A systematic review and meta-analysis of gestational diabetes mellitus and mental health among BAME populations

BACKGROUND: Gestational diabetes mellitus (GDM) is a common complication of pregnancy and is associated with an increased risk of mental health (MH) disorders including antenatal and postnatal depression (PND), anxiety and post-traumatic-stress-disorder (PTSD). We hypothesized GDM and MH disorders will disproportionately affect individuals from Black, Asian and Minority Ethnic backgrounds.METHODS: A systematic methodology was developed, and a protocol was published in PROSPERO (CRD42020210863) and a systematic review of publications between 1st January 1990 and 30th January 2021 was conducted. Multiple electronic databases were explored using keywords and MeSH terms. The finalised dataset was analysed using statistical methods such as random-effect models, subgroup analysis and sensitivity analysis. These were used to determine odds ratio (OR) and 95% confidence intervals (CI) to establish prevalence using variables of PND, anxiety, PTSD and stress to name a few.FINDINGS: Sixty studies were finalised from the 20,040 data pool. Forty-six studies were included systematically with 14 used to meta-analyze GDM and MH outcomes. A second meta-analysis was conducted using 7 studies to determine GDM risk among Black, Asian and Minority Ethnic women with pre-existing MH disorders. The results indicate an increased risk with pooled adjusted OR for both reflected at 1.23, 95% CI of 1.00-1.50 and 1.29, 95% CI of 1.11-1.50 respectively.INTERPRETATION: The available studies suggest a MH sequalae with GDM as well as a sequalae of GDM with MH among Black, Asian and Minority Ethnic populations. Our findings warrant further future exploration to better manage these patients.FUNDING: Not applicable.</p

Gabapentin for chronic pelvic pain in women (GaPP2):a multicentre, randomised, double-blind, placebo-controlled trial

BackgroundChronic pelvic pain affects 2–24% of women worldwide and evidence for medical treatments is scarce. Gabapentin is effective in treating some chronic pain conditions. We aimed to measure the efficacy and safety of gabapentin in women with chronic pelvic pain and no obvious pelvic pathology.MethodsWe performed a multicentre, randomised, double-blind, placebo-controlled randomised trial in 39 UK hospital centres. Eligible participants were women with chronic pelvic pain (with or without dysmenorrhoea or dyspareunia) of at least 3 months duration. Inclusion criteria were 18–50 years of age, use or willingness to use contraception to avoid pregnancy, and no obvious pelvic pathology at laparoscopy, which must have taken place at least 2 weeks before consent but less than 36 months previously. Participants were randomly assigned in a 1:1 ratio to receive gabapentin (titrated to a maximum dose of 2700 mg daily) or matching placebo for 16 weeks. The online randomisation system minimised allocations by presence or absence of dysmenorrhoea, psychological distress, current use of hormonal contraceptives, and hospital centre. The appearance, route, and administration of the assigned intervention were identical in both groups. Patients, clinicians, and research staff were unaware of the trial group assignments throughout the trial. Participants were unmasked once they had provided all outcome data at week 16–17, or sooner if a serious adverse event requiring knowledge of the study drug occurred. The dual primary outcome measures were worst and average pain scores assessed separately on a numerical rating scale in weeks 13–16 after randomisation, in the intention-to-treat population. Self-reported adverse events were assessed according to intention-to-treat principles. This trial is registered with the ISRCTN registry, ISCRTN77451762.FindingsParticipants were screened between Nov 30, 2015, and March 6, 2019, and 306 were randomly assigned (153 to gabapentin and 153 to placebo). There were no significant between-group differences in both worst and average numerical rating scale (NRS) pain scores at 13–16 weeks after randomisation. The mean worst NRS pain score was 7·1 (standard deviation [SD] 2·6) in the gabapentin group and 7·4 (SD 2·2) in the placebo group. Mean change from baseline was −1·4 (SD 2·3) in the gabapentin group and −1·2 (SD 2·1) in the placebo group (adjusted mean difference −0·20 [97·5% CI −0·81 to 0·42]; p=0·47). The mean average NRS pain score was 4·3 (SD 2·3) in the gabapentin group and 4·5 (SD 2·2) in the placebo group. Mean change from baseline was −1·1 (SD 2·0) in the gabapentin group and −0·9 (SD 1·8) in the placebo group (adjusted mean difference −0·18 [97·5% CI −0·71 to 0·35]; p=0·45). More women had a serious adverse event in the gabapentin group than in the placebo group (10 [7%] of 153 in the gabapentin group compared with 3 [2%] of 153 in the placebo group; p=0·04). Dizziness, drowsiness, and visual disturbances were more common in the gabapentin group.InterpretationThis study was adequately powered, but treatment with gabapentin did not result in significantly lower pain scores in women with chronic pelvic pain, and was associated with higher rates of side-effects than placebo. Given the increasing reports of abuse and evidence of potential harms associated with gabapentin use, it is important that clinicians consider alternative treatment options to off-label gabapentin for the management of chronic pelvic pain and no obvious pelvic pathology.FundingNational Institute for Health Research

