The REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) Study. Rationale and Design

Operational Development and Launch of an Adaptive Platform Trial in Amyotrophic Lateral Sclerosis: Processes and Learnings From the First Four Regimens of the HEALEY ALS Platform Trial

INTRODUCTION/AIMS

Platform trials present several advantages over traditional interventional clinical trials. Here, we provide a detailed description of the operational framework of the HEALEY ALS Platform Trial.

METHODS

Platform-level procedures for regulatory oversight, safety, and site management were developed prior to trial launch. Central vendors and a single Institutional Review Board (sIRB) were used. An Investigational New Drug (IND) application was submitted for the master protocol, and each regimen was added as an amendment.

RESULTS

The HEALEY ALS Platform Trial was launched in 2020. Fifty-four geographically diverse sites from the Northeast ALS Consortium (NEALS), all highly experienced in ALS care and research, were selected. Three investigational products were selected to launch concurrently at the start of the trial as individual regimens. A fourth investigational product was selected and added to the trial after the initial launch. The Master Protocol and the first three regimens (Regimens A-C) were sIRB approved in 120 days. sIRB amendment for Regimen D was approved in 21 days. Enrollment for regimens A-C was completed in 15 months, whereas Regimen D was completed in 11 months from the start of enrollment. Results of all regimens were available within approximately 2 years from the initial trial launch.

DISCUSSION

The HEALEY ALS Platform Trial capitalized on the benefits of the platform approach, including an adaptable operational infrastructure, concurrent enrollment into four distinct regimens, and an accelerated start-up time for a new regimen added after initial trial launch.

Characteristics, design, and statistical methods in platform trials: a systematic review

BACKGROUND AND OBJECTIVE

Platform trials (PTs) are gaining popularity in clinical research due to their innovative and flexible methodologies. The objective was to determine the characteristics, methodological, and statistical practices in PTs.

METHODS

We identified PTs from trial registries and bibliographic databases up to August 2024. Eligible PTs were randomized controlled trials studying multiple interventions within a single population, with flexibility to add or drop arms. Data were extracted on trial status, design, statistical methods, and reporting practices.

RESULTS

We identified 189 PTs. Most focused on infectious diseases (77, including 57 for COVID-19) and oncology (68). PT initiation peaked during the COVID-19 pandemic but has since stabilized at 84 active PTs, with 25 in planning. A complete master protocol was available for 47% (89/189) of PTs. Bayesian designs featured in 58/189 PTs vs. 56/189 frequentist trials, 20/189 trials utilizing both (unclear in 55/189 PTs). Overall, 25/111 trials (23%) were designed without a predetermined target sample size, all of which were Bayesian. Among these, 16 were explicitly reported as "perpetual" trials. The number of interim analyses was predetermined in 18% (10/57) of Bayesian trials vs. 58% (28/48) of frequentist trials. Simulations to evaluate operating characteristics were used in 93% (39/42) of Bayesian trials. Simulation reports were available in 67% (26/39) of cases, and the procedures were detailed for 62% (24/39) of trials. Only two trials shared the simulation code.

CONCLUSION

PTs remain popular and increasingly diverse. Efforts to enhance transparency and reporting, especially in complex Bayesian PTs, are essential to ensure reliability and broader acceptance.

StrokeNet Thrombectomy Endovascular Platform (STEP): A Catalyst for Stroke Research

Endovascular thrombectomy is a critical treatment for acute ischemic stroke. Challenges remain in extending treatment windows, refining patient selection, optimizing posttreatment interventions, and expanding endovascular thrombectomy indications. Traditional stroke trials have played a pivotal role in advancing care but are hindered by inefficiencies, rigid designs, high costs, long timelines, and slow enrollment. The StrokeNet Thrombectomy Endovascular Platform (STEP) represents an innovative paradigm shift in stroke research, offering a more agile, cost-effective, and flexible trial design. Here, we explore the architecture of STEP and its implications on stroke research.

Effect of hydrocortisone on mortality in patients with severe community-acquired pneumonia : The REMAP-CAP Corticosteroid Domain Randomized Clinical Trial

PURPOSE

To determine whether hydrocortisone improves mortality in severe community-acquired pneumonia (CAP).

METHODS

In an international adaptive randomized controlled platform trial testing multiple interventions, adults admitted to the intensive care unit (ICU) with severe CAP were randomized to a 7-day course of intravenous hydrocortisone (50 mg every 6 h) or control (no corticosteroid). The primary end point was 90-day all-cause mortality, analyzed iteratively by a Bayesian hierarchical model estimating distinct treatment effects for patients presenting with influenza (Y/N) and shock (Y/N).

RESULTS

Fixed 7-day course hydrocortisone enrollment was stopped for futility (< 5% probability of > 20% relative improvement). Of 658 patients enrolled, 536 were randomized to hydrocortisone and 122 to control. Vital status at day 90 was missing for 15 patients. Day 90 mortality was 15% (78/521) and 9.8% (12/122) for the hydrocortisone and control groups. The adjusted odds ratio ranged from 1.52 to 1.63 (with all 95% CrI crossing 1), while the probability of > 20% relative reduction of day 90 mortality ranged from 7.1 to 3.3% across influenza and shock strata. Results were consistent in sensitivity and pre-specified secondary outcomes. In exploratory analyses, the duration of shock appeared lower in the hydrocortisone group compared with control (median (IQR) of 2 (2-5) days compared to control 3 (2-6.75) days, p value = 0.05).

CONCLUSIONS

Among patients with severe CAP, treatment with a 7-day course of hydrocortisone, compared with no hydrocortisone, appears unlikely to yield a large reduction in mortality. Smaller benefits and possible harm are not excluded.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT02735707 (registration date: November 4th, 2016).

