Using online search activity for earlier detection of gynaecological malignancy.

BACKGROUND: Ovarian cancer is the most lethal and endometrial cancer the most common gynaecological cancer in the UK, yet neither have a screening program in place to facilitate early disease detection. The aim is to evaluate whether online search data can be used to differentiate between individuals with malignant and benign gynaecological diagnoses. METHODS: This is a prospective cohort study evaluating online search data in symptomatic individuals (Google user) referred from primary care (GP) with a suspected cancer to a London Hospital (UK) between December 2020 and June 2022. Informed written consent was obtained and online search data was extracted via Google takeout and anonymised. A health filter was applied to extract health-related terms for 24 months prior to GP referral. A predictive model (outcome: malignancy) was developed using (1) search queries (terms model) and (2) categorised search queries (categories model). Area under the ROC curve (AUC) was used to evaluate model performance. 844 women were approached, 652 were eligible to participate and 392 were recruited. Of those recruited, 108 did not complete enrollment, 12 withdrew and 37 were excluded as they did not track Google searches or had an empty search history, leaving a cohort of 235. RESULTS: The cohort had a median age of 53 years old (range 20-81) and a malignancy rate of 26.0%. There was a difference in online search data between those with a benign and malignant diagnosis, noted as early as 360 days in advance of GP referral, when search queries were used directly, but only 60 days in advance, when queries were divided into health categories. A model using online search data from patients (n = 153) who performed health-related search and corrected for sample size, achieved its highest sample-corrected AUC of 0.82, 60 days prior to GP referral. CONCLUSIONS: Online search data appears to be different between individuals with malignant and benign gynaecological conditions, with a signal observed in advance of GP referral date. Online search data needs to be evaluated in a larger dataset to determine its value as an early disease detection tool and whether its use leads to improved clinical outcomes.

Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound.

Ultrasound-based models exist to support the classification of adnexal masses but are subjective and rely upon ultrasound expertise. We aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses. In this retrospective study, transvaginal ultrasound scan images with linked diagnoses (ultrasound subjective assessment or histology) were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n = 577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n = 184 masses; 476 images). A segmentation and classification model was developed using convolutional neural networks and traditional radiomics features. Dice surface coefficient (DICE) was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance. The ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) years old and consisted of 23.1% and 31.5% malignant cases, respectively. The best segmentation model achieved a DICE score of 0.85 ± 0.01, 0.88 ± 0.01 and 0.85 ± 0.01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1.00 and F1-score of 0.88 (AUC:0.93), 0.94 (AUC:0.89) and 0.83 (AUC:0.90) in the ICH training, ICH validation and MPH test sets, respectively. We have developed an end-to-end radiomics-based model capable of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0.90) to the published performance of expert subjective assessment (gold standard), and current risk models. Further prospective evaluation of the classification performance of this ML model against existing methods is required.

Beta-human chorionic gonadotrophin point of care testing for the management of pregnancy of unknown location.

RESEARCH QUESTION: Does a commercially available quantitative beta-human chorionic gonadotrophin (BHCG) point of care testing (POCT) device improve workflow management in early pregnancy by performing comparably to gold standard laboratory methods, and is the performance of a validated pregnancy of unknown location (PUL) triage strategy maintained using POCT BHCG results? DESIGN: Women classified with a PUL between 2018 and 2021 at three early pregnancy units were included. The linear relationship of untreated whole-blood POCT and serum laboratory BHCG values was defined using coefficients and regression. Paired serial BHCG values were then incorporated into the validated M6 multinomial logistic regression model to stratify the PUL as at high risk or at low risk of clinical complications. The sensitivity and negative predictive value were assessed. The timings required for equivocal POCT and laboratory care pathways were compared. RESULTS: A total of 462 PUL were included. The discrepancy between 571 laboratory and POCT BHCG values was -5.2% (-6.2 IU/l), with a correlation coefficient of 0.96. The 133 PUL with paired 0 and 48 h BHCG values were compared using the M6 model. The sensitivity for high-risk outcomes (96.2%) and negative predictive values (98.5%) was excellent for both. Sample receipt and laboratory processing took 135 min (421 timings), compared with 12 min (91 timings) when using POCT (P < 0.0001). CONCLUSIONS: POCT BHCG values correlated well with laboratory testing measurements. The M6 model retained its performance when using POCT BHCG values. Using the model with POCT may improve workflow and patient care without compromising on effective PUL triage.

Ambulatory human chorionic gonadotrophin (hCG) testing: a verification of two hCG point of care devices.

