Construction and Clinical Validation of a Risk Prediction Model for Vaginal In-traepithelial Neoplasia Grade 2 or Worse Lesions
- VernacularTitle:阴道上皮内病变2及以上的风险预测模型构建及临床验证
- Author:
Ziyu FAN
1
;
Jiechun SHI
1
;
Chenjie GU
1
;
Xinyu MA
1
;
Yan XING
1
Author Information
1. 南京医科大学第一附属医院妇科,江苏 南京 210029
- Publication Type:Journal Article
- Keywords:
Vaginal intraepithelial neoplasia;
Risk factors;
Prediction model;
Nomogram
- From:
Journal of Practical Obstetrics and Gynecology
2025;41(1):42-47
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a risk prediction model for Vaginal Intraepithelial Neoplasia Grade 2 or Worse(VaIN 2+)lesions,and to establish a nomogram for individual diagnosis of VaIN 2+and risk stratification,so as to provide guidance for the treatment of vaginal lesions.Methods:A total of 248 women diagnosed with VaIN through colposcopic biopsy at the Center for Gynecologic and Cervical Diseases,First Affiliated Hospital of Nanjing Medical University,from January 2021 to January 2024 were included in this study.Based on the gold standard established by histological and pathological findings,these patients were categorized into a lower VaIN 2 group and a VaIN 2+group.Univariate comparative analysis was performed on the two groups.Multivariate Logis-tic regression analysis was used to determine the risk factors of VaIN 2+and to construct a diagnostic model.The nomogram model was established by using R Studio software.The discrimination,calibration and clinical practical value of the model were evaluated by the area under the receiver operating characteristic(ROC)curve and cali-bration curve.Results:Univariate analysis identified that HPV type,cervical lesion grade,acetowhite change,vagi-nal lesion duration,vaginal lesion location,and cervical lesion duration as influencing factors for diagnosing VaIN 2+(P<0.1).Multivariate binary Logistic regression analysis indicated that HPV16/18 positivity,cervical lesion grade≥CIN 2,thick acetowhite change,vaginal lesion duration≥5 years,and vaginal lesion location at the upper 1/3 of the vagina were independent risk factors for diagnosing VaIN 2+(OR>1,P<0.05),while cervical lesion duration<3 years was a protective factor(OR<1,P<0.05),with acetowhite change having the greatest impact(OR4.54).A regression model was established based on the multivariate binary Logistic regression analysis,with an AUC of 0.813.A nomogram model was constructed and internally validated,yielding a consistency index(C-index)of 0.81.Patients were stratified into risk groups using the X-tile software,with higher total scores indi-cating a greater risk of developing VaIN 2+.Conclusions:The nomogram model constructed in this study can in-dividually predict the risk of VaIN 2+lesions in patients,with high accuracy and clinical practicability.
