The prediction and treatment of postpartum myofascial pelvic pain

Abstract

BACKGROUND:

The clinical manifestations of myofascial pelvic pain (MFPP) are mainly acute or chronic muscle pain at one or more trigger points in the pelvic cavity or pelvic floor.

OBJECTIVE:

This study aims to explore the predictive value of pelvic floor myoelectric parameters with respect to MFPP and the effect of its clinical treatment.

METHODS:

Two hundred and one women followed up in the Wenzhou People’s Hospital 6–12 weeks postpartum between July 2020 and July 2021. They were divided into an MFPP group ( $n=$ 90) and a non-MFPP group ( $n=$ 102), but 9 MFPP patients without a pelvic floor electromyography evaluation were not included. The general demographic data and pelvic floor electromyography evaluation parameters of the two groups were compared; the related factors of postpartum women suffering from MFPP were analyzed, and a nomogram model of the postpartum risk of suffering from MFPP was established. The 99 patients with postpartum MFPP were divided into a treatment group ( $n=$ 10) and a control group ( $n=$ 89). The difference in visual analog scale scores between the two groups initially and after three months of treatment was compared to evaluate the effective remission rate of postpartum MFPP after treatment.

RESULTS:

A significant difference was observed in the relaxation time at the rapid contraction stage ( $z=$ 4.369, $p<$ 0.05) and the tension contraction stage ( $z=$ 135.645, $p<$ 0.01) between the MFPP group and the non-MFPP group. The nomogram model for predicting postpartum MFPP was established with nine variables as potential predictors. The calibration chart and C index of 0.68 (95% CI: 0.65–0.71) proved that the model had a certain degree of discrimination. The clinical decision-making curve showed that the model could increase the net benefit rate of patients. The pain relief rate in the treatment group was significantly higher than that in the control group ( $p<$ 0.01).

CONCLUSION:

There is a significant correlation between postpartum MFPP and relaxation time at rapid contraction stage and tension contraction stage. The risk prediction nomogram model of postpartum MFPP established with nine potential predictors has a certain prediction capability, and clinical treatment can effectively relieve MFPP in postpartum patients.

Keywords

Postpartum pelvic floor muscle myofascial pelvic pain pelvic floor myoelectric parameter nomogram model trigger point manipulation therapy machine learning artificial intelligence

1. Introduction

Myofascial pelvic pain (MFPP) refers to the shortening, tension, and tenderness of pelvic floor muscle tissues, connective tissues, and related fascia. It can be accompanied by urinary and intestinal symptoms [1]. The clinical manifestations are mainly acute or chronic muscle pain at one or more trigger points in the pelvic cavity or pelvic floor. The pain may be persistent or intermittent and vary in intensity. The most common symptoms include abdominal and lower back pain, suprapubic pain, coital pain, frequent micturition, urgent micturition, constipation or tenesmus. Its pathogenesis remains unclear, but it is thought to be the result of the interaction between the dysfunction of the immune, nervous, and endocrine systems and psychological factors. To date, some basic studies have found that MFPP is mainly related to the injury of the inferior hypogastric plexus caused by delivery, gynecological laparoscopic surgery, cesarean section, pelvic inflammatory diseases or trauma, and the associated nerve reconstruction [2, 3]. To arrive at a diagnosis of MFPP it is necessary to first exclude other causes of the pain, such as inflammation and a tumor of the reproductive system, urinary system, or digestive system. This can be done with the aid of a clinical history inquiry, physical examination, laboratory tests, imaging studies, and an endoscopic examination.

A diagnosis of MFPP can only be made when there is a trigger point of pelvic floor muscle and fascia pain [4]. The main criteria are as follows: a chief complaint of regional pain; sensory abnormalities in the expected scattered distribution area of the chief complaint pain or a trigger-point-involved pain; the tension band is palpated by the involved myofascial; a point of the tension band has severely punctate tenderness; and there is some degree of motion limitation during measurement. The secondary criteria are as follows: the main complaints of clinical pain or sensory abnormalities are repeated at tender points; lateral grasping or inserting needles into the trigger point of the banded area induces local convulsive response; and stretching muscles or injecting tenderness points (TPs) can relieve pain.

Studies have shown that there is a strong correlation between pelvic floor muscle tenderness scores and MFPP [5], a diagnosis of MFPP is reliable through the evaluation and analysis of pelvic floor surface electromyography [6], and there are significant changes in resting potential and variability before and after MFPP treatment. The above studies suggest that musculoskeletal assessment is feasible and well-tolerated. Pelvic floor surface electromyography (EMG) assessment parameters can be used as reference indexes for the diagnosis and treatment assessment of patients with MFPP. However, there are relatively few studies on postpartum MFPP, and the correlation between pelvic floor surface EMG assessment results and MFPP in postpartum women is not clear yet, Therefore, the correlation between postpartum MFPP and EMG evaluation parameters will be investigated in this study.

