Negative trials in critical care: why most research is probably wrong

[Specialty-specific knowledge as prerequisite for effective treatment of critically ill patients]

Since the last meeting of the German Medical Association in May 2024, there has been a discussion in Germany about the shortening of primary specialty training and a transfer of the contents of additional supra-specialty training to the existing primary specialty training. This also affects intensive care medicine, with the prospect of creating a subspecialty for subspecialties in intensive care medicine (e.g., a specialty in surgical intensive care medicine). We consider the associated reduction of general specialty-specific contents to be inappropriate for several reasons. Knowledge of the specialty-specific trigger factors (foci) of a critical illness (organ dysfunction) as well as knowledge of the respective trigger factor-specific symptoms, diagnostics and pathways for initiating a causal treatment, are decisive for the prognosis. Recent evidence suggests that in the case of septic foci a time span between making the diagnosis and treatment of the focus should not exceed ca. 6h in order to avoid a worsening of the prognosis. To ensure that the time between symptom onset and effective treatment of the causal factors is not too long, an in-depth expertise in the primary specialty is required throughout the process. This expertise is independent of training in intensive care medicine and can only be acquired through adequate training in the specialty, followed by additional training in intensive care medicine. Expertise in the primary specialty is a prerequisite for the effective treatment of critically ill patients. Maintaining the training specific to the primary specialty and the associated acquisition of specific knowledge in the respective specialty also enables a wider deployment of specialists in clinical practice and a more economical use of diagnostic and therapeutic resources. The additional training in intensive care medicine (supraspecialty) should not be at the expense of content specific to the primary specialty and must remain accessible to all surgical specialties in the field of surgery in the next revision of the training regulations. Due to the unavoidable extent, the additional training in intensive care medicine can itself only be provided on a full-time basis.

Treatment effect of intravenous high-dose selenium in sepsis phenotypes: a retrospective analysis of a large multicenter randomized controlled trial

BACKGROUND

Treatment effect of high-dose intravenous selenium remains controversial in patients with sepsis or septic shock. Here, we reanalyzed data from the randomized placebo-controlled trial of Sodium Selenite and Procalcitonin Guided Antimicrobial Therapy in Severe Sepsis (SISPCT) to reveal possible treatment differences according to established sepsis phenotypes.

METHODS

In this secondary data analysis of the SISPCT trial all 1089 patients of the original study were included. Patients were assigned to one of the four phenotypes by comparing patient variables with the Sepsis Endotyping in Emergency Care (SENECA) validation cohort. Survival analyses were performed using Kaplan-Meier and log-rank tests.

RESULTS

No robust effect of selenium on mortality and other outcome parameters could be determined in any sepsis phenotype. Phenotype frequencies were markedly different in our study cohort compared to previous reports (α: 2.2%, β: 6.3%, γ: 68.0%, δ: 23.4%). Differences in mortality between the respective phenotypes were not significant overall; however, 28-day mortality showed a lower mortality for the α- (20.8%) and β-phenotype (20.3%), followed by the γ- (27.1%), and δ-phenotype (28.5%).

CONCLUSIONS

Application of the four sepsis phenotypes to the SISPCT study cohort showed discrete but non-significant mortality differences within 28 days. However, beneficial treatment effects of high-dose intravenous selenium were still not detectable after categorizing the SISPCT study cohort according to four phenotype criteria.

Synthesis of expert opinions on fluid management in severe sepsis: A contextual review of human albumin and crystalloids

BACKGROUND

Sepsis is a critical condition associated with high mortality rates that necessitates effective fluid resuscitation. Crystalloids are widely utilized; however, human albumin solutions have been attributed potential oncotic and anti-inflammatory benefits. Given the ongoing debate and the absence of definitive empirical evidence, expert opinions provide valuable insights into the contextual and practical aspects of fluid management.

OBJECTIVES

This review synthesizes expert opinions on the utilization of albumin compared to crystalloids in critically ill sepsis patients, emphasizing the contextual and practical considerations rather than drawing conclusions about clinical efficacy.

