Thromboxane biosynthesis and future events in diabetes: the ASCEND trial.

BACKGROUND AND AIMS: Thromboxane (TX) A2, released by activated platelets, plays an important role in atherothrombosis. Urinary 11-dehydro-TXB2 (U-TXM), a stable metabolite reflecting the whole-body TXA2 biosynthesis, is reduced by ∼70% by daily low-dose aspirin. The U-TXM represents a non-invasive biomarker of in vivo platelet activation and is enhanced in patients with diabetes. This study assessed whether U-TXM is associated with the risk of future serious vascular events or revascularizations (SVE-R), major bleeding, or cancer in patients with diabetes. METHODS: The U-TXM was measured pre-randomization to aspirin or placebo in 5948 people with type 1 or 2 diabetes and no cardiovascular disease, in the ASCEND trial. Associations between log U-TXM and SVE-R (n = 618), major bleed (n = 206), and cancer (n = 700) during 6.6 years of follow-up were investigated by Cox regression; comparisons of these associations with the effects of randomization to aspirin were made. RESULTS: Higher U-TXM was associated with older age, female sex, current smoking, type 2 diabetes, higher body size, urinary albumin/creatinine ratio of ≥3 mg/mmol, and higher estimated glomerular filtration rate. After adjustment for these, U-TXM was marginally statistically significantly associated with SVE-R and major bleed but not cancer [hazard ratios per 1 SD higher log U-TXM (95% confidence interval): 1.09 (1.00-1.18), 1.16 (1.01-1.34), and 1.06 (0.98-1.14)]. The hazard ratio was similar to that implied by the clinical effects of randomization to aspirin for SVE-R but not for major bleed. CONCLUSIONS: The U-TXM was log-linearly independently associated with SVE-R in diabetes. This is consistent with the involvement of platelet TXA2 in diabetic atherothrombosis.

Towards an understanding of the ethics of electronic consent in clinical trials.

There are good practical reasons to use electronic consent (e-consent) in randomised trials, especially when conducting large-scale clinical trials to answer population-level health research questions. However, determining ethical reasons for e-consent is not so clear and depends on a proper understanding of what e-consent means when used in clinical trials and its ethical significance. Here we focus on four features of ethical significance which give rise to a range of ethical considerations relating to e-consent and merit further focused ethics research.

Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis.

BACKGROUND: Ascertainment of heart failure (HF) hospitalizations in cardiovascular trials is costly and complex, involving processes that could be streamlined by using routinely collected healthcare data (RCD). The utility of coded RCD for HF outcome ascertainment in randomized trials requires assessment. We systematically reviewed studies assessing RCD-based HF outcome ascertainment against "gold standard" (GS) methods to study the feasibility of using such methods in clinical trials. METHODS: Studies assessing International Classification of Disease (ICD) coded RCD-based HF outcome ascertainment against GS methods and reporting at least one agreement statistic were identified by searching MEDLINE and Embase from inception to May 2021. Data on study characteristics, details of RCD and GS data sources and definitions, and test statistics were reviewed. Summary sensitivities and specificities for studies ascertaining acute and prevalent HF were estimated using a bivariate random effects meta-analysis. Heterogeneity was evaluated using I2 statistics and hierarchical summary receiver operating characteristic (HSROC) curves. RESULTS: A total of 58 studies of 48,643 GS-adjudicated HF events were included in this review. Strategies used to improve case identification included the use of broader coding definitions, combining multiple data sources, and using machine learning algorithms to search free text data, but these methods were not always successful and at times reduced specificity in individual studies. Meta-analysis of 17 acute HF studies showed that RCD algorithms have high specificity (96.2%, 95% confidence interval [CI] 91.5-98.3), but lacked sensitivity (63.5%, 95% CI 51.3-74.1) with similar results for 21 prevalent HF studies. There was considerable heterogeneity between studies. CONCLUSIONS: RCD can correctly identify HF outcomes but may miss approximately one-third of events. Methods used to improve case identification should also focus on minimizing false positives.

