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Contains fulltext : 291250.pdf (Publisher’s version ) (Open Access) INTRODUCTION: Opportunistic salpingectomy (OS) refers to additional removal of the fallopian tubes during abdominal surgery performed for another medical indication, as prevention for ovarian cancer. As OS has been inconsistently implemented, its clinical practice varies worldwide. To reduce this variation, insight is required into current clinical practice and its determinants. Therefore, the study aim was to determine the implementation of counseling and performance of OS between 2015 and 2018, and its patient, surgical, physician, and hospital characteristics. MATERIAL AND METHODS: Retrospective study using electronic medical records from six different Dutch hospitals: two academic, two large teaching, and two non-teaching hospitals. Patients were considered eligible for OS if they underwent elective non-obstetric abdominal surgery for a gynecological indication from January 2015 through December 2018. Primary outcomes were uptake of counseling and performance of OS. Multilevel multivariable logistic regression analyses were conducted to identify characteristics associated with OS. RESULTS: A total of 3214 patients underwent elective non-obstetric abdominal surgery for a gynecological indication and were eligible for OS. Counseling on OS increased significantly from 2.9% in 2015 to 29.4% in 2018. In this period, 440 patients were counseled on OS, of which 95.9% chose OS. Performance of OS increased significantly from 6.9% in 2015 to 44.5% in 2018. Counseling for and performance of OS were more likely in patients who had surgery by laparoscopic approach, were counseled by a gynecological resident, or had more than three contact moments before surgery. Additionally, OS was less likely in patients who had vaginal surgery. CONCLUSIONS: Although the uptake of OS increased from 2015 to 2018, the majority of patients who were eligible for OS were not counseled and did not undergo OS. Its clinical practice varies on patient, surgery, and physician characteristics. Therefore, an implementation strategy tailored to associated determinants is recommended.

The clinical application of prediction models is increasing within the field of gynaecology and obstetrics. This is mostly due to the fact that clinicians and patients prefer individualized counselling and person specific, more objective outcome assessment. To prevent using inadequate models, it is important to construct and perform prediction model studies correctly. Therefore, the TRIPOD statement (the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) was developed. The aim of this review is to obtain an overview of the existing published prediction models for benign gynaecology and to investigate to what extent these studies meet the TRIPOD criteria. We performed a literature search in the databases PubMed, Embase and Cochrane Library from inception to August 2020. Searching the cross-references of the relevant studies within our search identified additional articles. Publications were included if the aim of the study was to develop a multivariable prediction model within the field of benign gynaecology. Two independent reviewers extracted the data. Analysis of the studies was performed by using a checklist derived from the TRIPOD criteria. Based on our search, 2487 studies were selected, including potential duplications. Eventually, a total of twenty-two studies were selected. 91% of these studies handled their predictors by univariable analysis before developing a multivariable prediction model. Fifteen studies described having missing data, but not all of them (9%) handled these missing data. Four different internal validation methods were used in twenty studies. Fifteen studies (68%) had prediction models with a C-index ≥ 0.7, which indicates a good model. Half of the studies (50%) did not measure the calibration, overall performance was described in two studies (9%). External validation was performed in 9% of the studies. The correct development of a prediction model within benign gynaecology and subsequent transparent reporting of the model development is important to facilitate clinical use. Without transparent reporting, wrong assumptions can be made leading to incorrect application of a specific prediction model. This overview shows that excepting carrying out an external validation, only one article met all the criteria. Therefore, we strongly recommend use of the TRIPOD criteria for developing and validating a prediction model (study). In addition, prior to publication, content experts should critically and statistically review the prediction model. If too many criteria are not met, refusing publication should be considered.

