中国实用外科杂志

中国实用外科杂志 ›› 2025, Vol. 45 ›› Issue (03): 340-345.DOI: 10.19538/j.cjps.issn1005-2208.2025.03.19

• 论著 • 上一篇    下一篇

胆囊结石的风险因素分析及临床预测模型的建立与验证

金素丽1，姜    霞2，张    征1，武    艳1，秘玉静1，梁涛涛1，高世超1   

  1. 1河北医科大学第一医院检验中心  河北省医学检验临床应用技术创新中心，河北石家庄 050030；2河北医科大学第一医院胃肠病诊疗中心  河北省大肠癌精准诊断与治疗重点实验室，河北石家庄 050030
  • 出版日期:2025-03-01 发布日期:2025-03-27

  • Online:2025-03-01 Published:2025-03-27

摘要： 目的    探究胆囊结石形成的危险因素，构建胆囊结石形成的列线图模型并进行验证。方法    选取2022年9月至2023年8月于河北医科大学第一医院体检中心检查出的854例胆囊结石病人作为疾病组，从同期体检人群中随机选1799名无胆囊结石者作为对照组。采用独立样本的Mann-Whitney U检验对两组的临床资料进行比较。将疾病组和对照组随机分为训练集和验证集。对训练集进行单因素及多因素二元Logistic回归筛选出胆囊结石形成的危险因素并构建临床预测模型列线图。采用受试者工作特征（ROC）曲线、校准曲线、一致性指数（C⁃Index）、决策曲线分析（DCA）对列线图的准确性和稳定性进行评估。结果    疾病组与对照组的年龄、体重指数（BMI）、空腹血糖（GLU）、甘油三酯（TG）、总胆固醇（TC）、高密度脂蛋白胆固醇（HDL-C）、低密度脂蛋白胆固醇（LDL-C）、尿素氮（BUN）、肌酐（CREA）、总胆红素（TB）差异均有统计学意义（P＜0.05）。经单因素及多因素Logistic回归分析后，选取年龄、BMI、GLU 3个变量建立列线图预测模型。训练集和验证集的曲线下面积（AUC）值分别为 0.731和0.725，Hosmer-Lemeshow 检验的拟合优度分别为（χ2=11.78，P =0.23）、（χ2=13.43，P=0.14），校准曲线的C⁃Index 分别为 0.462和 0.450（P＞0.05）。决策曲线分析表明训练集和验证集分别在 1%～70% 和 1%～50%范围内有较大的正向收益率。结论    年龄、BMI、空腹血糖是胆囊结石形成的危险因素，基于此建立的胆囊结石的风险列线图模型，经验证该模型准确且稳定，有利于临床进行个性化的风险评估。

关键词: 胆囊结石, 列线图, 临床预测模型

Abstract: To investigate the risk factors for gallbladder stone formation and to construct and validate a columnar line drawing model of gallbladder stone formation. Methods    854 cases of gallbladder stones examined at the Physical Examination Center of the First Hospital of Hebei Medical University from September 2022 to August 2023 were selected as the study group, and 1799 cases without gallbladder stones from the same period of physical examination were randomly selected as the control group. The Mann-Whitney U test for independent samples was used to compare the clinical data of the two groups. The study group and the control group were randomly divided into a training set and a validation set. Univariate and multivariate binary logistic regression was performed on the training set to screen the risk factors for gallbladder stone formation and construct a column-line graphical model. The accuracy and stability of the column charts were assessed using subject work characteristics (ROC) curves, calibration curves, consistency indices (C-Index), and decision curve analysis (DCA). Results    The differences in age, BMI, GLU, TG, TC, HDL-C, LDL-C, BUN, CREA, and TB between the study group and the control group were statistically significant (P<0.05). After univariate and multivariate Logistic regression analyses, three variables, age, BMI, and GLU, were selected to establish a column-line graph prediction model. The area under the curve (AUC) values for the training and validation sets were 0.731 and 0.725 respectively, the goodness-of-fits for the Hosmer-Lemeshow test were (χ2=11.78, P=0.23) and (χ2=13.43, P=0.14) respectively, and the C-Indices of the calibration curves were 0.462 and 0.450 respectively (P>0.05). Decision curve analysis shows large positive yields in the range of 1%-70% and 1%-50% for the training and validation sets, respectively. Conclusion    Age, BMI, and fasting glucose are risk factors for gallbladder stone formation, based on which we developed a columnar graphical model of the risk of gallbladder stones, with a good accuracy and stability after validation, facilitating personalized risk assessment in the clinic.

Key words: gallbladder stones, columnar line drawing, clinical predictive modeling