Analysis of risk factors and predictive model construction of chronic vaginitis complicated with vulvar intraepithelial neoplasia

Jin Yuni; Liu Wan; Xu Jing; Lu Xiaoning; Li Yamin

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Analysis of risk factors and predictive model construction of chronic vaginitis complicated with vulvar intraepithelial neoplasia

更新时间：2022-06-24

- Analysis of risk factors and predictive model construction of chronic vaginitis complicated with vulvar intraepithelial neoplasia
- Journal of Chongqing Medical University Vol. 47, Issue 2, Pages: 186-190(2022)
- 作者机构：
  
  昆明医科大学第一附属医院妇科,昆明,650031
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2022，
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Jin Yuni, Liu Wan, Xu Jing, et al. Analysis of risk factors and predictive model construction of chronic vaginitis complicated with vulvar intraepithelial neoplasia. [J]. Journal of Chongqing Medical University 47(2):186-190(2022)
DOI：

Jin Yuni, Liu Wan, Xu Jing, et al. Analysis of risk factors and predictive model construction of chronic vaginitis complicated with vulvar intraepithelial neoplasia. [J]. Journal of Chongqing Medical University 47(2):186-190(2022) DOI：

摘要

目的：探讨阴道炎患者发生外阴上皮内瘤变（vulvar intraepithelial neoplasia，VIN）的危险因素并构建预测模型。方法：采用回顾性分析，收集2015年2月至2020年2月昆明医科大学第一附属医院妇科门诊收治的368例慢性阴道炎且疑似VIN患者临床资料。根据患者病理组织检查确诊是否发生VIN分为VIN组（n=46）和非VIN组（n=322）。对2组来院诊时的临床资料进行单因素分析和多因素logistic回归分析，并构建预测模型。用灵敏度、特异度、受试者工作特征曲线（receiver operating characteristic，ROC）曲线下面积及预测正确率评价模型的效能。结果：VIN组与非VIN组的年龄、人类乳头瘤病毒（human papillomavirus，HPV）感染、白介素-17（interleukin-17，IL-17）、中性粒细胞与淋巴细胞的比值（ratio of neutrophils to lymphocytes，NLR）、巨噬细胞集落刺激因子（macrophage colony stimulating factor，M-CSF）、白蛋白（serum albumin，Alb）相比较，差异有统计学意义（P＜0.05）。经多因素logistic回归分析发现，HPV感染（OR=29.254，95%CI=4.124~153.574）、IL-17（OR=4.604，95%CI=2.209~97.683）、NLR（OR=2.835，95%CI=1.357~5.405）、M-CSF（OR=2.361，95%CI=1.135~3.975）和Alb（OR=1.099，95%CI=1.015~1.263）均是慢性阴道炎并发VIN的危险因素（P＜0.05）。根据危险因素得出预测模型的表达方程：Prob=1/（e-Y），Y=40.507－3.376×HPV感染－1.527×IL-17－1.042×NLR－0.859×M-CSF－0.095×Alb。验证得出模型的灵敏度为86.90%，特异度为85.70%，正确率为85.90%。结论：慢性阴道炎患者发生VIN的危险因素有HPV感染、IL-17高表达、NLR高表达、M-CSF高表达、Alb低表达，以此构建的预测模型区分能力良好，能有效评估阴道炎发生VIN的风险。

Abstract

Objective：To explore the risk factors of vulvar intraepithelial neoplasia（VIN） in patients with vaginitis and construct a predictive model. Methods：Using retrospective analysis，clinical data of 368 patients with chronic vaginitis and suspected VIN admitted to the gynecological clinic of the First Affiliated Hospital of Kunming Medical University from February 2015 to February 2020 were collected in the study. Patients were divided into VIN group（n=46） and non-VIN group（n=322） according to the diagnosis of VIN based on histopathological examination. Single factor analysis and multivariate logistic regression analysis were performed on the clinical data of the two groups when they came to the hospital，and a predictive model was built. Sensitivity，specificity，area under the receiver operating characteristic（ROC） curve and prediction accuracy were used to evaluate the effectiveness of the model. Results：The comparison of the age of VIN and non-VIN groups，human papillomavirus（HPV） infection，interleukin 17（IL-17），ratio of neu－trophils to lymphocytes（NLR），macrophage colony stimulating factor（M-CSF） and serum albumin（Alb） indicators showed statistical differences（P<0.05）. After multi-factor logistic regression analysis，it was found that，HPV infection（OR=29.254，95%CI=4.124-153.574），IL-17（OR=4.604，95%CI=2.209-97.683），NLR（OR=2.835，95%CI=1.357-5.405），M-CSF（OR=2.361，95%CI=1.135-3.975） and Alb（OR=1.099，95%CI=1.015-1.263） were risk factors for chronic vaginitis complicated with VIN（P<0.05）. According to the risk factors，the expression equation of the predictive model was derived：Prob=1/（e-Y），Y=40.507－3.376×HPV infection－1.527×IL-17－1.042×NLR－0.859×M-CSF－0.095×Alb. After verification，the sensitivity of the model was 86.90%，the specificity was 85.70%，and the accuracy was 85.90%. Conclusion：The risk factors for VIN in patients with chronic vaginitis include HPV infection，high expression of IL-17，high expression of NLR，high expression of M-CSF and low expression of Alb，thereby building a prediction model，with good distinguishing ability，which can effectively assess the risk of vaginitis complicated by VIN.

关键词

阴道炎外阴上皮内瘤变危险因素预测模型

Keywords

vaginitisvulvar intraepithelial neoplasiarisk factorpredictive model

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