Miscarriage Association

Miscarriage, the loss of a pregnancy before viability, can be sporadic or recurrent. Emerging evidence links miscarriage to specific microbiota compositions within the female reproductive tract (FRT). This systematic review aims to synthesise evidence on the association between sporadic and recurrent miscarriage and FRT microbiota composition, as assessed using metataxonomic profiling approaches. A systematic analysis of the 43 included studies, sampling the vaginal, cervical and endometrial microbiota supported an association between reduced Lactobacillus abundance and miscarriage, making it a potential target for therapeutic intervention. However, consistent changes in alpha and beta diversity were not observed and there was a lack of reproducibility for other compositional changes. This review also highlighted concerns about the significant bias introduced due to methodological variations and emphasises the need for future standardisation of microbial sampling, sequencing, and reporting to allow accurate comparison of results and to reduce research waste.

Recurrent miscarriage is a debilitating disorder associated with considerable physical and psychological morbidity. An estimated 50% of first trimester miscarriages remain unexplained. The aim of this study was to provide a personalised framework to guide the expectations of women experiencing recurrent miscarriage, with the ultimate goal of transforming clinical practice. We used real-world data from a UK longitudinal study of 1201 couples attending National Health Service (NHS) miscarriage clinics, with a history of previous miscarriages, comprising medical and obstetric history, results of investigations and pregnancy and neonatal outcome. We developed, parametrised, and validated predictive models for the probability that the next pregnancy is viable and for the time to next pregnancy. Time to next pregnancy separates couples into two groups, a group with subfertility, i.e., delay in conception, and a group with no significant delay in conception. We used Bayesian inference for the latter model.Trial registration number: The prospective data collections were pre-registered ISRCTN17732518; https://doi.org/10.1186/ISRCTN17732518. Predictive models of the time to pregnancy, the probability of the couple being subfertility and the probability of having a viable pregnancy can be parametrised from longitudinal study data. We identified several predictors for such models. In the viable pregnancy model, increased maternal age, higher Body Mass Index (BMI), having Polycystic Ovaries Syndrome (PCOS) and the number of previous miscarriages were associated with reduced odds of viable pregnancy. In contrast, having had previous live births increased the odds of a viable pregnancy. Model validation against a second external dataset gave an Area Under Curve (AUC) of 0.65 (95% Confidence Interval (CI): 0.55, 0.76). Of the 942 women referred to our recurrent miscarriage clinics and followed up over a period of 3 years, 10.7% (101) did not conceive during this time, indicating a potential subfertility problem. In the time to pregnancy model, increased maternal age, higher BMI, and smoking were associated with reduced likelihood of conception. Conversely, taking folic acid supplements and having a history of previous conceptions were associated with increased fertility. In our cohort, 53.4% (577 out of 1080 women) reported a pregnancy within 12 months. Additionally, 22.8% (277 out of 996 women who were followed up over a 2-year period) experienced a first pregnancy event in the second year. The area under the curve (AUC) for predicting pregnancy within 12 months was 0.60 (95% CI: 0.50-0.70) in an external validation using a second dataset. The pregnancy journey can be predicted on a personalised basis by integrating the validated models. We provide a framework for evidence-based management of women with miscarriage, comprising informed decision-making, including optimal referral to fertility services, and a tailored insight into fertility outcomes, thereby guiding expectations and providing psychological support. Tommy's National Centre for Miscarriage Research.

Abstract Study question Can an image recognition model, utilising automated deep learning, be trained to diagnose and classify the severity of adenomyosis on ultrasound? Summary answer The automated deep learning model developed in this study can accurately diagnose and classify the severity of adenomyosis from ultrasound images. What is known already Adenomyosis is prevalent across all stages of life, starting from adolescence to perimenopause, however it remains underdiagnosed. The diagnosis and classification of adenomyosis are essential for correlating with symptom severity and counselling about disease prognosis, management options and long-term impact. Diagnosing and classifying the severity is a time-consuming and complex process that depends on the operator’s experience. Machine learning (ML) has an enormous potential to assist healthcare professionals with image classification tasks. It can directly process and automatically learn mid to high-level abstract features acquired from images using a deep architecture model without requiring the manual definition of features. Study design, size, duration This observational cohort study utilised ultrasound images from four assisted conception units in the United Kingdom from February 2023 to February 2024. Labelled images were used as input for training and testing of the model. The complete dataset was manually split into 9:1 ratio; 90% of all images were used as training dataset (80% for training and 10% for validation) and 10% as test dataset. Ethics approval was obtained from the Institutional Review Board (IRB). Participants/materials, setting, methods An automated ML tool (Vertex AI Vision) was trained and tested to diagnose and classify adenomyosis severity. Two investigators, experienced in gynaecological ultrasound scanning, labelled two and three-dimensional images into four categories: normal uterus, mild, moderate, and severe adenomyotic uterus, as per standardised ultrasound criteria. The labelled images were formatted and uploaded on Google Cloud to train the neural network based on pattern recognition. The model’s performance was determined for an internal test dataset. Main results and the role of chance A total of 3,500 ultrasound images were obtained from 3,194 patients, and the internal test dataset consisted of 350 ultrasound images from 292 patients. An overall and per-category score threshold of 0.5 was adopted to provide a balance between reasonable precision and recall during the evaluation. The investigator’s categorization was used as the reference standard to evaluate the performance of the automated machine-learning model. The model’s overall accuracy for diagnosing and classifying adenomyosis, as determined by the area under the precision-recall curve, was 80.24% across all score thresholds. The overall precision was 83.00%, and recall was 65.75% at a confidence threshold of 0.5. The precision and recall were 91% and 78% for normal uterus, 67% and 53% for mild adenomyosis, 86% and 68% for moderate adenomyosis, and 88% and 64% for severe adenomyosis. The algorithm achieved a high accuracy for the internal dataset. The time taken by a trained healthcare professional to label and process 3,500 images into 4 categories was 70 hours. The time taken by an untrained automated ML tool to get trained, process, and label the images into 4 categories was 4 hours, saving 66 hours per healthcare professional. Limitations, reasons for caution Due to the inherent nature of non-invasive investigations for the population attending the assisted conception unit, histological confirmation of adenomyosis was not possible. In addition, the model’s performance has been tested using an internal test dataset and needs further validation using an external test dataset from a different population. Wider implications of the findings This novel study leverages the power of artificial intelligence to develop an automated machine learning model for diagnosing and classifying adenomyosis from ultrasound scans. This tool can potentially enhance the diagnostic capacity and efficiency of healthcare professionals and empower women by providing better care through early and uniform diagnosis. Trial registration number No

