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PurposeNew-onset atrial fibrillation (NOAF) is common and associated with increased morbidity and mortality. However, its clinical importance and management in critically ill patients are not well described. The aim of this scoping review is to assess the epidemiology and management strategies of NOAF during critical illness.MethodThe review was conducted in accordance with the PRISMA extension for scoping reviews. We searched PubMed, EMBASE and the Cochrane Library for studies assessing the incidence, outcome and management strategies of NOAF in adult critically ill patients. The quality of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.ResultsA total of 99 studies were included, of which 79 were observational and 20 were interventional. The incidence of NOAF varied from 1.7% to 43.9% with considerable inter-population variation (very low quality of evidence). Commonly identified risk factors for NOAF included higher age, cardiovascular comorbidities and sepsis. The occurrence of NOAF was associated with adverse outcomes, including stroke, prolonged length of stay and mortality (very low quality of evidence). We found limited data on the optimal management strategy with no evidence for firm benefit or harm for any intervention (very low/low quality of evidence).ConclusionsThe definition and incidence of NOAF in critically ill patients varied considerably and many risk factors were identified. NOAF seemed to be associated with adverse outcomes, but data were very limited and current management strategies are not evidence-based.

Background Days alive without life support (DAWOLS) and similar outcomes that seek to summarise mortality and non-mortality experiences are increasingly used in critical care research. The use of these outcomes is challenged by different definitions and non-normal outcome distributions that complicate statistical analysis decisions. Methods We scrutinized the central methodological considerations when using DAWOLS and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID STEROID 2 randomised clinical trial. We focused on readily available regression models of increasing complexity (linear, hurdle-negative binomial, zero–one-inflated beta, and cumulative logistic regression models) that allow comparison of multiple treatment arms, adjustment for covariates and interaction terms to assess treatment effect heterogeneity. Results In general, the simpler models adequately estimated group means despite not fitting the data well enough to mimic the input data. The more complex models better fitted and thus better replicated the input data, although this came with increased complexity and uncertainty of estimates. While the more complex models can model separate components of the outcome distributions (i.e., the probability of having zero DAWOLS), this complexity means that the specification of interpretable priors in a Bayesian setting is difficult. Finally, we present multiple examples of how these outcomes may be visualised to aid assessment and interpretation. Conclusions This summary of central methodological considerations when using, defining, and analysing DAWOLS and similar outcomes may help researchers choose the definition and analysis method that best fits their planned studies. Trial registration COVID STEROID 2 trial, ClinicalTrials.gov: NCT04509973, ctri.nic.in: CTRI/2020/10/028731.

Background Although light sedation levels are associated with several beneficial outcomes for critically ill patients on mechanical ventilation, the majority of patients are still deeply sedated. Organizational factors may play a role on adherence to light sedation levels. We aimed to identify organizational factors associated with a moderate to light sedation target on the first 48 h of mechanical ventilation, as well as the association between early achievement of within-target sedation and mortality. Methods This study is a secondary analysis of a multicenter two-phase study (prospective cohort followed by a cluster-randomized controlled trial) performed in 118 Brazilian ICUs. We included all critically ill patients who were on mechanical ventilation 48 h after ICU admission. A moderate to light level of sedation or being alert and calm (i.e., the Richmond Agitation-Sedation Scale of − 3 to 0) was the target for all patients on mechanical ventilation during the study period. We collected data on the type of hospital (public, private, profit and private, nonprofit), hospital teaching status, nursing and physician staffing, and presence of sedation, analgesia, and weaning protocols. We used multivariate random-effects regression with ICU and study phase as random-effects and correction for patients’ Simplified Acute Physiology Score 3 and Sequential Organ Failure Assessment. We also performed a mediation analysis to explore whether sedation level was just a mediator of the association between organizational factors and mortality. Results We included 5719 patients. Only 1710 (29.9%) were on target sedation levels on day 2. Board-certified intensivists on the morning and afternoon shifts were associated with an adequate sedation level on day 2 (OR = 2.43; CI 95%, 1.09–5.38). Target sedation levels were associated with reduced hospital mortality (OR = 0.63; CI 95%, 0.55–0.72). Mediation analysis also suggested such an association, but did not suggest a relationship between the physician staffing model and hospital mortality. Conclusions Board-certified intensivists on morning and afternoon shifts were associated with an increased number of patients achieving lighter sedation goals. These findings reinforce the importance of organizational factors, such as intensivists’ presence, as a modifiable quality improvement target.

ObjectivesThe acute respiratory distress syndrome (ARDS) is a heterogeneous condition, and identification of subphenotypes may help in better risk stratification. Our study objective is to identify ARDS subphenotypes using new simpler methodology and readily available clinical variables.SettingThis is a retrospective Cohort Study of ARDS trials. Data from the US ARDSNet trials and from the international ART trial.Participants3763 patients from ARDSNet data sets and 1010 patients from the ART data set.Primary and secondary outcome measuresThe primary outcome was 60-day or 28-day mortality, depending on what was reported in the original trial. K-means cluster analysis was performed to identify subgroups. Sets of candidate variables were tested to assess their ability to produce different probabilities for mortality in each cluster. Clusters were compared with biomarker data, allowing identification of subphenotypes.ResultsData from 4773 patients were analysed. Two subphenotypes (A and B) resulted in optimal separation in the final model, which included nine routinely collected clinical variables, namely heart rate, mean arterial pressure, respiratory rate, bilirubin, bicarbonate, creatinine, PaO2, arterial pH and FiO2. Participants in subphenotype B showed increased levels of proinflammatory markers, had consistently higher mortality, lower number of ventilator-free days at day 28 and longer duration of ventilation compared with patients in the subphenotype A.ConclusionsRoutinely available clinical data can successfully identify two distinct subphenotypes in adult ARDS patients. This work may facilitate implementation of precision therapy in ARDS clinical trials.