An Exploratory Study Assessing Data Synchronising Methods to Develop Machine Learning-Based Prediction Models:Application to Multimorbidity Among Endometriosis Women

Background: Endometriosis is a complex health condition with an array of physical and psychological symptoms, often leading to multimorbidity. Multimorbidity consists of the co-existence of two or more chronic medical conditions in one individual without any condition being considered an index condition, and therefore could be prevented if the initial conditions are managed effectively. It is a remarkably challenging heath condition and a good understanding of the complex mechanisms involved could enable timely diagnosis and effective management plans. This study aimed to develop an exploratory machine learning model that can predict multimorbidity among endometriosis women using real-world and synthetic data.Methods: A sample size of 1012 was used from 2 endometriosis specialized centers in the UK. The patients record includedlarge spectrum of variables, such as patient demographics, symptoms, diseases, previous treatments, and conditions in women with a confirmed diagnosis of endometriosis. In addition, 1000 more synthetic data records, for each center, was generated using a widely used synthetic Data Vault’s Gaussian Copula model using the data characteristic from patients’ records. Three standard classification models Logistic Regression (LR), Support Vector Machine (SVM) Random Forest (RF), were used for classification based on their intrinsic behavior in separating/classifying data. Hence, their performance was compared on realworldand synthetic data. All models were trained on both synthetic and real-world data but tested using real-world data. Their performance was assessed using quality assessment test, heatmaps and average accuracies.Results: The quality assessment test and heatmaps comparing synthetic and real-world datasets show that the synthetic data follow the same distribution. The average accuracies for all three models (LR, SVM and RF), given as “model accuracy-centre1:accuracy-centre2” was found to be: LR 64.26%:69.04%, SVM 67.35%:68.61%, and RF 58.67%:73.76% on real-world data and LR 69.9%:72.29%, SVM 69.39%:70.13, and RF 68.88%:74.62 on synthetic data, respectively.Conclusion: The findings of this exploratory study show that machine learning models trained on synthetic data performed better than models trained on real-world data. This suggests that synthetic data shows much promise for conducting clinical epidemiology and clinical trials that could devise better precision treatments for endometriosis and, possibly prevent multimorbidity

An Exploratory Study Assessing Data Synchronising Methods to Develop Machine Learning-Based Prediction Models:Application to Multimorbidity Among Endometriosis Women

Background: Endometriosis is a complex health condition with an array of physical and psychological symptoms, often leading to multimorbidity. Multimorbidity consists of the co-existence of two or more chronic medical conditions in one individual without any condition being considered an index condition, and therefore could be prevented if the initial conditions are managed effectively. It is a remarkably challenging heath condition and a good understanding of the complex mechanisms involved could enable timely diagnosis and effective management plans. This study aimed to develop an exploratory machine learning model that can predict multimorbidity among endometriosis women using real-world and synthetic data.Methods: A sample size of 1012 was used from 2 endometriosis specialized centers in the UK. The patients record includedlarge spectrum of variables, such as patient demographics, symptoms, diseases, previous treatments, and conditions in women with a confirmed diagnosis of endometriosis. In addition, 1000 more synthetic data records, for each center, was generated using a widely used synthetic Data Vault’s Gaussian Copula model using the data characteristic from patients’ records. Three standard classification models Logistic Regression (LR), Support Vector Machine (SVM) Random Forest (RF), were used for classification based on their intrinsic behavior in separating/classifying data. Hence, their performance was compared on realworldand synthetic data. All models were trained on both synthetic and real-world data but tested using real-world data. Their performance was assessed using quality assessment test, heatmaps and average accuracies.Results: The quality assessment test and heatmaps comparing synthetic and real-world datasets show that the synthetic data follow the same distribution. The average accuracies for all three models (LR, SVM and RF), given as “model accuracy-centre1:accuracy-centre2” was found to be: LR 64.26%:69.04%, SVM 67.35%:68.61%, and RF 58.67%:73.76% on real-world data and LR 69.9%:72.29%, SVM 69.39%:70.13, and RF 68.88%:74.62 on synthetic data, respectively.Conclusion: The findings of this exploratory study show that machine learning models trained on synthetic data performed better than models trained on real-world data. This suggests that synthetic data shows much promise for conducting clinical epidemiology and clinical trials that could devise better precision treatments for endometriosis and, possibly prevent multimorbidity

