Participant Diversity in United States Randomized Controlled Trials of Antibacterials for Staphylococcus aureus Infections, 2000-2021.

BACKGROUND: Equitable representation of members from historically marginalized groups is important in clinical trials, which inform standards of care. The goal of this study was to characterize the demographics and proportional subgroup reporting and representation of participants enrolled in randomized controlled trials (RCTs) of antibacterials used to treat Staphylococcus aureus infections. METHODS: We examined randomized controlled registrational and strategy trials published from 2000 to 2021 to determine the sex, race, and ethnicity of participants. Participant to incidence ratios (PIRs) were calculated by dividing the percentage of study participants in each demographic group by the percentage of the disease population in each group. Underrepresentation was defined as a PIR < 0.8. RESULTS: Of the 87 included studies, 82 (94.2%) reported participant sex, 69 (79.3%) reported participant race, and 20 (23.0%) included ethnicity data. Only 17 (19.5%) studies enrolled American Indian/Alaskan Native participants. Median PIRs indicated that Asian and Black participants were underrepresented in RCTs compared with the incidence of methicillin-resistant S. aureus infections in these subgroups. Underrepresentation of Black participants was associated with a larger study size, international sites, industry sponsorship, and phase 2/3 trials compared with phase 4 trials (P < .05 for each). Black participants had more than 4 times the odds of being underrepresented in phase 2/3 trials compared with phase 4 trials (odds ratio, 4.57; 95% confidence interval: 1.14-18.3). CONCLUSIONS: Standardized reporting methods for race and ethnicity and efforts to increase recruitment of marginalized groups would help ensure equity, rigor, and generalizability in RCTs of antibacterial agents and reduce health inequities.

An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.

BACKGROUND: The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. RESULTS: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. CONCLUSIONS: Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.

Species-level assessment of the molecular basis of fluoroquinolone resistance among viridans group streptococci causing bacteraemia in cancer patients.

Viridans group streptococci (VGS) are a major cause of bacteraemia in neutropenic cancer patients, particularly those receiving fluoroquinolone prophylaxis. In this study, we sought to understand the molecular basis for fluoroquinolone resistance in VGS causing bacteraemia in cancer patients by assigning 115 VGS bloodstream isolates to specific species using multilocus sequence analysis (MLSA), by sequencing the quinolone resistance-determining regions (QRDRs) of gyrA, gyrB, parC and parE, and by testing strain susceptibility to various fluoroquinolones. Non-susceptibility to one or more fluoroquinolones was observed for 78% of isolates, however only 68.7% of patients were receiving fluoroquinolone prophylaxis. All but one of the determinative QRDR polymorphisms occurred in GyrA or ParC, yet the pattern of determinative QRDR polymorphisms was significantly associated with the fluoroquinolone prophylaxis received. By combining MLSA and QRDR data, multiple patients infected with genetically indistinguishable fluoroquinolone-resistant Streptococcus mitis or Streptococcus oralis strains were discovered. Together these data delineate the molecular mechanisms of fluoroquinolone resistance in VGS isolates causing bacteraemia and suggest possible transmission of fluoroquinolone-resistant S. mitis and S. oralis isolates among cancer patients.