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

Competition for recruitment in SARS-CoV-2 Trials in the United States: a longitudinal cohort analysis

OBJECTIVE

Competition among trials for patient enrollment can impede recruitment. We hypothesized that this occurred early in the COVID-19 pandemic, when an unprecedented number of clinical trials were launched. We performed a simple and multivariable regression analysis evaluating the relationship between the proportion of SARS-CoV-2 investigational trial sites within each USA state with unsuccessful patient-participant recruitment and: (i) the proportion of cases required to reach state recruitment goals; (ii) state population based on data from the US Census; and, (iii) number of trial sites per state.

RESULTS

Our study included 151 clinical trials. The proportion of trials with successful recruitment was 72.19% (109 of 151 trials). We did not find a significant relationship between unsuccessful patient-participant recruitment, state recruitment goals, state population or the number of trial sites per state in both our simple and multivariable regression analyses. Our results do not suggest that early in the COVID-19 pandemic, competition for patient-participants impeded successful recruitment in SARS-CoV-2 trials. This may reflect the unique circumstances of the first few months of the pandemic in the United States, in which the number and location of SARS-CoV-2 cases was sufficient to meet trial recruitment requirements, despite the large number of trials launched.

Identification of the ideal recruitment situation in pandemic research: learning from the RECOVERY trial in Northern England: a qualitative study

Background

In early 2020, little was known about treatments for COVID-19. The UK responded by initiating a call for research, leading to the formation of the National Institute for Health Research (NIHR) Urgent Public Health (UPH) group. Fast-track approvals were initiated and support was offered to research sites via the NIHR. The Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial was designated UPH. High recruitment rates were required for timely results. Recruitment rates were inconsistent across different hospitals and places.

Purpose

The Factors Affecting Recruitment to the RECOVERY trial study was designed to seek out the facilitators and barriers to recruitment across a population of 3 million served by eight different hospitals and suggest recommendations for recruitment to UPH research during a pandemic situation.

Methods

A qualitative grounded theory study using situational analysis was used. This included a contextualisation of each recruitment site containing prepandemic operational status, prior research activity, COVID-19 admission rates and UPH activity. Additionally, one-to-one interviews using topic guides were completed with NHS staff involved in the RECOVERY trial. Analysis sought out the narratives that shaped recruitment activity.

Results

An ideal recruitment situation was identified. The closer sites were able to move towards that ideal situation, the easier they found it to implement the most significant factor on recruitment: embedding research recruitment into standard care. The ability to move to the ideal recruitment situation was mediated by five significant elements: uncertainty, prioritisation, leadership, engagement and communication.

Conclusion

Embedding recruitment into routine clinical care was the most influential factor on recruitment to the RECOVERY trial. To enable this, sites needed to attain the ideal recruitment situation. Prior research activity, size of site and regulator grading did not correlate with high recruitment rates. Research should be at the forefront of prioritisation during future pandemics.

Indicators of recent COVID-19 infection status: findings from a large occupational cohort of staff and postgraduate research students from a UK university

BACKGROUND

Researchers conducting cohort studies may wish to investigate the effect of episodes of COVID-19 illness on participants. A definitive diagnosis of COVID-19 is not always available, so studies have to rely on proxy indicators. This paper seeks to contribute evidence that may assist the use and interpretation of these COVID-indicators.

METHODS

We described five potential COVID-indicators: self-reported core symptoms, a symptom algorithm; self-reported suspicion of COVID-19; self-reported external results; and home antibody testing based on a 'lateral flow' antibody (IgG/IgM) test cassette. Included were staff and postgraduate research students at a large London university who volunteered for the study and were living in the UK in June 2020. Excluded were those who did not return a valid antibody test result. We provide descriptive statistics of prevalence and overlap of the five indicators.

RESULTS

Core symptoms were the most common COVID-indicator (770/1882 participants positive, 41%), followed by suspicion of COVID-19 (n = 509/1882, 27%), a positive symptom algorithm (n = 298/1882, 16%), study antibody lateral flow positive (n = 124/1882, 7%) and a positive external test result (n = 39/1882, 2%), thus a 20-fold difference between least and most common. Meeting any one indicator increased the likelihood of all others, with concordance between 65 and 94%. Report of a low suspicion of having had COVID-19 predicted a negative antibody test in 98%, but positive suspicion predicted a positive antibody test in only 20%. Those who reported previous external antibody tests were more likely to have received a positive result from the external test (24%) than the study test (15%).

CONCLUSIONS

Our results support the use of proxy indicators of past COVID-19, with the caveat that none is perfect. Differences from previous antibody studies, most significantly in lower proportions of participants positive for antibodies, may be partly due to a decline in antibody detection over time. Subsequent to our study, vaccination may have further complicated the interpretation of COVID-indicators, only strengthening the need to critically evaluate what criteria should be used to define COVID-19 cases when designing studies and interpreting study results.

Elicitation of quantitative, choice-based preferences for Person-Centered Care among People living with Dementia in comparison to physicians' judgements in Germany: study protocol for the mixed-methods PreDemCare-study

BACKGROUND

Person-Centered-Care (PCC) requires knowledge about patient preferences. Among People-living-with-Dementia (PlwD) data on quantitative, choice-based preferences, which would allow to quantify, weigh and rank patient-relevant elements of dementia-care, and identify most/least preferred choices, are limited. The Analytic-Hierarchy-Process (AHP) may be one approach to elicit quantitative, choice-based preferences with PlwD, due to simple pairwise comparisons of individual criteria from a complex decision-problem, e.g. health care decisions. Furthermore, data on congruence of patient preferences with physicians' judgements for PCC are missing. If patient preferences and physicians' judgements differ, provision of PCC becomes unlikely. An understanding of patient preferences compared to physician's judgements will support the implementation of truly PCC, i.e. state of the art dementia-care aligned with patient preferences.