2. Materials and method

2.1 Subjects

Women who attended the Wenzhou People’s Hospital between July 2020 and July 2021 for their first review in the sixth to twelfth week postpartum and were willing to participate in this study were selected for a medical history inquiry and gynecological examination. The subjects were divided into an MFPP group and a non-MFPP group using existing diagnostic methods, and they all signed informed consent forms.

The exclusion criteria were: 1) those with a previous history of pelvic surgery, urology, or spine surgery; 2) those with a history of diabetes, kidney disease, heart disease, or hypertension; 3) those with an acute stage urinary and reproductive system infection; 4) those with active vaginal bleeding or a tumor; 5) those who were pregnant; 6) those unable to cooperate with the examination because of mental disabilities or hearing impairment; 7) those who were in pelvic organ prolapse stage II or above; 8) those with urinary incontinence; 9) those equipped with cardiac pacemaker; and 10) those with a history of lumbar disc disease, sciatica, or other nervous system diseases.

With respect to the MFPP relief criteria, at least two of the following conditions had to be met: 1) the disappearance or relief of clinical pain and related symptoms; 2) a decrease in the scores of pelvic pain by $\geqslant$ 3 points, or all scores were $<$ 3 points; 3) the tenderness score of the pelvic floor muscle decreased by $\geqslant$ 3 points, or all pain evaluation scores were $<$ 3 points. The subjects were divided into an MFPP group and a non-MFPP group, according to the MFPP diagnostic criteria; the patients with MFPP were further divided into a treatment group and a control group according to whether they received clinical treatment (including manual massage, electromagnetic stimulation, and drug therapy).

2.2 Methods

2.2.1 General data

The general demographic data were collected using an in-house questionnaire and included the following: age, body mass index (BMI), anamnesis, pregnancy and delivery history, and surgery history; and obstetric related information, such as weight gain during pregnancy, mode of delivery, neonatal birth weight, and feeding mode.

2.2.2 Vaginal touch examination

To evaluate the pelvic floor muscle strength, the contraction force and duration were measured by the force exerted on the tester’s fingers. The scoring system was divided into five grade scales according to muscle contraction quality, retention time, and contraction times: Grade 0, no muscle contraction; Grade I, muscle fibrillation, only contracted once and lasted for 1 s; Grade II, incomplete muscle contraction, contracted twice, and lasted for 2 s; Grade III, complete muscle contraction without resistance, contracted three times and lasted for 3 s; Grade IV, complete muscle contraction, slight confrontation, contracted four times and lasted for 4 s; and Grade V, complete muscle contraction, continuous confrontation, contracted five times, each lasting for at least 5 s. The result is considered abnormal if it is lower than grade III.

2.2.3 The pelvic floor muscle tenderness examination method and the visual analogue scale (VAS) score

The examiner inserted a gloved index finger (lubricated with paraffin oil) into the vagina, slowly and gently, and encouraged the patient to relax and perform abdominal breathing. The patient was asked to contract the pelvic floor muscle and maintain tense contraction, and the examiner palpated the pubococcus muscle, iliac tail muscle, tail muscle, internal obturator muscle, and levator ani tendon arch with the pulp of one finger, and applied pressure to the symphysis pubis with the finger at an angle of 10–20 degrees in the paraurethral area close to the urethra. At the same time, the examiner paid attention to where there was pelvic floor muscle tenderness and a trigger point, recorded the trigger point position and pain score afterwards, and evaluated the degree of pain using the VAS: 0, no discomfort; 1–3 points, mild pain; 4–6 points, moderate pain; 7–9 points, severe pain; and 10 points, sharp pain. A VAS $\geqslant$ 3 points had a diagnostic significance.

2.2.4 Pelvic floor EMG assessment

All subjects received a pelvic floor EMG assessment. The Glazer pelvic floor function evaluation procedure was performed, and pelvic floor EMG assessment was carried out by specially trained pelvic floor specialist nurses to get the pelvic floor muscle surface electromyography values in $\mu$ v at the pre-resting stage, rapid muscle contraction stage, slow muscle contraction stage, endurance contraction stage, and post resting stage. The test results were recorded automatically.

2.3 Statistical analysis

R3.6.2 software was used for data analysis, and the counting data were expressed as a rate (%), and $\chi^{2}$ was used for inter group comparison; the measurement data conforming to the normal distribution were expressed with $\bar{x}\pm s$ , and a $t$ -test was used for comparison between the groups; M (Q1, Q3) was used to represent the non-normal distribution data, and then a rank sum test was used for the inter group comparison. Least Absolute Shrinkage Selector Operator (LASSO) regression combined with clinician judgment was used to screen appropriate variables to prevent over-fitting, and the related factors of postpartum women suffering from MFPP were analyzed through multiple logistic regression. A nomogram model was established by using the R3.6.2 software package and a regression modeling strategy program package. At the same time, the bootstrap method was used to repeatedly conduct sampling 1,000 times for internal verification, and the MFPP diagnostic prediction model was evaluated by drawing a calibration chart and calculating the C index. The net benefit rate under different threshold probabilities was quantified in the MFPP cohort by decision-making curve analysis to determine the clinical practicability of an MFPP diagnostic prediction model. This was a two-sided test, and the test level was $\alpha=$ 0.05.

