The optimal model of diagnosis to low-grade cervical intraepithelial neoplasia by combined detecting vaginal micro-environmental factors, based on the high-risk HPV infection
10.3760/cma.j.cn112338-20200808-01045
- VernacularTitle:高危型HPV感染状态下阴道微环境因子联合检测对低度宫颈上皮内瘤变诊断的优化模式研究
- Author:
Jie WANG
1
;
Ling DING
;
Yuanjing LYU
;
Dan MENG
;
Hong LIU
;
Li SONG
;
Zhuo QI
;
Haixia JIA
;
Ruixin PEI
;
Zhiqiang TIAN
;
Min HAO
;
Jintao WANG
Author Information
1. 山西医科大学公共卫生学院流行病学教研室，太原 030001
- Keywords:
Low-grade cervical intraepithelial neoplasia;
Vaginal micro-environmental factors;
High-risk human papillomavirus;
Optimization model
- From:
Chinese Journal of Epidemiology
2021;42(6):1108-1112
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the diagnostic value of different vaginal micro-environmental factors in low-grade cervical intraepithelial neoplasia (CIN Ⅰ) and determine the optimal model in high-risk human papillomavirus (HR-HPV) infection.Methods:A total of 926 women, including 623 with normal cervical (NC) condition and 303 CINⅠ patients, had undergone pathological examinations, and were enrolled in the study. All the women were from a community previously established cohort. Vaginal cleanliness, pH, H 2O 2, β-glucuronidase, coagulase, sialidase, and leukocyte esterase (LE) were detected by the combined detection method aerobic vaginitis/bacterial vaginosis in vaginal secretions. HPV genotyping was performed by using the flow-through hybridization technology. The data were analyzed by SAS 9.2 and SPSS 23.0. Results:The vaginal cleanliness, pH, sialidase, and LE were determined as the representative vaginal micro-environment factors by principal component analysis. Based on logistic regression theory to analyze the ROC curve, the results showed that the highest sensitivity was with pH value (76.2%), and the highest specificity was with sialidase (90.9%). The area under ROC curve were higher in combination detection modes of sialidase+LE (0.714), pH+sialidase+LE (0.719), vaginal cleanness+sialidase+LE (0.713) and pH+vaginal cleanness+sialidase+LE (0.709). According to HR-HPV infection status, the TOPSIS method was used to analyze the combined detection optimal model. Specifically, we found that the best diagnostic model was pH+sialidase +LE ( C i=0.585) in the HR-HPV positive group and vaginal cleanness+sialidase+LE ( C i=0.641) in the negative group. Conclusions:The combined detection of vaginal microenvironment factors could be used for auxiliary diagnosis for CINⅠ. It would be more effective when detecting pH, sialidase, and LE in HR-HPV positive women while vaginal cleanness, sialidase, and LE in HR-HPV negative women at the same time.
