Abstract
Objective
To develop and validate a nomogram prediction model for chronic pain after hysterectomy for uterine fibroids.
Methods
A retrospective study was conducted on 315 patients who visited our hospital from January 2022 to July 2024. The patients were stochastically assigned into training dataset (n=220) and validation dataset (n=95) in a 7:3 ratio. The training dataset was assigned into non chronic pain group (n=164) and chronic pain group (n=56) based on whether chronic pain persists for more than 3 months after surgery. Multivariate logistic regression was used to screen for predictive factors. R software was used to construct nomogram models. The calibration curve was used to evaluate the calibration degree of nomogram. ROC curve was used to evaluate the discrimination of nomogram. The clinical decision curve analysis was used to discuss and evaluate the net profit of the nomogram.
Results
Preoperative pain, history of abdominal or pelvic surgery, endometriosis, anxiety, and high Pain Catastrophic Scale (PCS) score were independent risk factors for chronic pain after hysterectomy for uterine fibroids (P<0.05). The nomogram model showed high predictive performance in both the training and validation datasets, and the calibration curves showed good consistency and calibration degree, the Hosmer-Lemeshow test showed χ2=1.654, 3.181, P=0.990, 0.922; the AUC values in ROC curve were 0.841 (95% CI: 0.781~0.900) and 0.825 (95% CI: 0.762~0.887). The clinical decision curve analysis indicated that decisions based on the nomogram model could provide higher net benefits for patients undergoing hysterectomy for uterine fibroids within a prediction probability threshold range of 0.08~0.75.
Conclusion
The nomogram developed in this study accurately predicts the risk of chronic pain after hysterectomy for uterine fibroids, which is beneficial for preoperative planning and patient consultation.
Keywords: uterine fibroids, hysterectomy, chronic pain, influencing factors, nomogram model
Introduction
Uterine fibroids are the most common benign tumors in women of reproductive age, and they are associated with heavy bleeding, reduced fertility, and decreased quality of life.1,2 Surgery is the preferred treatment for patients with clinically symptomatic uterine fibroids, with the most common traditional surgical options being myomectomy to remove the fibroids or total hysterectomy, with the latter being more frequently performed.3,4 Chronic postsurgical pain is defined by the International Association for the Study of Pain (IASP) as persistent or recurrent pain that arises after a surgical procedure, located in or around the surgical site, and lasting for at least three months.5,6 Postoperative chronic pain is a common complication after hysterectomy, typically related to changes in damaged neurons, inflammation, and the release of pain-inducing substances following tissue injury, which excite nociceptors and contribute to the development of chronic pain. Persistent pain hinders recovery and negatively affects patient well-being.7 Early identification of high-risk patients can provide a critical window for reducing the risk of postoperative chronic pain and improving patients’ quality of life. Studies have shown that the Numerical Rating Scale (NRS) is a validated and simple tool for assessing postoperative chronic pain and is now widely used in clinical practice.8,9 However, using the NRS alone to assess postoperative pain has relatively low efficiency and makes it difficult to predict pain early. Therefore, to assess the risk of developing chronic pain after uterine fibroid hysterectomy, this study analyzed several identified risk factors and combined them with new risk factors to develop a nomogram prediction model.
Objects and Methods
Patients
This study retrospectively included 315 patients who visited our hospital between January 2022 and July 2024. The patients were randomly divided into a training dataset (n=220) and a validation dataset (n=95) in a 7:3 ratio. Inclusion criteria: (1) Patients diagnosed with uterine fibroids in accordance with diagnostic standards,10 with indications for hysterectomy; (2) Ethics approval from the Meizhou people’s hospital was obtained for the study; (3) Age ≥18 years. Exclusion criteria: (1) History of uterine malignancies or other cancers; (2) Significant organ dysfunction; (3) Mental disorders or cognitive impairments; (4) Drug abuse or alcoholism; (5) Uterine adenomyosis or pelvic floor dysfunction. The flowchart for patient inclusion is shown in Figure 1.
