A limited number of medicines pragmatic trials had potential for waived informed consent following the 2016 CIOMS ethical guidelines

Rationale and design of the pragmatic clinical trial tREatment with Beta-blockers after myOcardial infarction withOut reduced ejection fracTion (REBOOT)

BACKGROUND

There is a lack of evidence regarding the benefits of β-blocker treatment after invasively managed acute myocardial infarction (MI) without reduced left ventricular ejection fraction (LVEF).

METHODS AND RESULTS

TREatment with Beta-blockers after myOcardial infarction withOut reduced ejection fraction (REBOOT) trial is a pragmatic, controlled, prospective, randomized, open-label blinded endpoint (PROBE design) clinical trial testing the benefits of β-blocker maintenance therapy in patients discharged after MI with or without ST-segment elevation. Patients eligible for participation are those managed invasively during index hospitalization (coronary angiography), with LVEF >40%, and no history of heart failure (HF). At discharge, patients will be randomized 1:1 to β-blocker therapy (agent and dose according to treating physician) or no β-blocker therapy. The primary endpoint is a composite of all-cause death, nonfatal reinfarction, or HF hospitalization over a median follow-up period of 2.75 years (minimum 2 years, maximum 3 years). Key secondary endpoints include the incidence of the individual components of the primary composite endpoint, the incidence of cardiac death, and incidence of malignant ventricular arrhythmias or resuscitated cardiac arrest. The primary endpoint will be analyzed according to the intention-to-treat principle.

CONCLUSION

The REBOOT trial will provide robust evidence to guide the prescription of β-blockers to patients discharged after MI without reduced LVEF.

High-impact RCTs without prospective informed consent: a systematic review

The prevalence of randomized controlled trials (RCTs) performed without fully informed prospective consent from subjects is unknown. We performed this study to estimate the prevalence of high-impact RCTs performed without informed consent from all subjects and examine whether such trials are becoming more prevalent. We performed a systematic review of English-language RCTs published from 2014 through 2018 identified in Scopus and sorted to identify the top 100 most highly cited RCTs each year. Text search of title and abstract included terms randomized controlled or clinical trial and spelling variants thereof, and excluded metaanalyses and systematic reviews. We independently identified the most highly cited RCTs based on predefined criteria and negotiated to agreement, then independently performed keyword searches, read, abstracted and coded information regarding informed consent from each paper and again negotiated to agreement. No quality indicators were assessed. We planned descriptive qualitative analysis and appropriate quantitative analysis to examine the prevalence and characteristics of trials enrolling subjects with other than fully informed prospective consent. We find that 44 (8.8%, binomial exact 95% CI 6.5% to 11.6%) of 500 high-impact RCTs did not secure informed consent from at least some subjects. The prevalence of such trials did not change over the 5 years (OR=1.09, z=0.78, p=0.44). A majority (66%) of the trials involved emergency situations, and 40 of 44 (90.9%) of the trials involved emergency interventions, pragmatic designs, were cluster randomized, or a combination of these factors. A qualitative analysis explores the methods of and justifications for waiving informed consent in our sample of RCTs.

Leveraging Data Science for a Personalized Haemodialysis

Background

The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed.

Summary

Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis.

Key messages

Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

Participants' written informed consent in low-risk pragmatic clinical trials with medicines

Introduction: An important gap within modern medicine is the lack of enough comparative effectiveness research of marketed medicines. Low-risk pragmatic randomized controlled trials (pRCTs) are those conducted resembling usual clinical practice that poses no or minimal incremental risk compared with normal clinical practice.Areas covered: This review addresses one important hurdle in the conduct of low-risk pRCTs: the need to seek participants' written informed consent.Expert opinion: The CIOMS ethical guidelines consider that any research that (a) would not be feasible or practicable to carry out without the waiver or modification, (b) has important social value, and (c) poses no more than minimal risks to participants, and that is approved by the relevant research ethics committee, could be conducted without participants' consent. It is clear that these provisions are applicable to some low-risk RCTs. Recently a research on the EU-CTR registry showed that only 2% of all ongoing phase 4 RCTs could have fulfilled the CIOMS provisions following the investigators' assessment. The EU clinical trial regulation - and that of other jurisdictions - should be debated on the suitableness of the conduct with an alteration or waiver of participants' consent of those low-risk pRCTs that fulfill the three CIOMS provisions.

Online randomized controlled experiments at scale: lessons and extensions to medicine

Background

Many technology companies, including Airbnb, Amazon, Booking.com, eBay, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, and Yahoo!/Oath, run online randomized controlled experiments at scale, namely hundreds of concurrent controlled experiments on millions of users each, commonly referred to as A/B tests. Originally derived from the same statistical roots, randomized controlled trials (RCTs) in medicine are now criticized for being expensive and difficult, while in technology, the marginal cost of such experiments is approaching zero and the value for data-driven decision-making is broadly recognized.

Methods and results

This is an overview of key scaling lessons learned in the technology field. They include (1) a focus on metrics, an overall evaluation criterion and thousands of metrics for insights and debugging, automatically computed for every experiment; (2) quick release cycles with automated ramp-up and shut-down that afford agile and safe experimentation, leading to consistent incremental progress over time; and (3) a culture of ‘test everything’ because most ideas fail and tiny changes sometimes show surprising outcomes worth millions of dollars annually.

Technological advances, online interactions, and the availability of large-scale data allowed technology companies to take the science of RCTs and use them as online randomized controlled experiments at large scale with hundreds of such concurrent experiments running on any given day on a wide range of software products, be they web sites, mobile applications, or desktop applications. Rather than hindering innovation, these experiments enabled accelerated innovation with clear improvements to key metrics, including user experience and revenue. As healthcare increases interactions with patients utilizing these modern channels of web sites and digital health applications, many of the lessons apply. The most innovative technological field has recognized that systematic series of randomized trials with numerous failures of the most promising ideas leads to sustainable improvement.

Conclusion

While there are many differences between technology and medicine, it is worth considering whether and how similar designs can be applied via simple RCTs that focus on healthcare decision-making or service delivery. Changes – small and large – should undergo continuous and repeated evaluations in randomized trials and learning from their results will enable accelerated healthcare improvements.