METHODS

This mixed-methods-study will be based on the results from a previous systematic review and conducted in three phases: (I) literature-based key intervention-categories of PCC will be investigated during qualitative interviews with Dementia-Care-Managers (DCMs) and PlwD to identify actually patient-relevant (sub) criteria of PCC; (II) based on findings from phase I, an AHP-survey will be designed and pre-tested for face- and content-validity, and consistency during face-to-face "thinking-aloud"-interviews with PlwD and two expert panels (DCMs and physicians); (III) the developed survey will elicit patient preferences and physicians' judgements for PCC. To assess individual importance weights for (sub) criteria in both groups, the Principal-Eigenvector-Method will be applied. Weights will be aggregated per group by Aggregation-of-Individual-Priorities-mode. Descriptive and interferential statistical analyses will be conducted to assess congruence of importance-weights between groups. Subgroup-analyses shall investigate participant-heterogeneities, sensitivity of AHP-results shall be tested by inclusion/exclusion of inconsistent respondents.

DISCUSSION

Little research is published on quantitative, choice-based preferences in dementia care. We expect that (1) PlwD have preferences and can express these, (2) that the AHP is a suitable technique to elicit quantitative, choice-based preferences among PlwD, and (3) to identify a divergence between patient preferences and physicians' judgements for PCC. With the help of the AHP-technique, which supports systematic decision-making including multiple criteria, it may be possible to involve PlwD in future care decisions (patient participation) and ensure implementation of truly Person-Centered-Dementia-Care.

TRIAL REGISTRATION

Approval of the study was granted by the Ethics Committee at the University Medicine Greifswald the 09Apr2021 (Reg.-Nr.: BB 018-21, BB 018-21a, BB 018-21b).

How informative were early SARS-CoV-2 treatment and prevention trials? a longitudinal cohort analysis of trials registered on ClinicalTrials.gov

Background

Early in the SARS-CoV-2 pandemic, commentators warned that some COVID trials were inadequately conceived, designed and reported. Here, we retrospectively assess the prevalence of informative COVID trials launched in the first 6 months of the pandemic.

Methods

Based on prespecified eligibility criteria, we created a cohort of Phase 1/2, Phase 2, Phase 2/3 and Phase 3 SARS-CoV-2 treatment and prevention efficacy trials that were initiated from 2020-01-01 to 2020-06-30 using ClinicalTrials.gov registration records. We excluded trials evaluating behavioural interventions and natural products, which are not regulated by the U.S. Food and Drug Administration (FDA). We evaluated trials on 3 criteria of informativeness: potential redundancy (comparing trial phase, type, patient-participant characteristics, treatment regimen, comparator arms and primary outcome), trials design (according to the recommendations set-out in the May 2020 FDA guidance document on SARS-CoV-2 treatment and prevention trials) and feasibility of patient-participant recruitment (based on timeliness and success of recruitment).

Results

We included all 500 eligible trials in our cohort, 58% of which were Phase 2 and 84.8% were directed towards the treatment of SARS-CoV-2. Close to one third of trials met all three criteria and were deemed informative (29.9% (95% Confidence Interval 23.7–36.9)). The proportion of potentially redundant trials in our cohort was 4.1%. Over half of the trials in our cohort (56.2%) did not meet our criteria for high quality trial design. The proportion of trials with infeasible patient-participant recruitment was 22.6%.

Conclusions

Less than one third of COVID-19 trials registered on ClinicalTrials.gov during the first six months met all three criteria for informativeness. Shortcomings in trial design, recruitment feasibility and redundancy reflect longstanding weaknesses in the clinical research enterprise that were likely amplified by the exceptional circumstances of a pandemic.

General practice attendances among patients attending a post-COVID-19 clinic: a pilot study

Background

About 10–35% of people with COVID-19 need medical care within 3 weeks of infection. However, the prevalence of ongoing care needs among those experiencing severe COVID-19 illness is unclear.

Aim

This pilot study aimed to address this knowledge gap by examining GP attendance trends among patients attending a post-COVID-19 hospital follow-up clinic, 3–6 months after an initial clinic visit.

Design & setting

Data were collected from adult patients attending a post-COVID-19 follow-up clinic at the Mater Misericordiae University Hospital (MMUH), Dublin, Ireland.

Method

Participants completed questionnaires outlining their demographics; medical histories; emergency hospital admissions and readmissions where applicable; and, where relevant, GP attendances following hospital discharge. Analyses were conducted using descriptive and inferential statistics.

Results

Participants’ (n = 153) median age was 43.5 years (interquartile range [IQR] = 30.9–52.1 years). There were 105 females (68.6%, 95% confidence interval [CI] = 61.3% to 75.9%). Various medical histories were reported among participants. Sixty-seven (43.2%, 95% CI = 35.9% to 51.6%) received emergency COVID-19 hospital care. Older adults, males, intensive care unit [ICU] admissions, and readmissions were common among hospital attendees. Of the hospital attendees, 16 (24%, 95% CI = 13.7% to 34.2%) attended GPs within 7 days of hospital discharge, and 26 (39%, 95% CI = 27.3% to 50.7%) within 30 days. Older adults, people with pre-existing medical conditions, and individuals admitted to ICU and/or readmitted to hospital were common among general practice attendees.

Conclusion

Persistent health issues appear to be common among patients with severe COVID-19, particularly those who are older adults, have pre-existing health problems, and who had been in ICU and/or readmission care. Larger scale studies of ongoing COVID-19 care needs in primary care and general practice are required.

Clinical Trial Data Sharing for COVID-19-Related Research

This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.