Figure 1.
Case collection process diagram.
Collection of Clinical Data
Clinical data were collected for patients undergoing uterine fibroid hysterectomy, including: age, body mass index (BMI), marital status, education level, preoperative pain, menopause status, hypertension, diabetes, surgical approaches (laparoscopic, abdominal, vaginal) and anesthesia methods (general anesthesia, neuraxial anesthesia), history of abdominal/pelvic surgery, number of pregnancies, number of deliveries, endometriosis, surgical method, anxiety, Pain Catastrophizing Scale (PCS) score, adnexal removal, surgical duration, intraoperative blood loss, postoperative infection, postoperative nausea and vomiting, and uterine weight. Preoperative pain was assessed using the Numerical Rating Scale (NRS), with a total score of 10, where a higher score indicates more severe pain (0 indicates no pain, and >0 indicates pain). Anxiety was assessed using the GAD-7 Anxiety Disorder Scale, which includes 7 items with a total score of 21; a higher score indicates more severe anxiety, and a score ≥5 was considered indicative of anxiety. The PCS scale consists of 13 items, each scored on a 5-point scale (0–4), with a total score of 52. A higher total score indicates more severe pain catastrophizing. Chronic pain assessment: Patients were followed up via telephone or outpatient visits 4 months postoperatively to determine if they experienced persistent postoperative pain due to surgical factors (NRS score >0), lasting more than 3 months. The training dataset was divided into two groups based on the occurrence of chronic pain: a non-chronic pain group (n=164) and a chronic pain group (n=56).
Statistical Analysis
All statistical analyses were performed using SPSS version 25.0. Categorical variables were presented as n (%) and continuous variables (after testing for normal distribution) as mean ± standard deviation (
). Comparisons between groups were made using the χ²-test for categorical variables and the independent samples t-test for continuous variables. All variables with P < 0.05 in the univariate analysis showed no multicollinearity after collinearity analysis. Multivariate logistic regression analysis was used to identify predictors of chronic pain following uterine fibroid hysterectomy. A nomogram model was constructed using R software version 4.3.3 to predict the risk of chronic pain after uterine fibroid hysterectomy. Internal validation was performed using the bootstrap method. Calibration curves were used to compare the predicted probabilities with actual outcomes. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the discriminative ability of the nomogram. Clinical decision curve analysis was performed to assess the net benefit of the nomogram. A p-value of <0.05 was considered statistically significant.
Results
Comparison of Clinical Data Between the Training and Validation Datasets
There were no statistically significant differences between the training and validation datasets in terms of age, BMI, marital status, education level, preoperative pain, menopause status, hypertension, diabetes, anesthesia duration, type of anesthesia, postoperative analgesia, history of abdominal/pelvic surgery, number of pregnancies, number of deliveries, endometriosis, surgical method, anxiety, PCS score, adnexal removal, surgical duration, intraoperative blood loss, postoperative infection, postoperative nausea and vomiting, and uterine weight (P > 0.05). See Table 1.
Table 1.