3. Results

3.1 General conditions

Between July 2020 and July 2021, 201 eligible adult women were recruited for this study, comprising 99 patients with MFPP (9 patients with MFPP did not undergo a pelvic floor EMG evaluation and were not included in the comparison of general data parameters) and 102 patients without MFPP.

3.2 The comparison of general demographic data and clinical data

There was no significant difference between the MFPP group and the non-MFPP group in age, time of delivery, BMI, weight gain during pregnancy, delivery mode, and feeding mode ( $p>$ 0.05). The proportion of newborns with a weight $>$ 4,000 g was higher in the MFPP group than in the non-MFPP group ( $p<$ 0.05), and the difference was statistically significant. See Table 1.

Table 1
Comparison of general demographic data and clinical characteristics between MFPP and non-MFPP groups

Characteristic	$n$ %
	MFPP group ( $n=$ 90)	Non-MFPP group ( $n=$ 102)	$\chi^{2}$ value	$p$ value
Age (years)			0.81	0.67
$<$ 25	6 (6.67)	9 (8.82)
25–33	67 (74.44)	78 (76.47)
$>$ 33	17 (18.89)	15 (14.71)
Pregnancy (times)			0.36	0.55
$\leqslant$ 1	59 (65.56)	71 (69.61)
$\geqslant$ 2	31 (34.44)	31 (30.39)
BMI (kg/m ${}^{2}$ )			2.36	0.31
$<$ 18.5	6 (6.67)	9 (8.82)
18.5–24	58 (64.44)	73 (71.57)
$>$ 24	26 (28.89)	20 (19.61)
Weight gain during pregnancy (kg)			0.36	0.55
$\leqslant$ 12.5	42 (46.67)	52 (50.98)
$>$ 12.5	48 (53.33)	50 (49.02)
Mode of delivery			0.41	0.52
Vaginal delivery	63 (0.70)	67 (65.69)
Cesarean delivery	27 (0.30)	35 (34.31)
Neonatal weight (g)			4.99	0.03
$\leqslant$ 4000	84 (93.33)	102 (100.00)
$>$ 4000	6 (6.67)	0 (0.00)
Feeding mode			2.04	0.36
Breast feeding	46 (51.11)	58 (56.86)
Artificial feeding	10 (11.11)	15 (14.71)
Mixed feeding	34 (37.78)	29 (28.43)

3.3 The comparison of pelvic floor muscle strength grading and pelvic floor electromyography parameters

The relaxation time of the rapid contraction stage in the MFPP group was 0.87 s (0.72, 1.13), and in the non-MFPP group it was 1.00 s (0.77, 1.84). There was a significant difference between the two groups ( $p<$ 0.05). The relaxation time of the tension contraction stage in the MFPP group was 1.10 s (0.82, 2.07), and in the non-MFPP group it was 0.09 s (0.06, 0.13). There was a significant difference between the two groups ( $p<$ 0.01). Among the other parameters, the average value of pre-resting stage ( $p=$ 0.077), the variation coefficient of the tense contraction stage ( $p=$ 0.060), the variation coefficient of the endurance contraction stage ( $p=$ 0.111) and the variation coefficient of the post-resting stage ( $p=$ 0.106) also have a correlation with MFPP, as shown in Table 2.

Table 2
Comparison of pelvic floor muscle strength grading and pelvic floor electromyography test parameters between MFPP and non-MFPP groups

Items M (Q ${}_{1}$ , Q ${}_{3}$ )	MFPP group		Non-MFPP group		$Z$ value	$p$ value
Evaluation of pelvic floor muscle strength by	2	(1, 2)	2	(1, 2)	0.124	0.724
Vaginal touch examination (grade)
Mean value of pre resting state ( $\mu$ V)	4.26	(2.84, 7.04)	5.28	(3.18, 8.01)	3.126	0.077
Variation coefficient at pre resting state	0.09	(0.07, 0.16)	0.09	(0.04, 0.14)	0.731	0.393
Maximum value in rapid contraction stage ( $\mu$ V)	26.62	(20.76, 37.79)	29.01	(20.4, 37.24)	0.069	0.793
Relaxation time during rapid contraction stage (s)	0.87	(0.72, 1.13)	1.00	(0.77, 1.84)	4.369	0.037
Average value of tense contraction stage ( $\mu$ V)	20.54	(14.34, 30.51)	22.63	(15.62, 29.01)	0.338	0.561
Variation coefficient at tense contraction stage	0.28	(0.23, 0.32)	0.26	(0.22, 0.30)	3.544	0.060
Relaxation time at tension contraction stage (s)	1.10	(0.82, 2.07)	0.09	(0.06, 0.13)	135.645	0.000 ${}^{\ast}$
Average value of endurance contraction stage ( $\mu$ V)	19.19	(13.41.28.74)	20.94	(15.58, 27.61)	0.285	0.593
Variation coefficient at endurance contraction stage	0.20	(0.16, 0.24)	0.18	(0.15, 0.22)	2.541	0.111
Average value of post resting stage ( $\mu$ V)	4.10	(2.57, 6.96)	4.50	(3.01, 6.97)	0.732	0.392
Variation coefficient at post resting stage	0.09	(0.07, 0.16)	0.08	(0.06, 0.15)	2.614	0.106

