[Prediction of pelvic floor muscle function in stress urinary incontinence].

Objective: To explore the predictive effect of pelvic floor muscle function on stress urinary incontinence (SUI) . Methods: A total of 258 women in gynecological outpatients at Fuzhou General Hospital were evaluated the pelvic floor muscle function by intravaginal manometery, then all of outpatients were divided into urinary incontinence group and non-incontinence group, and compared pelvic floor muscle function and clinical characteristic to establish prediction model of SUI by classification tree and analyse the predictive role of pelvic floor muscle function for SUI. Results: There were significant difference in body mass index [BMI; (22.8±2.9) vs (21.5±2.7) kg/m2, P<0.05], maximum newborn weight [ (3 396±424) vs (3 284±384) g, P<0.05] between urinary incontinence group (n=114) and non-incontinence group (n=144) . However, there were no significant differences in age, parity and mode of delivery between two groups (all P>0.05) . There were significant differences (all P<0.01) in maximum vaginal pressure [ (21±7) vs (35±9) mmHg (1 mmHg=0.133 kPa) ], average pressure [ (13±7) vs (23±9) mmHg], fatigue [ (-65±20) % vs (-46±17) %] and collecting time [ (1.0±0.6) vs (0.8±0.5) s] between two groups. Prediction model, which obtained by classification tree analysis with the affecting factors of SUI (including BMI, maximum vaginal pressure, fatigue and collecting time) , suggested that the incidence of SUI was 88.6% (70/79) , when maximum vaginal pressure ≤26.2 mmHg. While, when maximum vaginal pressure was greater than 28.2 mmHg, there was no occurrence of SUI (0/7) . But it would increase, when BMI >22.6 kg/m2. Conclusions: The occurrence of SUI is related to the BMI and pelvic floor muscles function. It would increase the risk of SUI with vaginal maximum pressure (≤26.2 mmHg) and BMI (>22.6 kg/m2) . While there is almost no SUI, while vaginal maximum pressure >28.2 mmHg. To select high-risk group of SUI and intervene early according to the prediction model, which may be make sense of reducing incidence of SUI.