How achievable are COVID-19 clinical trial recruitment targets? A UK observational cohort study and trials registry analysis.

OBJECTIVES: To analyse enrolment to interventional trials during the first wave of the COVID-19 pandemic in England and describe the barriers to successful recruitment in the circumstance of a further wave or future pandemics. DESIGN: We analysed registered interventional COVID-19 trial data and concurrently did a prospective observational study of hospitalised patients with COVID-19 who were being assessed for eligibility to one of the RECOVERY, C19-ACS or SIMPLE trials. SETTING: Interventional COVID-19 trial data were analysed from the clinicaltrials.gov and International Standard Randomized Controlled Trial Number databases on 12 July 2020. The patient cohort was taken from five centres in a respiratory National Institute for Health Research network. Population and modelling data were taken from published reports from the UK government and Medical Research Council Biostatistics Unit. PARTICIPANTS: 2082 consecutive admitted patients with laboratory-confirmed SARS-CoV-2 infection from 27 March 2020 were included. MAIN OUTCOME MEASURES: Proportions enrolled, and reasons for exclusion from the aforementioned trials. Comparisons of trial recruitment targets with estimated feasible recruitment numbers. RESULTS: Analysis of trial registration data for COVID-19 treatment studies enrolling in England showed that by 12 July 2020, 29 142 participants were needed. In the observational study, 430 (20.7%) proceeded to randomisation. 82 (3.9%) declined participation, 699 (33.6%) were excluded on clinical grounds, 363 (17.4%) were medically fit for discharge and 153 (7.3%) were receiving palliative care. With 111 037 people hospitalised with COVID-19 in England by 12 July 2020, we determine that 22 985 people were potentially suitable for trial enrolment. We estimate a UK hospitalisation rate of 2.38%, and that another 1.25 million infections would be required to meet recruitment targets of ongoing trials. CONCLUSIONS: Feasible recruitment rates, study design and proliferation of trials can limit the number, and size, that will successfully complete recruitment. We consider that fewer, more appropriately designed trials, prioritising cooperation between centres would maximise productivity in a further wave.

A patient decision aid to support shared decision-making on anti-thrombotic treatment of patients with atrial fibrillation: randomised controlled trial.

OBJECTIVE: To determine the efficacy of a computerised decision aid in patients with atrial fibrillation making decisions on whether to take warfarin or aspirin therapy. DESIGN: Two-armed open exploratory randomised controlled trial. SETTING: Two research clinics deriving participants from general practices in Northeast England. PARTICIPANTS: 109 patients with atrial fibrillation aged over 60. INTERVENTIONS: Computerised decision aid applied in shared decision-making clinic compared to evidence-based paper guidelines applied as direct advice. MAIN OUTCOME MEASURES: Primary outcome measure was the decision conflict scale. Secondary outcome measures included anxiety, knowledge, decision-making preference, treatment decision, use of primary and secondary care services and health outcomes. RESULTS: Decision conflict was lower in the computerised decision aid group immediately after the clinic; mean difference -0.18 (95% CI -0.34 to -0.01). Participants in this group not already on warfarin were much less likely to start warfarin than those in the guidelines arm (4/16, 25% compared to the guidelines group 15/16, 93.8%, RR 0.27, 95% CI 0.11 to 0.63). CONCLUSIONS: Decision conflict was lower immediately following the use of a computerised decision aid in a shared decision-making consultation than immediately following direct doctor-led advice based on paper guidelines. Furthermore, participants in the computerised decision aid group were significantly much less likely to start warfarin than those in the guidelines arm. The results show that such an approach has a positive impact on decision conflict comparable to other studies of decision aids, but also reduces the uptake of a clinically effective treatment that may have important implications for health outcomes.

Risk factors for first-ever stroke in older people in the north East of England: a population-based study.

BACKGROUND AND PURPOSE: Although older people potentially have most to gain from prevention, they have been excluded from or underrepresented in many stroke incidence studies. We sought to determine the risk factors for stroke in older people. METHODS: A 5-year follow-up study of a population-based cohort of 4440 subjects aged >65 years in northern England. Subjects were recruited from 27 general practices between 1995 and 1997. RESULTS: A total of 329 out of 4351 subjects with follow-up data suffered a first-ever stroke. On multivariate analysis, risk factors for stroke in older people included atrial fibrillation (hazard ratio [HR], 2.03 [95%CI, 1.31 to 3.16]); previous transient ischemic attack (1.87 [95% CI, 1.27 to 2.76]); smoking (1.72 [95% CI,1.28 to 2.32]); and cardiovascular disease (1.55 [95% CI, 1.19 to 2.03]). The HR per 10-mm Hg increase in systolic blood pressure was 1.15 (95% CI, 1.06 to 1.24). Age was associated with a HR of 1.74 (95% CI, 1.42 to 2.12) per 10-year increase. CONCLUSIONS: "Classic risk factors" increase the risk of stroke in older people. Stroke is not an inevitable consequence of aging, so by identifying and modifying risk factors in older people there are opportunities to reduce the incidence and mortality of this devastating condition.

Development and description of a decision analysis based decision support tool for stroke prevention in atrial fibrillation.

BACKGROUND: There is an increasing move towards clinical decision making that engages the patient, which has led to the development and use of decision aids to support better decisions. The treatment of patients in atrial fibrillation (AF) with warfarin to prevent stroke is a decision that is sensitive to patient preferences as shown by a previous decision analysis. AIM: To develop a computerised decision support tool, building upon a previous decision analysis, which would engage individual patient preferences in reaching a shared decision on whether to take warfarin to prevent stroke. METHODS: The development process had two main phases: (1) the development phase which employed focus groups and repeated interviews with GPs/practice nurses and patients alongside an iterative development of a computerised tool; (2) the training and testing phase in which GPs and practice nurses underwent training in the use of the tool, including the use of simulated patients. The tool was then used in a feasibility study in a small number of patients with AF to inform the design of a subsequent randomised controlled trial. RESULTS: The prototype tool had three components: (1) derivation of an individual patient's values for relevant health states using a standard gamble; (2) presentation/discussion of a patient's risks of stroke using the Framingham equation and the benefits/risks of warfarin from a systematic literature review; and (3) decision making component incorporating the outcome of a Markov decision analysis model. Older patients could be taken through the decision analysis based computerised tool, and patients and clinicians welcomed information on risks and benefits of treatments. The tool required time and training to use. Patients' decisions in the feasibility phase did not necessarily coincide with the output of the decision analysis model, but decision conflict appeared to be reduced and both patients and GPs were satisfied with the process. CONCLUSIONS: It is feasible to develop a decision analysis based computer software package that is acceptable to elderly patients and clinicians, but it requires time and expertise to use. It is most likely that a tool of this type will best be used by a small number of clinicians who have developed experience of its use and can maintain their skills.