Using advanced analytics to help identify women who are more likely to have a severe subjective experience of vulvovaginal atrophy: a modeling study

Abstract

Objective To develop a model to identify women likely to be severely impacted by vulvovaginal atrophy (VVA), based on their experience of symptoms and non-clinical factors. Methods Multivariate statistics and machine-learning algorithms were used to develop models using data from a cross-sectional, observational, multinational European survey. A set of independent variables were chosen to assess subjective VVA severity and its impact on daily activities. Results A final composite model was selected that included three categories of variables: clinical severity, patient demographics/clinical characteristics and Day-to-Day Impact of Vaginal Aging (DIVA) variables related to emotion/mood, impact on lifestyle and frequency of sex. The model accurately classified 71% of women. Three DIVA variables (feeling bad about yourself, desire/interest in sex, physical comfort related to sitting) explained much of the variation in the dependent variable of the model. Over 90% of the impact of VVA relates to certain psychosocial and behavioral aspects that can be identified without the need to consider physical signs/symptoms. Conclusion Non-clinical factors can contribute significantly to the overall VVA burden. Questions used in developing the composite model could form the basis of an instrument to help screen women prior to clinical consultation and improve VVA management.