Implementation of a fully remote randomized clinical trial with cardiac monitoring

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has challenged researchers performing clinical trials to develop innovative approaches to mitigate infectious risk while maintaining rigorous safety monitoring. Methods In this report we describe the implementation of a novel exclusively remote randomized clinical trial (ClinicalTrials.gov NCT04354428) of hydroxychloroquine and azithromycin for the treatment of the SARS-CoV-2–mediated COVID-19 disease which included cardiovascular safety monitoring. All study activities were conducted remotely. Self-collected vital signs (temperature, respiratory rate, heart rate, and oxygen saturation) and electrocardiographic (ECG) measurements were transmitted digitally to investigators while mid-nasal swabs for SARS-CoV-2 testing were shipped. ECG collection relied on a consumer device (KardiaMobile 6L, AliveCor Inc.) that recorded and transmitted six-lead ECGs via participants’ internet-enabled devices to a central core laboratory, which measured and reported QTc intervals that were then used to monitor safety. Results Two hundred and thirty-one participants uploaded 3245 ECGs. Mean daily adherence to the ECG protocol was 85.2% and was similar to the survey and mid-nasal swab elements of the study. Adherence rates did not differ by age or sex assigned at birth and were high across all reported race and ethnicities. QTc prolongation meeting criteria for an adverse event occurred in 28 (12.1%) participants, with 2 occurring in the placebo group, 19 in the hydroxychloroquine group, and 7 in the hydroxychloroquine + azithromycin group. Conclusions Our report demonstrates that digital health technologies can be leveraged to conduct rigorous, safe, and entirely remote clinical trials.

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The age of randomized clinical trials: three important aspects of randomized clinical trials in cardiovascular pharmacotherapy with examples from lipid, diabetes, and antithrombotic trials

European Heart Journal - Cardiovascular Pharmacotherapy ◽

10.1093/ehjcvp/pvaa126 ◽

2020 ◽

Author(s):

Heinz Drexel ◽

Basil S Lewis ◽

Giuseppe M C Rosano ◽

Christoph H Saely ◽

Gerda Tautermann ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Randomized Clinical Trial ◽

Randomized Clinical Trials ◽

Safety Monitoring ◽

Review Article ◽

Early Termination ◽

Regulatory Aspects ◽

Cardiovascular Pharmacotherapy ◽

Insight Into

Abstract This review article aims to explain the important issues that data safety monitoring boards (DSMB) face when considering early termination of a trial and is specifically addressed to the needs of clinical and research cardiologists. We give an insight into the overall background and then focus on the three principal reasons for stopping trials, i.e. efficacy, futility, and harm. The statistical essentials are also addressed to familiarize clinicians with the key principles. The topic is further highlighted by numerous examples from lipid trials and antithrombotic trials. This is followed by an overview of regulatory aspects, including an insight into industry–investigator interactions. To conclude, we summarize the key elements that are the basis for a decision to stop a randomized clinical trial (RCT).

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Is the future for clinical trials internet-based? A cluster randomized clinical trial

Clinical Trials ◽

10.1191/1740774505cn069oa ◽

2005 ◽

Vol 2 (1) ◽

pp. 72-79 ◽

Cited By ~ 29

Author(s):

Jennifer Litchfield ◽

Jenny Freeman ◽

Henrik Schou ◽

Mark Elsley ◽

Robert Fuller ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Randomized Clinical Trial ◽

The Future ◽

Cluster Randomized

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Melatonin reduces the mortality of severely-infected COVID-19 patients

Melatonin Research ◽

10.32794/mr112500115 ◽

2021 ◽

Vol 4 (4) ◽

pp. 613-616

Author(s):

Dun-Xian Tan ◽

Russel J Reiter

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Mortality Rate ◽

Randomized Clinical Trial ◽

Control Group ◽

Mortality Reduction ◽

Single Center ◽

Open Label ◽

Melatonin Treatment ◽

The World

SARS-CoV-2 has ravaged the population of the world for two years. Scientists have not yet identified an effective therapy to reduce the mortality of severe COVID-19 patients. In a single-center, open-label, randomized clinical trial, it was observed that melatonin treatment lowered the mortality rate by 93% in severely-infected COVID-19 patients compared with the control group (see below). This is seemingly the first report to show such a huge mortality reduction in severe COVID-19 infected individuals with a simple treatment. If this observation is confirmed by more rigorous clinical trials, melatonin could become an important weapon to combat this pandemic.

