Pelvic pain: What are the symptoms and predictors for surgery, endometriosis and endometriosis severity

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

A preoperative predictive model for stage IV endometriosis

This was a retrospective study that evaluated a total of 280 patients who underwent surgery for complete removal of endometriosis to develop and validate the predictive model for stage IV endometriosis. The differences between stage I-III and stage IV endometriosis were performed by logistic regression. A model for the prediction of stage IV endometriosis was constructed, which was subsequently validated. The independent variables were visual analogue scale (VAS)≥4 [3.855, 95% confidence interval (CI): 1.675-8.871, p = 0.002], painful nodularity on uterosacral ligaments (13.954, 95% CI: 1.658-117.423, p = 0.015), and bilateral endometriosis (5.933, 95% CI: 1.931-18.225, p = 0.002). The AUC of the model was 0.777, with a sensitivity of 71.9% and specificity of 76.3% for stage IV endometriosis. Therefore, a complete collection of patient information prior to surgery, asking about pain and VAS scores, careful completion of pelvic examinations, and application of imaging techniques are conducive to better diagnosis and prediction of advanced endometriosis.IMPACT STATEMENTWhat is already known on this subject? Endometriosis, a chronic disease causing pain and infertility, is characterised by endometrial-like tissue outside the uterine cavity, which is often treated via surgery at present. Considering the risks of surgery, it is necessary to identify patients with stage IV endometriosis through non-invasive predictive models for adequate preparation for surgery. However, there is no reliable non-invasive predictive model now, despite utilisation of patient medical history, symptoms especially pain-related ones, pelvic examinations, laboratory examinations, and images in the preoperative diagnosis of endometriosis in the clinic.What do the results of this study add? A model developed based on three simple, accessible and non-invasive indicators displays good performance in predicting stage IV endometriosis.What are the implications of these findings for clinical practice and/or further research? It is conducive to diagnosing and predicting advanced endometriosis before surgery, so as to reduce the difficulty and improve the safety of surgery.

Short review on Adverse Childhood Experiences, Pelvic Pain and Endometriosis

The exposure to adverse childhood experiences has been associated with the subsequent development of several chronic health conditions, including pelvic pain. Endometriosis is a chronic disease characterized by the growth of endometrial-like tissue outside of the uterus, and is considered a common cause of chronic pelvic pain and infertility in reproductive-age women. However, the topic of pelvic pain and endometriosis is fraught with many challenges. This applies not just to clinical practice but also to research where many inconsistencies relating to pelvic pain and endometriosis definitions are encountered. A review was carried out for articles exploring the association of adverse childhood experiences and endometriosis. Studies addressing self-reported endometriosis suggested a relationship with childhood adversity, whilst papers relying on surgically diagnosed lesions of endometriosis irrespective of clinical presentation did not. This highlights the potential bias associated with the inconsistent use of the expression "endometriosis" in research.

The Association between Childhood Adversity and Risk of Dysmenorrhea, Pelvic Pain, and Dyspareunia in Adolescents and Young Adults: A Systematic Review

PURPOSE

Exposure to adverse childhood experiences (ACEs) has been associated with poor health outcomes, including chronic pain. However, little is known about the potential impact on the development of pelvic pain in adolescents and young adults. This systematic review was conducted to explore the association between ACEs and dysmenorrhea, pelvic pain, and dyspareunia in adolescents and young adults.

METHODS

Medline, Embase, and PsycNET were searched, using keywords related to childhood adversity, dysmenorrhea, pelvic pain, and dyspareunia.

RESULTS

Of the 566 articles identified, 19 studies were included. There was an association between the number and severity of ACEs and the risk of dysmenorrhea. Sexual abuse and posttraumatic stress disorder appeared to be associated with dysmenorrhea, pelvic pain, and dyspareunia, but it was unclear whether this relationship was mediated by poorer mental health. No association was found for immigration and bullying, and findings were inconsistent regarding female genital mutilation, parental separation, and parental death.

CONCLUSIONS

Future research should include longitudinal follow-up and use validated tools to assess childhood adversity. A greater understanding of the risk of ACEs among adolescents and young adults with dysmenorrhea, chronic pelvic pain, and dyspareunia could provide insight into the development of these conditions.