Comparison of Clinical Data Between Training Dataset and Validation Dataset [n (%)/ (
)]
| Index | Training Dataset (n=220) | Validation Dataset (n=95) | χ2/t | P |
|---|---|---|---|---|
| Age (years) | 47.81±7.95 | 48.26±8.12 | 0.458 | 0.647 |
| BMI (kg/m2) | 22.92±2.96 | 22.76±3.08 | 0.435 | 0.664 |
| Married | 185 (84.09) | 84 (88.42) | 0.998 | 0.318 |
| Education | 2.325 | 0.127 | ||
| High school and above | 158 (71.82) | 76 (80.00) | ||
| Junior high school and below | 62 (28.18) | 19 (20.00) | ||
| Preoperative pain | 74 (33.64) | 22 (23.16) | 3.438 | 0.064 |
| Menopause | 48 (21.82) | 25 (26.32) | 0.754 | 0.385 |
| Hypertension | 59 (26.82) | 18 (18.95) | 2.226 | 0.136 |
| Diabetes | 19 (8.64) | 13 (13.68) | 1.852 | 0.174 |
| Anesthesia duration (min) | 118.23±26.10 | 113.42±24.98 | 1.520 | 0.129 |
| Type of anesthesia | 1.035 | 0.309 | ||
| General anesthesia | 177 (80.45) | 81 (85.26) | ||
| Intrathecal Block | 43 (19.55) | 14 (14.74) | ||
| Postoperative analgesia | 0.450 | 0.502 | ||
| Yes | 166 (75.45) | 75 (78.95) | ||
| No | 54 (24.55) | 20 (21.05) | ||
| History of abdominal or pelvic surgery | 19 (8.64) | 15 (15.79) | 3.526 | 0.060 |
| Pregnancy times (times) | 2.37±0.85 | 2.26±0.75 | 1.091 | 0.276 |
| Delivery times (times) | 1.85±0.52 | 1.88±0.56 | 0.459 | 0.647 |
| Endometriosis | 13 (5.91) | 11 (11.58) | 3.030 | 0.082 |
| Operation | 0.924 | 0.336 | ||
| Laparoscopic | 194 (88.18) | 80 (84.21) | ||
| Open | 26 (11.82) | 15 (15.79) | ||
| Anxiety | 42 (19.09) | 16 (16.84) | 0.223 | 0.636 |
| PCS score (Points) | 18.61±5.65 | 19.27±5.84 | 0.942 | 0.347 |
| Adnexectomy | 37 (16.82) | 22 (23.16) | 1.752 | 0.186 |
| Operative time (min) | 111.33±22.85 | 108.34±26.46 | 1.015 | 0.311 |
| Intraoperative bleeding volume (mL) | 24.31±4.96 | 23.75±3.68 | 0.989 | 0.324 |
| Postoperative infection | 22 (10.00) | 16 (16.84) | 2.928 | 0.087 |
| Postoperative nausea and vomiting | 56 (25.45) | 18 (18.95) | 1.563 | 0.211 |
Univariate Analysis of Chronic Pain After Uterine Fibroid Hysterectomy in the Training Dataset
There were no statistically significant differences between the non-chronic pain group and the chronic pain group in terms of age, BMI, marital status, education level, menopause status, hypertension, diabetes, anesthesia duration, type of anesthesia, postoperative analgesia, number of pregnancies, number of deliveries, surgical method, adnexal removal, surgical duration, intraoperative blood loss, postoperative infection, postoperative nausea and vomiting, and uterine weight (P > 0.05). However, compared with the non-chronic pain group, the chronic pain group had significantly higher levels of preoperative pain, history of abdominal/pelvic surgery, endometriosis, anxiety, and PCS scores (P < 0.05). See Table 2.
Table 2.
