Primary HPV Infection in Postmenopausal Women: A Cohort Study to Enable Risk-Stratified Management
Year: 2026
Type: article
Study design: Observational Study
Abstract: Jing Ye, Gengyu Wu Department of Gynecologic Oncology, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of ChinaCorrespondence: Jing Ye, Email [email protected]: This study aims both to identify the independent risk factors for high-grade cervical intraepithelial neoplasia (HSIL) and to develop and validate a clinical risk stratification model.Patients and Methods: This study retrospectively enrolled 400 postmenopausal women with HR-HPV high-risk human papillomavirus (HR-HPV) positivity and biopsy-confirmed cervical intraepithelial neoplasia. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of HSIL, and a predictive model was constructed. This model was subsequently validated using an independent cohort of 172 patients.Results: Multivariate analysis identified older age, higher parity, HPV 16/18, and hypertension as independent risk factors for HSIL. The regression model based on these factors demonstrated good discrimination in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.805 in the training set and 0.778 in the validation set. Risk stratification according to the model revealed a significant ascending trend in HSIL prevalence across the defined risk groups. The diagnostic performance for HSIL was evaluated for key predictors, showing an AUC of 0.769 for HPV 16/18 genotype alone and 0.792 for its combination with age.Conclusion: A predictive model was constructed to effectively stratify the risk of HSIL in postmenopausal women with an initial HR-HPV infection. This tool facilitates the identification of high-risk individuals and provides a basis for individualized clinical management.Keywords: high-risk human papillomavirus, postmenopausal, cervical intraepithelial neoplasia, risk model, predictive factors
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7133574456
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