Table 3

Prediction of postpartum MFPP variables

Variables	Prediction model
	$\beta$	OR value (95% CI)	$P$ value
Age	$-$ 0.9265	3.96e ${}^{-01}$ (3.43e ${}^{-02}$ –3.6e ${}^{+00}$ )	0.428
BMI	0.8361	2.31e ${}^{+00}$ (7.29e ${}^{-01}$ –9.68e ${}^{+00}$ )	0.185
Feeding mode	1.7292	5.64e ${}^{+00}$ (1.89e ${}^{+00}$ –2.45e ${}^{+01}$ )	0.006
Evaluation of pelvic floor muscle strength by	1.6522	5.22e ${}^{+00}$ (1.19e ${}^{+00}$ –3.64e ${}^{+01}$ )	0.049
Vaginal touch examination
Variation coefficient at pre resting state	$-$ 5.8074	3.01e ${}^{-03}$ (4.88e ${}^{-05}$ –4.25e ${}^{-02}$ )	$<$ 0.001
Relaxation time during rapid contraction stage	$-$ 1.4102	2.44e ${}^{-01}$ (2.63e ${}^{-02}$ –1.47e ${}^{+00}$ )	0.159
Average value of endurance contraction stage	$-$ 1.3442	2.61e ${}^{-01}$ (2.54e ${}^{-02}$ –1.96e ${}^{+00}$ )	0.792
Variation coefficient at endurance contraction stage	0.5623	1.75e ${}^{+00}$ (4.06e ${}^{-01}$ –8.49e ${}^{+00}$ )	0.712
Average value of post resting stage	1.2839	3.61e ${}^{+00}$ (5.23e ${}^{-01}$ –4.12e ${}^{+01}$ )	0.235

Figure 1.

Select variable characteristics using LASSO regression model. (A) The selection of the best parameter lambda in LASSO model is verified by the minimum standard using fivefold cross validation. The binomial deviation curve is plotted relative to log (lambda). The vertical dotted line (1-se standard) is drawn at the optimal value using the minimum standard and 1-standard deviation, where the best lambda filters out 9 variables with non-zero coefficients. (B) LASSO coefficient distribution of 22 variables. Generate coefficient distribution diagram for log (lambda) sequence.

Figure 2.

The risk prediction nomogram model of postpartum MFPP. The risk prediction nomogram model of postpartum MFPP was constructed by using age, BMI, feeding mode, touching measure of pelvic floor muscle strength, variation coefficient of pre-resting stage, relaxation time of rapid contraction stage, variation coefficient of endurance contraction stage, average value of endurance contraction stage and average value of post resting stage.

Figure 3.

The calibration curve of the risk prediction nomogram of postpartum MFPP in the cohort. The x-axis represents the risk of postpartum MFPP. The y-axis represents the incidence of diagnosed postpartum MFPP. The diagonal dotted line represents the perfect prediction of an ideal model. The solid line represents the performance of the nomogram, and the closer the solid line is to the dotted line closer, the prediction would be better.

Figure 4.

The analysis of clinical decision-making curve of risk prediction model for postpartum MFPP. For postpartum MFPP, the risk prediction model shows the net benefit curve. The deep solid line shows the net benefit when all postpartum women are considered to be free of postpartum MFPP; the light solid line shows the net benefit when all postpartum women are diagnosed with postpartum MFPP. The blue solid line indicates that the threshold probability of all postpartum women being diagnosed with postpartum MFPP and all postpartum women not being diagnosed with postpartum MFPP are 19% and 70%, respectively, the use of the risk prediction model of postpartum MFPP can increase the net benefit rate of the disease.

