Javier Zamora

OBJECTIVE: To determine the clinical manifestations, risk factors, and maternal and perinatal outcomes in pregnant and recently pregnant women with suspected or confirmed coronavirus disease 2019 (covid-19). DESIGN: Living systematic review and meta-analysis. DATA SOURCES: Medline, Embase, Cochrane database, WHO COVID-19 database, China National Knowledge Infrastructure (CNKI), and Wanfang databases from 1 December 2019 to 6 October 2020, along with preprint servers, social media, and reference lists. STUDY SELECTION: Cohort studies reporting the rates, clinical manifestations (symptoms, laboratory and radiological findings), risk factors, and maternal and perinatal outcomes in pregnant and recently pregnant women with suspected or confirmed covid-19. DATA EXTRACTION: At least two researchers independently extracted the data and assessed study quality. Random effects meta-analysis was performed, with estimates pooled as odds ratios and proportions with 95% confidence intervals. All analyses will be updated regularly. RESULTS: 192 studies were included. Overall, 10% (95% confidence interval 7% to 12%; 73 studies, 67 271 women) of pregnant and recently pregnant women attending or admitted to hospital for any reason were diagnosed as having suspected or confirmed covid-19. The most common clinical manifestations of covid-19 in pregnancy were fever (40%) and cough (41%). Compared with non-pregnant women of reproductive age, pregnant and recently pregnant women with covid-19 were less likely to have symptoms (odds ratio 0.28, 95% confidence interval 0.13 to 0.62; I2=42.9%) or report symptoms of fever (0.49, 0.38 to 0.63; I2=40.8%), dyspnoea (0.76, 0.67 to 0.85; I2=4.4%) and myalgia (0.53, 0.36 to 0.78; I2=59.4%). The odds of admission to an intensive care unit (odds ratio 2.13, 1.53 to 2.95; I2=71.2%), invasive ventilation (2.59, 2.28 to 2.94; I2=0%) and need for extra corporeal membrane oxygenation (2.02, 1.22 to 3.34; I2=0%) were higher in pregnant and recently pregnant than non-pregnant reproductive aged women. Overall, 339 pregnant women (0.02%, 59 studies, 41 664 women) with confirmed covid-19 died from any cause. Increased maternal age (odds ratio 1.83, 1.27 to 2.63; I2=43.4%), high body mass index (2.37, 1.83 to 3.07; I2=0%), any pre-existing maternal comorbidity (1.81, 1.49 to 2.20; I2=0%), chronic hypertension (2.0, 1.14 to 3.48; I2=0%), pre-existing diabetes (2.12, 1.62 to 2.78; I2=0%), and pre-eclampsia (4.21, 1.27 to 14.0; I2=0%) were associated with severe covid-19 in pregnancy. In pregnant women with covid-19, increased maternal age, high body mass index, non-white ethnicity, any pre-existing maternal comorbidity including chronic hypertension and diabetes, and pre-eclampsia were associated with serious complications such as admission to an intensive care unit, invasive ventilation and maternal death. Compared to pregnant women without covid-19, those with the disease had increased odds of maternal death (odds ratio 2.85, 1.08 to 7.52; I2=0%), of needing admission to the intensive care unit (18.58, 7.53 to 45.82; I2=0%), and of preterm birth (1.47, 1.14 to 1.91; I2=18.6%). The odds of admission to the neonatal intensive care unit (4.89, 1.87 to 12.81, I2=96.2%) were higher in babies born to mothers with covid-19 versus those without covid-19. CONCLUSION: Pregnant and recently pregnant women with covid-19 attending or admitted to the hospitals for any reason are less likely to manifest symptoms such as fever, dyspnoea, and myalgia, and are more likely to be admitted to the intensive care unit or needing invasive ventilation than non-pregnant women of reproductive age. Pre-existing comorbidities, non-white ethnicity, chronic hypertension, pre-existing diabetes, high maternal age, and high body mass index are risk factors for severe covid-19 in pregnancy. Pregnant women with covid-19 versus without covid-19 are more likely to deliver preterm and could have an increased risk of maternal death and of being admitted to the intensive care unit. Their babies are more likely to be admitted to the neonatal unit. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020178076. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 1 of the original article published on 1 September 2020 (BMJ 2020;370:m3320), and previous updates can be found as data supplements (https://www.bmj.com/content/370/bmj.m3320/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

BACKGROUND: Systematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis. RESULTS: Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities. CONCLUSION: Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at http://www.hrc.es/investigacion/metadisc_en.htm.