In Silico and In Vitro Studies of the Approved Antibiotic Ceftaroline Fosamil and Its Metabolites as Inhibitors of SARS-CoV-2 Replication

The SARS-CoV-2 proteases Mpro and PLpro are critical targets for antiviral drug development for the treatment of COVID-19. The 1,2,4-thiadiazole functional group is an inhibitor of cysteine proteases, such as papain and cathepsins. This chemical moiety is also present in ceftaroline fosamil (CF), an FDA-approved fifth-generation cephalosporin antibiotic. This study investigates the interactions between CF, its primary metabolites (M1 is dephosphorylated CF and M2 is an opened β-lactam ring) and derivatives (protonated M1H and M2H), and its open 1,2,4-thiadiazole rings derivatives (open-M1H and open-M2H) with SARS-CoV-2 proteases and evaluates CF's effects on in vitro viral replication. In silico analyses (molecular docking and molecular dynamics (MD) simulations) demonstrated that CF and its metabolites are potential inhibitors of PLpro and Mpro. Docking analysis indicated that the majority of the ligands were more stable with Mpro than PLpro; however, in vitro biochemical analysis indicated PLpro as the preferred target for CF. CF inhibited viral replication in the human Calu-3 cell model at submicromolar concentrations when added to cell culture medium at 12 h. Our results suggest that CF should be evaluated as a potential repurposing agent for COVID-19, considering not only viral proteases but also other viral targets and relevant cellular pathways. Additionally, the reactivity of sulfur in the 1,2,4-thiadiazole moiety warrants further exploration for the development of viral protease inhibitors.

Structural insights into the RNA binding inhibitors of the C-terminal domain of the SARS-CoV-2 nucleocapsid

The SARS-CoV-2 nucleocapsid (N) protein is an essential structural element of the virion, playing a crucial role in enclosing the viral genome into a ribonucleoprotein (RNP) assembly, as well as viral replication and transmission. The C-terminal domain of the N-protein (N-CTD) is essential for encapsidation, contributing to the stabilization of the RNP complex. In a previous study, three inhibitors (ceftriaxone, cefuroxime, and ampicillin) were screened for their potential to disrupt the RNA packaging process by targeting the N-protein. However, the binding efficacy, mechanism of RNA binding inhibition, and molecular insights of binding with N-CTD remain unclear. In this study, we evaluated the binding efficacy of these inhibitors using isothermal titration calorimetry (ITC), revealing the affinity of ceftriaxone (18 ± 1.3 μM), cefuroxime (55 ± 4.2 μM), and ampicillin (28 ± 1.2 μM) with the N-CTD. Further inhibition assay and fluorescence polarisation assay demonstrated RNA binding inhibition, with IC50 ranging from ∼ 12 to 18 μM and KD values between 24 μM to 32 μM for the inhibitors, respectively. Additionally, we also determined the inhibitor-bound complex crystal structures of N-CTD-Ceftriaxone (2.0 Å) and N-CTD-Ampicillin (2.2 Å), along with the structure of apo N-CTD (1.4 Å). These crystal structures revealed previously unobserved interaction sites involving residues K261, K266, R293, Q294, and W301 at the oligomerization interface and the predicted RNA-binding region of N-CTD. These findings provide valuable molecular insights into the inhibition of N-CTD, highlighting its potential as an underexplored but promising target for the development of novel antiviral agents against coronaviruses.

Statistical documentation for multi-disease, multi-domain platform trials: our experience with the Staphylococcus aureus Network Adaptive Platform trial

Platform trials have become widely adopted across multiple disease areas over recent years, however, guidelines for operationalising these trials have not kept pace. We outline a series of documents that summarise the statistical components, and implicit processes, of the Staphylococcus aureus Network Adaptive Platform (SNAP) trial to provide an informal template for other researchers and reviewers of platform trials. We briefly summarise the content and role of the core protocol, statistical appendix, domain-specific appendices, simulation report, statistical implementation guides, data safety and monitoring committee (DSMC) reports, and domain-specific statistical analysis plans and final reports, and a transparent governance structure that ensures separate blinded and unblinded statistical teams. In the absence of guidelines or checklists for platform trial statistical documents, we hope to provide useful guidance to others in terms of what has worked so far for the SNAP trial, stimulate discussion, and inform a future consensus.Trial registration NCT05137119 . Registered on 30 November 2021.

Treatment of Complicated Gram-Positive Bacteremia and Infective Endocarditis

The Gram-positive cocci Staphylococcus aureus, Streptococcus spp., and Enterococcus spp. are the most frequent causative organisms of bloodstream infections and infective endocarditis. "Complicated bacteremia" is a term used in S. aureus bloodstream infections and originally implied the presence of metastatic infectious foci (i.e. complications of S. aureus bacteremia). These complications demand longer antimicrobial treatment durations and, frequently, interventional source control. Several risk factors for the incidence of bacteremia complications have been identified and are often used for the definition of complicated bacteremia. Here, we discuss management and diagnostic approaches and treatment options for patients with complicated bacteremia, with particular focus on infective endocarditis. We also summarize the available evidence regarding imaging modalities and the choice of antimicrobial mono- or combination therapy according to resistance patterns for these pathogens as well as treatment durations and optimized application routes. Finally, we synopsize current and future areas of research in complicated bacteremia and infective endocarditis.

The legacy of the COVID-19 pandemic on critical care research: A descriptive interview study

Background

The COVID-19 pandemic challenged both research and clinical teams in critical care to collaborate on research solutions to new clinical problems. Although an effective, nationally coordinated response helped facilitate critical care research, reprioritisation of research efforts towards COVID-19 studies had significant consequences for existing and planned research activity in critical care.

Aims

Our aim was to explore the impact of the COVID-19 pandemic research prioritisation policies and practices on critical care research funded prior to the pandemic, the conduct of pandemic research, and implications for ongoing and future critical care research.

Methods

We undertook a descriptive qualitative study recruiting research-active clinician researchers and research delivery team members working in critical care. We conducted digitally recorded, semi-structured interviews in 2021-2022. Framework Analysis was used to analyse the data.

Results

We interviewed 22 participants comprising principal investigators, senior trial coordinators and research delivery nurses from across the UK. Six themes were identified: Unit, organisational and national factors; Study specific factors; Resources; Individual/clinician factors; Family/patient factors; Contextual factors. These themes explained how a nationally coordinated response during the pandemic affected individuals, studies and wider organisations in managing the research response in critical care, highlighting future implications for critical care research.

Conclusion

Harnessing the collective response seen in the COVID-19 pandemic in critical care could better support integration of research activity into routine critical care activities. Future endeavours should focus on workforce preparations, contingency planning, strategies for study prioritisation and integration of research as part of the continuum of clinical care.

Composite endpoints in COVID-19 randomized controlled trials: a systematic review

BACKGROUND/AIMS

This study aimed to determine the prevalence of ordinal, binary, and numerical composite endpoints among coronavirus disease 2019 trials and the potential bias attributable to their use.