OBJECTIVES: Quantitative human chorionic gonadotropin (hCG) measurements are used to manage women classified with a pregnancy of unknown location (PUL). Two point of care testing (POCT) devices that quantify hCG are commercially available. We verified the i-STAT 1 (Abbott) and the AQT 90 FLEX (Radiometer) prior to use in PUL triage. METHODS: Tests for precision, external quality assurance (EQA), correlation, hook effect and recovery were undertaken alongside a POCT usability assessment during this prospective multi-center verification. RESULTS: Coefficients of variation ranged between 4.0 and 5.1 % for the three i-STAT 1 internal quality control (IQC) solutions and between 6.8 and 7.3 % for the two AQT IQC solutions. Symmetric differences in POCT EQA results when compared with laboratory and EQA stock values ranged between 3.2 and 24.5 % for the i-STAT 1 and between 3.3 and 36.9 % for the AQT. Correlation coefficients (i-STAT 1: 0.96, AQT: 0.99) and goodness of fit curves (i-STAT 1: 0.92, AQT: 0.99) were excellent when using suitable whole blood samples. An hCG hook effect was noted with the i-STAT 1 between 572,194 and 799,089 IU/L, lower than the hook effect noted with the AQT, which was between 799,089 and 1,619,309 IU/L. When hematocrit concentration was considered in sample types validated for use with each device, hCG recovery was 108 % with the i-STAT 1 and 98 % with the AQT. The i-STAT 1 scored lower on usability overall (90/130) than the AQT (121/130, p<0.001, Mann-Whitney). CONCLUSIONS: Both hCG POCT devices were verified for use in clinical practice. Practical factors must also be considered when choosing which device to use in each unit.

Digital consent in gynecology: an evaluation of patient experience.

INTRODUCTION: The surgical consent process is a crucial discussion between patient and surgeon, which is predominantly documented utilizing hand-written forms. The exchange of individualized information allows the patient to make a truly informed decision. Digital consent (also known as electronic consent or e-consent) has been shown to improve accuracy of information provided without increasing the time taken to consent patients. We aimed to evaluate patient experience and effectiveness of digital consent in a gynecology department in a tertiary London Teaching Hospital. METHODS: A questionnaire was designed and completed by 100 patients undergoing gynecological surgery: 50 consented using paper and 50 consented digitally. The questionnaire included 8 statements, with five possible answers to select, ranging from strongly agree to strongly disagree, on a standard five-point Likert Scale. Patients were all female and categorized into age groups (deciles) and asked whether consent was taken digitally or on paper. Data were collected between January and July 2021. RESULTS: Most responses were positive with 87% (694/800) of responses to the questions being either strongly agree or agree. Patients who were consented using paper selected 'strongly agree' 43.5% (174/400) of the time in comparison to 64.8% (259/400) of the time when they were consented digitally. The majority, 86% (43/50), of digitally consented patients received a copy of the consent form in comparison to 18% (9/50) of those consented using paper. On average, the patients consented digitally were older than their paper-consented counterparts (49-58 and 59-68 respectively). The mean scores for the questions relating to the ease of reading the form, ease of understanding the form, understanding of the potential complications, and overall satisfaction were higher in those digitally consented (p < 0.05). DISCUSSION: Overall, patients were satisfied with both methods of consent. However, individuals who were consented digitally reported higher levels of satisfaction throughout the consent process, compared to paper consent. These data suggest that digital consent is an acceptable alternative to paper consent for patients and facilitates adherence to national consent guidance, which stipulates patients should be given the information they request.

Does advanced paternal age affect outcomes following assisted reproductive technology? A systematic review and meta-analysis.

Infertility affects more than 14% of couples, 30% being caused by male factor infertility. This meta-analysis includes 28 studies, selected according to PRISMA guidelines. Data were extracted from these studies to collate cycles separating paternal age at 30, 35, 40, 45 and 50 years (±1 year). Primary outcomes of interest were clinical pregnancy, live birth and miscarriage rates. Secondary outcomes were the number of fertilized eggs, cleavage-stage embryos and blastocysts, and embryo quality per cycle. Fixed-effects and random-effects models giving pooled odds ratios (OR) were used to assess the effect of paternal age. This meta-analysis included a total 32,484 cycles from 16 autologous oocyte studies and 12 donor oocyte studies. In autologous cycles, a statistically significant effect of paternal age <40 years was noted in clinical pregnancy (OR 1.65, 95% confidence interval [CI] 1.27-2.15), live birth (OR 2.10, 95% CI 1.25-3.51) and miscarriage (OR 0.74, 95% CI 0.57-0.94) rates. Paternal age <50 years significantly reduced miscarriage rate (OR 0.68, 95% CI 0.54-0.86), and increased blastocyst rate (OR 1.61, 95% CI 1.08-2.38) and number of cleavage-stage embryos (OR 1.67, 95% CI 1.02-2.75) in donor oocyte cycles, where maternal age is controlled. This is an important public and societal health message highlighting the need to also consider paternal age alongside maternal age when planning a family.

Fertility treatment and cancers-the eternal conundrum: a systematic review and meta-analysis.

