Development of a predictive model integrating urogenital pathogens and inflammatory markers for human papillomavirus infection

Objective: The objective of the present study was to evaluate the relationship between common urogenital pathogens and human papillomavirus (HPV) infection and to assess the impact of coinfections on vaginal microecology. Methods: (MG). HPV DNA genotyping was also performed. Clinical characteristics and laboratory results were evaluated using univariate and multivariate logistic regression analyses to identify independent risk factors for HPV infection. A logistic regression model was constructed, and its predictive performance was evaluated by receiver operating characteristic (ROC) curve and calibration analyses. Results: < 0.05). Multivariate analysis revealed that CT infection (aOR=4.115), UU infection (aOR=3.937), elevated leukocyte count (aOR=2.076), and abnormal vaginal discharge (aOR=2.987) were independent predictors for HPV. The prediction model demonstrated good discrimination (AUC≈0.85). In addition, UU, MH, CT, and UP infections were significantly linked to bacterial vaginosis and other vaginal disorders. Conclusions: CT and UU infections are strongly associated with HPV infection. Elevated leukocyte count and abnormal vaginal discharge serve as inflammatory indicators that increase prediction accuracy. The proposed model, which integrates pathogen detection and host inflammatory status, performs well in identifying women at high risk of HPV, thereby supporting early screening and cervical cancer prevention.