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

Importance: Pain 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. Objective: The 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 sources: We conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021. Study selection: Our 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 synthesis: The 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 measures: Before 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. Results: 38 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 relevance: The 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 Registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343.

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.

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.

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care.

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, 'holistically' developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.

Usability Testing of a Reusable Pulse Oximeter Probe Developed for Health-Care Workers Caring for Children < 5 Years Old in Low-Resource Settings.

Hypoxemia measured by pulse oximetry predicts child pneumonia mortality in low-resource settings (LRS). Existing pediatric oximeter probes are prohibitively expensive and/or difficult to use, limiting LRS implementation. Using a human-centered design, we developed a low-cost, reusable pediatric oximeter probe for LRS health-care workers (HCWs). Here, we report probe usability testing. Fifty-one HCWs from Malawi, Bangladesh, and the United Kingdom participated, and seven experts provided reference measurements. Health-care workers and experts measured the peripheral arterial oxyhemoglobin saturation (SpO2) independently in < 5 year olds. Health-care worker measurements were classed as successful if recorded in 5 minutes (or shorter) and physiologically appropriate for the child, using expert measurements as the reference. All expert measurements were considered successful if obtained in < 5 minutes. We analyzed the proportion of successful SpO2 measurements obtained in < 1, < 2, and < 5 minutes and used multivariable logistic regression to predict < 1 minute successful measurements. We conducted four testing rounds with probe modifications between rounds, and obtained 1,307 SpO2 readings. Overall, 67% (876) of measurements were successful and achieved in < 1 minute, 81% (1,059) < 2 minutes, and 90% (1,181) < 5 minutes. Compared with neonates, increasing age (infant adjusted odds ratio [aOR]; 1.87, 95% confidence interval [CI]: 1.16, 3.02; toddler aOR: 4.33, 95% CI: 2.36, 7.97; child aOR; 3.90, 95% CI: 1.73, 8.81) and being asleep versus being calm (aOR; 3.53, 95% CI: 1.89, 6.58), were associated with < 1 minute successful measurements. In conclusion, we designed a novel, reusable pediatric oximetry probe that was effectively used by LRS HCWs on children. This probe may be suitable for LRS implementation.

The use of anti-Müllerian hormone and antral follicle count to predict the potential of oocytes and embryos.

OBJECTIVE: To investigate whether anti-Müllerian hormone (AMH) is better than antral follicle count (AFC) in predicting oocyte yield and embryo quality after controlled ovarian hyperstimulation for in vitro fertilization (IVF). STUDY DESIGN: This is a prospective observational study involving 162 women (<40 years old) undergoing their first IVF cycle at an IVF unit within a university hospital. AMH and AFC measurements were made on day 3 of the cycle within 3 months of starting ovarian stimulation. A standard long down-regulation protocol using gonadotrophin releasing hormone agonist and recombinant follicle stimulating hormone was used. A maximum of two embryos were transferred on day 2 or 3 following oocyte retrieval. The primary outcome was the number of good quality embryos available for transfer and freezing. Embryos were graded according to the number of blastomeres, the difference in blastomere size and the degree of fragmentation, into grades 1-4. Secondary outcomes included the number of oocytes retrieved and fertilized and the live birth rate. Correlation between different parameters was calculated using Spearman's correlation coefficient. Receiver operating characteristic (ROC) curves were generated for AMH and AFC to compare ability of parameters to predict top quality or frozen embryos and the occurrence of a live birth. RESULTS: Of the 137 women who had fresh embryo transfer, 52 became pregnant (32.1% pregnancy rate per cycle started) and 38 had a live birth (23.5% live birth rate per cycle started). Both AMH and AFC had highly significant correlations with the number of oocytes retrieved and the number of oocytes fertilized (P<0.001). The two markers were also significantly associated with the number of top quality embryos available for transfer and the number of embryos frozen (P<0.01). With regard to live birth, AMH performed better than AFC (P<0.01 and P<0.05, respectively), but both markers were more valuable in predicting the absence rather than the occurrence of live birth (negative predictive value 84%). CONCLUSIONS: AMH and AFC are comparable predictors of oocytes retrieved and of the number of good quality embryos available for transfer and freezing. Prediction of live birth may help clinicians selecting patients suitable for single embryo transfer.