Outcomes and statistical power in adult critical care randomized trials

Oxygen Targets for Mechanically Ventilated Adults with Sepsis: Secondary Analysis of the PILOT Trial

Background: Patients with sepsis frequently require invasive mechanical ventilation. How oxygenation during mechanical ventilation affects clinical outcomes for patients with sepsis remains uncertain. Research Question: To evaluate the effects of different oxygen saturation targets on clinical outcomes for patients with sepsis receiving mechanical ventilation. Study Design and Methods: We performed a secondary analysis of the Pragmatic Investigation of optimaL Oxygen Targets (PILOT) trial dataset among patients who met criteria for sepsis by the Sepsis-3 definition at the time of enrollment. We compared patients randomized to a lower oxygen saturation target (90%; range, 88-92%), an intermediate target (94%; range, 92-96%), and a higher target (98%; range, 96-100%) with regard to the outcomes of 28-day in-hospital mortality and ventilator-free days to study day 28. Results: Of 2541 patients in the PILOT dataset, 805 patients with sepsis were included in the current analysis. In-hospital mortality by day 28 did not differ significantly between the lower target group (48%; 95% confidence interval [CI], 42% to 54%), the intermediate target group (50%; 95% CI, 43% to 56%), and the higher target group (51%; 95% CI, 45% to 56%) (P = 0.83). The number of ventilator-free days to day 28 did not significantly differ between the trial groups, with a mean of 9.9 (standard deviation [SD], 11.8) in the lower oxygen saturation target group, 9.5 (SD, 11.2) in the intermediate group, and 9.4 (SD, 11.4) in the higher group (P = 0.65). Interpretation: Among mechanically ventilated patients with sepsis in a large, randomized trial, the incidence of 28-day in-hospital mortality was not statistically significantly different between the use of a lower, intermediate, or higher oxygen target. However, the confidence intervals included treatment effects that would be clinically meaningfully and further randomized trials of oxygen targets in sepsis are required. Referenced trial name Pragmatic Investigation of optimaL Oxygen Targets Trial (PILOT) ClinicalTrials.gov number NCT03537937URL: https://clinicaltrials.gov/study/NCT03537937.

Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill

Clinicians aim to provide treatments that will result in the best outcome for each patient. Ideally, treatment decisions are based on evidence from randomised clinical trials. Randomised trials conventionally report an aggregated difference in outcomes between patients in each group, known as an average treatment effect. However, the actual effect of treatment on outcomes (treatment response) can vary considerably between individuals, and can differ substantially from the average treatment effect. This variation in response to treatment between patients-heterogeneity of treatment effect-is particularly important in critical care because common critical care syndromes (eg, sepsis and acute respiratory distress syndrome) are clinically and biologically heterogeneous. Statistical approaches have been developed to analyse heterogeneity of treatment effect and predict individualised treatment effects for each patient. In this Review, we outline a framework for deriving and validating individualised treatment effects and identify challenges to applying individualised treatment effect estimates to inform treatment decisions in clinical care.

Dynamic risk stratification and treatment optimization in sepsis: the role of NLPR

Background

Sepsis, characterized by immune dysregulation, inflammatory cascades, and coagulation dysfunction, remains a global health challenge with high mortality, particularly in patients with multiple organ dysfunction syndrome (MODS). Existing prognostic tools, such as SOFA and APACHE II scores, are limited by complexity and lack of real-time monitoring, necessitating simple and reliable biomarkers for risk stratification and individualized management.

Objective

This study aimed to evaluate the prognostic value of the neutrophil-to-lymphocyte-to-platelet ratio (NLPR) for mortality in sepsis patients and explore its potential utility in dynamic risk stratification and treatment optimization.

Methods

We conducted a retrospective cohort study using the MIMIC-IV database (v3.1), including adult sepsis patients meeting Sepsis-3.0 criteria. NLPR was calculated based on neutrophil, lymphocyte, and platelet counts within 24 h of admission. Patients were stratified into quartiles (Q1-Q4) based on NLPR values. Kaplan-Meier survival analysis, Cox regression models, and restricted cubic spline (RCS) analysis were performed to assess NLPR's association with 28-day, 90-day, and 365-day mortality. Subgroup analyses examined NLPR's performance in diverse clinical populations.

Results

NLPR was a strong and independent predictor of mortality at all time points. Patients in the highest NLPR quartile (Q4) had significantly higher 28-day (28.22% vs. 12.64%), 90-day (36.82% vs. 18.06%), and 365-day (44.94% vs. 25.58%) mortality compared to the lowest quartile (Q1, all P < 0.001). Cox regression confirmed the independent association of high NLPR with mortality after adjusting for confounders such as age, gender, BMI, and SOFA scores. RCS analysis identified nonlinear relationships between NLPR and mortality, with critical thresholds (e.g.,NLPR = 6.5 for 365-day mortality) providing actionable targets for early risk identification. Subgroup analysis revealed consistent predictive performance across clinical populations, with amplified risks in younger patients, malnourished individuals, and those with acute kidney injury.

Conclusion

NLPR is a simple, accessible, and robust biomarker for sepsis risk stratification, integrating inflammation and coagulation data. It complements traditional scoring systems, provides actionable thresholds for early intervention, and facilitates dynamic monitoring. These findings underscore NLPR's potential to improve clinical decision-making and outcomes in sepsis management, warranting validation in prospective multicenter studies.

A Core Outcome Set for Adult General ICU Patients

OBJECTIVES

Randomized clinical trials informing clinical practice (e.g., like large, pragmatic, and late-phase trials) should ideally mostly use harmonized outcomes that are important to patients, family members, clinicians, and researchers. Core outcome sets for specific subsets of ICU patients exist, for example, respiratory failure, delirium, and COVID-19, but not for ICU patients in general. Accordingly, we aimed to develop a core outcome set for adult general ICU patients.

DESIGN

We developed a core outcome set in Denmark following the Core Outcome Measures in Effectiveness Trials Handbook. We used a modified Delphi consensus process with multiple methods design, including literature review, survey, semi-structured interviews, and discussions with initially five Danish research panels. The core outcome set was internationally validated and revised based on feedback from research panels in all countries.

SETTING

There were five Danish research panels and 17 panels in 13 other countries. Interviews and the three-round Delphi survey was conducted in Denmark, followed by validation of the core outcome set across 14 countries in Europe, Australasia, and India.

SUBJECTS

Adult ICU survivors, family members, clinicians, and researchers.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We identified 329 published outcomes, of which 50 were included in the 264 participant Delphi survey. In semi-structured interviews of 82, no additional outcomes were added. The first Delphi survey round was completed by 249 (94%) participants, and 202 (82%) contributed to the third and final round. The initial core outcome set comprised six outcomes. International validation involved 217 research panel members and resulted in the final core outcome set comprising survival, free of life support, free of delirium, out of hospital, health-related quality of life, and cognitive function.