Clinical trial results in context: comparison of baseline characteristics and outcomes of 38,510 RECOVERY trial participants versus a reference population of 346,271 people hospitalised with COVID-19 in England.

BACKGROUND: Randomised trials are essential to reliably assess medical interventions. Nevertheless, interpretation of such studies, particularly when considering absolute effects, is enhanced by understanding how the trial population may differ from the populations it aims to represent. METHODS: We compared baseline characteristics and mortality of RECOVERY participants recruited in England (n = 38,510) with a reference population hospitalised with COVID-19 in England (n = 346,271) from March 2020 to November 2021. We used linked hospitalisation and mortality data for both cohorts to extract demographics, comorbidity/frailty scores, and crude and age- and sex-adjusted 28-day all-cause mortality. RESULTS: Demographics of RECOVERY participants were broadly similar to the reference population, but RECOVERY participants were younger (mean age [standard deviation]: RECOVERY 62.6 [15.3] vs reference 65.7 [18.5] years) and less frequently female (37% vs 45%). Comorbidity and frailty scores were lower in RECOVERY, but differences were attenuated after age stratification. Age- and sex-adjusted 28-day mortality declined over time but was similar between cohorts across the study period (RECOVERY 23.7% [95% confidence interval: 23.3-24.1%]; vs reference 24.8% [24.6-25.0%]), except during the first pandemic wave in the UK (March-May 2020) when adjusted mortality was lower in RECOVERY. CONCLUSIONS: Adjusted 28-day mortality in RECOVERY was similar to a nationwide reference population of patients admitted with COVID-19 in England during the same period but varied substantially over time in both cohorts. Therefore, the absolute effect estimates from RECOVERY were broadly applicable to the target population at the time but should be interpreted in the light of current mortality estimates. TRIAL REGISTRATION: ISRCTN50189673- Feb. 04, 2020, NCT04381936- May 11, 2020.

Patient and public involvement and engagement in the ASCEND PLUS trial: reflections from the design of a streamlined and decentralised clinical trial.

INTRODUCTION: ASCEND PLUS is a randomised controlled trial assessing the effects of oral semaglutide on the primary prevention of cardiovascular events in around 20,000 individuals with type 2 diabetes in the UK. The trial's innovative design includes a decentralised direct-to-participant invitation, recruitment, and follow-up model, relying on self-completion of online forms or telephone or video calls with research nurses, with no physical sites. Extensive patient and public involvement and engagement (PPIE) was essential to the design and conduct of ASCEND PLUS. AIM: To report the process and conduct of PPIE activity in ASCEND PLUS, evaluate effects on trial design, reflect critically on successes and aspects that could have been improved, and identify themes and learning relevant to implementation of PPIE in future trials. METHODS: PPIE activity was coordinated centrally and included six PPIE focus groups and creation of an ASCEND PLUS public advisory group (PAG) during the design phase. Recruitment to these groups was carefully considered to ensure diversity and inclusion, largely consisting of adults living with type 2 diabetes from across the UK. Two members of the PAG also joined the trial Steering Committee. Steering Committee meetings, focus groups, and PAG meetings were conducted online, with two hybrid workshops to discuss PPIE activity and aspects of the trial. RESULTS: PPIE activity was critical to shaping the design and conduct of ASCEND PLUS. Key examples included supporting choice for participants to either complete the screening/consent process independently online, or during a telephone or video call interview with a research nurse. A concise 'initial information leaflet' was developed to be sent with the initial invitations, with the 'full' information leaflet sent later to those interested in joining the trial. The PAG reviewed the content and format of participant- and public-facing materials, including written documents, online screening forms, animated videos, and the trial website, to aid clarity and accessibility, and provided input into the choice of instruments to assess quality of life. CONCLUSIONS: PPIE is integral in ASCEND PLUS and will continue throughout the trial. This involvement has been critical to optimising the trial design, successfully obtaining regulatory and ethical approval, and conducting the trial.

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome.

Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials.

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS. METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status. DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.