Univariate Analysis of Chronic Pain After Hysterectomy for Uterine Fibroids in the Training Dataset [n (%)/ (
)]
| Index | Non Chronic Pain Group (n=164) | Chronic Pain Group (n=56) | χ2/t | P |
|---|---|---|---|---|
| Age (years) | 47.36±8.02 | 49.13±7.85 | 1.434 | 0.153 |
| BMI (kg/m2) | 22.98±3.02 | 22.75±2.94 | 0.495 | 0.621 |
| Married | 136 (82.93) | 49 (87.50) | 0.653 | 0.419 |
| Education | 1.226 | 0.268 | ||
| High school and above | 121 (73.78) | 37 (66.07) | ||
| Junior high school and below | 43 (26.22) | 19 (33.93) | ||
| Preoperative pain | 42 (25.61) | 32 (57.14) | 18.595 | <0.001 |
| Menopause | 35 (21.34) | 13 (23.21) | 0.086 | 0.770 |
| Hypertension | 41 (25.00) | 18 (32.14) | 1.085 | 0.298 |
| Diabetes | 12 (7.32) | 7 (12.50) | 0.840 | 0.359 |
| Anesthesia duration (min) | 119.45±24.56 | 121.64±25.18 | 0.572 | 0.568 |
| Type of anesthesia | 0.136 | 0.712 | ||
| General anesthesia | 131 (79.88) | 46 (82.14) | ||
| Intrathecal Block | 33 (20.12) | 10 (17.86) | ||
| Postoperative analgesia | 0.975 | 0.323 | ||
| Yes | 121 (73.78) | 45 (80.36) | ||
| No | 43 (26.22) | 11 (19.64) | ||
| History of abdominal or pelvic surgery | 7 (4.27) | 12 (21.43) | 15.580 | <0.001 |
| Pregnancy times (times) | 2.35±0.80 | 2.43±0.91 | 0.623 | 0.534 |
| Delivery times (times) | 1.87±0.56 | 1.79±0.45 | 0.967 | 0.334 |
| Endometriosis | 3 (1.83) | 10 (17.86) | 16.513 | <0.001 |
| Operation | 0.439 | 0.508 | ||
| Laparoscopic | 146 (89.02) | 48 (85.71) | ||
| Open | 18 (10.98) | 8 (14.29) | ||
| Anxiety | 20 (12.20) | 22 (39.29) | 19.835 | <0.001 |
| PCS score (Points) | 17.05±5.15 | 23.18±6.30 | 7.250 | <0.001 |
| Adnexectomy | 25 (15.24) | 12 (21.43) | 1.141 | 0.285 |
| Operative time (min) | 112.58±20.75 | 116.45±19.09 | 1.229 | 0.220 |
| Intraoperative bleeding volume (mL) | 24.02±4.71 | 25.15±5.69 | 1.832 | 0.068 |
| Postoperative infection | 15 (9.15) | 7 (12.50) | 0.522 | 0.470 |
| Postoperative nausea and vomiting | 37 (22.56) | 19 (33.93) | 2.843 | 0.092 |
Identification of Predictive Factors for Chronic Pain After Uterine Fibroid Hysterectomy
Based on the univariate analysis of the training dataset, the following factors were included in the multivariate logistic regression analysis: preoperative pain, history of abdominal/pelvic surgery, endometriosis, anxiety, and PCS score. In the regression analysis, the dependent variable was the occurrence of chronic pain, and the variable value assignments are shown in Table 3. The results indicated that preoperative pain, history of abdominal/pelvic surgery, endometriosis, anxiety, and higher PCS scores were independent risk factors for chronic pain following uterine fibroid hysterectomy (P < 0.05). The analysis results are shown in Table 4.
Table 3.
Logistic Regression Analysis Variable Assignment Table
| Influence Factor | Assignment |
|---|---|
| Preoperative pain | Yes=1, No=0 |
| History of abdominal or pelvic surgery | Yes=1, No=0 |
| Endometriosis | Yes=1, No=0 |
| Anxiety | Yes=1, No=0 |
| PCS score | Continuous variable |
| Dependent variable | Chronic pain=1, Non chronic pain=0 |
Table 4.
Logistic Regression Analysis of Factors Related to Chronic Pain After Hysterectomy for Uterine Fibroids
| Influence Factor | β | SE | Wald χ2 | OR | 95% CI | P |
|---|---|---|---|---|---|---|
| Preoperative pain | 0.986 | 0.385 | 6.568 | 2.681 | 1.261~5.701 | 0.010 |
| History of abdominal or pelvic surgery | 1.758 | 0.611 | 8.274 | 5.799 | 1.751~19.206 | 0.004 |
| Endometriosis | 2.628 | 0.780 | 11.346 | 13.848 | 3.001~13.905 | 0.001 |
| Anxiety | 1.139 | 0.444 | 6.575 | 3.125 | 1.308~7.465 | 0.010 |
| PCS score | 0.157 | 0.037 | 18.245 | 1.170 | 1.089~1.258 | <0.001 |
| Constant | −5.254 | 0.845 | 38.691 | 0.005 | – | <0.001 |
Nomogram Model Construction
Based on the multivariate results from the logistic regression analysis, a nomogram was constructed to predict the likelihood of chronic pain following uterine fibroid hysterectomy. See Figure 2. Each risk factor (preoperative pain, history of abdominal/pelvic surgery, endometriosis, anxiety, and PCS score) was assigned a corresponding score. The total score was calculated by summing the individual scores, which were then plotted on the total score scale. A vertical line was drawn down from the total score point to determine the probability of developing chronic pain.
