Assessment of the feasibility of transperineal ultrasound combined with clinical factors in predicting female stress urinary incontinence factors / 中华超声影像学杂志

ABSTRACT

Objective To investigate the feasibility and accuracy of transperineal real‐time three‐dimensional ultrasound combined with clinical factors in predicting the risk of female stress urinary incontinence( SUI ) . Methods T hree hundred and forty‐eight female patients with SUI diagnosed were selected as the case group ,and 102 healthy people in the same period were selected as the control group . All subjects underwent transperineal real‐time three‐dimensional ultrasound . T he ultrasonic parameters of resting state ,contraction and Valsalva were measured ,and the clinical parameters such as age ,height , weight ,history of pregnancy and childbirth were collected . According to the time sequence ,all the subjcets were divided into derivation cohort and verification cohort inproportion to 2∶1 ,single factor screening and logistic multiple regression analysis were carried out on 24 factors ,and the risk model was established . T he cut‐off value of the disease probability P was determined by the ROC curve of the subjects ,and then the accuracy of the cut‐off value in predicting SUI was verified in the verification group . Results Single factor analysis showed that 13 parameters were associated with SUI( all P ＜0 .05) . Logit P＝2 .014+1 .870× Z1 was established by multivariate logistic regression analysis . T he cut‐off value of the disease probability P determined by ROC curve was 0 .823 . T he predictive sensitivity of the model was 68 .1% ( 95% CI 59 .6% －76 .6% ) ,specificity was 91 .2% ( 95% CI86 .0% －96 .4% ) ,positive predictive value was 64 .3%( 95% CI 55 .6% － 73 .0% ) and negative predictive value was 92 .5% ( 95% CI 86 .2% － 98 .8% ) . Conclusions It is feasible to predict the risk of female stress urinary incontinence by transperineal real‐time three‐dimensional ultrasound combined with clinical factors . Although ,some limitations with the prediction model ,it has accuracy in predicting SUI with obvious symptoms .