BackgroundThe COVID-19 pandemic tested the capacity of intensive care units (ICU) to respond to a crisis and demonstrated their fragility. Unsurprisingly, higher than usual mortality rates, lengths of stay (LOS), and ICU-acquired complications occurred during the pandemic. However, worse outcomes were not universal nor constant across ICUs and significant variation in outcomes was reported, demonstrating that some ICUs could adequately manage the surge of COVID-19.MethodsIn the present editorial, we discuss the concept of a resilient Intensive Care Unit, including which metrics can be used to address the capacity to respond, sustain results and incorporate new practices that lead to improvement.ResultsWe believe that a resiliency analysis adds a component of preparedness to the usual ICU performance evaluation and outcomes metrics to be used during the crisis and in regular times.ConclusionsThe COVID-19 pandemic demonstrated the need for a resilient health system. Although this concept has been discussed for health systems, it was not tested in intensive care. Future studies should evaluate this concept to improve ICU organization for standard and pandemic times.

The choosing wisely campaign has highlighted for each medical profession the five practices that both physicians and patients should question (http://www.choosingwisely.org/). Achieving informed test selection was named as one of the five challenges our profession should address in the years to come. However, the means of achieving this aim remains unclear. Some call for realtime disclosure of the costs and consequences of excessive testing. Others believe in better education on the topic. Both approaches are challenging, and neither is likely to suffice alone. There is also a need to change the medical system and societal expectations from a good doctor.

BackgroundCritical care medicine has historically relied on syndromic diagnoses such as sepsis, acute respiratory distress syndrome (ARDS) and acute kidney injury to guide research and treatment. While this approach has advanced clinical practice, the growing recognition of patient heterogeneity presents significant challenges for treatment optimisation and trial interpretation. Understanding heterogeneous treatment effects (HTE) has emerged as a crucial methodological frontier, particularly for complex critical care syndromes where patient responses to interventions vary substantially.FindingsThere are three major methodological frameworks for analysing HTE: (1) Risk-based analyses, guided by the Predictive Approaches to Treatment effect Heterogeneity statement, provide an accessible framework for examining treatment effect variation across baseline risk strata but may overlook important effect modifiers. (2) Clustering techniques have successfully identified distinct phenotypes in both ARDS and sepsis, though external validation remains challenging. (3) Effect-based methods employing new methods offer sophisticated capabilities for identifying treatment effect modifiers but require careful consideration to model specification.ConclusionThis review examines these methodological approaches through both theoretical framework and practical application. Considerations on the applicability of HTE are also provided. We conclude that while HTE methods offer promising tools for personalising critical care interventions, their successful implementation requires careful consideration of both methodological rigour and practical feasibility.

Randomised clinical trials (RCTs) are the gold standard for providing unbiased evidence of intervention effects. Here, we provide an overview of the history of RCTs and discuss the major challenges and limitations of current critical care RCTs, including overly optimistic effect sizes; unnuanced conclusions based on dichotomization of results; limited focus on patient-centred outcomes other than mortality; lack of flexibility and ability to adapt, increasing the risk of inconclusive results and limiting knowledge gains before trial completion; and inefficiency due to lack of re-use of trial infrastructure. We discuss recent developments in critical care RCTs and novel methods that may provide solutions to some of these challenges, including a research programme approach (consecutive, complementary studies of multiple types rather than individual, independent studies), and novel design and analysis methods. These include standardization of trial protocols; alternative outcome choices and use of core outcome sets; increased acceptance of uncertainty, probabilistic interpretations and use of Bayesian statistics; novel approaches to assessing heterogeneity of treatment effects; adaptation and platform trials; and increased integration between clinical trials and clinical practice. We outline the advantages and discuss the potential methodological and practical disadvantages with these approaches. With this review, we aim to inform clinicians and researchers about conventional and novel RCTs, including the rationale for choosing one or the other methodological approach based on a thorough discussion of pros and cons. Importantly, the most central feature remains the randomisation, which provides unparalleled restriction of confounding compared to non-randomised designs by reducing confounding to chance.

PurposeClinical characteristics and management of COVID-19 patients have evolved during the pandemic, potentially changing their outcomes. We analyzed the associations of changes in mortality rates with clinical profiles and respiratory support strategies in COVID-19 critically ill patients.MethodsA multicenter cohort of RT-PCR-confirmed COVID-19 patients admitted at 126 Brazilian intensive care units between February 27th and October 28th, 2020. Assessing temporal changes in deaths, we identified distinct time periods. We evaluated the association of characteristics and respiratory support strategies with 60-day in-hospital mortality using random-effects multivariable Cox regression with inverse probability weighting.ResultsAmong the 13,301 confirmed-COVID-19 patients, 60-day in-hospital mortality was 13%. Across four time periods identified, younger patients were progressively more common, non-invasive respiratory support was increasingly used, and the 60-day in-hospital mortality decreased in the last two periods. 4188 patients received advanced respiratory support (non-invasive or invasive), from which 42% underwent only invasive mechanical ventilation, 37% only non-invasive respiratory support and 21% failed non-invasive support and were intubated. After adjusting for organ dysfunction scores and premorbid conditions, we found that younger age, absence of frailty and the use of non-invasive respiratory support (NIRS) as first support strategy were independently associated with improved survival (hazard ratio for NIRS first [95% confidence interval], 0.59 [0.54–0.65], p < 0.001).ConclusionAge and mortality rates have declined over the first 8 months of the pandemic. The use of NIRS as the first respiratory support measure was associated with survival, but causal inference is limited by the observational nature of our data.