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, H2O2, 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 (Ci=0.585) in the HR-HPV positive group and vaginal cleanness+sialidase+LE (Ci=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.

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