The Intensive Care Platform Trial

Among critically ill patients, many die, and many of the survivors and their family members struggle for years with reduced quality of life. Critically ill patients are treated in intensive care units (ICUs). Here, they receive life support, e.g., mechanical ventilation and advanced support of the circulation (heart and blood vessels) and kidneys. In addition, ICU patients receive many other treatments. It is, however, uncertain if all the treatments provide value for the patients. The desirable effects of many treatments are uncertain, and some may be wasteful or even harmful. Clinical trials are necessary to validly assess the desirable and undesirable effects of different treatments. However, conventional clinical trials have limitations: * They typically only assess a single question related to a single comparison of treatments at a time. * They are often not very flexible, including with regards to the number of participants needed, and this increases the risk that a trial will end up as inconclusive. * There is no or limited re-use or sharing of infrastructure across trials, leading to duplicate work and resource use. * Trial participants do usually not benefit from the obtained knowledge before the trial concludes. * Involvement of patients, family members, and other stakeholders is typically limited, which may decrease the relevance of the questions addressed. With the Intensive Care Platform Trial (INCEPT), we aim to tackle these challenges by establishing a flexible platform trial that continuously learns from the obtained results. The platform trial may run forever with simultaneous and continuous assessment of many treatments. INCEPT will continuously learn from the accrued data and use these to improve the treatment of both participating and future patients. With INCEPT, we are also building a framework for thorough and extensive involvement of key stakeholders, including patients and family members. INCEPT will improve the way clinical trials are done and increase the probabilities that treatments are improved. This will: * Directly improve outcomes for ICU patients. * Relieve a strained healthcare system by discarding inefficient or harmful treatments. * Ensure that new treatments are beneficial or cost-effective before implementation. * Lower the costs and burdens of assessing more treatments in the critically ill.

Background: Randomised clinical trials (RCTs) are the gold standard for evaluating intervention effects, however, conventional RCTs are bureaucratic, costly, inflexible, and often inconclusive. Adaptive platform trials are increasingly used as they can reduce barriers and are more flexible, and thus come with a higher probability of obtaining conclusive results faster at lower costs. Objectives: The Intensive Care Platform Trial (INCEPT) will be used to assess the effects of interventions used in adults acutely admitted to the intensive care unit (ICU). Design: INCEPT is an investigator-initiated, pragmatic, randomised, embedded, multifactorial, international, adaptive platform trial. INCEPT uses adaptive stopping and arm-dropping rules, as well as fixed and response-adaptive randomisation. Specific domains may be either open label or blinded. Domains and interventions: Comparable groups of interventions will be nested in domains, which have conceptual similarities with stand-alone randomised trials. Domains will continuously be added to INCEPT and conducted following domain-specific appendices to the core protocol. Inclusion and exclusion criteria: Adults acutely admitted to the ICU will be screened if they are eligible for at least one active domain. The only platform-level exclusion criteria are 1) informed consent after inclusion expected to be unobtainable and 2) patients admitted under coercive measures. Additional inclusion and exclusion criteria will be domain-specific. Stakeholder involvement: Stakeholder involvement is central in INCEPT and ensured through a central advisory board comprising various key stakeholders, and consultations with national and international research panels consisting of ICU survivors, family members, clinicians, and researchers. Stakeholders will be involved in the development of the overall platform trial and specific domains with pre-specified minimum requirements for involvement. Outcomes: Each domain will use one of the core outcomes (defined elsewhere in the registration) as the primary outcome and the guiding outcome driving all adaptations. Statistical methods Primary analyses will generally be conducted in the intention-to-treat population of each domain. INCEPT primarily uses Bayesian statistical methods with neutral priors conveying either minimal information or some scepticism, although specific domains may use conventional, frequentist statistical methods. Outcomes will generally be analysed using logistic and linear regression models adjusted for pre-specified anticipated prognostic baseline characteristics, followed by calculation of sample-average estimates and intervention effects using G-computation. Results will be presented for each intervention and comparisons presented on both the absolute (risk differences and mean differences) and relative (risk ratios and ratios of means) scales with 95% credible intervals and probabilities of superiority. INCEPT will generally use constant, symmetric stopping rules for superiority/inferiority based on the guiding outcome; domains may use stopping rules for practical equivalence or futility based on the posterior distribution of the guiding outcome on the absolute scale. All stopping rules will be binding. Response-adaptive randomisation, either with or without restrictions, may be used based on the posterior distribution for the guiding outcome. Missing data will be multiply imputed. Additional secondary analyses (e.g., per-protocol analyses), sensitivity analyses, and analyses of heterogeneity in intervention effects according to pre-defined baseline characteristics may be specified for each domain and undertaken once a domain has stopped. Domains will be designed and evaluated using statistical simulation.