Dataset Information

Equity in vaccine trials for higher weight people? Protocol for a rapid review of inclusion and exclusion criteria for higher weight people in clinical trials for COVID-19.

ABSTRACT:

Introduction

Vaccination is a public health strategy that aims to reduce the burden of viral illness, especially important for populations known or likely to be at increased risk for inequitable outcomes due to the disease itself or disparities in care accessed and received. The role of weight status in COVID-19 susceptibility and disease burden remains unclear. Despite this, higher weight is frequently described as a definitive risk factor for both susceptibility and disease severity. Therefore, COVID-19 vaccine trials should recruit a study group representative of the full weight spectrum, and undertake appropriate subgroup analysis by weight status to evaluate response and titrate dose regimes where indicated to ensure equitable outcomes for higher weight people.

Methods and analysis

We aim to review inclusion and exclusion criteria of clinical trial protocols registered with ClinicalTrials.gov, ISRCTN Register, the WHO official vaccine trial register, and 'The COVID-19 Vaccine Tracker'. To determine the number of trials including higher weight (body mass index >30 kg/m²) individuals and the number of trials conducting efficacy subgroup analyses by weight status. Screening, data extraction and quality appraisal of trial protocols will be completed independently by a minimum of two reviewers. Clinical trials will be assessed for risk of bias using the Risk of Bias-2 tool. We will conduct a descriptive analysis of extracted data. The following subsets are proposed: participation of higher weight people in COVID-19 vaccine trials by trial phase, country and vaccine platform.

Ethics and dissemination

Ethical approval was not required for this review. The results of this rapid review will be presented at appropriate conferences and published in a suitable peer reviewed journal.

Prospero registration number

CRD42020226573.

SUBMITTER: Campbell J

PROVIDER: S-EPMC8154291 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The coronavirus disease 2019 (COVID-19) pandemic that has been ongoing since 2019 is still ongoing and how to control it is one of the international issues to be addressed. Antiviral drugs that reduce the viral load in terms of reducing the risk of secondary infection are important. For the general control of emerging infectious diseases, establishing an efficient method to evaluate candidate therapeutic agents will lead to a rapid response. We evaluated clinical trial designs for viral entry inhibitors that have the potential to be effective pre-exposure prophylactic drugs in addition to reducing viral load after infection. We used a previously developed simulation of clinical trials based on a mathematical model of within-host viral infection dynamics to evaluate sample sizes in clinical trials of viral entry inhibitors against COVID-19. We assumed four measures as outcomes, namely change in log10-transformed viral load from symptom onset, PCR positive ratio, log10-transformed viral load, and cumulative viral load, and then sample sizes were calculated for drugs with 99 % and 95 % antiviral efficacy. Consistent with previous results, we found that sample sizes could be dramatically reduced for all outcomes used in an analysis by adopting inclusion/exclusion criteria such that only patients in the early post-infection period would be included in a clinical trial. A comparison of sample sizes across outcomes demonstrated an optimal measurement schedule associated with the nature of the outcome measured for the evaluation of drug efficacy. In particular, the sample sizes calculated from the change in viral load and from viral load tended to be small when measurements were taken at earlier time points after treatment initiation. For the cumulative viral load, the sample size was lower than that from the other outcomes when the stricter inclusion/exclusion criteria to include patients whose time since onset is earlier than 2 days was used. We concluded that the design of efficient clinical trials should consider the inclusion/exclusion criteria and measurement schedules, as well as outcome selection based on sample size, personnel and budget needed to conduct the trial, and the importance of the outcome regarding the medical and societal requirements. This study provides insights into clinical trial design for a variety of situations, especially addressing infectious disease prevalence and feasible trial sizes. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Project description:BACKGROUND:Low back pain is a common health complaint resulting in substantial economic burden. Each year, upwards of 20 randomised controlled trials (RCTs) evaluating interventions for non-specific low back pain are published. Use of the term non-specific low back pain has been criticised on the grounds of encouraging heterogeneity and hampering interpretation of findings due to possible heterogeneous causes, challenging meta-analyses. We explored selection criteria used in trials of treatments for nsLBP. METHODS:A systematic review of English-language reports of RCTs in nsLBP population samples, published between 2006 and 2012, identified from MEDLINE, EMBASE, and the Cochrane Library databases, using a mixed-methods approach to analysis. Study inclusion and exclusion criteria were extracted, thematically categorised, and then descriptive statistics were used to summarise the prevalence by emerging category. RESULTS:We included 168 studies. Two inclusion themes (anatomical area, and symptoms and signs) were identified. Anatomical area was most reported as between costal margins and gluteal folds (n =?8, 5%), while low back pain (n =?150, 89%) with or without referred leg pain (n =?27, 16%) was the most reported symptom. Exclusion criteria comprised 21 themes. Previous or scheduled surgery (n =?84, 50%), pregnancy (n =?81, 48%), malignancy (n =?78, 46%), trauma (n =?63, 37%) and psychological conditions (n =?58, 34%) were the most common. Sub-themes of exclusion criteria mostly related to neurological signs and symptoms: nerve root compromise (n =?44, 26%), neurological signs (n =?34, 20%) or disc herniation (n =?30, 18%). Specific conditions that were most often exclusion criteria were spondylolisthesis (n =?35, 21%), spinal stenosis (n =?31, 18%) or osteoporosis (n =?27, 16%). CONCLUSION:RCTs of interventions for non-specific low back pain have incorporated diverse inclusion and exclusion criteria. Guidance on standardisation of inclusion and exclusion criteria for nsLBP trials will increase clinical homogeneity, facilitating greater interpretation of between-trial comparisons and meta-analyses. We propose a template for reporting inclusion and exclusion criteria.