CONCLUSIONS

We developed and internationally validated a core outcome set with six core outcomes to be used in research, specifically clinical trials involving adult general ICU patients.

Death by p-value: the overreliance on p-values in critical care research

The p-value has changed from a versatile tool for scientific reasoning to a strict judge of medical information, with the usual 0.05 cutoff frequently deciding a study's significance and subsequent clinical use. Through an examination of five critical care interventions that demonstrated meaningful treatment effects yet narrowly missed conventional statistical significance, this paper illustrates how rigid adherence to p-value thresholds may obscure therapeutically beneficial findings. By providing a clear, step-by-step illustration of a basic Bayesian calculation, we demonstrate that clinical importance can remain undetected when relying solely on p-values. These observations challenge current statistical paradigms and advocate for hybrid approaches-including both frequentist and Bayesian methodologies-to provide a more comprehensive understanding of clinical data, ultimately leading to better-informed medical decisions.

Aiming for precision: personalized medicine through sepsis subtyping

According to the latest definition, sepsis is characterized by life-threatening organ dysfunction caused by a dysregulated host response to an infection. However, this definition fails to grasp the heterogeneous nature and the underlying dynamic pathophysiology of the syndrome. In response to this heterogeneity, efforts have been made to stratify sepsis patients into subtypes, either based on their clinical presentation or pathophysiological characteristics. Subtyping introduces the possibility of the implementation of personalized medicine, whereby each patient receives treatment tailored to their individual disease manifestation. This review explores the currently known subtypes, categorized by subphenotypes and endotypes, as well as the treatments that have been researched thus far in the context of sepsis subtypes and personalized medicine.

Composite Primary Outcomes Reported in Studies of Critical Care: A Scoping Review

OBJECTIVE

Composite primary outcomes (CPO) (incorporating both mortality and non-mortality outcomes) offer several advantages over mortality as an outcome for critical care research. Our objective was to explore and map the literature to report on CPO evaluated in critical care research.

DATA SOURCES

PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Scopus, and Cochrane Library from January 2000 to January 2024.

STUDY SELECTION

All studies (both non-randomized controlled trial [RCT] and RCT) conducted in ICUs treating adult patients (18 yr old or older) that described CPOs and their definitions, were included for mapping, reporting, and analyzing CPOs without any restrictions.

DATA EXTRACTION

Independent double-screening of abstracts/full texts and data extraction was performed using a pilot-tested extraction template. The data collected included characteristics of CPO, definitions, trends, and death handling techniques used while reporting the CPO.

DATA SYNTHESIS

Seventeen CPOs were extracted from 71 studies, predominantly reported in the setting of pharmaceutical studies (48/71, 67.6%), used RCT methodology (60/71, 84.5%), and were mostly single-center studies (55/71, 77.5%). Ventilator-free days were the most commonly reported CPO (29/71, 40.8%) with marked variability in the definition used and death handling (0 d in 33 studies and -1 d in 7 studies). The most common statistical paradigm used was frequentist (63/71, 88.7%) and the study follow-up time was 90 days with 28 studies using this timeline (28/71, 39.4%). Narrative synthesis highlighted the variability in defining CPO.

CONCLUSIONS

CPOs are an emerging set of outcomes increasingly reported in critical care research. There was significant heterogeneity in definitions used, follow-up times, and reporting trends.

Non-adrenergic vasopressors for vasodilatory shock or perioperative vasoplegia: a meta-analysis of randomized controlled trials

BACKGROUND

Excessive exposure to adrenergic vasopressors may be harmful. Non-adrenergic vasopressors may spare adrenergic agents and potentially improve outcomes. We aimed to conduct a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy of non-adrenergic vasopressors in adult patients receiving vasopressor therapy for vasodilatory shock or perioperative vasoplegia.

METHODS

We searched PubMed, Embase, and Cochrane Library for RCTs comparing non-adrenergic vasopressors with adrenergic vasopressors alone or placebo in critically ill or perioperative patients. Each eligible study was categorized into septic shock, cardiac surgery, or non-cardiac surgery. Non-adrenergic vasopressors included vasopressin, terlipressin, selepressin, angiotensin II, methylene blue, and hydroxocobalamin. The primary outcome was mortality at longest follow-up. We conducted a random-effects meta-analysis. We registered the protocol in PROSPERO International Prospective Register of Systematic Reviews (CRD42024505039).

RESULTS

Among 51 eligible RCTs totaling 5715 patients, the predominant population was septic shock in 30 studies, cardiac surgery in 11 studies, and non-cardiac surgery in 10 studies. Cochrane risk-of-bias tool for randomized trials version 2 identified 17 studies as low risk of bias. In septic shock, mortality was significantly lower in the non-adrenergic group (960/2232 [43%] vs. 898/1890 [48%]; risk ratio [RR], 0.92; 95% confidence interval [95% CI], 0.86-0.97; P = 0.03; I2 = 0%), with none of the individual non-adrenergic vasopressors showing significant survival benefits. No significant mortality difference was observed in patients undergoing cardiac surgery (34/410 [8.3%] vs. 47/412 [11%]; RR, 0.82; 95% CI, 0.55-1.22; P = 0.32; I2 = 12%) or those undergoing non-cardiac surgery (9/388 [2.3%] vs. 18/383 [4.7%]; RR, 0.66; 95% CI, 0.31-1.41; P = 0.28; I2 = 0%).

CONCLUSIONS

Administration of non-adrenergic vasopressors was significantly associated with reduced mortality in patients with septic shock. However, no single agent achieved statistical significance in separate analyses. Although the pooled effects of non-adrenergic vasopressors on survival did not reach statistical significance in patients undergoing cardiac or non-cardiac surgery, the confidence intervals included the possibility of both no effect and a clinically important benefit from non-adrenergic agents. These findings justify the conduct of further RCTs comparing non-adrenergic vasopressors to usual care based on noradrenaline alone.

Delta Inflation, Optimism Bias, and Uncertainty in Clinical Trials

The phenomenon of delta inflation, in which design treatment effects tend to exceed observed treatment effects, has been documented in several therapeutic areas. Delta inflation has often been attributed to investigators' optimism bias, or an unwarranted belief in the efficacy of new treatments. In contrast, we argue that delta inflation may be a natural consequence of clinical equipoise, that is, genuine uncertainty about the relative benefits of treatments before a trial is initiated. We review alternative methodologies that can offer more direct evidence about investigators' beliefs, including Bayesian priors and forecasting analysis. The available evidence for optimism bias appears to be mixed, and can be assessed only where uncertainty is expressed explicitly at the trial design stage.

