摘要
目的分析白癜风共病糖尿病危险因素,并构建、验证预测模型。方法110例白癜风共病糖尿病患者(共病组)及4505例白癜风未共病糖尿病患者(对照组)来源于第四军医大学西京医院2010年1月至2021年9月的病历数据库,按照性别、年龄进行1∶4倾向性得分匹配。匹配完成后,按照4∶1随机划分为训练集和测试集。使用训练集通过logistic回归分析患者的人口学和临床特征,筛选差异因素,构建预测模型,使用五折交叉验证进行内部验证,并通过测试集进行区分度(曲线下面积,AUC)、校准度(Hosmer-Lemeshow检验)和准确性(灵敏度、特异度、阳性预测值、阴性预测值)评价。结果共病组107例与对照组428例成功匹配,训练集430例,测试集105例。根据多因素logistic回归结果,预测模型纳入白癜风病程[比值比(OR)=1.04,95%可信区间(CI):1.02~1.07,P<0.001]、高糖/高脂/高盐饮食(OR=3.19,95%CI:1.38~7.38,P=0.007)、糖尿病家族史(OR=23.23,95%CI:9.72~55.50,P<0.001)、代谢性合并症(OR=12.53,95%CI:5.60~28.07,P<0.001)、自身免疫性合并症(OR=5.89,95%CI:2.52~13.76,P<0.001)及肢端型白癜风(OR=3.84,95%CI:1.45~10.19,P=0.007)6个预测指标。五折交叉验证显示该模型预测效果良好,训练集AUC为0.902(95%CI:0.864~0.940),验证集AUC为0.895(95%CI:0.815~0.974)。在测试集上应用构建的模型,评价结果显示其具有良好的区分度(AUC=0.814,95%CI:0.715~0.913)、校准度(Hosmer-Lemeshow检验P=0.068)及准确性(灵敏度=0.810,95%CI:0.574~0.937;特异度=0.786,95%CI:0.680~0.865;阳性预测值=0.486,95%CI:0.317~0.657;阴性预测值=0.943,95%CI:0.853~0.982)。结论基于白癜风病程、肢端型白癜风、高糖/高脂/高盐饮食、糖尿病家族史、代谢性合并症和自身免疫性合并症6个预测指标构建的白癜风共病糖尿病风险预测模型具有较好的区分度、校准度及准确性,能够为筛查高危人群提供依据。
Objective To analyze risk factors for diabetes mellitus in patients with vitiligo,and to construct and validate a prediction model.Methods A total of 110 vitiligo patients with diabetes mellitus(comorbidity group)and 4505 vitiligo patients without diabetes mellitus(control group)were collected from the medical record database in Xijing Hospital,the Fourth Military Medical University from January 2010 to October 2021,and matched for gender and age at a ratio of 1∶4 by using a propensity score method.After matching,the matched pairs were randomly divided into a training set and a test set at a ratio of 4∶1.Univariate and multivariate logistic regression analyses were used to assess demographic and clinical characteristics of patients in the training set,screen differential factors,and construct a prediction model.A five-fold cross-validation method was used for internal validation after construction of the prediction model.The discrimination(area under the curve[AUC]),calibration(Hosmer-Lemeshow test)and accuracy(sensitivity,specificity,positive predictive value,and negative predictive value)of the prediction model were evaluated in the test set.Results A total of 107 cases in the comorbidity group and 428 cases in the control group were successfully matched.The training set included 430 cases,and the test set included 105 cases.Based on multivariate logistic regression results,a total of 6 factors were included in the prediction model,including course of vitiligo(odds ratio[OR]=1.04,95%confidence interval[CI]:1.02-1.07,P<0.001),high-sugar/high-fat/high-salt diet(OR=3.19,95%CI:1.38-7.38,P=0.007),family history of diabetes(OR=23.23,95%CI:9.72-55.50,P<0.001),metabolic comorbidities(OR=12.53,95%CI:5.60-28.07,P<0.001),autoimmune comorbidities(OR=5.89,95%CI:2.52-13.76,P<0.001),and acral vitiligo(OR=3.84,95%CI:1.45-10.19,P=0.007).Five-fold cross-validation results showed a good predictive performance of the prediction model,with the AUC being 0.902(95%CI:0.864-0.940)in the training set and 0.895(95%CI:0.815-0.974)in the test set.The prediction model also showed favourable discrimination(AUC=0.814,95%CI:0.715-0.913),calibration(Hosmer-Lemeshow test,P=0.068),and accuracy(sensitivity=0.810,95%CI:0.574-0.937;specificity=0.786,95%CI:0.680-0.865;positive predictive value=0.486,95%CI:0.317-0.657;negative predictive value=0.943,95%CI:0.853-0.982)in the test set.Conclusion A risk prediction model was constructed for diabetes mellitus in patients with vitiligo based on 6 factors(course of vitiligo,high-sugar/high-fat/high-salt diet,family history of diabetes,metabolic comorbidities,autoimmune comorbidities,and acral vitiligo),which showed favourable discrimination,calibration and accuracy,and might provide a reference for screening the high-risk diabetic population in vitiligo patients.
作者
李柏樟
亢盼
张肖莹
朱冠男
李舒丽
李春英
Li Baizhang;Kang Pan;Zhang Xiaoying;Zhu Guannan;Li Shuli;Li Chunying(Department of Dermatology,Xijing Hospital,The Fourth Military Medical University,Xi’an 710032,China)
出处
《中华皮肤科杂志》
CAS
CSCD
北大核心
2022年第7期576-582,共7页
Chinese Journal of Dermatology
基金
国家自然科学基金(12126606、81930087)。
关键词
白癜风
糖尿病
共病现象
危险因素
预测模型
Vitiligo
Diabetes mellitus
Comorbidity
Risk factors
Prediction model