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Guidelines on how to assess the validity of results presented in subgroup analysis of clinical trials

Revista do Hospital das Clínicas ◽

10.1590/s0041-87812002000200007 ◽

2002 ◽

Vol 57 (2) ◽

pp. 83-88 ◽

Cited By ~ 5

Author(s):

Edson Duarte Moreira ◽

Ezra Susser

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Randomized Clinical Trial ◽

Subgroup Analysis ◽

Observational Studies ◽

Randomized Clinical Trials ◽

Usual Method ◽

Validity Of Results ◽

Results Of Treatment ◽

Trial Participants

In observational studies, identification of associations within particular subgroups is the usual method of investigation. As an exploratory method, it is the bread and butter of epidemiological research. Nearly everything that has been learned in epidemiology has been derived from the analysis of subgroups. In a randomized clinical trial, the entire purpose is the comparison of the test subjects and the controls, and when there is particular interest in the results of treatment in a certain section of trial participants, a subgroup analysis is performed. These subgroups are examined to see if they are liable to a greater benefit or risk from treatment. Thus, analyzing patient subsets is a natural part of the process of improving therapeutic knowledge through clinical trials. Nevertheless, the reliability of subgroup analysis can often be poor because of problems of multiplicity and limitations in the numbers of patients studied. The naive interpretation of the results of such examinations is a cause of great confusion in the therapeutic literature. We emphasize the need for readers to be aware that inferences based on comparisons between subgroups in randomized clinical trials should be approached more cautiously than those based on the main comparison. That is, subgroup analysis results derived from a sound clinical trial are not necessarily valid; one must not jump to conclusions and accept the validity of subgroup analysis results without an appropriate judgment.

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Extracorporeal Membrane Oxygenation and the Play the Winner Rule

PEDIATRICS ◽

10.1542/peds.76.4.622 ◽

1985 ◽

Vol 76 (4) ◽

pp. 622-623

Author(s):

NIGEL PANETH ◽

SYLVAN WALLENSTEIN

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Extracorporeal Membrane Oxygenation ◽

Randomized Clinical Trial ◽

Conventional Treatment ◽

Randomized Clinical Trials ◽

Ethical Problem ◽

Therapeutic Trial ◽

Membrane Oxygenation ◽

Treatment Comparisons

The therapeutic trial comparing extracorporeal membrane oxygenation with conventional treatment in neonatal respiratory failure reported by Bartlett et al (Pediatrics 1985;76:479-487) uses a method of comparing treatments unlikely to be familiar to most pediatricians. Known as the "randomized play the winner" method, it has thus far been little used in clinical research. Most clinical investigators consider the conventional randomized clinical trial to be the last word in treatment comparisons. But randomized clinical trials are costly, cumbersome, and to some observers less than ideal ethically. The ethical problem arises from the fact that during a "successful" randomized clinical trial (ie, one that demonstrates a significant advantage to one treatment) about half of the trial subjects will receive a treatment which, at the end of the trial, will be known to be inferior.

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Clinical Technologies ◽

10.4018/978-1-60960-561-2.ch609 ◽

2011 ◽

pp. 1738-1758

Author(s):

Tillal Eldabi ◽

Robert D. Macredie ◽

Ray J. Paul

Keyword(s):

Clinical Trial ◽

Decision Making ◽

Clinical Trials ◽

Economic Evaluation ◽

Simulation Model ◽

Randomized Clinical Trial ◽

Simulation Modeling ◽

Randomized Clinical Trials ◽

Healthcare Systems ◽

Modeling Process

This chapter reports on the use of simulation in supporting decision-making about what data to collect in a randomized clinical trial (RCT). We show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” Healthcare systems pose many of the challenges, including difficulty in understanding the system being studied, uncertainty over which data to collect, and problems of communication between problem owners. In this chapter we show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” The experience of developing the simulation model leads us to suggest simple but extremely valuable lessons. The first relates to the inclusion of stakeholders in the modeling process and the accessibility of the resulting models. The ownership and confidence felt by stakeholders in our case is, we feel, extremely important and may provide an example to others developing models.