METHODS

We systematically reviewed the Cochrane COVID-19 Study Register to assess the prevalence, characteristics, and bias associated with using composite endpoints in coronavirus disease 2019 randomized clinical trials. We compared the effect measure (relative risk) of composite outcomes and that of its most critical component (i.e. death) by estimating the Bias Attributable to Composite Outcomes index [ln(relative risk for the composite outcome)/ln(relative risk for death)].

RESULTS

Composite endpoints accounted for 152 out of 417 primary endpoints in coronavirus disease 2019 randomized trials, being more frequent among studies published in high-impact journals. Ordinal endpoints were the most common (54% of all composites), followed by binary or time-to-event (34%), numerical (11%), and hierarchical (1%). Composites predominated among trials enrolling patients with severe disease when compared to trials with a mild or moderate case mix (odds ratio = 1.72). Adaptations of the seven-point World Health Organization scale occurred in 40% of the ordinal primary endpoints, which frequently underwent dichotomization for the statistical analyses. Mortality accounted for a median of 24% (interquartile range: 6%-48%) of all events when included in the composite. The median point estimate of the Bias Attributable to Composite Outcomes index was 0.3 (interquartile range: -0.1 to 0.7), being significantly lower than 1 in 5 of 24 comparisons.

DISCUSSION

Composite endpoints were used in a significant proportion of coronavirus disease 2019 trials, especially those involving severely ill patients. This is likely due to the higher anticipated rates of competing events, such as death, in such studies. Ordinal composites were common but often not fully appreciated, reducing the potential gains in information and statistical efficiency. For studies with binary composites, death was the most frequent component, and, unexpectedly, composite outcome estimates were often closer to the null when compared to those for mortality death. Numerical composites were less common, and only two trials used hierarchical endpoints. These newer approaches may offer advantages over traditional binary and ordinal composites; however, their potential benefits warrant further scrutiny.

CONCLUSION

Composite endpoints accounted for more than a third of coronavirus disease 2019 trials' primary endpoints; their use was more common among studies that included patients with severe disease and their point effect estimates tended to underestimate those for mortality.

Global collaboration between platform trials in surgery and anaesthesia

Large, randomised trials are the bedrock of evidence-based medicine, but the resources required to complete such trials greatly limit the number of important clinical questions that can be addressed within a reasonable period of time. Adaptive platform trials can identify effective, ineffective, or harmful treatments faster. These trials have been shown to deliver rapid evidence through the COVID-19 pandemic and are now being adopted across surgery and anaesthesia, with many opportunities for surgeons, anaesthetists, and other perioperative physicians to conduct and collaborate in platform trials.

Review article: Primer for clinical researchers on innovative trial designs for emergency medicine

Randomised trials have long been recognised as the gold standard research tool for evidence-based medicine. The past decade has seen the emergence of several innovative trial designs that are revolutionising how trials are conducted. These innovative designs enable more efficient, pragmatic trials that can address complex research questions which were previously not possible. In this paper, we provide an overview of the key innovative designs that are likely to be useful in the emergency medicine context, namely cluster crossover and stepped wedge designs, sequential multiple assignment randomised trial (SMART) designs, and platform trials. We describe the main features of each design, outline their pros and cons, and describe when they may or may not be useful. We also provide examples of these innovative designs in contexts that are relevant to emergency medicine.

Adapt to survive and thrive: the time is now for adaptive platform trials for preterm birth

In this Viewpoint, we discuss the challenges facing perinatal clinical researchers, many of which are unique to this field, and how traditional two-arm randomised trials using frequentist analysis might no longer be fit for purpose for perinatology. We propose a solution: the adoption of adaptive platform trials (APTs) with Bayesian methodology to address perinatal research questions to improve outcomes of preterm birth. APTs use a master protocol as a foundation to efficiently assess multiple interventions simultaneously for a particular disease. APTs can study these interventions in a perpetual manner, with interventions allowed to enter or leave the platform on the basis of preplanned decision algorithms. In this Viewpoint, we outline the ways in which APTs can overcome some of the issues facing perinatal clinical research, and the challenges and essential requirements for the design and implementation of perinatal APTs that should be considered.

Why and how should we simulate platform trials? Learnings from EU-PEARL

BACKGROUND

Platform trials are innovative clinical trials governed by a master protocol that allows for the evaluation of multiple investigational treatments that enter and leave the trial over time. Interest in platform trials has been steadily increasing over the last decade. Due to their highly adaptive nature, platform trials provide sufficient flexibility to customize important trial design aspects to the requirements of both the specific disease under investigation and the different stakeholders. The flexibility of platform trials, however, comes with complexities when designing such trials. In the past, we reviewed existing software for simulating clinical trials and found that none of them were suitable for simulating platform trials as they do not accommodate the design features and flexibility inherent to platform trials, such as staggered entry of treatments over time.

RESULTS

We argued that simulation studies are crucial for the design of efficient platform trials. We developed and proposed an iterative, simulation-guided "vanilla and sprinkles" framework, i.e. from a basic to a more complex design, for designing platform trials. We addressed the functionality limitations of existing software as well as the unavailability of the coding therein by developing a suite of open-source software to use in simulating platform trials based on the R programming language. To give some examples, the newly developed software supports simulating staggered entry of treatments throughout the trial, choosing different options for control data sharing, specifying different platform stopping rules and platform-level operating characteristics. The software we developed is available through open-source licensing to enable users to access and modify the code. The separate use of two of these software packages to implement the same platform design by independent teams obtained the same results.

CONCLUSION

We provide a framework, as well as open-source software for the design and simulation of platform trials. The software tools provide the flexibility necessary to capture the complexity of platform trials.

MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration

Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.

