Critical Care Trials Research Articles

The uncertainty principle: a novel approach to optimizing trials in critical care.

The uncertainty principle: a novel approach to optimizing trials in critical care.

In adults with TBI and anemia, liberal vs. restrictive RBC transfusion did not reduce unfavorable neurologic outcomes by 10% at 6 mo.

Turgeon AF, Fergusson DA, Clayton L, et al; HEMOTION Trial Investigators on behalf of the Canadian Critical Care Trials Group, the Canadian Perioperative Anesthesia Clinical Trials Group, and the Canadian Traumatic Brain Injury Research Consortium. Liberal or restrictive transfusion strategy in patients with traumatic brain injury. N Engl J Med. 2024;391:722-735. 38869931.

Barriers, Solutions, and Opportunities for Adapting Critical Care Clinical Trials in the COVID-19 Pandemic

The COVID-19 pandemic created unprecedented challenges for clinical trials worldwide, threatening premature closure and trial integrity. Every phase of research operations was affected, often requiring modifications to protocol design and implementation. To identify the barriers, solutions, and opportunities associated with continuing critical care trials that were interrupted during the pandemic, and to generate suggestions for future trials. This mixed-methods study performed an explanatory sequential analysis involving a self-administered electronic survey and focus groups of principal investigators (PIs) and project coordinators (PCs) conducting adult and pediatric individual-patient randomized trials of the Canadian Critical Care Trials Group during the COVID-19 pandemic. Eligible trials were actively enrolling patients on March 11, 2020. Data were analyzed between September 2023 and January 2024. Importance ratings of barriers to trial conduct and completion, solutions employed, opportunities arising, and suggested strategies for future trials. Quantitative data examining barriers were analyzed using descriptive statistics. Data addressing solutions, opportunities, and suggestions were analyzed by qualitative content analysis. Integration involved triangulation of data sources and perspectives about 13 trials, synthesized by an interprofessional team incorporating reflexivity and member-checking. A total of 13 trials run by 29 PIs and PCs (100% participation rate) were included. The highest-rated barriers (on a 5-point scale) to ongoing conduct during the pandemic were decisions to pause all clinical research (mean [SD] score, 4.7 [0.8]), focus on COVID-19 studies (mean [SD] score, 4.6 [0.8]), and restricted family presence in hospitals (mean [SD] score, 4.1 [0.8]). Suggestions to enable trial progress and completion included providing scientific leadership, implementing technology for communication and data management, facilitating the informed consent process, adapting the protocol as necessary, fostering site engagement, initiating new sites, streamlining ethics and contract review, and designing nested studies. The pandemic necessitated new funding opportunities to sustain trial enrollment. It increased public awareness of critical illness and the importance of randomized trial evidence. While underscoring the vital role of research in society and drawing the scientific community together with a common purpose, the pandemic signaled the need for innovation to ensure the rigor and completion of ongoing trials. Lessons learned to optimize research procedures will help to ensure a vibrant clinical trials enterprise in the future.

Composite outcome measures in high-impact critical care randomised controlled trials: a systematic review

BackgroundThe use of composite outcome measures (COM) in clinical trials is increasing. Whilst their use is associated with benefits, several limitations have been highlighted and there is limited literature exploring their use within critical care. The primary aim of this study was to evaluate the use of COM in high-impact critical care trials, and compare study parameters (including sample size, statistical significance, and consistency of effect estimates) in trials using composite versus non-composite outcomes.MethodsA systematic review of 16 high-impact journals was conducted. Randomised controlled trials published between 2012 and 2022 reporting a patient important outcome and involving critical care patients, were included.Results8271 trials were screened, and 194 included. 39.1% of all trials used a COM and this increased over time. Of those using a COM, only 52.6% explicitly described the outcome as composite. The median number of components was 2 (IQR 2–3). Trials using a COM recruited fewer participants (409 (198.8–851.5) vs 584 (300–1566, p = 0.004), and their use was not associated with increased rates of statistical significance (19.7% vs 17.8%, p = 0.380). Predicted effect sizes were overestimated in all but 6 trials. For studies using a COM the effect estimates were consistent across all components in 43.4% of trials. 93% of COM included components that were not patient important.ConclusionsCOM are increasingly used in critical care trials; however effect estimates are frequently inconsistent across COM components confounding outcome interpretations. The use of COM was associated with smaller sample sizes, and no increased likelihood of statistically significant results. Many of the limitations inherent to the use of COM are relevant to critical care research.