Figure 2.
Nomogram model.
Calibration Curve and ROC Curve Validation of the Nomogram Model in the Training and Validation Datasets
The nomogram prediction model demonstrated high predictive efficacy in both the training and validation datasets. Calibration curves for both datasets showed good consistency and calibration (Figure 3A and B). The Hosmer-Lemeshow test results were χ² = 1.654, 3.181, P = 0.990, 0.922. The area under the ROC curve (AUC) was 0.841 (95% CI: 0.781–0.900) in the training dataset and 0.825 (95% CI: 0.762–0.887) in the validation dataset (Figure 3C and D).
Figure 3.
Calibration curve and ROC curve of nomogram model. (A) Calibration curve in the training datasets; (B) Calibration curve in the validation datasets; (C) ROC curve of the training datasets; (D) ROC curve of the validation datasets.
Clinical Decision Curve Analysis of the Nomogram Model
The clinical decision curve analysis showed that, compared with the “All treatment” (All line) and “No treatment” (None line) strategies, the decisions based on the nomogram model provided a higher net benefit within the prediction probability threshold range of 0.08 to 0.75 for patients undergoing uterine fibroid hysterectomy. See Figure 4.
Figure 4.
Clinical decision curve analysis of nomogram model.
Discussion
In this study, the incidence of chronic pain following hysterectomy was 25.45%, lower than the results of an earlier study by Benlolo et al.11 Additionally, preoperative pain, history of abdominal/pelvic surgery, endometriosis, anxiety, and PCS scores were identified as factors influencing the occurrence of chronic pain after uterine fibroid hysterectomy in this study. After constructing a nomogram based on the independent risk factors identified in the multivariate analysis, it was found that the nomogram demonstrated good predictive performance, with high calibration, discrimination, and clinical net benefit.
As-Sanie et al12 found that each one-point increase in preoperative localized pain raises the likelihood of persistent pelvic pain six months after hysterectomy by 27%. Similarly, in this study, preoperative pain was identified as a risk factor for chronic pain following hysterectomy, with preoperative pain contributing an additional 17.92 points to the nomogram. Preoperative localized pain (or central sensitization) results from changes in the central nervous system’s pain processing and has been shown to be closely related to pain severity and disability, as well as predictive of both acute and chronic postoperative pain.13 These changes in the central nervous system, caused by preoperative localized pain, cannot be easily resolved through hysterectomy, leading to persistent postoperative pain. Tan et al14 also found that a history of abdominal/pelvic surgery could predict pain four months after hysterectomy. Consistent with prior reports, this study identified a history of abdominal/pelvic surgery as a contributing factor to chronic pain after hysterectomy, with this factor adding 32.48 points to the nomogram. It is suggested that a history of abdominal/pelvic surgery, particularly the increasingly common cesarean section history, may lead to postoperative adhesions between the bladder and cervix, making the separation of these structures during hysterectomy more complex. In this study, endometriosis contributed 46.82 points to the constructed nomogram model. Endometriosis is an estrogen-dependent inflammatory disease that affects women of reproductive age. Its pain pathophysiology involves both sensory and somatic pain mechanisms.15 Women with endometriosis are more sensitive to pain, and this abnormal pain perception results from the chronic inflammatory process of the disease. Over time, this inflammatory process leads to weakened pain inhibition and amplified sensory input, which in turn results in central sensitization. As a result, women with endometriosis are more likely to experience chronic pain after hysterectomy.16 However, since the number of patients with combined endometriosis in this study is relatively small, the results of the multivariate analysis may have some bias and therefore require further validation in future studies. The uterus is a uniquely significant organ, performing essential physiological functions such as pregnancy and childbirth, and plays an important role in maintaining a woman’s self-esteem. Some women may feel that losing their uterus equates to losing their femininity, as perceived by other societal groups.17 Therefore, hysterectomy can have a profound impact on a