构建及其临床价值
于琳琳1, 金黑鹰2,†()   
  1. 1. 210046　南京中医药大学研究生院
    2. 210017　南京中医药大学第二附属医院肛肠中心
  • 收稿日期:2022-03-14 出版日期:2022-10-25
  • 通信作者: 金黑鹰

Construction of a risk prediction model for colorectal polyps and its clinical value

Linlin Yu1, Heiying Jin2,†()   

  1. 1. Graduate School, Nanjing University of Traditional Chinese Medicine, Nanjing 210046, China
    2. Department of Colorectal Surgery, the Second Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing 210017, China
  • Received:2022-03-14 Published:2022-10-25
  • Corresponding author: Heiying Jin
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于琳琳, 金黑鹰. 结直肠息肉发病风险预测模型构建及其临床价值[J]. 中华结直肠疾病电子杂志, 2022, 11(05): 409-414.

Linlin Yu, Heiying Jin. Construction of a risk prediction model for colorectal polyps and its clinical value[J]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2022, 11(05): 409-414.

目的

探讨行结肠镜检查者罹患结直肠息肉的相关危险因素并构建预测模型。

方法

回顾性分析2019年1月至2021年10月于南京中医药大学第二附属医院行结肠镜检查者共1 671例的临床资料。根据结肠镜结果将968例结直肠息肉患者纳入息肉组，703名无息肉病变的患者纳入无息肉组。收集患者年龄、性别、身高、体重、吸烟史、饮酒史、实验室检查结果和既往肠镜检查结果等多种因素，分析影响结直肠息肉发生的相关危险因素。应用R语言建立预测结直肠息肉发生风险的列线图模型，用Bootstrap法进行模型内部验证，采用列线图验证曲线及ROC曲线评价列线图的预测性能。

结果

息肉组患者年龄（t=151.531，P＜0.001）、男性比例（χ2=50.843，P＜0.001）、长期吸烟史比例（χ2=5.034，P=0.013）、BMI（t=0.813，P＜0.001）、既往息肉史（χ2=8.323，P=0.004）高于无息肉组，差异有统计学意义。多因素Logistic回归模型分析结果显示：年龄，性别，BMI，长期吸烟，既往息肉大小、病理类型及生长位置为结直肠息肉发病的独立危险因素（均P＜0.05）。ROC曲线显示AUC为0.908，敏感性和特异性分别为76.9%和83.2%。

结论

年龄，性别，长期吸烟史，BMI，既往息肉大小、病理类型及生长位置为结直肠息肉发病的独立危险因素。该研究所建立的列线图模型具有良好的区分度和准确度，可为直观、个体化地分析结直肠息肉发生风险，甄别高危人群，为临床制订筛查方案提供参考依据。

关键词: 息肉, 结直肠息肉, 危险因素, 发病预测模型
Objective

To investigate the risk factors of colorectal polyps in patients undergoing colonoscopy and to construct a predictive model.

Methods

A total of 1 671 patients who underwent colonoscopy in the Second Affiliated Hospital of Nanjing University of Traditional Chinese Medicine from January 2019 to October 2021 were analyzed retrospectively. According to the endoscopic results, 968 patients with colorectal polyps were included in the polyp group, and 703 patients without polyp lesions were included in the polyp-free group. Various factors such as age, gender, height, weight, history of smoking and drinking, laboratory examination and previous colonoscopy results were collected, and the related risk factors affecting the occurrence of colorectal polyps were analyzed. R software was used to establish a nomogram model for predicting the risk of colorectal polyps, and the Bootstrap method was used for internal validation of the model, and the C-index and ROC curve were used to evaluate the predictive performance of the nomogram.

Results

The age (t=151.531, P＜0.001) , male ratio (χ2=50.843, P＜0.001), the proportion of patients with long-term smoking (χ2=5.034, P=0.013), BMI (t=0.813, P＜0.001), past polyps historical proportions in the polyp group (χ2=8.323, P=0.004) were higher than those in the control group, and the difference was statistically significant (P＜0.05). Multivariate logistic regression model analysis showed that age, gender, BMI, the proportion of patients with long-term smoking, previous polyp size, pathological type and growth location were independent risk factors for colorectal polyps (P＜0.05). The ROC curve showed that the AUC was 0.908, and the sensitivity and specificity were 76.9% and 83.2%, respectively.