Barriers to participation in biosampling-based translational research: A cross-sectional survey of Canadian critical care researchers.

Collection of biosamples for translational research studies is vital for understanding biological pathways, discovering disease-related biomarkers, and identifying novel therapeutic targets. However, a lack of infrastructure for sample procurement, processing, storage, and shipping may hinder the ability of clinical research units to effectively engage in translational research. The purpose of this study was to identify the barriers to biosampling-based translational research in the critical care setting in Canada. We administered an online survey to members of the Canadian Critical Care Trials Group (CCCTG), the Canadian Critical Care Translational Biology Group (CCCTBG), and the Canadian Critical Care Research Coordinators Group (CCCRCG). The survey focused on participants' personal experience of biosampling research, research infrastructure, motivating factors, and perceived barriers. We received 59 responses from 31 sites, including 6 community intensive care unit (ICU) sites. The overall response rate was 11.3%. The majority of respondents were research coordinators (44%), followed by clinician-investigators (33.8%), graduate students (10.2%), and PhD-investigators (8.5%). Although most (63.8%) respondents reported an interest in participating in translational research, they also reported that their ICUs were currently contributing to a third of the number of translational studies compared to clinical studies. For respondents with experience in participating in translational research studies, the most common barriers were lack of funding, lack of time, and insufficient research staff. For respondents without previous experience, the perceived facilitators were more interest from their research group, improved training/mentorship, increased funding, and better access to laboratory equipment. Our survey found that the majority of participants were interested in and recognize the value of participating in biosampling-based translational research but lacked funding, time, and research personnel trained in biosampling protocols. Our survey also identified factors that might encourage participation at new sites. Addressing these barriers will be a key step towards increasing translational research capacity across Canada.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer.

To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect. The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research. The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

Use of the Win Ratio Analysis in Critical Care Trials.

Composite outcomes are commonly used in critical care trials to estimate the treatment effect of an intervention. A significant limitation of classical analytic approaches is that they assign equal statistical importance to each component in a composite, even if these do not have the same clinical importance (i.e., in a composite of death and organ failure, death is clearly more important). The win ratio (WR) method has been proposed as an alternative for trial outcomes evaluation, as it effectively assesses events based on their clinical relevance (i.e., hierarchical order) by comparing each patient in the intervention group with their counterparts in the control group. This statistical approach is increasingly used in cardiovascular outcome trials. However, WR may be useful to unveil treatment effects also in the critical care setting, because these trials are typically moderately sized, thus limiting the statistical power to detect small differences between groups, and often rely on composite outcomes that include several components of different clinical importance. Notably, the advantages of this approach may be offset by several drawbacks (such as ignoring ties and difficulties in selecting and ranking endpoints) and challenges in appropriate clinical interpretation (i.e., establishing clinical meaningfulness of the observed effect size). In this perspective article, we present some key elements to implementing WR statistics in critical care trials, providing an overview of strengths, drawbacks, and potential applications of this method. To illustrate, we conduct a reevaluation of the HYPO-ECMO (Hypothermia during Venoarterial Extracorporeal Membrane Oxygenation) trial using the WR framework as a case example.

Public perception of participation in low-risk clinical trials in critical care using waived consent: a Canadian national survey.