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Two-stage Bayesian hierarchical modeling for blinded and unblinded safety monitoring in randomized clinical trials

BMC Medical Research Methodology ◽

10.1186/s12874-020-01097-6 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Junhao Liu ◽

Jo Wick ◽

Renee’ H. Martin ◽

Caitlyn Meinzer ◽

Dooti Roy ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Drug Safety ◽

Safety Data ◽

Safety Monitoring ◽

Safety Signal ◽

Two Stage ◽

Bayesian Hierarchical ◽

Potential Safety ◽

Stage 1

Abstract Background Monitoring and reporting of drug safety during a clinical trial is essential to its success. More recent attention to drug safety has encouraged statistical methods development for monitoring and detecting potential safety signals. This paper investigates the potential impact of the process of the blinded investigator identifying a potential safety signal, which should be further investigated by the Data and Safety Monitoring Board with an unblinded safety data analysis. Methods In this paper, two-stage Bayesian hierarchical models are proposed for safety signal detection following a pre-specified set of interim analyses that are applied to efficacy. At stage 1, a hierarchical blinded model uses blinded safety data to detect a potential safety signal and at stage 2, a hierarchical logistic model is applied to confirm the signal with unblinded safety data. Results Any interim safety monitoring analysis is usually scheduled via negotiation between the trial sponsor and the Data and Safety Monitoring Board. The proposed safety monitoring process starts once 53 subjects have been enrolled into an eight-arm phase II clinical trial for the first interim analysis. Operating characteristics describing the performance of this proposed workflow are investigated using simulations based on the different scenarios. Conclusions The two-stage Bayesian safety procedure in this paper provides a statistical view to monitor safety during the clinical trials. The proposed two-stage monitoring model has an excellent accuracy of detecting and flagging a potential safety signal at stage 1, and with the most important feature that further action at stage 2 could confirm the safety issue.

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Digital Health Applications for Pharmacogenetic Clinical Trials

Genes ◽

10.3390/genes11111261 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1261

Author(s):

Hetanshi Naik ◽

Latha Palaniappan ◽

Euan A. Ashley ◽

Stuart A. Scott

Keyword(s):

Clinical Trials ◽

Trial Design ◽

Digital Health ◽

Clinical Care ◽

Vital Signs ◽

Medication Administration ◽

Therapeutic Interventions ◽

Participant Engagement ◽

Patient Reported ◽

Health Related

Digital health (DH) is the use of digital technologies and data analytics to understand health-related behaviors and enhance personalized clinical care. DH is increasingly being used in clinical trials, and an important field that could potentially benefit from incorporating DH into trial design is pharmacogenetics. Prospective pharmacogenetic trials typically compare a standard care arm to a pharmacogenetic-guided therapeutic arm. These trials often require large sample sizes, are challenging to recruit into, lack patient diversity, and can have complicated workflows to deliver therapeutic interventions to both investigators and patients. Importantly, the use of DH technologies could mitigate these challenges and improve pharmacogenetic trial design and operation. Some DH use cases include (1) automatic electronic health record-based patient screening and recruitment; (2) interactive websites for participant engagement; (3) home- and tele-health visits for patient convenience (e.g., samples for lab tests, physical exams, medication administration); (4) healthcare apps to collect patient-reported outcomes, adverse events and concomitant medications, and to deliver therapeutic information to patients; and (5) wearable devices to collect vital signs, electrocardiograms, sleep quality, and other discrete clinical variables. Given that pharmacogenetic trials are inherently challenging to conduct, future pharmacogenetic utility studies should consider implementing DH technologies and trial methodologies into their design and operation.

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