3.4 The establishment of a nomogram model

In order to avoid the overfitting of the prediction model, the LASSO regression model had a non-zero coefficient (see Fig. 1). At the same time, combined with the professional judgment of clinicians, 22 variables based on 192 patients in the cohort were reduced to 9 potential variables, namely age, BMI, feeding mode, manual pelvic floor muscle strength testing, the variation coefficient at the pre-resting stage, the relaxation time at the rapid contraction stage, the variation coefficient at the endurance contraction stage, the average value at the endurance contraction stage, and the average value at the post resting stage (see Table 3). A nomogram model for predicting the risk of MFPP was established, using the results of the multivariate logistic regression analysis. The higher the total score of the nomogram model, the higher the risk of postpartum MFPP, as shown in Fig. 2. The calibration chart (see Fig. 3) and C index 0.68 (95% CI: 0.65–0.71) show that the model has a certain degree of discrimination ability. The clinical decision-making curve analysis of the risk prediction nomogram model for predicting the occurrence of postpartum MFPP shows that if the threshold probability of all postpartum women being diagnosed with postpartum MFPP and all postpartum women being diagnosed without postpartum MFPP are 19% and 70% respectively, the net benefit rate of postpartum MFPP predicted by this prediction model will increase, as shown in Fig. 4.

3.5 The comparison of outcomes between the treatment group and the non-treatment group

Ninety-nine postpartum MFPP patients (including 9 MFPP patients without a pelvic floor EMG evaluation) were divided into a treatment group (10 cases) and a control group (89 cases); the VAS score of pelvic floor muscle tenderness was accessed and re-evaluated after three months. The natural relief rate of the control group was lower than that of the treatment group, and the pelvic floor muscle pain relief rate of the treatment group was significantly higher than that of the control group. There was a significant statistical difference between the two groups ( $p<$ 0.01), as shown in Table 4.

Table 4
Symptom remission rate of MFPP treatment group and non-treatment group (%)

Items	Treatment group ( $n=$ 10)	Control group ( $n=$ 89)	$\chi^{2}$ value	$P$ value
Right
Pubococcygeus	5 (50.0)	5 (5.6)	14.9	$<$ 0.01
Obturator internus	8 (80.0)	8 (9.0)	28.5	$<$ 0.01
Iliococcygeus	8 (80.0)	9 (10.1)	26.2	$<$ 0.01
Coccygeus	9 (90.0)	9 (10.1)	33.4	$<$ 0.01
Piriformis muscle	8 (80.0)	7 (7.9)	31.0	$<$ 0.01
Left
Pubococcygeus	3 (30.0)	4 (4.5)	–	$<$ 0.01
Obturator internus	8 (80.0)	10 (11.2)	24.1	$<$ 0.01
Iliococcygeus	7 (70.0)	5 (5.6)	29.2	$<$ 0.01
Coccygeus	8 (80.0)	7 (7.9)	31.0	$<$ 0.01
Piriformis muscle	8 (80.0)	7 (7.9)	31.0	$<$ 0.01

4. Discussion

MFPP is an important part of chronic pelvic pain (CPP) syndrome. Its clinical manifestations have no obvious specificity, the pathogenesis is complex, the diagnosis involves multiple departments, the cognitive rate is low, and the onset can be acute or subacute. About 14–23% of CPP women and 78% of interstitial cystitis women have MFPP, and it has become a common disease endangering women’s health [7]. Postpartum MFPP is often seen in women within three months of delivery, accounting for about 21.38% of MFPP [8], and is considered to be related to pelvic floor injury caused by pregnancy and delivery, since substantial changes may take place in the skeletal muscles of pregnant women [9]. The pelvic floor myofascial will be subjected to long-term load, and the lasting tense contraction of muscle fibers will lead to spasm and compress the nerve tissue passing through them, resulting in the formation of local myofascial trigger points. In addition, a series of mechanical injuries to the pelvic floor during delivery and poor postpartum recovery can eventually lead to postpartum MFPP.

In order to further understand the pathophysiology of MFPP, researchers at home and abroad have conducted a great deal of research into the examination of pelvic floor nerve and muscle function in patients with MFPP. Glazer began to analyze the surface EMG data of patients with pelvic floor dysfunction in 1995. He put forward the pelvic floor surface EMG evaluation scheme and established an international pelvic floor surface EMG database [10]. Itza et al. diagnosed MFPP through analyzing EMG and rotation amplitude and considered that the result was reliable, with a sensitivity of 83% and a specificity of 100% [11]. Tatiana et al. discussed the feasibility of applying systemic musculoskeletal TPs to assess pain sensitivity in women with different types of CPP. The results showed that the score of the pelvic floor muscle TPs had a strong correlation in the MFPP group, suggesting that musculoskeletal assessment was feasible and well-tolerated [12]. Jantos et al. showed that pelvic floor muscle hyperactivity could lead to the increase of the baseline and variation coefficient of resting potential before and after pelvic floor surface EMG data [13].

