Prediction of late-pregnancy pelvic floor dysfunction: a nomogram model based on proton density fat fraction and synthetic magnetic resonance imaging.

BACKGROUND

Pelvic floor dysfunction (PFD) is a highly prevalent condition that significantly compromises women's quality of life. To date, the majority of studies and clinical rehabilitation interventions have focused on the assessment and management of pelvic floor muscle (PFM) function in the postpartum period, whereas investigations into the monitoring of pelvic floor function during the third trimester of pregnancy remain relatively understudied. This study aimed to develop a radiology-clinical nomogram integrating proton density fat fraction (PDFF; Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation, IDEAL-IQ sequence), synthetic magnetic resonance imaging (MAGnetic Resonance Image Compilation, MAGiC sequence), and diffusion tensor imaging (DTI) for predicting PFD in late pregnancy.

METHODS

A total of 114 third-trimester pregnant women were classified into PFD and non-PFD groups. Fat fraction (FF), T1, T2, proton density (PD), and fractional anisotropy (FA) of bilateral levator ani muscles (LAMs) were measured from IDEAL-IQ, MAGiC, and DTI by two radiologists (with 15 years of experience). Measurement reliability was assessed using intraclass correlation coefficients (ICCs). Univariate analysis screened potential predictors [age, gestational week, gravida, parity, delivery mode, fetal weight, pre- and post-pregnancy body mass index (BMI), and quantitative imaging parameters]. Significant variables were included in a multivariate logistic regression to build the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

RESULTS

Of 114 participants, 52 were assigned to the PFD group and 62 to the non-PFD group. Univariate analysis showed significant between-group differences in gestational week (P=0.025), gravida (P=0.032), parity (P=0.023), fetal weight (P<0.001), left LAM FF (L-LAM FF, P=0.005), and right LAM T2 (R-LAM T2, P=0.004). The final Akaike information criterion (AIC)-selected multivariable prediction model, gravida, L-LAM FF, and R-LAM T2 were retained as nomogram predictors. The nomogram achieved moderate discrimination with an area under the curve (AUC) of 0.726 [95% confidence interval (CI): 0.632-0.815]. Calibration indicated good model fit, and DCA demonstrated positive net benefit within a threshold probability range of 25-83%. Interobserver agreement was moderate/excellent (ICC 0.49-0.86). FA was only an exploratory imaging biomarker and was not retained in the final combined prediction model.

CONCLUSIONS

The nomogram incorporating gravida, L-LAM FF, and R-LAM T2 effectively predicts PFD in late pregnancy.