The acceptability of waiver of consent for participation in clinical research in intensive care unit (ICU) settings is uncertain. We sought to survey the Canadian public to assess levels of support, comfort, and acceptability for waived consent for low-risk clinical trials. We performed a prospective cross-sectional survey of the Canadian public aged 18yr or older. The survey was conducted by Ipsos between 19 and 23 November 2020. The survey content was derived from a literature review and in consultation with a patient and family partnership committee. The survey focused on attitudes and beliefs on waived consent for participation in low-risk clinical trials in ICU settings. The survey contained 35 items focused on sociodemographics, general health status, participation in medical research, and levels of support and comfort with research and with waived consent. The survey used a case study of a low-risk clinical trial intervention in ICU patients. Analysis was descriptive. We included 2,000 participants, 38% of whom reported experience with ICU and 16% with medical research. Participation in medical research was more common among those with postsecondary education, those with chronic disease, and those who were employed in health care. Most (80%) would support a model of waived consent for low-risk clinical trials, citing medical benefits (36%) and low perceived risk (34%). Most (77%) were comfortable with personally participating in a low-risk clinical trial. Most (80%) believed waived consent approaches were acceptable. Half (52%) believed the waived consent process should provide information about the research and include the option of opting out. When asked whether participants should always give full informed consent, regardless of the practicality or level of risk, 74% and 72% agreed, respectively. There is public support for models of waived consent for participation in low-risk pragmatic clinical trials in ICU settings in Canada; however, this is not universal. This information can inform and guide education, ethics, policy, and legal discussion on consent models.

A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.

Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants in which case principal stratification is an appealing framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate mean models for the potential outcomes and latent stratum membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by biologic sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and degree of hypoxemia). These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field.

Barriers and Facilitators of Surrogates Providing Consent for Critically Ill Patients in Clinical Trials: A Qualitative Study

BackgroundEnrollment into critical care clinical trials is often hampered by the need to rely on surrogate decision-makers. To identify potential interventions facilitating enrollment into critical care clinical trials, a better understanding of surrogate decision-making for critical care clinical trial enrollment is needed. Research QuestionWhat are the barriers and facilitators of critical care trial enrollment? What are surrogate decision makers’ perspectives on proposed interventions to facilitate trial enrollment? Study Design and MethodsThe authors conducted semi-structured interviews with 20 surrogate decision-makers of critically ill patients receiving mechanical ventilation. The interviews were recorded and transcribed verbatim, and analyzed for themes using an inductive approach. ResultsTwenty surrogate decision-makers of critically ill patients participated in the semi-structured interviews. Thematic analysis confirmed previous research showing that trust in the system, assessing the risks and benefits of trial participation, the desire to help others, and building medical knowledge as important motivating factors for trial enrollment. Two previously undescribed concerns among surrogate decision makers of critically ill patients were identified, including the potential to interfere with clinical treatment decisions and negative sentiment about placebos. Surrogates viewed public recognition and charitable donations for participation as favorable potential interventions to encourage trial enrollment. However, participants viewed direct financial incentives and prioritizing research participants during medical rounds negatively. InterpretationThis study confirms and extends previous findings that health system trust, study risks and benefits, altruism, knowledge generation, interference with clinical care and placebos are key concerns and barriers for surrogate decision-makers to enroll patients in critical care trials. Future studies are needed to evaluate if charitable giving on the patient's behalf and public recognition are effective strategies to promote enrollment into critical care trials.

The Rise of Adaptive Platform Trials in Critical Care.

As durable learning research systems, adaptive platform trials represent a transformative new approach to accelerating clinical evaluation and discovery in critical care. This Perspective provides a brief introduction to the concept of adaptive platform trials, describes several established and emerging platforms in critical care, and surveys some opportunities and challenges for their implementation and impact.

Adaptive designs in critical care trials: a simulation study

BackgroundAdaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation.MethodsUsing two large critical care trials (ADRENAL. ClinicalTrials.gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR. ClinicalTrials.gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05.ResultsAcross all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O’Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased.ConclusionsThis study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method.

Student-led podcasts to enhance literature evaluation skills

Background and purposeCritical evaluation and understanding of primary literature is essential to pharmacy practice. This project evaluated student confidence and learning outcomes in a critical care elective course where podcasts were used as an active learning assignment for literature evaluation. Educational activity and settingThird-year pharmacy students created a 15- to 20-min podcast episode that reviewed a landmark critical care trial. A pre/post survey was used to assess student perceptions of confidence in evaluating primary literature. A standardized rubric was used to assess literature evaluation skills in several domains. FindingsNinety-two third-year pharmacy students were enrolled in the critical care elective course, and 93% (86 of 92) completed the survey. Student perceptions of their ability to evaluate literature improved for all seven questions, including comfort in interpreting study methodology and applying findings of literature to patient care. Students scored proficient on most components of the standardized rubric, with ability to discuss results and conclusions being the lowest performing domain (78.2% fully proficient). After completing the activity, students felt more confident in interpreting results, applying findings, and orally presenting a journal article to peers. Most students felt the podcast activity helped them strengthen their literature evaluation skills and recommended this activity to a peer. SummaryThird-year pharmacy students reported an improvement in confidence in the interpretation and application of primary literature after creating a podcast episode reviewing a critical care trial. Additional research with creation of podcasts or other audio/visual methods should be studied in the future.