The critical care literature 2023

The number of critically ill patients that present to emergency departments across the world continues to rise. In fact, the proportion of critically ill patients in emergency departments is now higher than pre-COVID-19 pandemic levels. [1] The emergency physician (EP) is typically the first physician to evaluate and resuscitate the critically ill patient. Given the continued shortage of intensive care unit (ICU) beds, persistent staff shortages, and overall inefficient hospital throughput, EPs are often tasked with providing intensive care to these patients long beyond the initial resuscitation phase. Prolonged boarding of critically ill patients in the ED is associated with increased ICU and hospital length of stay, increased adverse events, ED staff burnout, decreased patient and family satisfaction, and, most importantly, increased mortality. [2-5]. As such, it is imperative for the EP to be knowledgeable about recent literature in resuscitation and critical care medicine, so that critically ill ED patients can continue to receive the best, most up-to-date evidence-based care. This review summarizes important articles published in 2023 that pertain to the resuscitation and management of select critically ill ED patients. Topics included in this article include cardiac arrest, post-cardiac arrest care, septic shock, rapid sequence intubation, severe pneumonia, transfusions, trauma, and critical procedures.

Effect of Ventilator Mode on Ventilator-Free Days in Critically Ill Adults: A Randomized Trial

Rationale

For critically ill adults receiving invasive mechanical ventilation, the ventilator mode determines how breaths are delivered. Whether the choice of ventilator mode affects outcomes for critically ill patients is unknown. To compare the effects of three common ventilator modes (volume control, pressure control, and adaptive pressure control) on death and duration of mechanical ventilation.

Methods

We conducted a pragmatic, cluster-randomized, crossover trial among adults receiving invasive mechanical ventilation in a medical ICU between November 1, 2022 and July 31, 2023. Each month, patients in the participating unit were assigned to receive volume control, pressure control, or adaptive pressure control during continuous mandatory ventilation. The primary outcome was ventilator-free days through 28 days.

Results

Among 566 patients included in the primary analysis, the median number of ventilator-free days was 23 [IQR, 0-26] in the volume control group, 22 [0-26] in the pressure control group, and 24 [0-26] in the adaptive pressure control group (P=0.60). The median tidal volume was similar in the three groups, but the percentage of breaths larger than 8mL/kg of predicted body weight differed between volume control (median, 4.0%; IQR, 0.0-14.1), pressure control (10.6%; 0.0-31.5), and adaptive pressure control (4.7%; 0.0-19.2). Incidences of hypoxemia, acidemia, and barotrauma were similar in the three groups.

Conclusions

Among critically ill adults receiving invasive mechanical ventilation, the use of volume control, pressure control, or adaptive pressure control did not affect the number of ventilator-free days, however, confidence intervals included differences that may be clinically meaningful.

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.

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

PURPOSE

To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect.

SUMMARY

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

CONCLUSION

The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

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

BACKGROUND

Enrollment into critical care clinical trials is often hampered by the need to rely on surrogate decision-makers. To identify potential interventions facilitating enrollment into critical care clinical trials, a better understanding of surrogate decision-making for critical care clinical trial enrollment is needed.

RESEARCH QUESTION

What are the barriers and facilitators of critical care trial enrollment? What are surrogate decision-makers' perspectives on proposed interventions to facilitate trial enrollment?

STUDY DESIGN AND METHODS

We conducted semistructured interviews with 20 surrogate decision-makers of critically ill patients receiving mechanical ventilation. The interviews were recorded and transcribed verbatim, and analyzed for themes using an inductive approach.

RESULTS

Thematic analysis confirmed previous research showing that trust in the system, assessing the risks and benefits of trial participation, the desire to help others, and building medical knowledge as important motivating factors for trial enrollment. Two previously undescribed concerns among surrogate decision-makers of critically ill patients were identified, including the potential to interfere with clinical treatment decisions and negative sentiment about placebos. Surrogates viewed public recognition and charitable donations for participation as favorable potential interventions to encourage trial enrollment. However, participants viewed direct financial incentives and prioritizing research participants during medical rounds negatively.

INTERPRETATION

This study confirms and extends previous findings that health system trust, study risks and benefits, altruism, knowledge generation, interference with clinical care, and placebos are key concerns and barriers for surrogate decision-makers to enroll patients in critical care trials. Future studies are needed to evaluate if charitable giving on the patient's behalf and public recognition are effective strategies to promote enrollment into critical care trials.

Incidence of acute kidney injury and attributive mortality in acute respiratory distress syndrome randomized trials

PURPOSE

The development of acute kidney injury (AKI) after the acute respiratory distress syndrome (ARDS) reduces the chance of organ recovery and survival. The purpose of this study was to examine the AKI rate and attributable mortality in ARDS patients.

METHODS

We performed an individual patient-data analysis including 10 multicenter randomized controlled trials conducted over 20 years. We employed a Super Learner ensemble technique, including a time-dependent analysis, to estimate the adjusted risk of AKI. We calculated the mortality attributable to AKI using an inverse probability of treatment weighting estimator integrated with the Super Learner.

RESULTS

There were 5148 patients included in this study. The overall incidence of AKI was 43.7% (n = 2251). The adjusted risk of AKI ranged from 38.8% (95% confidence interval [CI], 35.7 to 41.9%) in ARMA, to 55.8% in ROSE (95% CI, 51.9 to 59.6%). 37.1% recovered rapidly from AKI, with a significantly lower recovery rate in recent trials (P < 0.001). The 90-day excess in mortality attributable to AKI was 15.4% (95% CI, 12.8 to 17.9%). It decreased from 25.4% in ARMA (95% CI, 18.7 to 32%), to 11.8% in FACTT (95% CI, 5.5 to 18%) and then remained rather stable over time. The 90-day overall excess in mortality attributable to acute kidney disease was 28.4% (95% CI, 25.3 to 31.5%).

CONCLUSIONS

The incidence of AKI appears to be stable over time in patients with ARDS enrolled in randomized trials. The development of AKI remains a significant contributing factor to mortality. These estimates are essential for designing future clinical trials for AKI prevention or treatment.

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

BACKGROUND

The use of composite outcome measures (COM) in clinical trials is increasing. Whilst their use is associated with benefits, several limitations have been highlighted and there is limited literature exploring their use within critical care. The primary aim of this study was to evaluate the use of COM in high-impact critical care trials, and compare study parameters (including sample size, statistical significance, and consistency of effect estimates) in trials using composite versus non-composite outcomes.

METHODS

A systematic review of 16 high-impact journals was conducted. Randomised controlled trials published between 2012 and 2022 reporting a patient important outcome and involving critical care patients, were included.

RESULTS

8271 trials were screened, and 194 included. 39.1% of all trials used a COM and this increased over time. Of those using a COM, only 52.6% explicitly described the outcome as composite. The median number of components was 2 (IQR 2-3). Trials using a COM recruited fewer participants (409 (198.8-851.5) vs 584 (300-1566, p = 0.004), and their use was not associated with increased rates of statistical significance (19.7% vs 17.8%, p = 0.380). Predicted effect sizes were overestimated in all but 6 trials. For studies using a COM the effect estimates were consistent across all components in 43.4% of trials. 93% of COM included components that were not patient important.

