Pelvic floor dysfunction (PFD) is a clinical syndrome caused by weakened pelvic floor support structures, leading to displacement and functional abnormalities of pelvic organs. Its primary manifestations include pelvic organ prolapse (POP), urinary incontinence (UI), fecal incontinence, and sexual dysfunction (1). PFD is now recognized as a “hidden epidemic”, with epidemiological data indicating that 21%−26% of women are affected by this condition (2). Although PFD is not life-threatening, it significantly impairs patients' quality of life and increases the risk of psychological issues such as anxiety and depression, making it a critical focus in global obstetrics and gynecology research. Among the numerous risk factors for PFD, pregnancy and childbirth are the most influential independent risk factors (1, 3). Previous studies have confirmed that 75% of POP cases are directly attributable to pregnancy- and childbirth-related trauma (4). Among women who undergo vaginal delivery, 25%−50% develop varying degrees of UI, fecal incontinence, or POP within 1 year postpartum (5); this phenomenon is closely linked to mechanical damage to the pelvic floor muscles (PFM) and nerves during delivery (6).

PFM includes type I slow-twitch fibers (which are responsible for static pelvic support) and type II fast-twitch fibers (which are responsible for regulating dynamic contraction). Dysfunction in PFM, which is characterized by reduced muscle tone or loss of coordinated contraction, leads to anatomical displacement and functional impairment of organs such as the bladder, uterus, and rectum, which underlies the pathophysiological mechanism of PFD (7). Current methods for assessing PFM function include visual inspection, vaginal palpation, ultrasound, magnetic resonance imaging (MRI), manometry, infrared thermography (IRT), and surface electromyography (sEMG) (8–11). However, no gold standard exists for scientific research or clinical practice, as each method has specific advantages and limitations. Among these, the Glazer protocol for pelvic floor surface electromyography, proposed by Glazer and Marinoff in 1997, has been clinically validated as a reliable and effective non-invasive method for PFM electromyographic evaluation (12, 13).

Reported risk factors for PFD include maternal and obstetric indicators such as mode of delivery (vaginal delivery poses greater risks than cesarean section), instrument-assisted delivery (e.g., forceps), parity, advanced maternal age at first delivery, high BMI, and fetal birth weight (14–19). Socioeconomic factors (low income, rural residence and physical labor) and perinatal management (e.g., absence of oxytocin use) have also been implicated in the risk profile (20, 21). Nevertheless, independent predictors of PFMW in primiparous women after vaginal delivery, and the reason remain unclear, particularly because few large-scale studies have quantified the associations between maternal-obstetric indicators and functional impairment in PFM.

This study employs the Glazer protocol to evaluate early postpartum PFM function in primiparous women following vaginal delivery. By constructing a clinical prediction model that integrates maternal characteristics and obstetric indicators, we aimed to develop a nomogram for predicting the risk of PFMW. This tool will enable early screening and targeted interventions for high-risk populations, thus providing evidence-based strategies for preventing postpartum and long-term PFD.

Continuous variables are presented as the mean ± standard deviation or as the median (interquartile range). Variables were compared via Student's t test or the Mann–Whitney U test, as appropriate. Categorical variables are presented as frequencies and percentages and were compared using the chi-square test (or Fisher's exact test when the expected frequency was < 5).

In the entire training cohort, univariable analysis was used to identify factors that were significantly associated with PFMW. Variables that were found to be associated with PFMW in the univariate analysis (P < 0.2) were subsequently added to the multivariate analysis, and backward stepwise selection was carried out with an improvement in goodness of fit measured by a decrease in the Akaike information criterion. A nomogram for PFMW likelihood was developed on the basis of the findings of the final regression analysis.

Concordance statistics (C-statistics) and 95% confidence intervals (CIs) were computed to evaluate the ability of the nomogram to identify patients who will suffer from PFMW. Furthermore, the C-statistics between the nomogram and each independent predictor were compared using the Delong test. Calibration curves were developed via 1,000 bootstrap resamples to analyze the level of agreement between the nomogram predictions and actual observations in the training cohort. Decision curve analysis was performed by estimating the net benefits at various threshold probabilities of PFMW to evaluate the clinical utility of the predictive nomogram.