STUDY QUESTION: Does fertility treatment (FT) significantly increase the incidence of breast, ovarian, endometrial or cervical cancer? SUMMARY ANSWER: Overall, FT does not significantly increase the incidence of breast, ovarian or endometrial cancer and may even reduce the incidence of cervical cancer. WHAT IS KNOWN ALREADY: Infertility affects more than 14% of couples. Infertility and nulliparity are established risk factors for endometrial, ovarian and breast cancer, yet the association with FT is more contentious. STUDY DESIGN, SIZE, DURATION: A literature search was carried out using Cochrane Library, EMBASE, Medline and Google Scholar up to December 2019. Peer-reviewed studies stating cancer incidence (breast, ovarian, endometrial or cervical) in FT and no-FT groups were identified. Out of 128 studies identified, 29 retrospective studies fulfilled the criteria and were included (n = 21 070 337). PARTICIPANTS/MATERIALS, SETTING, METHODS: In the final meta-analysis, 29 studies were included: breast (n = 19), ovarian (n = 19), endometrial (n = 15) and cervical (n = 13), 17 studies involved multiple cancer types and so were included in each individual cancer meta-analysis. Primary outcome of interest was cancer incidence (breast, ovarian, endometrial and cervical) in FT and no-FT groups. Secondary outcome was cancer incidence according to specific fertility drug exposure. Odds ratio (OR) and random effects model were used to demonstrate treatment effect and calculate pooled treatment effect, respectively. A meta-regression and eight sub-group analyses were performed to assess the impact of the following variables, maternal age, infertility, study size, outliers and specific FT sub-types, on cancer incidence. MAIN RESULTS AND THE ROLE OF CHANCE: Cervical cancer incidence was significantly lower in the FT group compared with the no-FT group: OR 0.68 (95% CI 0.46-0.99). The incidences of breast (OR 0.86; 95% CI 0.73-1.01) and endometrial (OR 1.28; 95% CI 0.92-1.79) cancers were not found to be significantly different between the FT and no-FT groups. Whilst overall ovarian cancer incidence was not significantly different between the FT and no-FT groups (OR 1.19; 95% CI 0.98-1.46), separate analysis of borderline ovarian tumours (BOT) revealed a significant association (OR 1.69; 95% CI 1.27-2.25). In further sub-group analyses, ovarian cancer incidence was shown to be significantly higher in the IVF (OR 1.32; 95% CI 1.03-1.69) and clomiphene citrate (CC) treatment group (OR 1.40; 95% CI 1.10-1.77), respectively when compared with the no-FT group. Conversely, the incidences of breast (OR 0.75; 95% CI 0.61-0.92) and cervical cancer (OR 0.58; 95% CI 0.38-0.89) were significantly lower in the IVF treatment sub-group compared to the no-FT group. LIMITATIONS, REASONS FOR CAUTION: The large, varied dataset spanning a wide study period introduced significant clinical heterogeneity. Thus, results have to be interpreted with an element of caution. Exclusion of non-English citations, unpublished work and abstracts, in order to ensure data accuracy and reliability was maintained, may have introduced a degree of selection bias. WIDER IMPLICATIONS OF THE FINDINGS: The results for breast, ovarian, endometrial and cervical cancer are reassuring, in line with previously published meta-analyses for individual cancers but the association between IVF and CC treatment and an increase in ovarian cancer incidence requires additional work to understand the potential mechanism driving this association. In particular, focusing on (i) discriminating specific treatments effects from an inherent risk of malignancy; (ii) differential risk profiles among specific patient sub-groups (refractory treatment and obesity); and (iii) understanding the impact of FT outcomes on cancer incidence. STUDY FUNDING/COMPETING INTEREST(S): This study did not receive any funding. The authors have no financial, personal, intellectual and professional conflicts of interest to declare. PROSPERO REGISTRATION NUMBER: CRD42019153404.

Risk of vasopressin use: a case of acute pulmonary oedema, post intramyometrial infiltration of vasopressin in laparoscopic myomectomy.

A 34-year-old patient underwent a laparoscopic myomectomy, complicated by a profound episode of bradycardia and hypotension following intramyometrial infiltration of vasopressin (20 IU), promptly corrected with intravenous ephedrine (6 mg) and glycopyrrolate (200 µg). At extubation, pink frothy fluid was noted in the endotracheal tube; she was visibly distressed, desaturated to 89% in air and was coughing up pink stained fluid. Acute pulmonary oedema secondary to vasopressin was suspected. A tight-fitting oxygen mask (100%) with positive end expiratory pressure was applied and intravenous furosemide (20 mg) and diamorphine (4 mg, 1 mg increments) were administered to facilitate diuresis and oxygenation. Chest X-ray confirmed acute pulmonary oedema. Arterial blood gas demonstrated type 2 respiratory failure. Over 12 hours, the oxygen was weaned to 1 L/min. She demonstrated excellent diuresis. Troponin and brain-natriuretic peptide were elevated, but echocardiogram was normal. The cardiology diagnosis was vasopressin-induced coronary vasospasm, precipitating acute pulmonary oedema. She was discharged home on day 5.