Adaptive Platform Trials in Stroke

Clinical trials of treatments for stroke have generally utilized 2-arm, randomized designs to evaluate a single intervention against a control. Running separate clinical trials, with each addressing a single therapeutic question, is resource intensive and slows evidence generation, especially in a field with rapidly expanding treatment options and evolving practices. Platform trials-randomized clinical trials designed to evaluate multiple interventions that may enter and exit the ongoing platform based on a master protocol-accelerate the investigation of multiple therapeutic options within a single infrastructure. This in turn has the potential to accelerate access to new interventions for patients with stroke that can save lives and improve outcomes. In the context of acute ischemic stroke, 2 new platform trials have been established, the STEP trial (StrokeNet Thrombectomy Endovascular Platform) and ACT-GLOBAL (A Multi-Factorial, Multi-Arm, Multi-Stage, Randomised, Global Adaptive Platform Trial for Stroke), to address multiple therapeutic questions simultaneously using a multifactorial design including Bayesian modeling and other adaptive features. These trials are designed to maximize the information obtained from each participant, to align clinical research more closely with the complexities of clinical care, and to accelerate the identification of effective therapies. This article explores conceptual, practical, and statistical considerations in the design and implementation of adaptive platform trials and highlights their potential to accelerate the identification of new therapies, management, and rehabilitation in stroke.

A randomized embedded multifactorial adaptive platform for extra corporeal membrane oxygenation (REMAP ECMO) - design and rationale of the left ventricular unloading trial domain

BACKGROUND

The use of Extracorporeal Membrane Oxygenation (ECMO) remains associated with high rates of complications, weaning failure and mortality which can be partly explained by a knowledge gap on how to properly manage patients on ECMO support. To address relevant patient management issues, we designed a "Randomized Embedded Multifactorial Adaptive Platform (REMAP)" in the setting of ECMO (REMAP ECMO) and a first embedded randomized controlled trial (RCT) investigating the effects of routine early left ventricular (LV) unloading through intra-aortic balloon pumping (IABP).

METHODS

REMAP ECMO describes a registry-based platform allowing for the embedding of multiple response adaptive RCTs (trial domains) which can perpetually address the effect of relevant patient management issues on ECMO weaning success. A first trial domain studies the effects of LV unloading by means of an IABP as an adjunct to veno-arterial (V-A) ECMO versus V-A ECMO alone on ECMO weaning success at 30 days in adult cardiogenic shock patients admitted to the Intensive Care Unit (ICU). The primary outcome of this trial is "successful weaning from ECMO" being defined as a composite of survival without the need for mechanical circulatory support, heart transplantation, or left ventricular assist device (LVAD) at 30 days after initiation of ECMO. Secondary outcomes include the need for interventional escalation of LV unloading strategy, mechanistic endpoints, survival characteristics until 1 year after ECMO initiation, and quality of life. Trial data will be analysed using a Bayesian statistical framework. The adaptive design allows for a high degree of flexibility, such as response adaptive randomization and early stopping of the trial for efficacy or futility. The REMAP ECMO LV unloading study is approved by the Medical Ethical Committee of the Erasmus Medical Center and is publicly registered.

CONCLUSION

This REMAP ECMO trial platform enables the efficient roll-out of multiple RCTs on relevant patient management issues. A first embedded trial domain will compare routine LV unloading by means of an IABP as an adjunct to V-A ECMO versus V-A ECMO alone.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT05913622.

Group Response-Adaptive Randomization With Delayed and Missing Responses

Response-adaptive randomization (RAR) procedures have been extensively studied in the literature, but most of the procedures rely on updating the randomization after each response, which is impractical in many clinical trials. In this article, we propose a new family of RAR procedures that dynamically update based on the responses of a group of individuals, either when available or at fixed time intervals (weekly or biweekly). We show that the proposed design retains the essential theoretical properties of Hu and Zhang's doubly adaptive biased coin designs (DBCD), and performs well in scenarios involving delayed and randomly missing responses. Numerical studies have been conducted to demonstrate that the new proposed group doubly adaptive biased coin design has similar properties to the Hu and Zhang's DBCDs in different situations. We also apply the new design to a real clinical trial, highlighting its advantages and practicality. Our findings open the door to studying the properties of other group response adaptive designs, such as urn models, and facilitate the application of response-adaptive randomized clinical trials in practice.

Can a Bayesian approach clarify if corticosteroids are beneficial for severe community-acquired pneumonia?

Despite advances in the antimicrobial treatment of sepsis and organ support in the intensive care unit (ICU), community-acquired pneumonia (CAP) remains a leading cause of mortality and disability-adjusted life years lost globally.1 Severe CAP, where CAP becomes complicated by acute hypoxaemic respiratory failure or shock, is also the most common cause of sepsis, where complex and heterogeneous biological mechanisms underlie a dysregulated inflammatory host response that ultimately leads to major organ dysfunction and death. Along with the emerging threats of respiratory pandemics, antimicrobial resistance, ageing populations and the rise of chronic diseases, much research has been conducted to improve the treatment outcomes of CAP via host immunomodulation. These efforts have focused almost exclusively on anti-inflammatory effects of corticosteroids, which have a proven track record of improving outcomes in other forms of sepsis, such as bacterial meningitis, Pneumocystis jirovecii pneumonia and severe COVID-19.

Benefit of early oseltamivir therapy for adults hospitalized with influenza A: an observational study

BACKGROUND

clinical guidelines recommend initiation of antiviral therapy as soon as possible for patients hospitalized with confirmed or suspected influenza.

METHODS

A multicenter US observational sentinel surveillance network prospectively enrolled adults (aged ≥18 years) hospitalized with laboratory-confirmed influenza at 24 hospitals during October 1, 2022-July 21, 2023. A multivariable proportional odds model was used to compare peak pulmonary disease severity (no oxygen support, standard supplemental oxygen, high-flow oxygen/non-invasive ventilation, invasive mechanical ventilation, or death) after the day of hospital admission among patients starting oseltamivir treatment on the day of admission (early) versus those who did not (late or not treated), adjusting for baseline (admission day) severity, age, sex, site, and vaccination status. Multivariable logistic regression models were used to evaluate the odds of intensive care unit (ICU) admission, acute kidney replacement therapy or vasopressor use, and in-hospital death.

RESULTS

A total of 840 influenza-positive patients were analyzed, including 415 (49%) who started oseltamivir treatment on the day of admission, and 425 (51%) who did not. Compared with late or not treated patients, those treated early had lower peak pulmonary disease severity (proportional aOR: 0.60, 95% CI: 0.49-0.72), and lower odds of intensive care unit admission (aOR: 0.24, 95% CI: 0.13-0.47), acute kidney replacement therapy or vasopressor use (aOR: 0.40, 95% CI: 0.22-0.67), and in-hospital death (aOR: 0.36, 95% CI: 0.18-0.72).

CONCLUSION

Among adults hospitalized with influenza, treatment with oseltamivir on day of hospital admission was associated reduced risk of disease progression, including pulmonary and extrapulmonary organ failure and death.