BACKGROUND: While live birth is the principal clinical outcome following in vitro fertilization (IVF) treatment, the number of eggs retrieved following ovarian stimulation is often used as a surrogate outcome in clinical practice and research. The aim of this study was to explore the association between egg number and live birth following IVF treatment and identify the number of eggs that would optimize the IVF outcome. METHODS: Anonymized data on all IVF cycles performed in the UK from April 1991 to June 2008 were obtained from the Human Fertilization and Embryology Authority (HFEA). We analysed data from 400 135 IVF cycles. A logistic model was fitted to predict live birth using fractional polynomials to handle the number of eggs as a continuous independent variable. The prediction model, which was validated on a separate HFEA data set, allowed the estimation of the probability of live birth for a given number of eggs, stratified by age group. We produced a nomogram to predict the live birth rate (LBR) following IVF based on the number of eggs and the age of the female. RESULTS: The median number of eggs retrieved per cycle was 9 [inter-quartile range (IQR) 6-13]. The overall LBR was 21.3% per fresh IVF cycle. There was a strong association between the number of eggs and LBR; LBR rose with an increasing number of eggs up to ∼15, plateaued between 15 and 20 eggs and steadily declined beyond 20 eggs. During 2006-2007, the predicted LBR for women with 15 eggs retrieved in age groups 18-34, 35-37, 38-39 and 40 years and over was 40, 36, 27 and 16%, respectively. There was a steady increase in the LBR per egg retrieved over time since 1991. CONCLUSION: The relationship between the number of eggs and live birth, across all female age groups, suggests that the number of eggs in IVF is a robust surrogate outcome for clinical success. The results showed a non-linear relationship between the number of eggs and LBR following IVF treatment. The number of eggs to maximize the LBR is ∼15.

BACKGROUND: The prevalence of congenital uterine anomalies in high-risk women is unclear, as several different diagnostic approaches have been applied to different groups of patients. This review aims to evaluate the prevalence of such anomalies in unselected populations and in women with infertility, including those undergoing IVF treatment, women with a history of miscarriage, women with infertility and recurrent miscarriage combined, and women with a history of preterm delivery. METHODS: Searches of MEDLINE, EMBASE, Web of Science and the Cochrane register were performed. Study selection and data extraction were conducted independently by two reviewers. Studies were grouped into those that used 'optimal' and 'suboptimal' tests for uterine anomalies. Meta-analyses were performed to establish the prevalence of uterine anomalies and their subtypes within the various populations. RESULTS: We identified 94 observational studies comprising 89 861 women. The prevalence of uterine anomalies diagnosed by optimal tests was 5.5% [95% confidence interval (CI), 3.5-8.5] in the unselected population, 8.0% (95% CI, 5.3-12) in infertile women, 13.3% (95% CI, 8.9-20.0) in those with a history of miscarriage and 24.5% (95% CI, 18.3-32.8) in those with miscarriage and infertility. Arcuate uterus is most common in the unselected population (3.9%; 95% CI, 2.1-7.1), and its prevalence is not increased in high-risk groups. In contrast, septate uterus is the most common anomaly in high-risk populations. CONCLUSIONS: Women with a history of miscarriage or miscarriage and infertility have higher prevalence of congenital uterine anomalies compared with the unselected population.

BACKGROUND: A false-negative case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is defined as a person with suspected infection and an initial negative result by reverse transcription-polymerase chain reaction (RT-PCR) test, with a positive result on a subsequent test. False-negative cases have important implications for isolation and risk of transmission of infected people and for the management of coronavirus disease 2019 (COVID-19). We aimed to review and critically appraise evidence about the rate of RT-PCR false-negatives at initial testing for COVID-19. METHODS: We searched MEDLINE, EMBASE, LILACS, as well as COVID-19 repositories, including the EPPI-Centre living systematic map of evidence about COVID-19 and the Coronavirus Open Access Project living evidence database. Two authors independently screened and selected studies according to the eligibility criteria and collected data from the included studies. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We calculated the proportion of false-negative test results using a multilevel mixed-effect logistic regression model. The certainty of the evidence about false-negative cases was rated using the GRADE approach for tests and strategies. All information in this article is current up to July 17, 2020. RESULTS: We included 34 studies enrolling 12,057 COVID-19 confirmed cases. All studies were affected by several risks of bias and applicability concerns. The pooled estimate of false-negative proportion was highly affected by unexplained heterogeneity (tau-squared = 1.39; 90% prediction interval from 0.02 to 0.54). The certainty of the evidence was judged as very low due to the risk of bias, indirectness, and inconsistency issues. CONCLUSIONS: There is substantial and largely unexplained heterogeneity in the proportion of false-negative RT-PCR results. The collected evidence has several limitations, including risk of bias issues, high heterogeneity, and concerns about its applicability. Nonetheless, our findings reinforce the need for repeated testing in patients with suspicion of SARS-Cov-2 infection given that up to 54% of COVID-19 patients may have an initial false-negative RT-PCR (very low certainty of evidence). SYSTEMATIC REVIEW REGISTRATION: Protocol available on the OSF website: https://tinyurl.com/vvbgqya.