Evidence suggesting that high-grade serous ovarian cancers originate in the fallopian tubes has led to the emergence of opportunistic salpingectomy (OS) as an approach to reduce ovarian-cancer risk. In the U.S., some national societies now recommend OS in place of tubal ligation for sterilization or during a benign hysterectomy in average-risk women. However, limited data exist on the dissemination of OS in clinical practice. We examined the uptake and predictors of OS in a nationwide sample of inpatient and outpatient claims (N = 48,231,235) from 2010 to 2017. Incidence rates of OS were calculated, and an interrupted time-series analysis was used to quantify changes in rates before (2010-2013) and after (2015-2017) national guideline release. Predictors of OS use were examined using Poisson regression. From 2010 to 2017, the age-adjusted incidence rate of OS for sterilization and OS during hysterectomy increased 17.8-fold [95% confidence interval (CI), 16.2-19.5] and 7.6-fold (95% CI, 5.5-10.4), respectively. The rapid increase (age-adjusted increase in quarterly rates of between 109% and 250%) coincided with the time of national guideline release. In multivariable-adjusted analyses, OS use was more common in young women and varied significantly by geographic region, rurality, family history/genetic susceptibility, surgical indication, inpatient/outpatient setting, and underlying comorbidities. Similar differences in OS uptake were noted in analyses limited to women with a family history/genetic susceptibility to breast/ovarian cancer. Our results highlight significant differences in OS uptake in both high- and average-risk women. Defining subsets of women who would benefit most from OS and identifying barriers to equitable OS uptake is needed. PREVENTION RELEVANCE: Opportunistic salpingectomy for ovarian-cancer risk reduction has been rapidly adopted in the U.S., with significant variation in uptake by demographic and clinical factors. Studies examining barriers to opportunistic salpingectomy access and the long-term effectiveness and potential adverse effects of opportunistic salpingectomy are needed.
Endometriosis, a systemic ailment, profoundly affects various aspects of life, often eluding detection for over a decade. This leads to enduring issues such as chronic pain, infertility, emotional strain, and potential organ dysfunction. The prolonged absence of diagnosis can contribute to unexplained obstetric challenges and fertility issues, necessitating costly and emotionally taxing treatments. While biopsy remains the gold standard for diagnosis, emerging noninvasive screening methods are gaining prominence. These tests can indicate endometriosis in cases of unexplained infertility, offering valuable insights to patients and physicians managing both obstetric and non-obstetric conditions. In a retrospective cross-sectional study involving 215 patients aged 25 to 45 with unexplained infertility, diagnostic laparoscopy was performed after unsuccessful reproductive technology attempts. Pathology results revealed tissue abnormalities in 98.6% of patients, with 90.7% showing endometriosis, confirmed by the presence of endometrial-like glands and stroma. The study underscores the potential role of endometriosis in unexplained infertility cases. Although the study acknowledges selection bias, a higher than previously reported prevalence suggests evaluating endometriosis in patients who have not responded to previous reproductive interventions may be justified. Early detection holds significance due to associations with ovarian cancer, prolonged fertility drug use, pregnancy complications, and elevated post-delivery stroke risk.
Opportunistic salpingectomy (OS) is an attractive method for primary prevention of ovarian cancer. Although OS has not been associated with a higher complication rate, it may be associated with earlier onset of menopause. To provide a systematic review and meta-analysis of the effect of OS on both age at menopause and ovarian reserve. A search was conducted in the Cochrane Library, Embase and MEDLINE databases from inception until March 2022. We included randomized clinical trials and cohort studies investigating the effect of OS on onset of menopause and/or ovarian reserve through change in anti-Müllerian hormone (AMH), antral follicle count (AFC), estradiol (E2), follicle stimulating hormone (FSH) and luteinizing hormone (LH). Data was extracted independently by two researchers. Random-effects meta-analyses were conducted to estimate the pooled effect of OS on ovarian reserve. The initial search yielded 1047 studies. No studies were found investigating the effect of OS on age of menopause. Fifteen studies were included in the meta-analysis on ovarian reserve. Meta-analyses did not result in statistically significant differences in mean change in AMH (MD -0.07 ng/ml, 95%CI -0.18;0.05), AFC (MD 0.20 n, 95 % CI -4.91;5.30), E2 (MD 3.97 pg/ml, 95%CI -0.92;8.86), FSH (MD 0.33mIU/ml, 95%CI -0.15;0.81) and LH (MD 0.03mIU/ml; 95%CI -0.47;0.53). Our study shows that OS does not result in a significant reduction of ovarian reserve in the short term. Further research is essential to confirm the absence of major effects of OS on menopausal onset since clear evidence on this subject is lacking. Registration number PROSPERO CRD42021260966.
Screening and Prevention · Cervical Cancer Screening
The purpose is to accurately identify women at high risk of developing cervical cancer so as to optimize cervical screening strategies and make better use of medical resources. However, the predictive models currently in use require clinical physiological and biochemical indicators, resulting in a smaller scope of application. Stacking-integrated machine learning (SIML) is an advanced machine learning technique that combined multiple learning algorithms to improve predictive performance. This study aimed to develop a stacking-integrated model that can be used to identify women at high risk of developing cervical cancer based on their demographic, behavioral, and historical clinical factors. The data of 858 women screened for cervical cancer at a Venezuelan Hospital were used to develop the SIML algorithm. The screening data were randomly split into training data (80%) that were used to develop the algorithm and testing data (20%) that were used to validate the accuracy of the algorithms. The random forest (RF) model and univariate logistic regression were used to identify predictive features for developing cervical cancer. Twelve well-known ML algorithms were selected, and their performances in predicting cervical cancer were compared. A correlation coefficient matrix was used to cluster the models based on their performance. The SIML was then developed using the best-performing techniques. The sensitivity, specificity, and area under the curve (AUC) of all models were calculated. The RF model identified 18 features predictive of developing cervical cancer. The use of hormonal contraceptives was considered as the most important risk factor, followed by the number of pregnancies, years of smoking, and the number of sexual partners. The SIML algorithm had the best overall performance when compared with other methods and reached an AUC, sensitivity, and specificity of 0.877, 81.8%, and 81.9%, respectively. This study shows that SIML can be used to accurately identify women at high risk of developing cervical cancer. This model could be used to personalize the screening program by optimizing the screening interval and care plan in highand low-risk patients based on their demographics, behavioral patterns, and clinical data.