The postpartum period is a special time for women, and myofascial injury during delivery, the inflammatory factors produced during the recovery of postpartum pelvic floor tissue, the injury and repair process of pelvic floor nerve, muscle, and visceral tissue, and the instability of the pelvis may lead to postpartum MFPP [14]. Research at home and abroad has shown that the myofascial trigger point is an important clinical diagnostic standard for MFPP. Treatment should involve relaxation training, manual massage, and neuromuscular electrical stimulation of the myofascial trigger points [15], which will relax the fascia to relieve spasm and pain [16]. However, postpartum MFPP is often ignored by patients as well as clinicians, and the continuous existence of myofascial trigger points results in a stress-pain-tension cycle [17], which gradually develops into chronic pelvic pain, affecting the quality of life of postpartum women. Therefore, if the diagnosis and treatment of postpartum MFPP can be carried out accurately and in a timely manner during this period, it will improve the quality of life of the women who are affected by it.

Due to the particularity of the pelvic muscle nerve position and the invasive nature of a needle electrode, this method of penetrating pelvic floor muscle is often not used as a routine examination in clinic. The examination of pelvic floor neuromuscular function mainly involves pelvic floor muscle strength detection, pelvic floor surface EMG, and neuromotor induced potential detection. At present, noninvasive surface EMG is mostly used as a means of evaluation in clinic. Surface EMG is the bioelectrical signal of neuromuscular system activity guided and recorded from the muscle surface through electrodes. Pelvic floor surface EMG evaluation converts these bioelectrical signals into digital signals. The data is analyzed and processed using analysis software, and the EMG physiological parameters are obtained. Many researchers recommend using the EMG parameters obtained from pelvic floor EMG evaluation to evaluate pelvic floor neuromuscular function [18, 19]. Therefore, large sample data could be collected under noninvasive conditions to reveal the correlation between pelvic floor muscle electrophysiological parameters and postpartum MFPP. It would also be possible to compare the differences in the pelvic floor muscle electrophysiological parameters of postpartum MFPP patients according to whether they received treatment or not. This knowledge would provide a basis for a clinical treatment scheme guided by pelvic floor muscle electrophysiological parameters in the future.

The movement of people was geographically restricted during COVID-19, the implementation of innovative developments in machine learning has adapted to this change, AI-based computer-aided detection and computer-aided diagnosis are used to increase efficiency and save costs, technological advances have contributed to the growing popularity of musculoskeletal assessments. Machine learning and deep learning methods for musculoskeletal and ultrasound imaging are booming. Using big data to build a predictive model, through more accurate and efficient medical diagnosis and treatment evaluation, will greatly reduce the burden of medical staff and improve the diagnosis of postpartum MFPP in women in the next few years. In addition, the evaluation impact of postpartum MFPP on different factors using machine learning approaches, which can detect complex relationships between postpartum MFPP factors, without the need for any previous training or knowledge, and is a unsupervised interaction method. This study found that the proportion of newborns weighing $>$ 4,000 g was higher in postpartum MFPP patients than in non-MFPP patients, which may be due to the fact that excessive fetal weight can cause greater damage to pelvic floor tissue and myofascial tissue. Being aged $<$ 25 or $>$ 33 years, having a BMI $>$ 24, and engaging in artificial feeding are high risk factors of postpartum MFPP, which may be because pelvic floor muscle and nerve function is not perfect when the women are young, pelvic floor muscles and nerves are more vulnerable to damage when the women are older, excessive BMI can cause excessive pelvic floor muscle load and easily form myofascial trigger points, and artificial feeding may lead to a poor postpartum repair effect. Patients with MFPP have a tense, spastic state of the pelvic floor muscles, so it takes longer to return to normal after both rapid contraction stage and tense contraction stage. The relaxation time at rapid contraction stage, relaxation time at tense contraction stage, average value at pre-resting stage, coefficient of variation at tense contraction stage, variation coefficient at endurance contraction stage, and variation coefficient at post-resting stage in pelvic floor surface EMG parameters all have a correlation with the occurrence of postpartum MFPP. This may be related to the change in EMG parameters caused by pelvic floor muscle tension spasm and the subsequent formation of myofascial trigger points. Pelvic floor muscle strength was evaluated by integrating patient pelvic floor electromyography evaluation data and combining the high-risk demographic factors of MFPP with vaginal finger diagnosis. Pelvic floor muscle strength was evaluated by age, BMI, feeding mode, the vaginal touch diagnosis, the variation coefficient at the pre-resting stage, the relaxation time at the rapid contraction stage, the average value at the endurance contraction stage, and the variation coefficient at the endurance contraction stage. The average value of nine variables in the post-resting stage can be used as parameter indicators to predict the occurrence of MFPP. It is considered that the prediction model has a certain degree of discrimination ability. Clinical decision curves showed that the threshold probabilities for all postpartum women to be diagnosed with postpartum MFPP and for all postpartum women to be diagnosed with postpartum MFPP were 19% and 70%, respectively. When the model is used to predict all postpartum women, postpartum MFPP patients can be identified earlier, reducing patient suffering and medical costs, so the predicted net benefit rate of postpartum MFPP predicted by this predictive model will increase. This research result needs to be verified with a larger sample, and a special pelvic floor surface EMG database needs to be established. At the same time, by continuously comparing the VAS scores of the treatment group and the non-treatment group of MFPP patients within three months, it was found that the natural remission rate of postpartum MFPP is low and treatment can significantly improve the remission rate of postpartum MFPP. This suggests that treatment can improve the quality of life of postpartum MFPP patients, reduce pain, and help doctors reasonably optimize clinical treatment. As the occurrence of MFPP involves many disciplines, it is difficult to determine its etiology, and the therapeutic effect of a single therapy is not likely to be ideal. Consequently, combined treatment of MFPP has become the first-line approach. The literature reports that the effective remission rate of electrical stimulation combined with manual massage in the treatment of MFPP is significantly better than that of other methods [20, 21]. Through prospective research and analysis, it has been confirmed that biofeedback treatment, using pelvic floor electromyography evaluation parameters as a signal, combined with myofascial manipulation has achieved good short- and long-term results [15]. Such treatment boasts safety, non-invasiveness, and cost-effectiveness. It can also significantly reduce the pain of patients, restore the coordination function of pelvic floor muscles, and is also conducive to the promotion of grass-roots hospitals. However, there are few clinical studies on biofeedback combined with myofascial manipulation in the treatment of postpartum MFPP at home or abroad.