Conclusion

Age, gender, BMI, the proportion of patients with long-term smoking, previous polyp size, pathological type and growth location are independent risk factors for colorectal polyps. The nomogram model established in this study has good discrimination and accuracy, which can intuitively and individually analyze the risk of colorectal polyps, identify high-risk groups, and provide a reference for clinical screening programs.

Key words: Polyps, Colorectal polyps, Risk factors, Incidence prediction model
表1 两组基本情况比较［
xˉ
±s，例（%）］
指标

有息肉组

（n=968）

无息肉组

（n=703）

 t/χ2值 P值
性别 50.843 ＜0.001
男 625（64.57） 331（47.08）
女 343（35.43） 372（52.92）
年龄（岁） 57.80±11.74 52.53±14.04 151.531 ＜0.001
吸烟史 5.034 0.013
有 391（40.39） 246（34.99）
无 577（59.61） 457（65.01）
饮酒史 1.172 0.191
有 383（39.57） 256（36.42）
无 585（60.43） 447（63.58）
BMI（kg/m2） 25.08±3.11 24.33±2.90 0.813 ＜0.001
CRP 1.05±1.94 1.16±2.12 4.377 0.498
TC 4.74±1.10 4.82±1.05 111.409 0.551
TG 1.41±0.83 1.43±0.84 109.628 0.162
HDL-C 1.24±0.30 1.27±0.30 79.447 0.402
LDL-C 3.12±0.79 3.14±0.76 122.299 0.238
既往息肉史 8.323 0.004
是 305（31.51） 176（25.03）
否 663（68.49） 527（74.96）
表2 Logistic回归赋值表
指标 赋值说明
性别 0=女 1=男
年龄 原值带入
吸烟史 0=无 1=有
既往息肉病理类型 0=非腺瘤性息肉 1=腺瘤性息肉
既往息肉大小 原值带入
既往息肉数目

1=1~2个 2=3~4个

3=5个及以上

既往息肉位置

1=左半结肠 2=右半结肠 3=直肠

4=分布于两个及以上位置

BMI 0= ≤25 1= ＞25
表3 结直肠息肉危险因素的单因素及多因素Logistic回归模型分析
指标 单因素Logistic回归 多因素Logistic回归
OR值 95%可信区间 P值 OR值 95%可信区间 P值 B
性别 0.488 0.401~0.595 ＜0.001 0.436 0.331~0.575 ＜0.001 2.251
年龄 1.032 1.024~1.041 ＜0.001 1.031 1.020~1.042 ＜0.001 1.032
吸烟史 6.694 1.635~17.603 0.008 3.118 1.108~8.776 0.031 1.137
既往息肉病理类型 0.047 0.024~0.092 ＜0.001 3.056
非腺瘤史 0.896 0.712~1.127 0.348
腺瘤史 1.298 1.010~1.668 0.041
既往息肉大小 2.599 2.318~2.914 ＜0.001 12.094 8.986~16.443 ＜0.001 2.493
既往息肉数目
1~2个 0.397 0.292~0.540 ＜0.001 0.099
3~4个 1.619 1.280~2.046 ＜0.001 1.157 0.600~2.232 0.663 1.146
5个及以上 0.913 0.656~1.270 0.588 1.681 0.389~1.191 0.178 1.385
既往息肉位置
左半结肠 0.375 0.272~0.517 ＜0.001 ＜0.001
右半结肠 0.496 0.333~0.739 0.001 0.180 0.101~0.320 ＜0.001 0.717
直肠 0.455 0.288~0.719 0.001 0.278 0.144~0.533 ＜0.001 0.282
全结肠 2.018 1.589~2.564 ＜0.001 0.225 0.104~0.485 ＜0.001 1.043
BMI 1.306 1.061~1.607 0.012 1.811 1.368~2.398 ＜0.001 1.594
图1 结直肠息肉多因素Logistic回归模型的列线图
图2 结直肠息肉多因素Logistic回归模型的列线图验证曲线。注：横轴表示预测发病概率，纵轴表示实际发病概率；点线、实线、短横线分别表示同样风险因素影响下结肠息肉的实际发病概率、预测发病概率和理想状态下的完美模型的发病概率
图3 ROC曲线
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