METHODS

Following the Joanna Briggs Institute (JBI) guidelines for systematic reviews of text and opinions, databases and registries were searched from 2015 to 2024. Two reviewers independently screened sources. Data extraction was conducted by one reviewer and verified by another reviewer. Of 1,917 sources, 38 met the inclusion criteria. Findings were synthesized narratively.

RESULTS

Expert consensus emphasizes crystalloids as the preferred first-line fluid for sepsis due to their safety, cost-effectiveness, and availability. Albumin is conditionally recommended in specific scenarios such as severe hypoalbuminemia, high vasopressor requirements, or volume-sensitive conditions. While theoretical benefits of albumin, including enhanced volume expansion and reduced fluid overload, are recognized, evidence for consistent clinical outcomes remains limited. Experts underscore the importance of individualized management tailored to patient-specific factors and dynamic monitoring, aligning with guideline recommendations that advise against routine albumin use.

CONCLUSIONS

This review highlights the contextual and practical aspects of fluid management in sepsis, underscoring the predominance of crystalloids as the initial choice. Expert insights suggest that albumin may have a supplementary role in specific clinical scenarios. These findings provide a refined understanding of current practice and serve as a foundation for informed decision-making and future research.

TRIAL REGISTRY

PROSPERO; Registration Number: CRD42024580521; URL: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=580521.

Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study

BACKGROUND

Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the treatment effect. However, individual significant interactions may not always yield clinically actionable subgroups, particularly for continuous covariates. Non-parametric causal machine learning approaches are flexible alternatives for estimating HTEs across many possible treatment effect modifiers in a single analysis.

METHODS

We conducted a secondary analysis of the VANISH RCT, which compared the early use of vasopressin with norepinephrine on renal failure-free survival for patients with septic shock at 28 days. We used classical (separate tests for interaction with Bonferroni correction), data-adaptive (hierarchical lasso regression), and non-parametric causal machine learning (causal forest) methods to analyse HTEs for the primary outcome of being alive at 28 days. Causal forests comprise honest causal trees, which use sample splitting to determine tree splits and estimate treatment effects separately. The modal initial (root) splits of the causal forest were extracted, and the mean value was used as a threshold to partition the population into subgroups with different treatment effects.

RESULTS

All three models found evidence of HTE with serum potassium levels. Univariable logistic regression OR 0.435 (95%CI [0.270, 0.683]. p = 0.0004), hierarchical lasso logistic regression standardised OR: 0.604 (95% CI 0.259, 0.701), lambda = 0.0049. Hierarchical lasso kept the interaction between the treatment and serum potassium, sodium level, minimum temperature, platelet count and presence of ischemic heart disease. The causal forest approach found some evidence of HTE (p = 0.124). When extracting root splits, the modal split was on serum potassium (mean applied threshold of 4.68 mmol/L). When dividing the patient population into subgroups based on the mean initial root threshold, risk differences in being alive at 28 days were 0.069 (95%CI [-0.032, 0.169]) and - 0.257 (95%CI [-0.368, -0.146]) with serum potassium ≤ 4.68 and > 4.68 respectively.

CONCLUSIONS

The causal forest agreed with the data-adaptive and classical method of subgroup analysis in identifying HTE by serum potassium. Whilst classical and data-adaptive methods may identify sources of HTE, they do not immediately suggest subgroup splits which are clinically actionable. The extraction of root splits in causal forests is a novel approach to obtaining data-derived subgroups, to be further investigated.