Internal validation of the model's stability was carried out via cross-validation, which involved randomly dividing the training cohort's patients into ten equal samples. To create logistic regression models, nine of these samples were used, and the final sample was then given the model coefficients. The mean C-statistics for each iteration was computed after this procedure was repeated ten times. Additionally, the model was applied to a validation dataset and evaluated via C statistics, calibration, and decision curve analysis to evaluate its external validity.

R version 4.4.1 was used for statistical analyses and for graphing. All statistical analyses were two–sided tests, and P < 0.05 was considered statistically significant.

In this study, we developed and validated a simple nomogram to quantify the risk of PFMW for primiparous women in the early postpartum period. This nomogram, which is based on maternal and obstetrical characteristics, has excellent discriminatory ability, calibration, and net benefit in predicting PFMW. Early rehabilitation is critically important for preventing the progression and persistence of PFD (24–26). Thus, early prediction of PFMW could help clinicians provide professional counseling as well as rehabilitation guidance to the appropriate population.

Some previous clinical studies have assessed the probability of early postpartum PFMW among primiparous women (27–30). However, their findings have been limited due to small sample sizes and the pooling of women who had cesarean sections with those who had vaginal deliveries. Pregnancy and delivery are the most important risk factors for PFD (31, 32), and there are significant differences in baseline factors between cesarean and vaginal deliveries. In the present study, we created a predictive model in a larger cohort on the basis of a detailed collection of obstetrical and especially labor-related factors, which have been well validated. To our knowledge, this is the first predictive model to collect detailed delivery information for predicting the occurrence of postpartum PFMW among primiparous women with vaginal deliveries. Five factors were identified to be predictive of postpartum PFMW in the model, namely, age, weight gain during pregnancy, mode of delivery, duration of the second stage of labor, and infant weight.

PFMW is generally considered strongly associated with maternal age. With increasing maternal age and decreasing estrogen levels, decreased collagen synthesis and reduced tissue elasticity collectively weaken the biomechanical capacity of pelvic floor muscles and connective tissues, thereby increasing their vulnerability to mechanical stress. During pregnancy, the sustained mechanical load from the gravid uterus exerts chronic compressive and stretching forces on these pelvic floor tissues (25). Both Peinado-Molina et al. (31) and Xu et al. (19) reported a significant association between age and PFD when age was treated as a continuous variable. Similarly, we found that the risk of postpartum PFMW increased with each additional year of age (OR 1.156, 95% CI 1.116–1.999). Therefore, more attention should be devoted to the occurrence of postpartum PFMW and the enhancement of pelvic floor muscle training in aging mothers (26).

We found that both gestational weight gain and fetal birth weight were valuable predictors of postpartum PFMW, which is consistent with the findings of previous studies (19, 30, 33). It is well known that weight gain above normal during pregnancy and pre-pregnancy obesity (BMI > 25) increase abdominal pressure and pelvic floor muscle burden, leading to chronic strain and decreased elasticity of muscles and connective tissues. Additionally, there is an increased risk of mechanical injuries during large for gestational age (LGA) delivery, such as overstretching of the pelvic floor muscles and nerve injuries, especially when LGA delivery is combined with prolonged labor or instrumental delivery (30). Nowak et al. (34) reported that high BMI was correlated with excessive gestational weight gain (overweight: OR 3.0, 95% CI 1.84–3.87; obese: OR 2.45, 95% CI 1.1–5.48), and obese women with adequate gestational weight gain had a greater risk of having LGA newborns (OR 5.48, 95% CI 1.15–26.13). Hence, the above three factors have a cumulative superimposed effect on pelvic floor muscle injury, and they themselves can influence each other to some extent. Several studies have shown an increase in the prevalence and severity of PFD with increasing pre-pregnancy BMI compared with normal weight (15, 33). However, there was no association between pre-pregnancy BMI and postpartum PFMW after confounding factors were corrected for in our study, which may have resulted from the combined effects of study population differences, hospital conditions, and postpartum screening methods. Alternatively, weight gain during pregnancy and fetal birth weight may mask the effect of pre-pregnancy BMI on postpartum PFMW to some extent.

