Routine Innovation in Perioperative Clinical Trials: The Best Chance to Answer Our Most Important Questions?

Every day in hospitals around the world, anesthesiologists face important questions about what treatments provide the best outcomes for patients. Advancements in methodology and data collection facilitate evaluation of different treatment strategies in real-world situations using observational routinely collected data. Unfortunately, even in large data sets with detailed patient and system characteristics, confounding and indication bias leave interpretation of estimates derived from observational comparative effectiveness studies uncertain. In fact, observational studies routinely estimate treatment effects that are directionally incongruent with subsequent randomized trials. Even when treatment effects are directionally congruent, observational studies typically overestimate the size of treatment effects identified in randomized trials.  These findings act as a clear reminder that randomized controlled trials remain the most important tool for understanding the true causal effects of novel and routine treatments, which highlights the clinical and methodologic implications of the Dexamethasone for Cardiac Surgery (DECS)–II trial reported this month in Anesthesiology. 

While in an ideal world we would have randomized trial results to inform all treatment decisions, conducting randomized trials is hard. Many barriers to successful trial conduct exist. Key barriers relevant to evaluating a routine treatment include (1) convincing centers to participate, which requires departures from their usual care; (2) having the resources required to enroll and randomize thousands of patients, the majority of whom may ultimately decide not to participate after staff have spent hours screening and contacting them; and (3) having the resources required to collect complete, valid, and meaningful outcome data.

Fortunately, there has been an explosion of innovative clinical trial methodologies that can help to overcome barriers to efficient conduct of randomized trials. In a seven-center, international randomized trial, Myles et al.  used several innovative techniques to conduct an efficient trial aimed at answering an important question in routine care of cardiac surgical patients, namely, does administering dexamethasone (1 mg/kg) lead to patients spending more days at home in the 30 days after surgery (primary outcome), having lower incidence of organ failure, less time in critical care, or better biomarker profiles?

First, DECS-II was a pragmatic trial meaning that the care provided to participants reflected what was done daily by providers and hospitals participating in the trial. In contrast to an explanatory design, where all aspects of care other than the treatment (e.g., induction drugs, bypass settings, ventilatory and hemodynamic management) are tightly controlled and protocolized patients in DECS-II received typical care from their providers, other than random assignment of dexamethasone. This approach allows the results of the trial to generalize more directly to real-world care.

Second, the primary outcome, days at home, as well as organ complications, were captured from routinely collected data. Use of registry-linked clinical trial methodology means that bias-reducing power of randomization can be used to efficiently generate causal treatment effect estimates by leveraging the scale and efficiency of data that are already being collected. While trialists (and readers) must always ensure that outcomes captured from routine data sources are accurate and valid, days at home is a validated, routinely collected metric, while national surgical registries can provide valid assessment of complications if standardized methods are used to prospectively capture events using trained assessors.

Third, to overcome the challenge of disrupting routine care with implementation of a randomized trial, randomization was designed to favor local practice. This means that even though allocation to corticosteroid treatment was random, centers in the Netherlands, where corticosteroids are almost always given, had two patients randomized to corticosteroids for each randomized to no corticosteroids, with the inverse approach carried out in Australian centers, where corticosteroids were rarely used. This approach supported acceptance of the study protocol with local providers, as without participation of study centers, multicenter trials cannot succeed.

Last, the investigators should be congratulated for having the adaptability required to conduct an international trial, where jurisdictional standards and processes can vary enormously. In Australia, where patients were already being routinely enrolled in a cardiac surgery quality assurance database, prerandomized consent (also called a Zelen design) was used. This meant that only patients randomized to dexamethasone (the local departure from routine care) were required to provide informed consent. In the Netherlands, all patients were required to provide preliminary consent. For anyone randomized to not receive dexamethasone (their local departure from usual care), further written consent was required. While these altered consent approaches provide efficiency in enrolling participants, they are not without risk. If patients choose to withdraw after randomization, serious bias can emerge.  Fortunately, the study team achieved low levels of attrition (less than 5% in each arm) and conducted multiple required sensitivity analyses that were consistent with primary results, substantially reducing concerns about bias.

Utilizing these innovative design strategies, what conclusions have emerged? Based on the combined results of three large multicenter trials of suprapharmacologic doses of corticosteroids in cardiac surgery to mitigate the inflammatory response to cardiopulmonary bypass some trends are evident (table 1). First, it appears unlikely that, in general, corticosteroids produce clinically meaningful improvements in outcomes and resource use. Second, any positive signals appear to emerge from use of dexamethasone whereas methylprednisolone was the only drug implicated in increased release of cardiac biomarkers and provided no signals of benefit.  For clinicians strongly in favor of dexamethasone administration, focusing use in subgroups of patients most likely to benefit (higher degree of critical illness, younger age, greater risk of postoperative pulmonary complications, female patients) may be an acceptable strategy.

For those who remain uncertain, but hopeful, that dexamethasone’s mechanistic promise will lead to improved outcomes, drawing on further examples of innovative clinical trial designs will be required. Clinicians intrinsically recognize that different patients often respond differently to the same treatment, a phenomenon known methodologically as heterogeneity of treatment effect. For steroids in acute inflammatory states, biologic mechanisms and some epidemiologic evidence suggest that heterogeneity in treatment effects is plausible. While typical approaches to heterogeneous treatment effects have involved exploratory approaches like testing for subgroup effects based on simple categorization of baseline variables such as age or sex (via tests of statistical interaction that are always underpowered), emerging evidence suggests that identifying optimal target populations for clinical trials likely requires a more granular approach. Specifically, advances in predictive modeling applied to large, high-quality clinical trial datasets, like those available from DECS-I and -II, can be used to identify the most likely group of responders based on combinations of baseline variables, including complex interactions.  With these predictions in hand, an enriched subpopulation where larger treatment effects were expected could be identified and randomized, providing robust evidence in a more precisely targeted population. Furthermore, with advancements in electronic health records and enterprise-based prediction models, such responder scores may be operationalizable via real-time decision support to facilitate translation of promising results into clinical care.

In our opinion, despite rapid advances in observational methods, randomized trials will remain our most important tool for evaluating treatments. However, an innovative environment where best practices in trial design are integrated with advancements in predictive modeling, and then leveraged together using high-quality routinely collected data and pragmatic trial design, holds enormous promise. For perioperative clinicians and patients, DECS-II provides crucial clinical and methodologic insights and should inspire further innovation in clinical trials that are likely to deliver increasingly robust and efficient answers to the treatment questions that we most highly prioritize.