Toward Precision in Nutrition Therapy

Precision in critical care nutrition is paramount, as it focuses nutrition interventions on those patients most likely to benefit, or those who might potentially be harmed. Critical care nutrition must therefore be tailored to individual metabolic needs as determined by factors that control the capacity for tissue homeostasis and anabolic responses. This ideally involves the accurate and timely assessment of macronutrient and micronutrient requirements, a careful evaluation of metabolic response mechanisms and the identification of circumstances that might interfere with the productive utilization of dietary substrates. Specific surrogate markers of metabolic response, such as blood glucose levels, urea levels, or nitrogen balance, might be used to evaluate the metabolic readiness for nutrition and to establish the timing, nature, and clinical effectiveness of nutrition interventions. Despite the pressing need to further develop more targeted approaches in critically ill patients, indices of immediate metabolic responses that correlate with favorable clinical outcomes are lacking. In addition, the development of precision approaches might address timely adjustments in protein, energy, or micronutrient supplementation based on evolving clinical conditions. Here, we review why precision tools are needed in critical care nutrition, our progress thus far, as well as promising approaches and technologies by which multidisciplinary healthcare teams can improve quality of care and clinical outcomes by individualizing nutrition interventions.

Brain Injury after Cardiac Arrest: Refining Prognosis

This study critically reviews prognostication, brings into focus its "refinement" over the decades, and provides a template for clinicians who must judge the functioning of patients who awaken. This includes the use of diagnostic tests, including neuroimaging, electrophysiology, and laboratory testing that may aid in evaluating neurologic recovery. The article reviews recent guidelines and provides advice informed by many years of clinical experience.

Metabolic phenotypes and vitamin D response in the critically ill: A metabolomic cohort study

BACKGROUND & AIMS

Although vitamin D deficiency is common in critically ill patients, randomized controlled trials fail to demonstrate benefits of supplementation. We aimed to identify distinct vitamin D3 responsive metabolic phenotypes prior to trial intervention of high-dose vitamin D3 by applying machine learning clustering method to metabolomics data from the Correction of Vitamin D Deficiency in Critically Ill Patients (VITdAL-ICU) trial.

METHODS

In the randomized, placebo-controlled VITdAL-ICU trial, critically ill adults received placebo or high-dose vitamin D3. To distinguish vitamin D3 responsive metabolic phenotypes prior to intervention, we implemented consensus clustering with partitioning around medoids algorithm to the plasma metabolome data before randomization. Individual metabolite differences were determined utilizing linear mixed-effects regression models stratified for metabolomic phenotypes with false discovery rate adjustment. The association between vitamin D3 supplementation and 180-day mortality was evaluated in each metabolic phenotype, applying multivariable logistic regression analysis.

RESULTS

In 453 critically ill adults, the study identified 4 distinct metabolic phenotypes (clusters A. N = 134; B. N = 123; C. N = 92; D. N = 104). We found differential metabolic pathway patterns in the four clusters. Specifically, branched chain amino acid catabolic metabolites, long-chain acylcarnitines and diacylglycerol species are significantly increased in a specific metabolic phenotype (cluster D) following high-dose vitamin D3. Further, in cluster D high-dose vitamin D3 supplementation had a significantly lower adjusted odds of 180-day mortality after controlling age, sex, Simplified Acute Physiology Score II, admission diagnosis, and baseline 25-hydroxyvitamin D (OR 0.28 (95%CI, 0.09-0.89); P = 0.03). In metabotype A, B, and C, high-dose vitamin D3 supplementation was not significantly associated with lower 180-day mortality following multivariable adjustment.

CONCLUSION

In this post-hoc cohort study of the VITdAL-ICU trial, the clustering analysis of plasma metabolome data identified biologically distinct metabolic phenotypes. Among clusters, we found the different associations between high-dose vitamin D3 supplementation and specific metabolite pathways as well as 180-day mortality. Our findings facilitate further research to validate metabolic phenotype-targeted strategies for critical illness treatments.

Low leucocyte, neutrophil and lymphocyte count (tri-low phenotype) in melioidosis: A predictor of early mortality

Introduction

Melioidosis is a bacterial disease caused by Burkholderia pseudomallei, a gram-negative bacillus endemic to parts of Asia and Northern Australia. This study aimed to identify the role of total and differential leucocyte count in predicting 48-h mortality in patients with melioidosis.

Methodology

This retrospective cohort study included patients diagnosed with culture-proven melioidosis at Kasturba Medical College between 2017 and 2023. Total leucocyte count (TLC), absolute neutrophil count (ANC), and absolute lymphocytic count (ALC) were classified into low (first quartile), medium (second and third quartile) and high (last quartile). The chi-square test was used to compare each group's early (48-h) mortality.

