Systematic Review of Women Leading and Participating in Nephrology Randomized Clinical Trials.

Introduction: Women are underrepresented in the leadership of and participation in randomized controlled trials (RCTs). We conducted a bibliometric review of nephrology RCTs to examine trial leadership by women and participation of women in nephrology RCTs. Methods: A bibliometric review of RCTs published in top medical, surgical, or nephrology journals was conducted using MEDLINE and EMBASE from January 2011 to December 2021. Leadership by women as corresponding authors, women trial participation, and trial characteristics were examined with duplicate independent data extraction. Logistic regression was used to examine associations between trial characteristics and women leadership and trial participation. Results: A total of 1770 studies were screened and 395 RCTs met eligibility criteria. The number (%) of women in corresponding, first, and last authorship positions were as follows: 89 (22%), 109 (28%), and 74 (19%), respectively, without change over time (P = 0.94). The median percentage (interquartile range [IQR]) of women trial participants was 39.0% (13.5%) with no difference between women or men lead authors (P = 0.15). Men lead authors were statistically less likely to enroll women in RCTs. Women lead authors were less likely to be funded by industry (odds ratio [OR]: 0.30; 95% confidence interval [CI]: 0.14-0.63; P = 0.002) or lead international trials (OR: 0.11; 95% CI: 0.01-0.83; P = 0.03). Trials with sex-specific eligibility criteria were more likely to have women leaders (OR: 2.56; 95% CI: 1.19-5.49; P = 0.02) than those without. Discussion: Gender inequalities in RCT leadership and RCT participation exist in nephrology and did not improve over time. Strategies to improve inequalities need to be implemented and evaluated.

Maternal glucose levels and future risk of developing cardiovascular disease: a systematic review and meta-analysis protocol.

INTRODUCTION: Hyperglycaemia during pregnancy has been considered as one of the risk factors for cardiovascular diseases (CVDs) among women. Although the evidence regarding the association between gestational diabetes mellitus (GDM) and subsequent CVD has been synthesised, there are no systematic reviews covering the evidence of the association among the non-GDM population. This systematic review and meta-analysis, therefore, aim to fill the gap by summarising existing evidence on the association between maternal glucose levels and the risk of future CVD in pregnant women with or without a diagnosis of GDM. METHODS AND ANALYSIS: This systematic review protocol was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines. Comprehensive literature searches were performed in the following electronic databases: MEDLINE, EMBASE and CINAHL to identify relevant papers from inception to 31 December 2022. All observational studies (case-control studies, cohort studies and cross-sectional studies) will be included. Two reviewers will perform the abstract and full-text screening based on the eligibility criteria through Covidence. The Newcastle-Ottawa Scale will be used to assess the methodological quality of included studies. Statistical heterogeneity will be assessed by using the I2 test and Cochrane's Q test. If the included studies are found to be homogeneous, pooled estimates will be calculated and meta-analysis will be performed using Review Manager 5 (RevMan) software. Random effects will be used to determine weights for meta-analysis, if needed. Pre-specified subgroup analysis and sensitivity analysis will be performed, if needed. The study results will be presented in the sequence of main outcomes, secondary outcomes and important subgroup analysis for each type of glucose level separately. ETHICS AND DISSEMINATION: Given no original data will be collected, ethics approval is not applicable for this review. The results of this review will be disseminated by publication and conference presentation. PROSPERO REGISTRATION NUMBER: CRD42022363037.

Prevalence of antimicrobial resistance and its clinical implications in Ethiopia: a systematic review.

BACKGROUND: Antimicrobial resistance is one of the major public health challenges in Ethiopia. However, there is no comprehensive summary of existing AMR data in the country. AIM: To determine the prevalence of antimicrobial resistance and its clinical implications in Ethiopia. METHODS: A systematic literature search was performed on the PubMed/Medline database. Original studies on antimicrobial resistance conducted in Ethiopia between 1st January 2009 and 31st July 2019 were included. The outcome measure was the number of isolates resistant to antimicrobial agents in terms of specific pathogens, and disease condition. Data was calculated as total number of resistant isolates relative to the total number of isolates per specific pathogen and medication. RESULTS: A total of 48,021 study participants enrolled from 131 original studies were included resulting in 15,845 isolates tested for antimicrobial resistance. The most common clinical sample sources were urine (28%), ear, nose, and throat discharge collectively (27%), and blood (21%). All the studies were cross-sectional and 83% were conducted in hospital settings. Among Gram-positive bacteria, the reported level of resistance to vancomycin ranged from 8% (Enterococcus species) to 20% (S. aureus). E. coli, K. pneumoniae and P. aeruginosa were the most common Gram-negative pathogens resistant to key antimicrobial agents described in the national standard treatment guideline and were associated with diverse clinical conditions: urinary tract infections, diarrhea, surgical site infections, pneumonia, ocular infections, and middle ear infections. CONCLUSION: Overall, there is a high prevalence of antimicrobial resistance in Ethiopia. Empirical treatment of bacterial infections needs to be guided by up-to-date national guidelines considering local antimicrobial susceptibility patterns. Equipping diagnostic laboratories with culture and drug susceptibility testing facilities, and establishing a strong antimicrobial stewardship program should be high priorities.

Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study.

Network meta-analysis (NMA) has emerged as a useful analytical tool allowing comparison of multiple treatments based on direct and indirect evidence. Commonly, a hierarchical Bayesian NMA model is used, which allows rank probabilities (the probability that each treatment is best, second best, and so on) to be calculated for decision making. However, the statistical properties of rank probabilities are not well understood. This study investigates how rank probabilities are affected by various factors such as unequal number of studies per comparison in the network, the sample size of individual studies, the network configuration, and effect sizes between treatments. In order to explore these factors, a simulation study of four treatments (three equally effective treatments and one less effective reference) was conducted. The simulation illustrated that estimates of rank probabilities are highly sensitive to both the number of studies per comparison and the overall network configuration. An unequal number of studies per comparison resulted in biased estimates of treatment rank probabilities for every network considered. The rank probability for the treatment that was included in the fewest number of studies was biased upward. Conversely, the rank of the treatment included in the most number of studies was consistently underestimated. When the simulation was altered to include three equally effective treatments and one superior treatment, the hierarchical Bayesian NMA model correctly identified the most effective treatment, regardless of all factors varied. The results of this study offer important insight into the ability of NMA models to rank treatments accurately under several scenarios. The authors recommend that health researchers use rank probabilities cautiously in making important decisions.

Network meta-analysis using R: a review of currently available automated packages.

Network meta-analysis (NMA)--a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously--has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.