5. Conclusion

This study followed cross-sectional survey standards and carried out strict quality control to reduce the measurement bias. The subjects were women who came to the hospital for a review 6–12 weeks postpartum, so the information bias has been reduced. Since there is no published literature on the individualized and quantitative evaluation of postpartum MFPP in China, this paper used a nomogram model to predict the risk of postpartum MFPP for the first time, and it has been shown that the model has a certain prediction efficiency from multiple angles, including internal validation, a calibration chart, and a clinical decision-making model, and it provides a new way of making a postpartum MFPP prediction. The shortcomings of the study are that the sample size was limited, and all the subjects came from the same research center, which may have led to selection bias. As this study was a case-control study, it could not prove the causal relationship between postpartum MFPP and abnormal pelvic floor surface EMG evaluation, so this model needs to be further improved in future research.

Funding

This study was funded by the 2020 Wenzhou Basic Scientific Research Project (No. Y2020492).

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki (as was revised in 2013). The study was approved by the Ethics Committee of Wenzhou People’s Hospital (No. 2020-200). Written informed consent was obtained from all participants.

Availability of data and materials

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.

Footnotes

Acknowledgments

The authors are particularly grateful to all people who helped them with the article.

Conflict of interest

The authors declare that they have no competing interests.

References

Jessmon

Boulanger

Zhou

Patwardhan

. Epidemiology and treatment patterns of epithelial ovarian cancer. Expert Rev Anticancer Ther. 2017 May; 17(5): 427-437.

Chen

Zhu

. Classification of chronic pelvic pain. Journal of Practical Obstetrics and Gynecology. 2016; 32(05): 321-323. [Article in Chinese] https://kns-cnki-net-443.web.bisu.edu.cn/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2016&filename=SFCZ201605002&uniplatform=NZKPT&v=v2_nnMJT5uKeYYzpYGAQxQgVoTyJoAs6YeOWqhD7a4pEMTz2T27o-Co8zuclPvWV.

Stein

. Chronic pelvic pain. Gastroenterol Clin North Am. 2013 Dec; 42(4): 785-800.

Montenegro

Gomide

Mateus-Vasconcelos

, et al. Abdominal myofascial pain syndrome must be considered in the differential diagnosis of chronic pelvic pain. Eur J Obstet Gynecol Reprod Biol. 2009; 147(1): 21-24.

Meister

Sutcliffe

Badu

Ghetti

Lowder

. Pelvic floor myofascial pain severity and pelvic floor disorder symptom bother: is there a correlation? Am J Obstet Gynecol. 2019; 221(3): 235.e1-235.e15.

Navarro Brazález

Torres Lacomba

de la Villa

, et al. The evaluation of pelvic floor muscle strength in women with pelvic floor dysfunction: A reliability and correlation study. Neurourol Urodyn. 2018; 37(1): 269-277.

Jarrell

Vilos

Allaire

, et al. No. 164-consensus guidelines for the management of chronic pelvic pain. J Obstet Gynaecol Can. 2018; 40(11): e747-e787.

Cheng

Tan

Liu

, et al. Curative effect of electrical stimulation combined with massage on postpartum myofascial pelvic pain. Chinese Journal of Woman and Child Health Research. 2014; 25(06): 1015-1018. [Article in Chinese] https://kns-cnki-net-443.web.bisu.edu.cn/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2016&filename=SANE201406040&uniplatform=NZKPT&v=k0drrEEkgJLZnZebfXR67U5OLvbJlBrXNgPXoZyO85VarqTodCC8ovmXNqJIFn0k.