Global collaboration between platform trials in surgery and anaesthesia

Large, randomized trials are the bedrock of evidence-based medicine, but the resources required to complete such trials greatly limit the number of important clinical questions that can be addressed within a reasonable period of time. Adaptive platform trials can identify effective, ineffective, or harmful treatments faster. These trials have been shown to deliver rapid evidence through the COVID-19 pandemic and are now being adopted across surgery and anaesthesia, with many opportunities for surgeons, anaesthetists, and other perioperative physicians to conduct and collaborate in platform trials.

Contribution of intrapulmonary shunt to the pathogenesis of profound hypoxaemia in viral infection: a mechanistic discussion with an illustrative case

Background

The formation of anastomoses between the pulmonary arteries and pulmonary veins, or the pulmonary and the bronchial circulation, is part of normal foetal lung development. They persist in approximately 30% of adults at rest, and open in almost all adults during exertion. Blood flowing through these anastomoses bypasses the alveolar surface and increases in such shunting can thus cause hypoxaemia. This is now known to contribute to the pathogenesis of hypoxaemia in COVID-19 disease. We here provide evidence to support a similar role in influenza A infection.

Illustrative case presentation

We describe a case of influenza A infection associated with severe hypoxaemia, poorly responsive to supplemental oxygen and which worsened following the application of continuous positive airway pressure (CPAP), despite the presence of a normal physical examination, chest radiograph and echocardiogram. This combination suggests a significant intrapulmonary (extra-alveolar) shunt as a cause of the severe hypoxaemia. The shunt fraction was estimated to be approximately 57%.

Discussion and conclusion

Intrapulmonary vascular shunts can contribute substantially to hypoxaemia in viral infection. Seeking to understand the pathogenesis of observed hypoxaemia can help guide respiratory therapy. Mechanistic research may suggest novel therapeutic targets which could assist in avoiding intubation and mechanical ventilatory support.

An Update on Recent Clinical Trial Data in Bloodstream Infection

Bloodstream infections (BSIs) remain a significant source of morbidity and mortality globally, exacerbated by an ageing population and rising antimicrobial resistance (AMR). This review offers an updated evaluation of randomized clinical trials (RCTs) in BSI management from 2018 onwards, focusing on the evolving landscape of diagnostics and treatment. New rapid diagnostic technologies and shorter antimicrobial courses have transformed clinical practice, reducing the time to appropriate therapy and hospital stays. Several RCTs demonstrated that rapid phenotypic and genotypic tests shorten the time to optimal therapy, especially when paired with antimicrobial stewardship. Ongoing trials are investigating novel antimicrobial regimens and the safety of early oral switch strategies, particularly for Gram-positive and Gram-negative BSIs. Recent RCTs on Staphylococcus aureus BSI (SAB) and multidrug-resistant Gram-negative bacteria highlight advances in treatment but emphasize the need for further study into the efficacy of combination therapies and the utility of rapid diagnostics in different healthcare settings. The review also explores challenges in trail design, with adaptive and pragmatic appropriates improving the efficacy of clinical trials. Finally, this paper identifies gaps in the research, including the need for further investigation into oral step-down therapy, optimal durations, and the role of rapid diagnostics in resource-limited settings.

Lessons Learned from National Heart, Lung, and Blood Institute Covid-19 Clinical Trials

AbstractIn response to the Covid-19 pandemic, the National Heart, Lung, and Blood Institute launched five multisite clinical trials testing candidate host tissue-directed medical interventions to hasten recovery, improve function, and reduce morbidity and mortality. Speed, flexibility, and collaboration were essential. This article from the Steering and Executive committees describes the Collaborating Network of Networks for Evaluating Covid-19 and Therapeutic Strategies (CONNECTS) research program that enrolled 6690 participants and evaluated 18 intervention strategies using 10 molecular agents across the care continuum (outpatient, inpatient, and post discharge), and reports lessons learned from this initiative. Successes include rapid trial execution through collaboration and adaptive platform designs. Challenges that impeded efficiency included time required to execute subcontracts, constraints on clinical research workforce, and limited research infrastructure in nonacademic settings.

Screen Failures in Clinical Trials in Retina

PURPOSE

Disparities in clinical trials are a major problem because of significant underrepresentation of certain gender, racial, and ethnic groups. Several factors including stringent eligibility criteria and recruitment strategies hinder our understanding of retinal disease. Thus, we aimed to study the various reasons of screen failures and specific patient and study characteristics among screen failures.

DESIGN

This is a cross-sectional retrospective study.

METHODS

Screening data of 87 trials from 6 centers were analyzed. Study characteristics (disease studied, phase of trial, and route of drug administration) and patient demographics (age, gender, race, ethnicity, and employment status) were compared among different causes of screen failures. Screen failures were broadly classified into 6 categories: exclusion because of vision-based criteria, exclusion because of imaging findings, exclusion because of other factors, patient-related criteria, physician-related criteria, and miscellaneous. Descriptive statistics, Pearson chi-square test, and analysis of variance were used for statistical analysis.

MAIN OUTCOME MEASURES

Prevalence of various reasons for screen failures in multiple trials and its trend among different study and patient characteristics.

RESULTS

Among 87 trials and 962 patients, 465 (48.2%) patients were successfully randomized and 497 (51.8%) patients were classified as screen failures. The trials were conducted for various retinal diseases. Mean age was 76.50 ± 10.45 years and 59.4% were females. Predominantly White patients (93.4%) and unemployed/retired patients (66.6%) were screened. Of the 497 screen failures, most were because of patients not meeting inclusion criteria of imaging findings (n = 221 [44.5%]) followed by inclusion of vision-based criteria (n = 73 [14.7%]), exclusion because of other factors (n = 75 [15.1%]), patient-related (n = 34 [6.8%]), physician-related (n = 28 [5.6%]), and miscellaneous reasons (n = 39 [7.8%]). Reason for screen failure was not available for 27 (5.4%) patients. A higher proportion of patients screened for surgical trials (15%) declined to participate in the study compared with noninvasive trials involving topical drugs and photobiomodulation (0%) (P = 0.02).

CONCLUSIONS

Patients not meeting the imaging and vision-cased criteria were the most common reasons for screen failures. White patients and unemployed patients predominantly participated in clinical trials. Patients are more inclined to continue participation in noninvasive clinical trials compared with surgical trials. Better recruitment strategies and careful consideration of study criteria can aid in decreasing the rate of screen failures.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.