woman’s psychological state. Xie et al18 found that psychological interventions could effectively reduce sexual dysfunction, pelvic organ prolapse, and chronic pelvic pain in patients after hysterectomy. The findings of this study on the impact of anxiety on postoperative chronic pain are consistent with the aforementioned research. Pain catastrophizing is defined as a negative cognitive and emotional response to anticipated or actual pain, and is a multidimensional concept that includes elements such as rumination, magnification, and helplessness.19 The presence of catastrophizing is associated with various pain conditions, including endometriosis, neuropathic pain, and the development of postoperative pain, with the PCS being used to assess catastrophizing.20 Chen et al21 found that the higher the PCS score, the more severe the pain catastrophizing, and the higher the risk of chronic pain following hysterectomy. The findings of this study are generally consistent with those of the previous research. In this study, for each 1-point increase in the PCS score, the incidence of chronic pain post-hysterectomy increased by 17% (OR = 1.170). In the nomogram, for each 5-point increase in the PCS score, the nomogram score increased by 14.76 points. This study validated the predictive efficacy of the nomogram using ROC curves, calibration curves, and clinical decision curve analysis, and found that it demonstrated high predictive effectiveness. The AUC in both the training and validation datasets was 0.841 and 0.825, respectively, and the actual outcomes were closely aligned with the predicted outcomes. Decisions based on the nomogram model provided a higher net benefit within a broad range of prediction probability thresholds for patients undergoing uterine fibroid hysterectomy.
This study is a retrospective investigation exploring the factors influencing chronic pain after uterine fibroid hysterectomy. The advantages of this study include the use of clinically validated postoperative pain measurement tools, the incorporation of both intraoperative and postoperative outcomes, and a 4-month follow-up with a relatively high retention rate of participants. Despite these advantages, the study still has several limitations: Firstly, like all previous retrospective cross-sectional studies, this study is unable to establish causal relationships. Second, although the retention rate was high, the results may still be biased due to the exclusion of some patients. Furthermore, the data in this study were derived from a single large academic medical center, which may introduce selection bias as well as variations in surgical techniques and experience, therebylimiting the generalizability of the findings to other populations. For example, the incidence of chronic pain after hysterectomy in this study was lower than in earlier research, which may be attributed to the fact that many of the hysterectomies at this institution were performed by a group of surgeons extensively trained in the multimodal management of uterine fibroids. Therefore, in the future, multicenter prospective studies will be conducted to validate the external applicability and reliability of the model.
In summary, the nomogram model based on independent risk factors demonstrated high predictive efficacy and can effectively predict the risk of chronic pain following uterine fibroid hysterectomy. This model is useful for developing more effective and personalized treatment plans in the future. Example of nomogram application: For a patient undergoing hysterectomy for uterine fibroids, with a PCS score of 15, a history of preoperative pain, no history of abdominal or pelvic surgery, presence of endometriosis, and no anxiety, the total score is 42.56 + 17.81 + 0 + 46.93 + 0 = 107.30 points. Drawing a vertical line downward from this total score to the predicted probability axis corresponds to a value of 0.62, indicating a 62% risk of developing postoperative chronic pain.
Funding Statement
There is no funding to report.
Data Sharing Statement
The datasets in this study are available from the corresponding author.
Research Involving Human Participants
The study was in accordance with Meizhou People’s Hospital Ethics Review Board (No.2025-C-13) and with the 1964 Helsinki Declaration. Written informed consent to participate in this study was provided by the participants.
Disclosure
The authors declared no conflicts of interest in this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets in this study are available from the corresponding author.