Project description:Clinical trials are essential parts of a medical study process, but studies are often cancelled due to a lack of participants. Clinical Trial Recruitment Support Systems are systems that help to increase the number of participants by seeking more suitable subjects. The software ATLAS (developed by Observational Health Data Sciences and Informatics) can support the launch of a clinical trial by building cohorts of patients who fulfill certain criteria. The correct use of medical classification systems aiming at clearly defined inclusion and exclusion criteria in the studies is an important pillar of this software. The aim of this investigation was to determine whether ATLAS can be used in a Clinical Trial Recruitment Support System to portray the eligibility criteria of clinical studies. Our analysis considered the number of criteria feasible for integration with ATLAS and identified its strengths and weaknesses. Additionally, we investigated whether nonrepresentable criteria were associated with the utilized terminology systems. We analyzed ATLAS using 223 objective eligibility criteria from 30 randomly selected trials conducted in the last 10 years. In the next step, we selected appropriate ICD, OPS, LOINC, or ATC codes to feed the software. We classified each criterion and study based on its implementation capability in the software, ensuring a clear and logical progression of information. Based on our observations, 51% of the analyzed inclusion criteria were fully implemented in ATLAS. Within our selected example set, 10% of the studies were classified as fully portrayable, and 73% were portrayed to some extent. Additionally, we conducted an evaluation of the software regarding its technical limitations and interaction with medical classification systems. To improve and expand the scope of criteria within a cohort definition in a practical setting, it is recommended to work closely with personnel involved in the study to define the criteria precisely and to carefully select terminology systems. The chosen criteria should be combined according to the specific setting. Additional work is needed to specify the significance and amount of the extracted criteria.

Project description:BackgroundControl-arm mortality varies between acute respiratory distress syndrome (ARDS) RCTs.MethodsWe systematically reviewed ARDS RCTs that commenced recruitment after publication of the American-European Consensus (AECC) definition (MEDLINE, Embase, and Cochrane central register of controlled trials; January 1994 to October 2020). We assessed concordance of RCT inclusion criteria to ARDS consensus definitions and whether exclusion criteria are strongly or poorly justified. We estimated the proportion of between-trial difference in control-arm 28-day mortality explained by the inclusion criteria and RCT design characteristics using meta-regression.ResultsA literature search identified 43 709 records. One hundred and fifty ARDS RCTs were included; 146/150 (97.3%) RCTs defined ARDS inclusion criteria using AECC/Berlin definitions. Deviations from consensus definitions, primarily aimed at improving ARDS diagnostic certainty, frequently related to duration of hypoxaemia (117/146; 80.1%). Exclusion criteria could be grouped by rationale for selection into strongly or poorly justified criteria. Common poorly justified exclusions included pregnancy related, age, and comorbidities (infectious/immunosuppression, hepatic, renal, and human immunodeficiency virus/acquired immunodeficiency syndrome). Control-arm 28-day mortality varied between ARDS RCTs (mean: 29.8% [95% confidence interval: 27.0-32.7%; I2=88.8%; τ2=0.02; P<0.01]), and differed significantly between RCTs with different Pao2:FiO2 ratio inclusion thresholds (26.6-39.9 kPa vs <26.6 kPa; P<0.01). In a meta-regression model, inclusion criteria and RCT design characteristics accounted for 30.6% of between-trial difference (P<0.01).ConclusionsIn most ARDS RCTs, consensus definitions are modified to use as inclusion criteria. Between-RCT mortality differences are mostly explained by the Pao2:FiO2 ratio threshold within the consensus definitions. An exclusion criteria framework can be applied when designing and reporting exclusion criteria in future ARDS RCTs.

Project description:PurposeTo develop a statistical approach that compares patient selection strategies across clinical trials and apply this approach to acute ischemic stroke clinical trials to identify the optimal inclusion criteria.MethodsWe developed a statistical approach that compares the number needed to treat to achieve one success (NNT) along with the number needed to screen to achieve one success (NNS) and assesses if there are significant differences in inclusion criteria, treatment course, and clinical outcome among patients that may have been included/excluded in the trials. We applied this approach to the study population from four recent positive acute stroke clinical trials: MR CLEAN, EXTEND-IA, ESCAPE, and SWIFT PRIME, applying published trial criteria to an independent registry of 612 acute stroke patients, since we did not have access to the complete trial data.ResultsAlthough reported NNT were similar for EXTEND-IA, SWIFT PRIME and ESCAPE, and somewhat higher for MR CLEAN, NNS varied across the trials from 21 for EXTEND-IA, 27 for MR CLEAN, to 46 for ESCAPE and 64 for SWIFT PRIME, reflecting less and more stringent inclusion criteria, respectively. Although there were significant differences in imaging biomarkers and other clinical characteristics among patients that may have been included/excluded in the trials, these differences did not translate to significant differences in treatment course or clinical outcomes.ConclusionsOur study proposes a robust statistical approach that can be applied to a larger pooled trial dataset, if made available, to objectively compare across clinical trials and inform inclusion criteria of future trials. Pooled analysis of the acute stroke trial data is needed to determine which imaging biomarker inclusion criteria are critical and which may be relaxed. If this procedure were applied across the pooled trial data, it could decrease costs and refine the design of future trials to be the most efficacious for the greatest number of patients.