Adaptive Trials in Stroke: Current Use and Future Directions

Inclusion of adaptive design features in a clinical trial provides preplanned flexibility to dynamically modify a trial during its conduct while preserving validity and integrity. Adaptive trials are needed to accelerate the conduct of more efficient, informative, and ethical clinical research in the field of neurology. Stroke is a natural candidate for adoption of these innovative approaches to trial design. This Research Methods in Neurology article is informed by a scoping review that identified 45 completed or ongoing adaptive clinical trials in stroke that were appraised: 15 trials had published results with or without a published protocol and 30 ongoing trials (14 trials had a published protocol, and 16 trials were registered only). Interventions spanned acute (n = 28), rehabilitation (n = 8), prevention (n = 8), and rehabilitation and prevention (n = 1). A subsample of these trials was selected to illustrate the utility of adaptive design features and discuss why each adaptive feature was incorporated in the design to best achieve the aim; whether each individual feature was used and whether it resulted in expected efficiencies; and any learnings during preparation, conduct, or reporting. We then discuss the operational, ethical, and regulatory considerations that warrant careful consideration during adaptive trial planning and reflect on the workforce readiness to deliver adaptive trials in practice. We conclude that adaptive trials can be designed, funded, conducted, and published for a wide range of research questions and offer future directions to support adoption of adaptive trial designs in stroke and neurologic research more broadly.

Overview of ACTIV trial-specific lessons learned

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) was an extraordinary example of a public-private partnership (PPP) that brought together over thirty organizations and hundreds of individuals to address one of the most pressing global health needs in recent decades. In particular, ACTIV provided a key avenue for testing numerous therapeutics for their potential benefit in treating the SARS-CoV-2 virus or the resulting symptoms of acute COVID-19 infection. Given the speed and scale at which ACTIV designed and implemented master protocols across global networks that it was simultaneously working to create, the PPP can provide valuable lessons for best practices and avoiding pitfalls the next time the world is faced with a global pandemic of a novel pathogen. This report provides a general overview of the ACTIV partnership to set the stage and context for the subsequent articles in this issue that will relay these lessons learned.

Association of COVID-19 risk factors with systemic fungal infections in hospitalized patients

Purpose

A new category of systemic co-infections that emerged with the COVID-19 pandemic is known as COVID-19-associated (CA) fungal infections, which include pulmonary aspergillosis (CAPA), candidiasis (CAC), and mucormycosis (CAM). We aimed to study the association between patient characteristics of hospitalized COVID-19 patients, COVID-19 comorbidities, and COVID-19 therapies with secondary non-superficial fungal infections.

Methods

We performed descriptive and regression analyses of data from 4,999 hospitalized COVID-19 patients from the University of Kentucky Healthcare (UKHC) system.

Results

The patients with secondary systemic fungal infections had a 6-fold higher risk of death than those without such infections. Generally, the risk factors for severe COVID-19 (age, obesity, cardiovascular disease, diabetes, and lack of COVID-19 vaccination) were strong predictors of a secondary fungal infection. However, several characteristics had much higher risks, suggesting that a causative link may be at play: ICU admission, mechanical ventilation, length of hospital stay, and steroid use.

Conclusions

In sum, this study found that the known risk factors for severe COVID-19 disease, age, diabetes, cardiovascular disease, obesity, ventilation, and high steroid doses were all predictors of a secondary fungal infection. Steroid therapy may need to be modified to account for a risk or a presence of a fungal infection in vulnerable patients.

Steroids in severe community-acquired pneumonia

There is conflicting evidence regarding the use of steroids in severe community-acquired pneumonia (CAP), with previous randomised controlled trials limited by small sample sizes. ESCAPe and CAPE COD are two recently published large trials on steroids in severe CAP. ESCAPe assessed the initiation of methylprednisolone within 72-96 h of hospital admission, while CAPE COD studied the use of hydrocortisone within 24 h of the development of severe CAP. ESCAPe did not show any differences in all-cause 60-day mortality or any of its secondary outcomes. CAPE COD showed that hydrocortisone improved all-cause 28-day mortality and reduced the risk of intubation or vasopressor-dependent shock. Important differences between the trials included the steroid regimens used, timing of steroid administration and baseline characteristics, with more diabetic patients included in ESCAPe. The results of CAPE COD support the initiation of hydrocortisone within 24 h of developing severe CAP, but more research is needed to evaluate long-term outcomes and optimum dosing regimens for steroids in severe CAP.

Statistical Power and Performance of Strategies to Analyze Composites of Survival and Duration of Ventilation in Clinical Trials

BACKGROUND

Patients with acute hypoxemic respiratory failure are at high risk of death and prolonged time on the ventilator. Interventions often aim to reduce both mortality and time on the ventilator. Many methods have been proposed for analyzing these endpoints as a single composite outcome (days alive and free of ventilation), but it is unclear which analytical method provides the best performance. Thus, we aimed to determine the analysis method with the highest statistical power for use in clinical trials.

METHODS

Using statistical simulation, we compared multiple methods for analyzing days alive and free of ventilation: the t, Wilcoxon rank-sum, and Kryger Jensen and Lange tests, as well as the proportional odds, hurdle-Poisson, and competing risk models. We compared 14 scenarios relating to: 1) varying baseline distributions of mortality and duration of ventilation, which were based on data from a registry of patients with acute hypoxemic respiratory failure and 2) the varying effects of treatment on mortality and duration of ventilation.

RESULTS AND CONCLUSIONS

All methods have good control of type 1 error rates (i.e., avoid false positive findings). When data are simulated using a proportional odds model, the t test and ordinal models have the highest relative power (92% and 90%, respectively), followed by competing risk models. When the data are simulated using survival models, the competing risk models have the highest power (100% and 92%), followed by the t test and a ten-category ordinal model. All models struggled to detect the effect of the intervention when the treatment only affected one of mortality and duration of ventilation. Overall, the best performing analytical strategy depends on the respective effects of treatment on survival and duration of ventilation and the underlying distribution of the outcomes. The evaluated models each provide a different interpretation for the treatment effect, which must be considered alongside the statistical power when selecting analysis models.