Health-related quality of life trajectories in critical illness: Protocol for a Monte Carlo simulation study.

Health-related quality of life (HRQoL) is a patient-centred outcome increasingly used as a secondary outcome in critical care research. It may cover several important dimensions of clinical status in intensive care unit (ICU) patients that arguably elude other more easily quantified outcomes such as mortality. Poor associations with harder outcomes, conflicting data on HRQoL in critically ill compared to the background population, and paradoxical effects on HRQoL and mortality complicate the current operationalisation in critical care trials. This protocol outlines a simulation study that will gauge if the areas under the HRQoL trajectories could be a viable alternative. We will gauge the behaviour of the proposed HRQoL operationalisation through Monte Carlo simulations, under clinical scenarios that reflect a broad critical care population eligible for inclusion in a large pragmatic trial. We will simulate 15,360 clinical scenarios based on a full factorial design with the following seven simulation parameters: number of patients per arm, relative mortality reduction in the interventional arm, acceleration of HRQoL improvement in the interventional arm, the relative improvement in final HRQoL in the interventional arm, dampening effect of mortality on HRQoL values at discharge from the ICU, proportion of so-called mortality benefiters in the interventional arm and mortality trajectory shape. For each clinical scenario, we will simulate 100,000 two-arm trials with 1:1 randomisation. HRQoL will be sampled fortnightly after ICU discharge. Outcomes will include HRQoL in survivors and all patients at the end of follow-up; mean areas under the HRQoL trajectories in both arms; and mean difference between areas under the HRQoL trajectories and single-sampled HRQoLs at the end of follow-up. In the outlined simulation study, we aim to assess whether the area under the HRQoL trajectory curve could be a candidate for reconciling the seemingly paradoxical effects on improved mortality and reduced HRQoL while remaining sensitive to early or accelerated improvement in patient outcomes. The resultant insights will inform subsequent methodological work on prudent collection and statistical analysis of such data from real critically ill patients.

Real-world causal evidence for planned predictive enrichment in critical care trials: A scoping review.

Randomised clinical trials in critical care are prone to inconclusiveness due, in part, to undue optimism about effect sizes and suboptimal accounting for heterogeneous treatment effects. Although causal evidence from rich real-world critical care can help overcome these challenges by informing predictive enrichment, no overview exists. We conducted a scoping review, systematically searching 10 general and speciality journals for reports published on or after 1 January 2018, of randomised clinical trials enrolling adult critically ill patients. We collected trial metadata on 22 variables including recruitment period, intervention type and early stopping (including reasons) as well as data on the use of causal evidence from secondary data for planned predictive enrichment. We screened 9020 records and included 316 unique RCTs with a total of 268,563 randomised participants. One hundred seventy-three (55%) trials tested drug interventions, 101 (32%) management strategies and 42 (13%) devices. The median duration of enrolment was 2.2 (IQR: 1.3-3.4) years, and 83% of trials randomised less than 1000 participants. Thirty-six trials (11%) were restricted to COVID-19 patients. Of the 55 (17%) trials that stopped early, 23 (42%) used predefined rules; futility, slow enrolment and safety concerns were the commonest stopping reasons. None of the included RCTs had used causal evidence from secondary data for planned predictive enrichment. Work is needed to harness the rich multiverse of critical care data and establish its utility in critical care RCTs. Such work will likely need to leverage methodology from interventional and analytical epidemiology as well as data science.

Family Engagement in the Adult Cardiac Intensive Care Unit: A Survey of Family Engagement Practices in the Cardiac Critical Care Trials Network.