Routine management of patients classified with a pregnancy of unknown location (PUL) involves serial monitoring of hCG levels to triage women as being at high or low risk of complications. This study aimed to explore the feasibility and accuracy of capillary hCG point-of-care testing (POCT). In this prospective observational study women were invited to participate following a classification of PUL at initial ultrasound. At the time of routine blood collection for standard laboratory serum hCG testing (SLT), patients were offered venous and capillary hCG POCT using the Abbott i-STAT 1 device. Capillary samples were derived using a lancet finger prick and direct application of the bleb to the i-STAT 1 cartridge. Data was collected between April and December 2022 at a single London early pregnancy unit. 195 capillary POCT samples were collected and compared with SLT and venous POCT results. hCG readings > 2000 IU/L were excluded from analyses as the i-STAT 1 has an upper detection limit of 2000 IU/L. Venous POCT results did not differ significantly from SLT (p = .224). Capillary POCT values were significantly lower than venous POCT and SLT (p < .001) but showed a strong correlation with both SLT (r(132) = .97, p < .001) and venous POCT (r(124) = .99, p < 0.001). Capillary POCT showed even higher correlations with SLT when hCG levels were < 1000 IU/L (r(115) = .99, p < .001). The cartridge failure rate for the i-STAT 1 was 26% with capillary testing. Capillary POCT cannot be used interchangeably with standard laboratory derived results but has a high correlation with both venous POCT and laboratory testing. This suggests potential for future application as a simpler, faster, and less invasive alternative to venous blood sampling. Further work is required to reduce cartridge failure and calibrate existing PUL triaging models to allow safe use with capillary POCT.

Objectives: Women are increasingly opting for expectant care of ectopic pregnancy to avoid surgical intervention and potentially safeguard future fertility.Robust prognostic markers to predict which patients are likely to have successful conservative management are few.The objective of this study was to investigate the biochemical and sonographic characteristics of patients with ectopic pregnancy and determine which were predictive of successful expectant management.Methods: This retrospective cohort study examined demographic details, routine biochemistry and sonographic parameters for all cases of ectopic pregnancy across a 24-month period at a single London hospital trust.Results: The mean patient age was 32.3 (standard deviation = 5.7).In total, there were 219 cases of tubal ectopic pregnancy diagnosed on ultrasound.Of these, 95 had initial expectant management (43.4%) and were included in the study.The failure rate was 41.1% for expectant care (39 patients), the most common reason being a static or rising hCG.hCG at point of decision was significantly higher in those with failed versus successful expectant care (p < 0.01), as was the 48-hour hCG ratio (p = 0.013).Progesterone level did not significantly differ in those with successful versus failed expectant care, though in the overall cohort progesterone levels were significantly higher in patients requiring surgery at any point compared to those with resolution following expectant or medical care.Neither endometrial thickness nor presence of any free fluid were prognostic for failure of expectant management.Patients presenting initially as pregnancy of unknown location were no more likely to have successful management than those diagnosed with ectopic at the first visit.Conclusions: HCG at point of decision and 48-hour hCG ratio have the greatest utility in predicting success of expectant management for ectopic pregnancy.No other significant differences in clinical, biochemical or ultrasound findings were identified.