Using real-world data to accelerate the search for long COVID therapies

Long COVID, a spectrum of symptoms and syndromes that can develop after SARS-COV-2 infection, can significantly affect patients' health, quality of life and impact their ability to productively function in society. There is currently no approved therapy for Long COVID and there is an urgent need for rigorous clinical trials to find such treatments. Although research into the pathophysiology of Long COVID is advancing, investigations into treatment for patients remain underfunded and, as a result, understudied. Owing to the urgency of the Long COVID pandemic and as a research collaborative across a diversity of biomedical innovation value propositions, we are calling for a new approach that parallelizes pathophysiologic and therapeutic research into this condition, leveraging patient-centered research and real-world data to generate hypotheses to assess the effectiveness of existing FDA approved drugs. Accelerated discovery of therapeutics for Long COVID can then be confirmed through efficient and cost-effective adaptive platform clinical trials.

Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies

Critical care trials evaluate the effect of interventions in patients with diverse personal histories and causes of illness, often under the umbrella of heterogeneous clinical syndromes, such as sepsis or acute respiratory distress syndrome. Given this variation, it is reasonable to expect that the effect of treatment on outcomes may differ for individuals with variable characteristics. However, in randomized controlled trials, efficacy is typically assessed by the average treatment effect (ATE), which quantifies the average effect of the intervention on the outcome in the study population. Importantly, the ATE may hide variations of the treatment's effect on a clinical outcome across levels of patient characteristics, which may erroneously lead to the conclusion that an intervention does not work overall when it may in fact benefit certain patients. In this review, we describe methodological approaches for assessing heterogeneity of treatment effect (HTE), including expert-derived subgrouping, data-driven subgrouping, baseline risk modeling, treatment effect modeling, and individual treatment rule estimation. Next, we outline how insights from HTE analyses can be incorporated into the design of clinical trials. Finally, we propose a research agenda for advancing the field and bringing HTE approaches to the bedside.

Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world

The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and-with the right planning-have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.

The future of clinical trials in idiopathic pulmonary fibrosis

PURPOSE OF REVIEW

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with a poor prognosis and limited therapeutic options. A multitude of promising compounds are currently being investigated; however, the design and conductance of late-phase clinical trials in IPF has proven particularly challenging.

RECENT FINDINGS

Despite promising phase 2 data, ziritaxestat, an autotaxin inhibitor, pentraxin-2, an endogenous protein that regulates wound healing and fibrosis, and pamrevlumab, a human monoclonal antibody against connective tissue growth factor, failed to show efficacy in phase 3 trials. Endpoint selection is critical for the design, execution, and success of clinical trials; recently, attention has been paid to the assessment of how patients feel, function, and survive with the aim of aligning scientific objectives and patient needs in IPF. External control arms are control patients that derive from historical randomized controlled trials, registries, or electronic health records. They are increasingly used to assess treatment efficacy in clinical trials owing to their potential to reduce study duration and cost and increase generalizability of findings.

SUMMARY

Advances in study design, end point selection and statistical analysis, and innovative strategies for more efficient enrolment of study participants have the potential to increase the likelihood of success of late-phase clinical trials in IPF.

The impact of heterogeneity on the analysis of platform trials with normally distributed outcomes

BACKGROUND

A platform trial approach allows adding arms to on-going trials to speed up intervention discovery programs. A control arm remains open for recruitment in a platform trial while intervention arms may be added after the onset of the study and could be terminated early for efficacy and/or futility when early stopping is allowed. The topic of utilising non-concurrent control data in the analysis of platform trials has been explored and discussed extensively. A less familiar issue is the presence of heterogeneity, which may exist for example due to modification of enrolment criteria and recruitment strategy.

METHOD

We conduct a simulation study to explore the impact of heterogeneity on the analysis of a two-stage platform trial design. We consider heterogeneity in treatment effects and heteroscedasticity in outcome data across stages for a normally distributed endpoint. We examine the performance of some hypothesis testing procedures and modelling strategies. The use of non-concurrent control data is also considered accordingly. Alongside standard regression analysis, we examine the performance of a novel method that was known as the pairwise trials analysis. It is similar to a network meta-analysis approach but adjusts for treatment comparisons instead of individual studies using fixed effects.

RESULTS

Several testing strategies with concurrent control data seem to control the type I error rate at the required level when there is heteroscedasticity in outcome data across stages and/or a random cohort effect. The main parameter of treatment effects in some analysis models correspond to overall treatment effects weighted by stage wise sample sizes; while others correspond to the effect observed within a single stage. The characteristics of the estimates are not affected significantly by the presence of a random cohort effect and/ or heteroscedasticity.

CONCLUSION

In view of heterogeneity in treatment effect across stages, the specification of null hypotheses in platform trials may need to be more subtle. We suggest employing testing procedure of adaptive design as opposed to testing the statistics from regression models; comparing the estimates from the pairwise trials analysis method and the regression model with interaction terms may indicate if heterogeneity is negligible.

Statistical considerations for the platform trial in COVID-19 vaccine priming and boosting

The Platform trial In COVID-19 priming and BOOsting (PICOBOO) is a multi-site, adaptive platform trial designed to generate evidence of the immunogenicity, reactogenicity, and cross-protection of different booster vaccination strategies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, specific for the Australian context. The PICOBOO trial randomises participants to receive one of three COVID-19 booster vaccine brands (Pfizer, Moderna, Novavax) available for use in Australia, where the vaccine brand subtypes vary over time according to the national vaccine roll out strategy, and employs a Bayesian hierarchical modelling approach to efficiently borrow information across consecutive booster doses, age groups and vaccine brand subtypes. Here, we briefly describe the PICOBOO trial structure and report the statistical considerations for the estimands, statistical models and decision making for trial adaptations. This paper should be read in conjunction with the PICOBOO Core Protocol and PICOBOO Sub-Study Protocol 1: Booster Vaccination. PICOBOO was registered on 10 February 2022 with the Australian and New Zealand Clinical Trials Registry ACTRN12622000238774.

Adaptive multi-interventional trial platform to improve patient care for fibrotic interstitial lung diseases

BACKGROUND

Fibrotic interstitial lung diseases (fILDs) are a heterogeneous group of lung diseases associated with significant morbidity and mortality. Despite a large increase in the number of clinical trials in the last 10 years, current regulatory-approved management approaches are limited to two therapies that prevent the progression of fibrosis. The drug development pipeline is long and there is an urgent need to accelerate this process. This manuscript introduces the concept and design of an innovative research approach to drug development in fILD: a global Randomised Embedded Multifactorial Adaptive Platform in fILD (REMAP-ILD).