Background The discovery of the Fallopian tube epithelium as origin of high grade serous ovarian cancer has brought a new option for ovarian cancer prevention. As the Fallopian tubes have no known function after completion of childbearing, they can be removed to reduce the life-time risk of ovarian cancer. Although the lifetime risk in the general population does not justify preventive surgery in itself, salpingectomy can be performed during abdominal surgery for other indications, also known as an opportunistic salpingectomy. The popularity of opportunistic salpingectomy is increasing globally, however substantial variation between gynecologists and hospitals in their advice on the opportunistic salpingectomy occurs due to remaining uncertainty of evidence. As a result, whether or not a woman is able to make her own decision depends on the hospital or gynecologist she visits. We aim to lower this practice variation by providing standardized and unbiased counseling material. Objective(s) To develop and test a patient decision aid for an opportunistic salpingectomy in women undergoing either pelvic gynecological surgery with the intention to retain the ovaries or opting for sterilization. Study Design We followed a systematic development process based on the International Patient Choice Aid Standard (IPDAS). Data were collected between June 2019 and June 2020, using both qualitative and quantitative methods. The development process which took place in collaboration with patients and healthcare professionals, was overseen by a multidisciplinary steering group, and was divided in four phases; 1. Assessment of decisional needs using individual telephone interviews and questionnaires; 2. Development of content and format based on decisional needs, current literature and guidelines; 3. Alpha-testing and first revision round; and 4. Alpha-testing and second revision round. Results An outline of the patient decision aid was developed based on decisional needs, current literature and guidelines. It became clear that the decision aid should consist of two separate paths: one with information specifically for salpingectomy in addition to abdominal surgery and one for salpingectomy as a sterilization method. Both paths contain information on the anatomy and function of ovaries and Fallopian tubes, the risk reduction of ovarian cancer and the potential benefits and risks of opportunistic salpingectomy. The sterilization path also contains information on the various sterilization methods and the risk of unwanted pregnancy. The patient decision aid was developed as an online tool that includes information chapters, a knowledge quiz, consideration statements, and a summary detailing patient’s preference and considerations. Adjustments were made following alpha-testing round one. The improved patient decision aid was subjected to usability tests (alpha-testing round two), in which it scored an ‘excellent’ in patient testing and a ‘good’ in tests with gynecologists. Furthermore, our patient decision aid met the requirements of 45 out of 49 applicable items from the International Patient Decision Aid Standards criteria. Conclusion In collaboration with patients and healthcare professionals, a patient decision aid was developed on opportunistic salpingectomy and salpingectomy as a sterilization method. Both patients and gynecologists thought it a usable aid which supports patients in making an informed decision whether to undergo an opportunistic salpingectomy, and supports the counseling process by gynecologists.

Background Five percent of pre-menopausal women experience abnormal uterine bleeding. Endometrial ablation (EA) is one of the treatment options for this common problem. However, this technique shows a decrease in patient satisfaction and treatment efficacy on the long term. Study objective To develop a prediction model to predict surgical re-intervention (for example re-ablation or hysterectomy) within 2 years after endometrial ablation (EA) by using machine learning (ML). The performance of the developed prediction model was compared with a previously published multivariate logistic regression model (LR). Design This retrospective cohort study, with a minimal follow-up time of 2 years, included 446 pre-menopausal women (18+) that underwent an EA for complaints of heavy menstrual bleeding. The performance of the ML and the LR model was compared using the area under the receiving operating characteristic (ROC) curve. Results We found out that the ML model (AUC of 0.65 (95% CI 0.56–0.74)) is not superior compared to the LR model (AUC of 0.71 (95% CI 0.64–0.78)) in predicting the outcome of surgical re-intervention within 2 years after EA. Based on the ML model, dysmenorrhea and duration of menstruation have the highest impact on the surgical re-intervention rate. Conclusion Although machine learning techniques are gaining popularity in development of clinical prediction tools, this study shows that ML is not necessarily superior to the traditional statistical LR techniques. Both techniques should be considered when developing a clinical prediction model. Both models can identify the clinical predictors to surgical re-intervention and contribute to the shared decision-making process in the clinical practice.

Background Mathematical predictive models for ovarian tumors have an advantage over subjective assessment due to their relative simplicity, and therefore usefulness for less experienced sonographers. It is currently unclear which predictive model is best at predicting the nature of an ovarian tumor. Purpose To compare the diagnostic predictive accuracy of the International Ovarian Tumour Analysis Simple Rules (IOTA SR) with Risk of Malignancy Index (RMI), to differentiate between benign and malignant ovarian tumors. Material and Methods A total of 202 women diagnosed with ovarian tumor(s) were included. Preoperatively, patients were examined through transvaginal ultrasonography and CA-125 (U/mL) levels were measured. RMI and IOTA SR were determined, and where possible compared to definitive histopathological diagnosis. Results Of the 202 women with ovarian tumors, 168 women were included in this cohort study. Of these tumors, 118 (70.2%) were benign, 17 (10.1%) were borderline, and 33 (19.7%) were malignant. The sensitivity, specificity, and area under the curve for the RMI were 72.0%, 90.7%, and 0.896, respectively. For the IOTA SR, these were 90.0%, 68.6%, and 0.793, respectively. Conclusion This cohort study shows that the RMI is a relatively useful diagnostic model in characterizing ovarian tumors, compared to the IOTA SR. However, due to the relatively low sensitivity of the RMI and high rate of inconclusive results of the IOTA SR, both diagnostic tests do not seem discriminative enough. Therefore, alternative diagnostic models are necessary.

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