Background/Objectives: Endometriosis is a chronic gynecological disorder characterized by ectopic endometrial-like tissue. The symptoms of this disease negatively affect the patient’s quality of life, both physically and mentally. This study aims to identify key factors impacting health-related quality of life in endometriosis patients. Methods: A total of 175 patients from the Endometriosis Centre of the RWTH Aachen University Hospital were assessed using the German version of the EHP-30. The EHP is a patient self-report tool used to measure the wide range of impacts that endometriosis can have on women’s lives (affecting pain levels, leading to feelings of powerlessness and a loss of control, and affecting their emotional well-being, social support, and self-image). Multivariate linear regression and random forest analyses were performed to evaluate predictors of health-related quality of life, focusing on demographic characteristics, pain severity, endometriosis symptoms and planned procedures. Results: Key factors that have a significant negative impact on QoL include higher pain scores, dysuria, and persistent endometriosis. Higher pain scores negatively affect the EHP-30 pain (p < 0.0001), control and powerlessness (p < 0.0001) and emotional well-being (p < 0.01) scores. Dysuria has a negative effect on pain (p < 0.001), control and powerlessness (p < 0.001), emotional well-being (p < 0.05), and social support (p < 0.05). Persistent endometriosis was negatively associated with pain (p < 0.01), control and powerlessness (p < 0.01), and social support. Previous endometriosis surgery has a positive effect on the EHP-30 scores for pain, control and powerlessness, emotional well-being, and self-image (p < 0.05). Conclusions: Our study highlights the multifactorial impact of endometriosis on health-related QoL. Personalized treatments focusing on pain management, emotional support and social interventions are crucial to improve patient outcomes.

Urinary incontinence (UI) could negatively affect a person’s quality of life (QoL). This study investigates the association among gynecological cancers, their treatments, UI, and its effect on the QoL of survivors of gynecological cancer. This cross-sectional questionnaire-based study included 121 cases from 405 patients who had undergone gyneco-oncological therapy. The participants were asked whether they experienced any form of UI and whether it impacted their QoL. The following therapies were used to treat the 12 gynecologic tumor types found in the participants: surgery (n = 116, 95.87%), chemotherapy (CTx) (n = 51, 42.2%), radiotherapy (RTx) (n = 31, 25.6%), and antibody therapy (ABT) (n = 11, 9.1%). No significant association was determined between tumor type and UI. However, body mass index (BMI), radical hysterectomy, vulvar or vaginal surgery, and presence of UI before treatment had significant impacts on the presence of UI. The surgical access routes, CTx, ABT, and hysterectomy had significant impacts on the severity of UI after treatment. Among all patients, 55.4% reported very good QoL. These reports of good QoL by patients could be due to their very good adjustment to the situation, with regard to being diagnosed with and receiving treatment for cancer, or due to the patients considering UI to not be much of an issue. Additionally, 34% of patients reported they had not been informed about the risk of UI before treatment. Informing patients about UI as one of the risks of therapy before initiating the treatment is crucial as patients who had been informed beforehand coped with UI far better than those who were not informed. Hence, the treatment of UI is often successful, so patients should be encouraged to receive urogynecological consultation.

Randomized controlled clinical trials are regarded as the gold standard for comparing different clinical interventions, but generally their conduct is operationally cumbersome, time-consuming, and expensive. Studies and investigations based on clinical routine data on the contrary utilize existing data acquired under real-life conditions and are increasingly popular among practitioners. In this paper, methodological aspects of studies based on clinical routine data are discussed. Important limitations and considerations as well as unique strengths of these types of studies are indicated and exemplarily demonstrated in a recent real-case study based on clinical routine data. In addition two simulation studies reveal the impact of bias in studies based on clinical routine data on the type I error rate and false decision rate in favor of the inferior intervention. It is concluded that correctly analyzing clinical routine data yields a valuable addition to clinical research; however, as a result of a lack of statistical foundation, internal validity, and comparability, generalizing results and inferring properties derived from clinical routine data to all patients of interest has to be considered with extreme caution.

It is still unclear how and why some patients develop painful and others painless polyneuropathy. The aim of this study was to identify multiple factors associated with painful polyneuropathies (NeuP). A total of 1181 patients of the multicenter DOLORISK database with painful (probable or definite NeuP) or painless (unlikely NeuP) probable or confirmed neuropathy were investigated clinically, with questionnaires and quantitative sensory testing. Multivariate logistic regression including all variables (demographics, medical history, psychological symptoms, personality items, pain-related worrying, life-style factors, as well as results from clinical examination and quantitative sensory testing) and machine learning was used for the identification of predictors and final risk prediction of painful neuropathy. Multivariate logistic regression demonstrated that severity and idiopathic etiology of neuropathy, presence of chronic pain in family, Patient-Reported Outcomes Measurement Information System Fatigue and Depression T-Score, as well as Pain Catastrophizing Scale total score are the most important features associated with the presence of pain in neuropathy. Machine learning (random forest) identified the same variables. Multivariate logistic regression archived an accuracy above 78%, random forest of 76%; thus, almost 4 out of 5 subjects can be classified correctly. This multicenter analysis shows that pain-related worrying, emotional well-being, and clinical phenotype are factors associated with painful (vs painless) neuropathy. Results may help in the future to identify patients at risk of developing painful neuropathy and identify consequences of pain in longitudinal studies.