CONCLUSIONS

COM are increasingly used in critical care trials; however effect estimates are frequently inconsistent across COM components confounding outcome interpretations. The use of COM was associated with smaller sample sizes, and no increased likelihood of statistically significant results. Many of the limitations inherent to the use of COM are relevant to critical care research.

Development and External Validation of Models to Predict Persistent Hypoxemic Respiratory Failure for Clinical Trial Enrichment

OBJECTIVES

Improving the efficiency of clinical trials in acute hypoxemic respiratory failure (HRF) depends on enrichment strategies that minimize enrollment of patients who quickly resolve with existing care and focus on patients at high risk for persistent HRF. We aimed to develop parsimonious models predicting risk of persistent HRF using routine data from ICU admission and select research immune biomarkers.

DESIGN

Prospective cohorts for derivation ( n = 630) and external validation ( n = 511).

SETTING

Medical and surgical ICUs at two U.S. medical centers.

PATIENTS

Adults with acute HRF defined as new invasive mechanical ventilation (IMV) and hypoxemia on the first calendar day after ICU admission.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We evaluated discrimination, calibration, and practical utility of models predicting persistent HRF risk (defined as ongoing IMV and hypoxemia on the third calendar day after admission): 1) a clinical model with least absolute shrinkage and selection operator (LASSO) selecting Pa o2 /F io2 , vasopressors, mean arterial pressure, bicarbonate, and acute respiratory distress syndrome as predictors; 2) a model adding interleukin-6 (IL-6) to clinical predictors; and 3) a comparator model with Pa o2 /F io2 alone, representing an existing strategy for enrichment. Forty-nine percent and 69% of patients had persistent HRF in derivation and validation sets, respectively. In validation, both LASSO (area under the receiver operating characteristic curve, 0.68; 95% CI, 0.64-0.73) and LASSO + IL-6 (0.71; 95% CI, 0.66-0.76) models had better discrimination than Pa o2 /F io2 (0.64; 95% CI, 0.59-0.69). Both models underestimated risk in lower risk deciles, but exhibited better calibration at relevant risk thresholds. Evaluating practical utility, both LASSO and LASSO + IL-6 models exhibited greater net benefit in decision curve analysis, and greater sample size savings in enrichment analysis, compared with Pa o2 /F io2 . The added utility of LASSO + IL-6 model over LASSO was modest.

CONCLUSIONS

Parsimonious, interpretable models that predict persistent HRF may improve enrichment of trials testing HRF-targeted therapies and warrant future validation.

Use of the Win Ratio Analysis in Critical Care Trials

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

The Rise of Adaptive Platform Trials in Critical Care

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

Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials

Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials.

Protocol and Statistical Analysis Plan for the Mode of Ventilation During Critical Illness (MODE) Trial

BACKGROUND

For every critically ill adult receiving invasive mechanical ventilation, clinicians must select a mode of ventilation. The mode of ventilation determines whether the ventilator directly controls the tidal volume or the inspiratory pressure. Newer hybrid modes allow clinicians to set a target tidal volume; the ventilator controls and adjusts the inspiratory pressure. A strategy of low tidal volumes and low plateau pressure improves outcomes, but the optimal mode to achieve these targets is not known.

RESEARCH QUESTION

Can a cluster-randomized trial design be used to assess whether the mode of mandatory ventilation affects the number of days alive and free of invasive mechanical ventilation among critically ill adults?

STUDY DESIGN AND METHODS

The Mode of Ventilation During Critical Illness (MODE) trial is a cluster-randomized, multiple-crossover pilot trial being conducted in the medical ICU at an academic center. The MODE trial compares the use of volume control, pressure control, and adaptive pressure control. The study ICU is assigned to a single-ventilator mode (volume control vs pressure control vs adaptive pressure control) for continuous mandatory ventilation during each 1-month study block. The assigned mode switches every month in a randomly generated sequence. The primary outcome is ventilator-free days to study day 28, defined as the number of days alive and free of invasive mechanical ventilation from the final receipt of mechanical ventilation to 28 days after enrollment. Enrollment began November 1, 2022, and will end on July 31, 2023.

RESULTS

This manuscript describes the protocol and statistical analysis plan for the MODE trial of ventilator modes comparing volume control, pressure control, and adaptive pressure control.

INTERPRETATION

Prespecifying the full statistical analysis plan prior to completion of enrollment increases rigor, reproducibility, and transparency of the trial results.

CLINICAL TRIAL REGISTRATION

The trial was registered with clinicaltrials.gov on October 3, 2022, before initiation of patient enrollment on November 1, 2022 (ClinicalTrials.gov identifier: NCT05563779).

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

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

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

BACKGROUND

Randomised clinical trials in critical care are prone to inconclusiveness due, in part, to undue optimism about effect sizes and suboptimal accounting for heterogeneous treatment effects. Although causal evidence from rich real-world critical care can help overcome these challenges by informing predictive enrichment, no overview exists.

METHODS

We conducted a scoping review, systematically searching 10 general and speciality journals for reports published on or after 1 January 2018, of randomised clinical trials enrolling adult critically ill patients. We collected trial metadata on 22 variables including recruitment period, intervention type and early stopping (including reasons) as well as data on the use of causal evidence from secondary data for planned predictive enrichment.

RESULTS

We screened 9020 records and included 316 unique RCTs with a total of 268,563 randomised participants. One hundred seventy-three (55%) trials tested drug interventions, 101 (32%) management strategies and 42 (13%) devices. The median duration of enrolment was 2.2 (IQR: 1.3-3.4) years, and 83% of trials randomised less than 1000 participants. Thirty-six trials (11%) were restricted to COVID-19 patients. Of the 55 (17%) trials that stopped early, 23 (42%) used predefined rules; futility, slow enrolment and safety concerns were the commonest stopping reasons. None of the included RCTs had used causal evidence from secondary data for planned predictive enrichment.

CONCLUSION

Work is needed to harness the rich multiverse of critical care data and establish its utility in critical care RCTs. Such work will likely need to leverage methodology from interventional and analytical epidemiology as well as data science.

Design and Execution of Clinical Trials in the Cardiac Intensive Care Unit

Clinical practice in the contemporary cardiac intensive care unit (CICU) has evolved significantly over the last several decades. With more frequent multisystem organ failure, increasing use of advanced respiratory support, and the advent of new mechanical circulatory support platforms, clinicians in the CICU are increasingly managing patients with complex comorbid disease in addition to their high-acuity cardiovascular illnesses. Here, the authors discuss challenges associated with traditional trial design in the CICU setting and review novel clinical trial designs that may facilitate better evidence generation in the CICU.

