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This study aimed to characterize static anatomical and dynamic functional parameters of the levator hiatus in women with bladder prolapse and to examine their diagnostic performance. Primiparous women with a diagnosis of bladder prolapse were recruited as the prolapse group (n = 62), while healthy nulliparous women were included as the control group (n = 60). Nulliparous controls were selected to provide a reference group without childbirth-related pelvic floor remodeling; however, because parity differed systematically between groups, parity-related effects cannot be separated from prolapse-related effects in this case-control design. The levator hiatus area was measured both at rest and during maximal Valsalva maneuver, and the difference between these 2 measurements was calculated. Hierarchical regression analysis was performed to evaluate the independent and joint contributions of group status and body mass index (BMI) to these parameters. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance within the study cohort. The levator hiatus resting area, Valsalva area, and area change were all significantly greater in the prolapse group than in the control group ( P < .001). Static anatomical parameters, including the resting and Valsalva areas, were associated with group status ( P < .001), whereas the dynamic functional parameter, namely the area change, was associated with both group status (standardized β = 0.829, P < .001) and BMI (standardized β = 0.139, P = .003). ROC analysis showed very high apparent diagnostic performance for area change in this cohort (AUC = 1.000), compared with the static anatomical parameters (AUC = 0.999 and 0.920, respectively). These findings indicate different statistical association patterns for levator hiatus morphology and dynamic area change. Dynamic functional assessment may provide complementary information beyond static anatomical measures; however, its diagnostic performance requires confirmation in parity-matched and independent validation cohorts.
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Xue et al. (2026) studied this question.
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