To date, the risk of postpartum PFMW for forceps delivery, vacuum delivery, and spontaneous vaginal birth has not been evaluated in large-sample randomized clinical trials. Three studies investigated the associations between postpartum PFMW and delivery mode. In the first cross-sectional study (29), the clinical demographic characteristics and vaginal sEMG data of 1,259 primiparous women at 6–8 weeks after birth were collected. The magnitude of fast and sustained contractions on sEMG in the forceps delivery group was significantly lower than that in the spontaneous vaginal birth group (P < 0.002, P < 0.001), and forceps delivery significantly inversely influenced PFM activity (fast contractions: OR 1.779, 95% CI 1.103–2.869; sustained contractions: OR 2.197, 95% CI 1.378–3.503). In the second study (28), 247 (85.5%) patients underwent vacuum-assisted delivery, and 42 (14.5%) patients underwent forceps delivery. The prevalence rates of levator ani muscle injury after vacuum and forceps delivery were 16.6% (41/247) (95% CI 12.0–21.2) and 40.5% (17/42) (95% CI 25.6–55.4), respectively (P = 0.001). Forceps delivery was identified as a risk factor for levator ani muscle injury, with an OR of 3.537 (95% CI 1.7–7.3). In the third study (35), among 45 participants in the forceps group and 28 participants in the vacuum group, the prevalence of levator ani muscle avulsion was significantly greater after forceps than after vacuum delivery [22/45 (49%) vs. 5/28 (18%), P = 0.012, prevalence ratio 2.74, 95% CI 1.17–6.40, OR 4.40, 95% CI 1.42–13.62]. However, the above two studies both lacked a spontaneous vaginal delivery group as a baseline control group, limiting the power. PFMW can be caused by levator ani muscle injuries and extensive levator hiatus (36). Thus, our findings were largely consistent with the conclusions of the above three studies, with the advantage of further quantifying the effect of vaginal delivery mode on postpartum PFMW, with a C-statistic of 0.549 (95% CI 0.522–0.576, P < 0.001). Forceps delivery was an independent risk factor (OR 1.904, 95% CI 1.336–2.714) and therefore was included in the nomogram model, whereas vacuum delivery was not.

The prolonged second stage of labor was shown to be another important predictor of postpartum PFMW in our study and previous studies (27, 36). During labor, the levator ani muscle and birth canal tissues must stretch to more than three times their original length. Continuous breath holding and exertion cause the pelvic floor muscle fibers and connective tissues to stretch repeatedly beyond their elastic limits, triggering microscopic tears. Prolonged compression of the pelvic floor nerves (e.g., pubic nerves) by the fetal head leads to nerve conduction dysfunction and weakens muscle contraction (36). Therefore, both the increased number of impairments and the prolonged duration of stress on the pelvic floor structure are associated with the onset of postpartum PFMW.

The current study has several limitations. First, we focused only on the detection of vaginal surface EMG signals, and further diagnoses of PFMW are lacking. Second, since third- and fourth-degree perineal lacerations are rare, analyzing them together with episiotomy may affect statistical validity. Third, several potential factors related to demographics and the postpartum recovery phase (e.g., economic level, postpartum breastfeeding, pelvic floor function exercise, etc.) were not considered. More potential indicators combined with clinical characteristics should be investigated to build a more accurate prediction model for postpartum PFMW. Finally, almost all the participants in this study were residents of Shanghai, an economically developed region of China. Therefore, further multicenter clinical trials are needed to establish a more general predictive model.

The novel clinical prediction model in the present study has practical implications since it is simple to adopt, shows good discrimination, and demonstrates good calibration for predicting the occurrence of early postpartum PFMW among primiparous women with vaginal delivery. We plan to integrate this predictive model into the routine 42-day postpartum examination at our hospital, enabling refined management of pelvic floor muscle function for postpartum women. By categorizing individuals into different risk groups for stratified management. The nomogram might help primiparous women with vaginal delivery benefit from early detection and rehabilitation of PFMW, thus facilitating the management of pelvic floor disorders. However, the benefits remain to be explored in prospective trials.

We created a nomogram that can be utilized to quantify the risk of postpartum PFMW for primiparous women with vaginal delivery. The model developed herein might contribute to the early identification of postpartum PFMW, thereby providing evidence-based strategies for preventing postpartum and long-term PFD.