Liberation from mechanical ventilation using Extubation Advisor Decision Support (LEADS): protocol for a multicentre pilot trial.

INTRODUCTION: Timely successful liberation from invasive ventilation has the potential to minimise critically ill patients exposure to invasive ventilation, save costs and improve outcomes; yet no trials have evaluated strategies to better inform extubation decision-making. The Liberation from mechanical ventilation using Extubation Advisor (EA) Decision Support (LEADS) Pilot Trial will assess the feasibility of a trial of a novel extubation decision support tool on feasibility metrics. The primary feasibility outcome will reflect our ability to recruit the desired population. Secondary feasibility outcomes will assess rates of (1) consent, (2) randomisation, (3) intervention adherence, (4) bidirectional crossovers and the (5) completeness of clinical outcomes collected. We will also evaluate physicians perceptions of the usefulness of the EA tool and measure costs related to EA implementation. METHODS AND ANALYSIS: We will include critically ill adults who are invasively ventilated for ≥48 hours and who are ready to undergo a spontaneous breathing trial (SBT) with a view to extubation. Patients in the intervention arm will undergo an EA assessment that measures respiratory rate variability to derive an estimate of extubation readiness. Treating clinicians (respiratory therapists, attending physicians and intensive care unit fellows) will receive an EA report for each SBT conducted. The EA report will assist, rather than direct, extubation decision-making. Patients in the control arm will receive standard care. SBTs will be directed by clinicians, using current best evidence, without EA assessments or reports. We aim to recruit 1 to 2 patients/month in approximately 10 centres, and to achieve >75% consent rate, >95% randomisation among consented patients, >80% of EA reports generated and delivered (intervention arm), <10% crossovers (both arms) and >90% of patients with complete clinical outcomes. We will also report physician point-of-care perceptions of the usefulness of the EA tool. ETHICS AND DISSEMINATION: The LEADS Pilot Trial is approved by the Research Ethics Boards of all participating centres and Clinical Trials Ontario (4008). We will disseminate the LEADS trial findings through conference presentations and publication. TRIAL REGISTRATION NUMBER: NCT05506904. PROTOCOL VERSION: 24 April 2024

Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of “modifiable risk factors”, measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details

Research priorities for the study of atrial fibrillation during acute and critical illness: recommendations from the Symposium on Atrial Fibrillation in Acute and Critical Care.

Atrial fibrillation (AF) is a common arrhythmia encountered in acute and critical illness and is associated with poor short and long-term outcomes. Given the consequences of developing AF, research into prevention, prediction and treatment of this arrhythmia in the critically ill are of great potential benefit, however, study of AF in critically ill patients faces unique challenges, leading to a sparse evidence base to guide management in this population. Major obstacles to the study of AF in acute and critical illness include absence of a common definition, challenges in designing studies that capture complex etiology and assess causality, lack of a clear outcome set, difficulites in recruitment in acute environments with respect to timing, consent, and workflow, and failure to embed studies into clinical care platforms and capitalize on emerging technologies. Collaborative effort by researchers, clinicians, and stakeholders should be undertaken to address these challenges, both through interdisciplinary cooperation for the optimization of research efficiency and advocacy to advance the understanding of this common and complex arrhythmia, resulting in improved patient care and outcomes. The Symposium on Atrial Fibrillation in Acute and Critical Care was convened to address some of these challenges and propose potential solutions

Redefining Critical Illness

Both research and practice in critical care medicine have long been defined by syndromes. Though clinically recognizable entities, these are in fact loose amalgams of heterogeneous states, within which responses to therapy may vary. Mounting translational evidence suggests the current syndrome-based framework of critical illness should be reconsidered. Moreover, research done during the COVID-19 pandemic illustrates how the study of a more biologically homogeneous condition – respiratory failure due to SARS-CoV-2 infection – can increase the efficiency with which actionable results are generated. We discuss recent findings from basic science and clinical research in critical care, and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus instead on the underlying biological changes that underpin critical illness states, and that may be amenable to treatment. We hypothesize that such an approach will accelerate translational critical care research, leading to a richer understanding of the pathobiology of critical illness, and of the proximate determinants of ICU outcomes. The specificity and granularity gained will support the design of more effective clinical trials, and inform a more precise, effective practice at the bedside.<br/

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A)

Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis

Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine—the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g., trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes.peer-reviewe