METHODS

Description of the REMAP-ILD concept and design: the specific terminology, design characteristics (multifactorial, adaptive features, statistical approach), target population, interventions, outcomes, mission and values, and organisational structure.

RESULTS

The target population will be adult patients with fILD, and the primary outcome will be a disease progression model incorporating forced vital capacity and mortality over 12 months. Responsive adaptive randomisation, prespecified thresholds for success and futility will be used to assess the effectiveness and safety of interventions. REMAP-ILD embraces the core values of diversity, equity, and inclusion for patients and researchers, and prioritises an open-science approach to data sharing and dissemination of results.

CONCLUSION

By using an innovative and efficient adaptive multi-interventional trial platform design, we aim to accelerate and improve care for patients with fILD. Through worldwide collaboration, novel analytical methodology and pragmatic trial delivery, REMAP-ILD aims to overcome major limitations associated with conventional randomised controlled trial approaches to rapidly improve the care of people living with fILD.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Enhancing sepsis biomarker development: key considerations from public and private perspectives

Implementation of biomarkers in sepsis and septic shock in emergency situations, remains highly challenging. This viewpoint arose from a public-private 3-day workshop aiming to facilitate the transition of sepsis biomarkers into clinical practice. The authors consist of international academic researchers and clinician-scientists and industry experts who gathered (i) to identify current obstacles impeding biomarker research in sepsis, (ii) to outline the important milestones of the critical path of biomarker development and (iii) to discuss novel avenues in biomarker discovery and implementation. To define more appropriately the potential place of biomarkers in sepsis, a better understanding of sepsis pathophysiology is mandatory, in particular the sepsis patient's trajectory from the early inflammatory onset to the late persisting immunosuppression phase. This time-varying host response urges to develop time-resolved test to characterize persistence of immunological dysfunctions. Furthermore, age-related difference has to be considered between adult and paediatric septic patients. In this context, numerous barriers to biomarker adoption in practice, such as lack of consensus about diagnostic performances, the absence of strict recommendations for sepsis biomarker development, cost and resources implications, methodological validation challenges or limited awareness and education have been identified. Biomarker-guided interventions for sepsis to identify patients that would benefit more from therapy, such as sTREM-1-guided Nangibotide treatment or Adrenomedullin-guided Enibarcimab treatment, appear promising but require further evaluation. Artificial intelligence also has great potential in the sepsis biomarker discovery field through capability to analyse high volume complex data and identify complex multiparametric patient endotypes or trajectories. To conclude, biomarker development in sepsis requires (i) a comprehensive and multidisciplinary approach employing the most advanced analytical tools, (ii) the creation of a platform that collaboratively merges scientific and commercial needs and (iii) the support of an expedited regulatory approval process.

The Integration of Clinical Trials With the Practice of Medicine: Repairing a House Divided

Importance

Optimal health care delivery, both now and in the future, requires a continuous loop of knowledge generation, dissemination, and uptake on how best to provide care, not just determining what interventions work but also how best to ensure they are provided to those who need them. The randomized clinical trial (RCT) is the most rigorous instrument to determine what works in health care. However, major issues with both the clinical trials enterprise and the lack of integration of clinical trials with health care delivery compromise medicine's ability to best serve society.

Observations

In most resource-rich countries, the clinical trials and health care delivery enterprises function as separate entities, with siloed goals, infrastructure, and incentives. Consequently, RCTs are often poorly relevant and responsive to the needs of patients and those responsible for care delivery. At the same time, health care delivery systems are often disengaged from clinical trials and fail to rapidly incorporate knowledge generated from RCTs into practice. Though longstanding, these issues are more pressing given the lessons learned from the COVID-19 pandemic, heightened awareness of the disproportionate impact of poor access to optimal care on vulnerable populations, and the unprecedented opportunity for improvement offered by the digital revolution in health care. Four major areas must be improved. First, especially in the US, greater clarity is required to ensure appropriate regulation and oversight of implementation science, quality improvement, embedded clinical trials, and learning health systems. Second, greater adoption is required of study designs that improve statistical and logistical efficiency and lower the burden on participants and clinicians, allowing trials to be smarter, safer, and faster. Third, RCTs could be considerably more responsive and efficient if they were better integrated with electronic health records. However, this advance first requires greater adoption of standards and processes designed to ensure health data are adequately reliable and accurate and capable of being transferred responsibly and efficiently across platforms and organizations. Fourth, tackling the problems described above requires alignment of stakeholders in the clinical trials and health care delivery enterprises through financial and nonfinancial incentives, which could be enabled by new legislation. Solutions exist for each of these problems, and there are examples of success for each, but there is a failure to implement at adequate scale.

Conclusions and Relevance

The gulf between current care and that which could be delivered has arguably never been wider. A key contributor is that the 2 limbs of knowledge generation and implementation-the clinical trials and health care delivery enterprises-operate as a house divided. Better integration of these 2 worlds is key to accelerated improvement in health care delivery.

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

Importance

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.

Objectives

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.

Design, Setting, and Participants

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.

Main Outcomes and Measures

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.

Results

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.

Conclusions and Relevance

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.

Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm survival trials

Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these studies are well-powered is crucial for identifying superior treatments. With the rise of personalized medicine, treatment efficacy may vary based on biomarker profiles. However, researchers often lack prior knowledge about which biomarkers are linked to varied treatment effects. Fixed or response-adaptive designs may not sufficiently account for heterogeneous patient characteristics, such as genetic diversity, potentially reducing the chance of selecting the optimal treatment for individuals. Recent advances in Bayesian nonparametric modeling pave the way for innovative trial designs that not only maintain robust power but also offer the flexibility to identify subgroups deriving greater benefits from specific treatments. Building on this inspiration, we introduce a Bayesian adaptive design for multi-arm trials focusing on time-to-event endpoints. We introduce a covariate-adjusted response adaptive randomization, updating treatment allocation probabilities grounded on causal effect estimates using a random intercept accelerated failure time BART model. After the trial concludes, we suggest employing a multi-response decision tree to pinpoint subgroups with varying treatment impacts. The performance of our design is then assessed via comprehensive simulations.