Renewed rationale for sex- and gender-disaggregated research: A COVID-19 commentary review

The COVID-19 pandemic provides a contemporaneous illustration of the need to consider sex and gender in research. Using surveillance, treatment and vaccine research examples, in this commentary review, we highlight opportunities for innovation in sex- and gender-sensitive and transformative health and medical research

Adaptive designs in critical care trials: a simulation study

BACKGROUND: Adaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation. METHODS: Using two large critical care trials (ADRENAL. CLINICALTRIALS: gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR. CLINICALTRIALS: gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05. RESULTS: Across all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased. CONCLUSIONS: This study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method

New sepsis definition changes incidence of sepsis in the intensive care unit

Sepsis lacks pathognomonic clinical features and a definitive biochemical or histological diagnostic test. As a result, since 1992, diagnosis of sepsis has been based on the presence of two or more of the criteria characterising the systemic inflammatory response syndrome (SIRS) (Table 1) arising from suspected or proven infection. In response to data questioning this construct, new criteria redefining sepsis, based on the Sequential Organ Failure Assessment (SOFA) score, have been proposed: Sepsis-3 (Table 1). The epidemiological and clinical implications of adopting these new criteria are currently unknown. We aimed to estimate the impact of adopting SOFA-based diagnostic criteria for sepsis on the diagnosis and apparent mortality of sepsis in Australian and New Zealand intensive care units