Sepsis and Adrenal Insufficiency

In sepsis, dysregulation of the hypothalamic-pituitary-adrenal axis, alterations in cortisol metabolism, and tissue resistance to glucocorticoids can all result in relative adrenal insufficiency or critical illness-related corticosteroid insufficiency (CIRCI). The symptoms and signs of CIRCI during sepsis are nonspecific, generally including decreased mental status, unexplained fever, or hypotension refractory to fluids, and the requirement of vasopressor therapy to maintain adequate blood pressure. While we have been aware of this syndrome for over a decade, it remains a poorly understood condition, challenging to diagnose, and associated with significantly diverging practices among clinicians, particularly regarding the optimal dosing and duration of corticosteroid therapy. The existing literature on corticosteroid use in patients with sepsis and septic shock is vast with dozens of randomized controlled trials conducted across the past 4 decades. These studies have universally demonstrated reduced duration of shock, though the effects of corticosteroids on mortality have been inconsistent, and their use has been associated with adverse effects including hyperglycemia, neuromuscular weakness, and an increased risk of infection. In this article, we aim to provide a thorough, evidence-based, and practical review of the current recommendations for the diagnosis and management of patients with sepsis who develop CIRCI, explore the controversies surrounding this topic, and highlight what lies on the horizon as new evidence continues to shape our practice.

Adaptive designs in critical care trials: a simulation study

BACKGROUND

Adaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation.

METHODS

Using two large critical care trials (ADRENAL.

CLINICALTRIALS

gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR.

CLINICALTRIALS

gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05.

RESULTS

Across all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased.

CONCLUSIONS

This study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method.

How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials

Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.

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

BACKGROUND

Ventilator-free days (VFDs) are a composite endpoint increasingly used as the primary outcome in critical care trials. However, because of the skewed distribution and competitive risk between components, sample size estimation remains challenging. This systematic review was conducted to systematically assess whether the sample size was congruent, as calculated to evaluate VFDs in trials, with VFDs' distribution and the impact of alternative methods on sample size estimation.

METHODS

A systematic literature search was conducted within the PubMed and Embase databases for randomized clinical trials in adults with VFDs as the primary outcome until December 2021. We focused on peer-reviewed journals with 2021 impact factors greater than five. After reviewing definitions of VFDs, we extracted the sample size and methods used for its estimation. The data were collected by two independent investigators and recorded in a standardized, pilot-tested forms tool. Sample sizes were calculated using alternative statistical approaches, and risks of bias were assessed with the Cochrane risk-of-bias tool.

RESULTS

Of the 26 clinical trials included, 19 (73%) raised "some concerns" when assessing risks of bias. Twenty-four (92%) trials were two-arm superiority trials, and 23 (89%) were conducted at multiple sites. Almost all the trials (96%) were unable to consider the unique distribution of VFDs and death as a competitive risk. Moreover, significant heterogeneity was found in the definitions of VFDs, especially regarding varying start time and type of respiratory support. Methods for sample size estimation were also heterogeneous, and simple models, such as the Mann-Whitney-Wilcoxon rank-sum test, were used in 14 (54%) trials. Finally, the sample sizes calculated varied by a factor of 1.6 to 17.4.

CONCLUSIONS

A standardized definition and methodology for VFDs, including the use of a core outcome set, seems to be required. Indeed, this could facilitate the interpretation of findings in clinical trials, as well as their construction, especially the sample size estimation which is a trade-off between cost, ethics, and statistical power. Systematic review registration PROSPERO ID: CRD42021282304. Registered 15 December 2021 ( https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021282304 ).

Protocol and statistical analysis plan for the Mode of Ventilation During Critical IllnEss (MODE) trial

Introduction

For every critically ill adult receiving invasive mechanical ventilation, clinicians must select a mode of ventilation. The mode of ventilation determines whether the ventilator directly controls the tidal volume or the inspiratory pressure. Newer hybrid modes allow clinicians to set a target tidal volume, for which the ventilator controls and adjusts the inspiratory pressure. A strategy of low tidal volumes and low plateau pressure improves outcomes, but the optimal mode to achieve these targets is not known.

Methods and analysis

The Mode of Ventilation During Critical Illness (MODE) trial is a cluster-randomized, multiple-crossover pilot trial being conducted in the medical intensive care unit (ICU) at an academic center. The MODE trial compares the use of volume control, pressure control, and adaptive pressure control. The study ICU is assigned to a single ventilator mode (volume control versus pressure control versus adaptive pressure control) for continuous mandatory ventilation during each 1-month study block. The assigned mode switches every month in a randomly generated sequence. The primary outcome is ventilator-free days (VFDs) to study day 28, defined as the number of days alive and free of invasive mechanical ventilation from the final receipt of mechanical ventilation to 28 days after enrollment. Enrollment began November 1, 2022 and will end on July 31, 2023.

Ethics and dissemination

The trial was approved by the Vanderbilt University Medical Center institutional review board (IRB# 220446). Results of this study will be submitted to a peer-reviewed journal and presented at scientific conferences.

Trial registration number

The trial was registered with clinicaltrials.gov on October 3, 2022, prior to initiation of patient enrollment on November 1, 2022 (ClinicalTrials.gov identifier: NCT05563779).

Association between days alive without life support/out of hospital and health-related quality of life

BACKGROUND

Trials in critically ill patients increasingly focus on days alive without life support (DAWOLS) or days alive out of hospital (DAOOH) and health-related quality of life (HRQoL). DAWOLS and DAOOH convey more information than mortality and are simpler and faster to collect than HRQoL. However, whether these outcomes are associated with HRQoL is uncertain. We thus aimed to assess the associations between DAWOLS and DAOOH and long-term HRQoL.

METHODS

Secondary analysis of the COVID STEROID 2 trial including adults with COVID-19 and severe hypoxaemia and the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial including adult intensive care unit patients with acute hypoxaemic respiratory failure. Associations between DAWOLS and DAOOH at day 28 and 90 and long-term HRQoL (after 6 or 12 months) using the EuroQol 5-dimension 5-level survey (EQ VAS and EQ-5D-5L index values) were assessed using flexible models and evaluated using measures of fit and prediction adequacy in both datasets (comprising internal performance and external validation), non-parametric correlation coefficients and graphical presentations.

RESULTS

We found no strong associations between DAWOLS or DAOOH and HRQoL in survivors at HRQoL-follow-up (615 and 1476 patients, respectively). There was substantial variability in outcomes, and predictions from the best fitted models were poor both internally and externally in the other trial dataset, which also showed inadequate calibration. Moderate associations were found when including non-survivors, although predictions remained uncertain and calibration inadequate.

CONCLUSION

DAWOLS and DAOOH were poorly associated with HRQoL in adult survivors of severe or critical illness included in the COVID STEROID 2 and HOT-ICU trials.

Use of days alive without life support and similar count outcomes in randomised clinical trials - an overview and comparison of methodological choices and analysis methods

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.

Comparative Effectiveness of Fludrocortisone and Hydrocortisone vs Hydrocortisone Alone Among Patients With Septic Shock

Importance

Patients with septic shock may benefit from the initiation of corticosteroids. However, the comparative effectiveness of the 2 most studied corticosteroid regimens (hydrocortisone with fludrocortisone vs hydrocortisone alone) is unclear.