Tenfelde

Tell

Brincat

Fitzgerald

. Musculoskeletal pelvic pain and sexual function in the first year after childbirth. J Obstet Gynecol Neonatal Nurs. 2019; 48(1): 59-68.

10.

Glazer

Romanzi

Polaneczky

. Pelvic floor muscle surface electromyography. Reliability and clinical predictive validity. J Reprod Med. 1999; 44(9): 779-782.

11.

Itza

Zarza

Salinas

Teba

Ximenez

. Turn-amplitude analysis as a diagnostic test for myofascial syndrome in patients with chronic pelvic pain. Pain Res Manag. 2015; 20(2): 96-100.

12.

Sanses

TVD

Pearson

Davis

, et al. Physical performance measures in older women with urinary incontinence: Pelvic floor disorder or geriatric syndrome? Int Urogynecol J. 2021; 32(2): 305-315.

13.

Jantos

. Vulvodynia: a psychophysiological profile based on electromyographic assessment. Appl Psychophysiol Biofeedback. 2008; 33(1): 29-38.

14.

Lee

McLaughlin

. Stability, continence and breathing: The role of fascia following pregnancy and delivery. J Bodyw Mov Ther. 2008; 12(4): 333-348.

15.

Pastore

Katzman

. Recognizing myofascial pelvic pain in the female patient with chronic pelvic pain. J Obstet Gynecol Neonatal Nurs. 2012; 41(5): 680-691.

16.

Dong

Guo

Huang

Chen

. The efficacy of manipulation as a treatment for myofascial pelvic pain. Int Urol Nephrol. 2021; 53(7): 1339-1343.

17.

Zou

Min

. Effect of biofeedback combined with myofascial manipulation on postpartum pelvic myofascial pain. Maternal and Child Health Care of China. 2020; 35(19): 3540-3543.

18.

Bertotto

Schvartzman

Uchôa

Wender

MCO

. Effect of electromyographic biofeedback as an add-on to pelvic floor muscle exercises on neuromuscular outcomes and quality of life in postmenopausal women with stress urinary incontinence: A randomized controlled trial. Neurourol Urodyn. 2017; 36(8): 2142-2147.

19.

Chen

Ren

Zhu

. Correlation between modified Oxford grading scale and pelvic floor surface electromyography in assessment of pelvic floor muscle function in female patients with stress urinary incontinence. National Medical Journal of China. 2020; 100(37): 2908-2912. [Article in Chinese] https://kns-cnki-net-443.web.bisu.edu.cn/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDZHYX&filename=ZHYX202037018&uniplatform=NZKPT&v=a8tHzAYwonj1C_–W-1m627NNdedJZJkN0_uYlVpLP8v-HRoFBmxpx717ayTuhhY.

20.

Liu

Long

Wang

. Electroacupuncture alleviates neuropathic pain by modulating Th2 infiltration and inhibiting microglial activation in the spinal cord of rats with spared nerve injury. World J Tradit Chin Med. 2020; 6: 448-55.

21.

Schwagerus

Dörner

Bender

, et al. Myofaszial bedingte chronische Unterbauchschmerzen bei Frauen: Eine retrospektive Auswertung der Auswirkungen einer spezifischen Diagnostik und Therapie [Myofascial chronic pelvic pain in women: A retrospective evaluation of the effects of specific diagnostics and therapy]. Schmerz. 2020; 34(5): 388-399.

The prediction and treatment of postpartum myofascial pelvic pain

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and method

2.1 Subjects

2.2 Methods

2.2.1 General data

2.2.2 Vaginal touch examination

2.2.3 The pelvic floor muscle tenderness examination method and the visual analogue scale (VAS) score

2.2.4 Pelvic floor EMG assessment

2.3 Statistical analysis

3. Results

3.1 General conditions

3.2 The comparison of general demographic data and clinical data

Table 1 Comparison of general demographic data and clinical characteristics between MFPP and non-MFPP groups

Table 2 Comparison of pelvic floor muscle strength grading and pelvic floor electromyography test parameters between MFPP and non-MFPP groups

3.5 The comparison of outcomes between the treatment group and the non-treatment group

Table 4 Symptom remission rate of MFPP treatment group and non-treatment group (%)

5. Conclusion

Funding

Ethics approval and consent to participate

Availability of data and materials

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Comparison of general demographic data and clinical characteristics between MFPP and non-MFPP groups

Table 2
Comparison of pelvic floor muscle strength grading and pelvic floor electromyography test parameters between MFPP and non-MFPP groups

Table 4
Symptom remission rate of MFPP treatment group and non-treatment group (%)