Cardiovascular and critical care professional societies recommend incorporating family engagement practices into routine clinical care. However, little is known about current family engagement practices in contemporary cardiac intensive care units (CICUs). We implemented a validated 12-item family engagement practice survey among site investigators participating in the Critical Care Cardiology Trials Network, a collaborative network of CICUs in North America. The survey includes 9 items assessing specific engagement practices, 1 item about other family-centered care practices, and 2 open-ended questions on strategies and barriers concerning family engagement practice. We developed an engagement practice score by assigning 1 point for each family engagement practice partially or fully adopted at each site (max score 9). We assessed for relationships between the engagement practice score and CICU demographics. All sites (N=39; 100%) completed the survey. The most common family engagement practices were open visitation (95%), information and support to families (85%), structured care conferences (n=82%), and family participation in rounds (77%). The median engagement practice score was 5 (interquartile range, 4). There were no differences in engagement practice scores by geographic region or CICU type. The most commonly used strategies to promote family engagement were family presence during rounds (41%), communication (28%), and family meetings (28%). The most common barriers to family engagement were COVID-related visitation policies (38%) and resource limitations (13%). Family engagement practices are routinely performed in many CICUs; however, considerable variability exists. There is a need for strategies to address the variability of family engagement practices in CICUs.

Sample size estimation in clinical trials using ventilator-free days as the primary outcome: a systematic review

BackgroundVentilator-free days (VFDs) are a composite endpoint increasingly used as the primary outcome in critical care trials. However, because of the skewed distribution and competitive risk between components, sample size estimation remains challenging. This systematic review was conducted to systematically assess whether the sample size was congruent, as calculated to evaluate VFDs in trials, with VFDs’ distribution and the impact of alternative methods on sample size estimation.MethodsA systematic literature search was conducted within the PubMed and Embase databases for randomized clinical trials in adults with VFDs as the primary outcome until December 2021. We focused on peer-reviewed journals with 2021 impact factors greater than five. After reviewing definitions of VFDs, we extracted the sample size and methods used for its estimation. The data were collected by two independent investigators and recorded in a standardized, pilot-tested forms tool. Sample sizes were calculated using alternative statistical approaches, and risks of bias were assessed with the Cochrane risk-of-bias tool.ResultsOf the 26 clinical trials included, 19 (73%) raised “some concerns” when assessing risks of bias. Twenty-four (92%) trials were two-arm superiority trials, and 23 (89%) were conducted at multiple sites. Almost all the trials (96%) were unable to consider the unique distribution of VFDs and death as a competitive risk. Moreover, significant heterogeneity was found in the definitions of VFDs, especially regarding varying start time and type of respiratory support. Methods for sample size estimation were also heterogeneous, and simple models, such as the Mann–Whitney–Wilcoxon rank-sum test, were used in 14 (54%) trials. Finally, the sample sizes calculated varied by a factor of 1.6 to 17.4.ConclusionsA standardized definition and methodology for VFDs, including the use of a core outcome set, seems to be required. Indeed, this could facilitate the interpretation of findings in clinical trials, as well as their construction, especially the sample size estimation which is a trade-off between cost, ethics, and statistical power.Systematic review registration PROSPERO ID: CRD42021282304. Registered 15 December 2021 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021282304).

Non-inferiority trial of a shorter (7 days) compared with a longer (14 days) duration of antimicrobial therapy for the treatment of bacteraemic urinary sepsis, measured by microbiological success after the completion of therapy: a substudy protocol for the Bacteraemia Antibiotic Length Actually Needed for Clinical Effectiveness (BALANCE) multicentre randomised controlled trial

IntroductionThe BALANCE study is a randomised clinical trial (3626 participants) designed to assess the non-inferiority of 7 days (short-course) antibiotic therapy compared with 14 days of therapy for bacteraemia using...

A negative trial for vasoactive intestinal peptide in COVID-19-associated acute hypoxaemic respiratory failure

A negative trial for vasoactive intestinal peptide in COVID-19-associated acute hypoxaemic respiratory failure

Driving Pressure: What Is the Harm?

Driving Pressure: What Is the Harm?