Objective

To compare the effectiveness of adding fludrocortisone to hydrocortisone vs hydrocortisone alone among patients with septic shock using target trial emulation.

Design, Setting, and Participants

This retrospective cohort study from 2016 to 2020 used the enhanced claims-based Premier Healthcare Database, which included approximately 25% of US hospitalizations. Participants were adult patients hospitalized with septic shock and receiving norepinephrine who began hydrocortisone treatment. Data analysis was performed from May 2022 to December 2022.

Exposure

Addition of fludrocortisone on the same calendar day that hydrocortisone treatment was initiated vs use of hydrocortisone alone.

Main Outcome and Measures

Composite of hospital death or discharge to hospice. Adjusted risk differences were calculated using doubly robust targeted maximum likelihood estimation.

Results

Analyses included 88 275 patients, 2280 who began treatment with hydrocortisone-fludrocortisone (median [IQR] age, 64 [54-73] years; 1041 female; 1239 male) and 85 995 (median [IQR] age, 67 [57-76] years; 42 136 female; 43 859 male) who began treatment with hydrocortisone alone. The primary composite outcome of death in hospital or discharge to hospice occurred among 1076 (47.2%) patients treated with hydrocortisone-fludrocortisone vs 43 669 (50.8%) treated with hydrocortisone alone (adjusted absolute risk difference, -3.7%; 95% CI, -4.2% to -3.1%; P < .001).

Conclusions and Relevance

In this comparative effectiveness cohort study among adult patients with septic shock who began hydrocortisone treatment, the addition of fludrocortisone was superior to hydrocortisone alone.

Determinants of Effect of Extracorporeal CO2 Removal in Hypoxemic Respiratory Failure

BACKGROUND: Dead space and respiratory system elastance (Ers) may influence the clinical benefit of a ventilation strategy combining very low tidal volume (VT) with extracorporeal carbon dioxide removal (ECCO2R) in patients with acute hypoxemic respiratory failure. We sought to evaluate whether the effect of ECCO2R on mortality varies according to ventilatory ratio (VR; a composite variable reflective of dead space and shunt) and Ers. METHODS: Secondary analysis of a trial of a strategy combining very low VT and low-flow ECCO2R planned before the availability of trial results. Bayesian logistic regression was used to estimate the posterior probability of effect moderation by VR, Ers, and severity of hypoxemia (ratio of arterial partial pressure of oxygen to fraction of inspired oxygen [PaO2:FiO2]) on 90-day mortality. Credibility of effect moderation was appraised according to the Instrument for Assessing the Credibility of Effect Modification Analyses criteria. RESULTS: A total of 405 patients were available for analysis. The effect of the intervention on mortality varied substantially with VR (posterior probability of interaction, 94%; high credibility). Benefit was more probable than harm in patients with VR 3 or higher. In patients with VR less than 3, the probability of increased mortality with intervention was high (>90%). The effect of the intervention also varied with PaO2:FiO2 (posterior probability of interaction, >99%; low credibility). Benefit was more probable than harm in patients with PaO2:FiO2 110 mm Hg or higher. The effect of the intervention did not vary substantially with Ers (posterior probability of interaction, 68%; low credibility). CONCLUSIONS: VR has a highly credible influence on the effect of a strategy combining very low VT and low-flow ECCO2R on mortality. This intervention may reduce mortality in patients with high VR. (Funded by an Early Career Investigator Award from the Canadian Institutes of Health Research to Dr. Goligher.)

Mortality As a Measure of Treatment Effect in Clinical Trials Recruiting Critically Ill Patients

OBJECTIVES

All-cause mortality is a common measure of treatment effect in ICU-based randomized clinical trials (RCTs). We sought to understand the performance characteristics of a mortality endpoint by evaluating its temporal course, responsiveness to differential treatment effects, and impact when used as an outcome measure in trials of acute illness.

DATA SOURCES

We searched OVID Medline for RCTs published from 1990 to 2018.

STUDY SELECTION

We reviewed RCTs that had randomized greater than or equal to 100 patients, were published in one of five high-impact general medical or eight critical care journals, and reported mortality at two or more distinct time points. We excluded trials recruiting pediatric or neonatal patients and cluster RCTs.

DATA EXTRACTION

Mortality by randomization group was recorded from the article or estimated from survival curves. Trial impact was assessed by inclusion of results in clinical practice guidelines.

DATA SYNTHESIS

From 2,592 potentially eligible trials, we included 343 RCTs (228,784 adult patients). While one third of all deaths by 180 days had occurred by day 7, the risk difference between study arms continued to increase until day 60 (p = 0.01) and possibly day 90 (p = 0.07) and remained stable thereafter. The number of deaths at ICU discharge approximated those at 28-30 days (95% [interquartile range [IQR], 86-106%]), and deaths at hospital discharge approximated those at 60 days (99% [IQR, 94-104%]). Only 13 of 43 interventions (30.2%) showing a mortality benefit have been adopted into widespread clinical practice.

CONCLUSIONS

Our findings provide a conceptual framework for choosing a time horizon and interpreting mortality outcome in trials of acute illness. Differential mortality effects persist for 60 to 90 days following recruitment. Location-based measures approximate time-based measures for trials conducted outside the United States. The documentation of a mortality reduction has had a modest impact on practice.

Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

Multicentre randomised trials in anaesthesia: an analysis using Bayesian metrics

Are the results of randomised trials reliable and are p values and confidence intervals the best way of quantifying efficacy? Low power is common in medical research, which reduces the probability of obtaining a 'significant result' and declaring the intervention had an effect. Metrics derived from Bayesian methods may provide an insight into trial data unavailable from p values and confidence intervals. We did a structured review of multicentre trials in anaesthesia that were published in the New England Journal of Medicine, The Lancet, Journal of the American Medical Association, British Journal of Anaesthesia and Anesthesiology between February 2011 and November 2021. We documented whether trials declared a non-zero effect by an intervention on the primary outcome. We documented the expected and observed effect sizes. We calculated a Bayes factor from the published trial data indicating the probability of the data under the null hypothesis of zero effect relative to the alternative hypothesis of a non-zero effect. We used the Bayes factor to calculate the post-test probability of zero effect for the intervention (having assumed 50% belief in zero effect before the trial). We contacted all authors to estimate the costs of running the trials. The median (IQR [range]) hypothesised and observed absolute effect sizes were 7% (3-13% [0-25%]) vs. 2% (1-7% [0-24%]), respectively. Non-zero effects were declared for 12/56 outcomes (21%). The Bayes factor favouring a zero effect relative to a non-zero effect for these 12 trials was 0.000001-1.9, with post-test zero effect probabilities for the intervention of 0.0001-65%. The other 44 trials did not declare non-zero effects, with Bayes factors favouring zero effect of 1-688, and post-test probabilities of zero effect of 53-99%. The median (IQR [range]) study costs reported by 20 corresponding authors in US$ were $1,425,669 ($514,766-$2,526,807 [$120,758-$24,763,921]). We think that inadequate power and mortality as an outcome are why few trials declared non-zero effects. Bayes factors and post-test probabilities provide a useful insight into trial results, particularly when p values approximate the significance threshold.