Introduction:Despite widespread recognition that communicating unexpected news during obstetric ultrasound examinations is challenging, there is a dearth of research investigating how to teach evidence-based communication to sonographers. Communication Coaching is a supportive, positive method that has previously been associated with improvements in communication, patient satisfaction, and reduced burnout in clinicians. However, to date, no study has coached sonographers. This study explored stakeholders’ views on a proposed Communication Coaching intervention and used these data to adapt the intervention for use with qualified obstetric sonographers.Methods:Semi-structured interviews were conducted with people who have a vested interest in unexpected news delivery and thematic analysis was conducted on the data. Eight sonographers, six people with lived experience of receiving unexpected news and six representatives from third-sector organisations who support expectant parents were recruited (18 women; 2 men, aged between 21 and 75 years).Results:Participants viewed the planned Communication Coaching intervention favourably and suggested adaptations. The two main themes were (1) the practicalities of coaching, and (2) content. The first theme had four subthemes: (a) brief and flexible structure, (b) online modality, (c) sensitive and positive coach and (d) organisational awareness. The second theme had three subthemes: (a) specific language and behaviour recommendations, (b) adaptable to different service-users and situations and (c) confer relevant emotional skills and techniques.Conclusions:Communication Coaching could be a feasible and acceptable intervention for qualified sonographers if specific, limited adaptations are made as recommended by the stakeholders. Further evaluation of the intervention in practice is necessary.

Abstract Study question What is the relative effectiveness and safety of existing COS protocols for women undergoing assisted reproductive technology (ART) treatment? Summary answer There was no difference in live birth between all protocols, but short antagonist protocols may reduce ovarian hyperstimulation syndrome in women with predicted normal/high response. What is known already Controlled ovarian stimulation is an essential step in most ART cycles. It involves the administration of exogenous gonadotrophins to induce multifollicular growth, usually in addition to drugs that prevent untimely ovulation by suppressing the pituitary gland. Different treatment combinations may be used in COS. These vary according to the type of drugs administered for pituitary suppression (e.g., gonadotrophin-releasing hormone [GnRH] agonists, antagonists) and ovarian stimulation (e.g., urinary or recombinant gonadotrophins). Drug dosages, timing and routes of administration also vary between different regimens. However, there is no consensus on how the existing COS protocols rank according to their effectiveness and safety. Study design, size, duration We searched the following databases to November 2021: MEDLINE, EMBASE, CINAHL, CENTRAL and ClinicalTrials.gov. We included randomised controlled trials (RCTs) comparing at least two COS protocols using GnRH agonists or antagonists for pituitary suppression; and human menopausal gonadotrophin (hMG), urinary or recombinant follicle-stimulating hormone (u/rFSH), with or without luteinising hormone (LH) for ovarian stimulation. The primary outcomes were the rates of live birth (LBR) and ovarian hyperstimulation syndrome (OHSS) per participant after one stimulation cycle. Participants/materials, setting, methods Two reviewers independently selected studies and extracted data. We conducted pairwise and network meta-analyses (NMA) according to participants’ predicted response to COS (normal, high and low). Using the Cochrane-RoB-1 tool, we restricted our primary analyses to RCTs at low risk of selection and other biases. We presented effect estimates as risk ratio (RR) with 95% confidence interval (CI) and considered I2&amp;gt;50% as representing substantial heterogeneity. For each outcome, we generated ranking plots comparing different interventions. Main results and the role of chance In total, our searches identified 9464 studies. The primary analysis included 68 RCTs assessing 17861 women and 34 different COS protocols. The evidence showed that in women with predicted normal or high response, the use of short GnRH antagonist protocols may result in little to no difference in LBR (RR 0.98, 95% CI 0.85 to 1.13; 6 studies; 2063 women; I2 = 0%; low-certainty evidence) and a reduction in OHSS (RR 0.88, 95% CI 0.78 to 0.99; 7 studies; 2246 women; I2 = 0%; low-certainty evidence) compared with long GnRH agonist protocols. The rankogram comparing different COS protocols showed a probability of 98% that short GnRH antagonist regimens are the best treatment to prevent OHSS. Sensitivity analyses including all studies showed that in women with predicted normal response undergoing long GnRH agonist cycles for pituitary suppression, the use of rFSH for ovarian stimulation may result in decreased fresh-cycle LBR compared to hMG (RR 0.80, 95% CI 0.68 to 0.95; 7 studies; 1575 women; I2 = 1%; low-certainty evidence). For the remaining interventions (e.g., agonist flare or progestogens for pituitary suppression, in combination with various gonadotrophin regimens) the evidence was uncertain of an effect or insufficient for quantitative synthesis. Limitations, reasons for caution The high number of interventions resulted in disconnected networks, limiting our ability to perform NMA for some comparisons. The certainty of the evidence was limited by serious risk of bias. Finally, the lack of data on cumulative LBR and differences in oocyte yield made comparisons between FSH preparations potentially unbalanced. Wider implications of the findings Our findings suggest that the use of short GnRH antagonist protocols may result in reduced OHSS rates in women with predicted normal or high ovarian response without compromising live birth rates. There is a paucity of high-quality RCTs comparing different gonadotrophin preparations (e.g., hMG versus rFSH) for COS. Trial registration number N/A