A systematic review and meta-analysis of the Endometriosis and Mental-Health Sequelae; The ELEMI Project

BACKGROUND: It is important to evaluate sequalae for complex chronic health conditions such as endometriosis and mental health disorders. Endometriosis impacts 1 in 10 women. Mental health outcomes can be a primary determinant in many physical health conditions although this is an area not well researched particularly in women's health. This has been problematic for endometriosis patients in particular, who report mental health issues as well as other key comorbidities such as chronic pelvic pain and infertility. This could be partly due to the complexities associated with comprehensively exploring overlaps between physical and mental health disorders in the presence of multiple comorbidities and their potential mechanistic relationship.METHODS: In this evidence synthesis, a systematic methodology and mixed-methods approaches were used to synthesize both qualitative and quantitative data to examine the prevalence of the overlapping sequalae between endometriosis and psychiatric symptoms and disorders. As part of this, an evidence synthesis protocol was developed which included a systematic review protocol that was published on PROSPERO (CRD42020181495). The aim was to identify and evaluate mental health reported outcomes and prevalence of symptoms and psychiatric disorders associated with endometriosis.FINDINGS: A total of 34 papers were included in the systematic review and 15 were included in the meta-analysis. Anxiety and depression symptoms were the most commonly reported mental health outcomes while a pooled analysis also revealed high prevalence of chronic pelvic pain and dyspareunia.INTERPRETATION: It is evident that small-scale cross-sectional studies have been conducted in a variety of settings to determine mental health outcomes among endometriosis patients. Further research is required to comprehensively evaluate the mental health sequalae with endometriosis.</p

A systematic review and meta-analysis of digital application use in clinical research in pain medicine.

ImportancePain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain. Accurate data collection and reporting treatment outcomes are vital to addressing the challenges faced. In light of this, we conducted a systematic evaluation of the current digital application landscape within chronic pain medicine.ObjectiveThe primary objective was to consider the prevalence of digital application usage for chronic pain management. These digital applications included mobile apps, web apps, and chatbots.Data sourcesWe conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021.Study selectionOur review included studies that involved the use of digital applications for chronic pain conditions. There were no restrictions on the country in which the study was conducted. Only studies that were peer-reviewed and published in English were included. Four reviewers had assessed the eligibility of each study against the inclusion/exclusion criteria. Out of the 84 studies that were initially identified, 38 were included in the systematic review.Data extraction and synthesisThe AMSTAR guidelines were used to assess data quality. This assessment was carried out by 3 reviewers. The data were pooled using a random-effects model.Main outcomes and measuresBefore data collection began, the primary outcome was to report on the standard mean difference of digital application usage for chronic pain conditions. We also recorded the type of digital application studied (e.g., mobile application, web application) and, where the data was available, the standard mean difference of pain intensity, pain inferences, depression, anxiety, and fatigue.Results38 studies were included in the systematic review and 22 studies were included in the meta-analysis. The digital interventions were categorised to web and mobile applications and chatbots, with pooled standard mean difference of 0.22 (95% CI: -0.16, 0.60), 0.30 (95% CI: 0.00, 0.60) and -0.02 (95% CI: -0.47, 0.42) respectively. Pooled standard mean differences for symptomatologies of pain intensity, depression, and anxiety symptoms were 0.25 (95% CI: 0.03, 0.46), 0.30 (95% CI: 0.17, 0.43) and 0.37 (95% CI: 0.05, 0.69), respectively. A sub-group analysis was conducted on pain intensity due to the heterogeneity of the results (I 2 = 82.86%; p = 0.02). After stratifying by country, we found that digital applications were more likely to be effective in some countries (e.g., United States, China) than others (e.g., Ireland, Norway).Conclusions and relevanceThe use of digital applications in improving pain-related symptoms shows promise, but further clinical studies would be needed to develop more robust applications.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343