A Bayesian Interpretation of a Pediatric Cardiac Arrest Trial (THAPCA-OH)

BACKGROUND: Pediatric out-of-hospital cardiac arrest results in high morbidity and mortality. Currently, there are no recommended therapies beyond supportive care. The THAPCA-OH (Therapeutic Hypothermia after Pediatric Cardiac Arrest Out-of-Hospital) trial compared hypothermia (33.0°C) with normothermia (36.8°C) in 295 children. Good neurobehavioral outcome and survival at 1 year were higher in the hypothermia group (20 vs. 12% and 38 vs. 29%, respectively). These differences did not meet the planned statistical threshold of P75% for all informative prior integrations with the THAPCA-OH results, except those with the most pessimistic priors. CONCLUSIONS: There is a high probability that hypothermia provides a modest benefit in neurobehavioral outcome and survival at 1 year. (ClinicalTrials.gov number, NCT00878644.)

Recruitment characteristics of randomised trials in critical care: A systematic review

BACKGROUND/AIMS

To summarise the temporal trends of recruitment and methodological characteristics of critical care randomised controlled trials with the primary outcome of mortality.

METHODS

PubMed was searched for articles meeting inclusion and exclusion criteria. Randomised controlled trials, with primary outcome of mortality, of adult and paediatric critical care patients treated in an intensive care unit, were included. Neonatal intensive care unit trials, non-English publications and conference proceedings or abstracts without full-length publications were excluded. Duplicate literature search, article selection and quality assessment were performed by two reviewers with disputes resolved through discussion. Data were extracted into a custom-designed Research Electronic Data Capture database.

RESULTS

The search identified 67,199 records of which 230 were included. The annual number of critical care randomised controlled trials published increased gradually over a 30-year period from 0 in 1990 to 19 in 2014 with stabilisation at 8-11 between 2015 and 2020. Twenty-seven percent of randomised controlled trials were low risk in all categories using the Cochrane Risk of Bias tool. Methodological characteristics such as registration on clinical trials registries and data safety monitoring committee presence significantly (p < 0.001) increased over time. The median recruitment was 376 patients (interquartile range 125-895) with significant increase (p = 0.002) from 62 (interquartile range: 33-486) in 1991 to 725 (interquartile range: 537-2600) in 2020. This was accompanied by an increase in recruitment times. Thus overall, recruitment rates did not increase. Early cessation occurred in 23% (54/230) of randomised controlled trials with no temporal trend.

CONCLUSION

The number, size and some methodological qualities of critical randomised controlled trials with primary outcome of mortality have increased over time, but rates of recruitment and early cessation have been unchanged.

Oxygen-Saturation Targets for Critically Ill Adults Receiving Mechanical Ventilation

BACKGROUND

Invasive mechanical ventilation in critically ill adults involves adjusting the fraction of inspired oxygen to maintain arterial oxygen saturation. The oxygen-saturation target that will optimize clinical outcomes in this patient population remains unknown.

METHODS

In a pragmatic, cluster-randomized, cluster-crossover trial conducted in the emergency department and medical intensive care unit at an academic center, we assigned adults who were receiving mechanical ventilation to a lower target for oxygen saturation as measured by pulse oximetry (Spo₂) (90%; goal range, 88 to 92%), an intermediate target (94%; goal range, 92 to 96%), or a higher target (98%; goal range, 96 to 100%). The primary outcome was the number of days alive and free of mechanical ventilation (ventilator-free days) through day 28. The secondary outcome was death by day 28, with data censored at hospital discharge.

RESULTS

A total of 2541 patients were included in the primary analysis. The median number of ventilator-free days was 20 (interquartile range, 0 to 25) in the lower-target group, 21 (interquartile range, 0 to 25) in the intermediate-target group, and 21 (interquartile range, 0 to 26) in the higher-target group (P = 0.81). In-hospital death by day 28 occurred in 281 of the 808 patients (34.8%) in the lower-target group, 292 of the 859 patients (34.0%) in the intermediate-target group, and 290 of the 874 patients (33.2%) in the higher-target group. The incidences of cardiac arrest, arrhythmia, myocardial infarction, stroke, and pneumothorax were similar in the three groups.

CONCLUSIONS

Among critically ill adults receiving invasive mechanical ventilation, the number of ventilator-free days did not differ among groups in which a lower, intermediate, or higher Spo₂ target was used. (Supported by the National Heart, Lung, and Blood Institute and others; PILOT ClinicalTrials.gov number, NCT03537937.).

Immune Modulation in Sepsis, ARDS, and Covid-19 - The Road Traveled and the Road Ahead

Immune Modulation in Sepsis, ARDS, and Covid-19Leligdowicz et al. consider the history and future of immunomodulating therapies in sepsis and ARDS, including ARDS due to Covid-19, and remark on the larger challenge of clinical research on therapies for syndromes with profound clinical and biologic heterogeneity.

Patient-Important Outcomes Other Than Mortality in Contemporary ICU Trials: A Scoping Review

OBJECTIVES

Randomized clinical trials (RCTs) conducted in adult ICU patients increasingly include patient-important outcomes other than mortality. This comes with challenges regarding outcome choices/definitions, handling of deceased patients and missing data in analyses, and choices of effect measures and statistical methods due to complex distributions. This scoping review aimed to characterize how these challenges are handled in relevant contemporary RCTs.

DATA SOURCES

We systematically searched 10 selected journals for RCTs conducted primarily in adult ICU patients published between 1 January 2018 and 5 May 2022 reporting at least one patient-important outcome other than mortality, including "days alive without"…-type outcomes, functional/cognitive/neurologic outcomes, health-related quality of life (HRQoL) outcomes, and ordinal/other outcomes.

STUDY SELECTION

Abstracts and full-texts were assessed independently and in duplicate by two reviewers.

DATA EXTRACTION

Data were extracted independently and in duplicate by two reviewers using predefined and pilot-tested extraction forms and subsequently categorized to facilitate analysis.

DATA SYNTHESIS

We included 687 outcomes from 167 RCTs, with 32% of RCTs using a patient-important outcome other than mortality as a (co-)primary outcome, most frequently "days alive without"…-type outcomes. Many different functional/cognitive/neurologic (103) and HRQoL (29) outcomes were reported. Handling of deceased patients varied, with analyses frequently restricted to survivors only for functional/cognitive/neurologic (62%) and HRQoL (89%) outcomes. Follow-up was generally longer and missing data proportions higher for functional/cognitive/neurologic and HRQoL outcomes. Most outcomes were analyzed using nonparametric tests (31%), linear regression/ t tests (27%), chi-square-like tests (12%), and proportional odds logistic regression (9%), often without presentation of actual treatment effects estimates (38%).

