国际医药卫生导报2025，Vol.31Issue(12)：2062-2068,7.DOI:10.3760/cma.j.cn441417-20241226-12026
logistic回归与贝叶斯方法构建老年患者结肠镜肠道准备失败预测模型及验证研究
A Study on the Construction and Validation of Prediction Models for Colonoscopy Bowel Preparation Failure in Elderly Patients Using Logistic Regression and Bayesian Methods
摘要
Abstract
Objective To construct and verify the prediction model of intestinal preparation failure for colonoscopy in elderly patients based on logistic regression and Bayes.Methods From January 2023 to December 2023,249 patients with failed bowel preparation for colonoscopy were enrolled from Guangzhou First People's Hospital.Using a 1∶1.88 ratio,467 patients without bowel preparation failure during the same period were selected.They were divided into a test group(501 cases)and a validation group(215 cases)according to the different treatment durations.In the test group,there were 174 cases of failed intestinal preparation,including 104 males and 70 females,with an age of(69.49±7.405)years.Intestinal preparation failed in 327 cases,including 139 males and 188 females,with an age of(68.54±5.779)years.In the verification group,there were 75 cases of failed intestinal preparation,including 36 males and 39 females,with an age of(70.32±8.076)years.Intestinal preparation failed in 140 cases,including 70 males and 70 females who were(67.94±6.115)years old.In this study,the data were collected using the General information Questionnaire and the Boston Bowel Readiness Assessment Scale.The differences in categorical variables and continuous variables between different groups were compared respectively through the x2 test and the mean t test.Subsequently,a multivariate Logistic regression model and a Bayesian model were constructed in the test set and verified using the validation set.Finally,the predictive efficacy of the two models was evaluated through the receiver operating characteristic curve(ROC).Results From January 2023 to December 2023,a total of 716 patients were admitted to Guangzhou First People's Hospital.Among them,249 patients failed in colonoscopy and intestinal preparation,with an incidence rate of 34.78％.In the test group,the following indicators in the failed bowel preparation group were significantly lower than those in the qualified bowel preparation group:the sequence of this colonoscopy,the grade of hypertension,the history of abdominal surgery,the detection rate of adenoma,the time of the first defecation after taking laxatives,and the compliance with a low-residue diet the day before the examination(all P＜0.05).A total of 716 patients were included in this study,among whom 349 were male(48.7％)and 367 were female(51.3％).The age range was 60 to 95 years old,with an average of 69.Through the multivariate logistic regression analysis,it found that the influencing factors of failed bowel preparation included:the number of colonoscopy examinations this time,the grade of hypertension,the history of abdominal surgery,adenoma,the time of the first defecation after taking laxatives,and whether there was a low-residue diet the day before the examination.The comparison of model performance showed that the AUC of the logistic regression model was 0.908(95％CI:0.881-0.934),which was significantly higher than 0.767(95％CI:0.701-0.833)of the Bayesian model,indicating that the logistic regression model has better classification performance.Conclusions The logistic regression model can provide parameter details among various variables,offer detailed scores for the prediction of failed bowel preparation,and can be used for clinical promotion.The Bayesian model is faster when the amount of data is large,and can be used as a clinical assistant.关键词
老年/肠道准备失败/logistics回归/贝叶斯/预测模型Key words
Old age/Intestinal preparation failure/logistic regression/Bayes/Prediction model引用本文复制引用
潘秋花,曹普源,陈爱真,冼彩连,刘志翔,王贺,李永强,何佳丽,李萍..logistic回归与贝叶斯方法构建老年患者结肠镜肠道准备失败预测模型及验证研究[J].国际医药卫生导报,2025,31(12):2062-2068,7.基金项目
广东省医学科学科研基金(A2024344) （A2024344）
广州市第一人民医院红棉项目(2023HL08) Guangdong Medical Science Research Foundation(A2024344) （2023HL08）
Red Cotton Project of Guangzhou First People's Hospital(2023HL08) （2023HL08）