CONCLUSIONS

In this sample of RCTs, substantial variation in practice and suboptimal methodological choices were observed. This calls for increased focus on standardizing outcome choices and definitions, adequate handling of missing data and deceased patients in analyses, and use of statistical methods quantifying effect sizes.

Trials with 'non-significant' results are not insignificant trials: a common significance threshold distorts reporting and interpretation of trial results

We discuss a newly published study examining how phrases are used in clinical trials to describe results when the estimated P-value is close to (slightly above or slightly below) 0.05, which has been arbitrarily designated by convention as the boundary for 'statistical significance'. Terms such as 'marginally significant', 'trending towards significant', and 'nominally significant' are well represented in biomedical literature, but are not actually scientifically meaningful. Acknowledging that 'statistical significance' remains a major determinant of publication, we propose that scientific journals de-emphasise the use of P-values for null hypothesis significance testing, a purpose for which they were never intended, and avoid the use of these ambiguous and confusing terms in scientific articles. Instead, investigators could simply report their findings: effect sizes, P-values, and confidence intervals (or their Bayesian equivalents), and leave it to the discerning reader to infer the clinical applicability and importance. Our goal should be to move away from describing studies (or trials) as positive or negative based on an arbitrary P-value threshold, and rather to judge whether the scientific evidence provided is informative or uninformative.

Hierarchical endpoint analysis using win ratio in critical care: An exploration using the balanced solutions in intensive care study (BaSICS)

PURPOSE

To reanalyze the results of the Balanced Solutions in Intensive Care Study (BaSICS) through hierarchical endpoint analysis with win ratio.

METHODS

All patients with full data in BaSICS trial were elected for the analysis. BaSICS compared balanced solutions (Plasma Lye 148) versus 0.9% saline in critically ill patients requiring fluid challenge. The win ratio was defined as a hierarchical endpoint of 90-day mortality, recepit of kidney replacement therapy, hospital length-of-stay (LOS), and intensive care unit (ICU) LOS. Both unstratified and stratified (by admission type: planned admission, unplanned admission with sepsis, and unplanned admission without sepsis) approaches were used. A subgroup analysis was performed in patients with traumatic brain injury.

RESULTS

A total of 10,490 patients were included in the analysis, resulting in 27,587,566 unique combinations for unstratified WR. Unstratified Win ratio was 1.02 (95% confidence interval 0.97; 1.07), which was similar to stratified WR. No stratum in the stratified analysis resulted in significant results. Subgroup analysis confirmed the possible harm of balanced solutions in traumatic brain injury patients (WR 0.80; 95% confidence interval 0.64; 0.99).

CONCLUSION

In this reanalysis of BaSICS, a win ratio analysis largely replicated the results of the main trial, yielding neutral results except for the subgroup of patients with traumatic brain injury where a signal of harm was found.

A dual-center cohort study on the association between early deep sedation and clinical outcomes in mechanically ventilated patients during the COVID-19 pandemic: The COVID-SED study

BACKGROUND

Mechanically ventilated patients have experienced greater periods of prolonged deep sedation during the coronavirus disease (COVID-19) pandemic. Multiple studies from the pre-COVID era demonstrate that early deep sedation is associated with worse outcome. Despite this, there is a lack of data on sedation depth and its impact on outcome for mechanically ventilated patients during the COVID-19 pandemic. We sought to characterize the emergency department (ED) and intensive care unit (ICU) sedation practices during the COVID-19 pandemic, and to determine if early deep sedation was associated with worse clinical outcomes.

STUDY DESIGN AND METHODS

Dual-center, retrospective cohort study conducted over 6 months (March-August, 2020), involving consecutive, mechanically ventilated adults. All sedation-related data during the first 48 h were collected. Deep sedation was defined as Richmond Agitation-Sedation Scale of - 3 to - 5 or Riker Sedation-Agitation Scale of 1-3. To examine impact of early sedation depth on hospital mortality (primary outcome), we used a multivariable logistic regression model. Secondary outcomes included ventilator-, ICU-, and hospital-free days.

RESULTS

391 patients were studied, and 283 (72.4%) experienced early deep sedation. Deeply sedated patients received higher cumulative doses of fentanyl, propofol, midazolam, and ketamine when compared to light sedation. Deep sedation patients experienced fewer ventilator-, ICU-, and hospital-free days, and greater mortality (30.4% versus 11.1%) when compared to light sedation (p < 0.01 for all). After adjusting for confounders, early deep sedation remained significantly associated with higher mortality (adjusted OR 3.44; 95% CI 1.65-7.17; p < 0.01). These results were stable in the subgroup of patients with COVID-19.

CONCLUSIONS

The management of sedation for mechanically ventilated patients in the ICU has changed during the COVID pandemic. Early deep sedation is common and independently associated with worse clinical outcomes. A protocol-driven approach to sedation, targeting light sedation as early as possible, should continue to remain the default approach.

The Impact of Sample Size Misestimations on the Interpretation of ARDS Trials: Systematic Review and Meta-analysis

BACKGROUND

Indeterminate randomized controlled trials (RCTs) in ARDS may arise from sample size misspecification, leading to abandonment of efficacious therapies.

RESEARCH QUESTIONS

If evidence exists for sample size misspecification in ARDS RCTs, has this led to rejection of potentially beneficial therapies? Does evidence exist for prognostic enrichment in RCTs using mortality as a primary outcome?

STUDY DESIGN AND METHODS

We identified 150 ARDS RCTs commencing recruitment after the 1994 American European Consensus Conference ARDS definition and published before October 31, 2020. We examined predicted-observed sample size, predicted-observed control event rate (CER), predicted-observed average treatment effect (ATE), and the relationship between observed CER and observed ATE for RCTs with mortality and nonmortality primary outcome measures. To quantify the strength of evidence, we used Bayesian-averaged meta-analysis, trial sequential analysis, and Bayes factors.

RESULTS

Only 84 of 150 RCTs (56.0%) reported sample size estimations. In RCTs with mortality as the primary outcome, CER was overestimated in 16 of 28 RCTs (57.1%). To achieve predicted ATE, interventions needed to prevent 40.8% of all deaths, compared with the original prediction of 29.3%. Absolute reduction in mortality ≥ 10% was observed in 5 of 28 RCTs (17.9%), but predicted in 21 of 28 RCTs (75%). For RCTs with mortality as the primary outcome, no association was found between observed CER and observed ATE (pooled OR: β = -0.04; 95% credible interval, -0.18 to 0.09). We identified three interventions that are not currently standard of care with a Bayesian-averaged effect size of > 0.20 and moderate strength of existing evidence: corticosteroids, airway pressure release ventilation, and noninvasive ventilation.

INTERPRETATION

Reporting of sample size estimations was inconsistent in ARDS RCTs, and misspecification of CER and ATE was common. Prognostic enrichment strategies in ARDS RCTs based on all-cause mortality are unlikely to be successful. Bayesian methods can be used to prioritize interventions for future effectiveness RCTs.