Results

Of the 170 patients with culture-confirmed melioidosis, 24 patients died within 48 h. The mortality was significantly higher in those with low TLC, ANC and ALC. When all three parameters were found to be low (tri-low phenotype), the specificity in predicting mortality was 93.2 %.

Conclusion

Low TLC, ANC and ALC are significant predictors of mortality among melioidosis patients. There is a need to explore new strategies to improve clinical outcomes among melioidosis patients with tri-low phenotype.

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.

Adverse Events during Prone Positioning of Patients with COVID-19 during a Surge in Hospitalizations-Results of an Observational Study

This study aimed to determine the prevalence of adverse events in mechanically ventilated adults with COVID-19 who have undergone prone positioning. A total of 100 patients were included retrospectively; 60% were males, the mean age was 64.8 ± 9.1 years, and hospital mortality was 47%. In all, we recorded 118 removals of catheters and tubes in 66 patients; 29.6% were removals of a nasogastric tube, 18.6% of an arterial line, 14.4% of a urinary catheter, and 12.7% of a central venous catheter. Reintubation or repositioning of a tracheotomy tube was required in 19 patients (16.1%), and cardiopulmonary resuscitation in 2 patients (1.7%). We recorded a total of 184 pressure ulcers in 79 patients (on anterior face in 38.5%, anterior thorax in 23.3% and any extremity anteriorly in 15.2%). We observed that body weight (p = 0.021; β = 0.09 (CI95: 0.01-0.17)) and the cumulative duration of prone positioning (p = 0.005; β = 0.06 (CI95: 0.02-0.11)) were independently associated with the occurrence of any adverse event. The use of prone positioning in our setting was associated with a greater number of adverse events than previously reported. Body weight and cumulative duration of prone positioning were associated with the occurrence of adverse events; however, other factors during a COVID-19 surge, such as working conditions, staffing, and staff education, could also have contributed to a high prevalence of adverse events.

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.

Host and Microbe Blood Metagenomics Reveals Key Pathways Characterizing Critical Illness Phenotypes

Rationale: Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood.Objectives: We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., Sepsis Response Signature [SRS1-2], molecular diagnosis and risk stratification of sepsis [MARS1-4], reactive and uninflamed).Methods: We analyzed data from 113 patients with hypoinflammatory sepsis and 76 patients with hyperinflammatory sepsis enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis.Measurements and Main Results: The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, whereas the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes and, notably, T cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance, and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive and uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in patients with the hypoinflammatory phenotype.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.

Future directions of lung-protective ventilation strategies in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is characterized by the heterogeneous distribution of lung aeration along a gravitational direction due to increased lung density. Therefore, the lung available for ventilation is usually limited to ventral, nondependent lung regions and has been called the "baby" lung. In ARDS, ventilator-induced lung injury is known to occur in nondependent "baby" lungs, as ventilation is shifted to ventral, nondependent lung regions, increasing stress and strain. To protect this nondependent "baby" lung, the clinician targets and limits global parameters such as tidal volume and plateau pressure. In addition, positive end-expiratory pressure (PEEP) is used to prevent dorsal, dependent atelectasis and, if successful, increases the size of the baby lung and lessens its susceptibility to injury from inspiratory stretch. Although many clinical trials have been performed in patients with ARDS over the last two decades, there are few successfully showing benefits on mortality (ie, prone positioning and neuromuscular blocking agents). These disappointing results contrast with other medical disciplines, especially in oncology, where the heterogeneity of diseases is recognized widely and precision medicine has been promoted. Thus, lung-protective ventilation strategies need to take an innovative approach that accounts for the heterogeneity of injured lungs. This article summarizes ventilator-induced lung injury and ARDS and discusses how to implement precision medicine in the field of ARDS. Potentially useful methods to individualize PEEP with esophageal balloon manometry, lung recruitability, and electrical impedance tomography were discussed.

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.

Deep Attention Q-Network for Personalized Treatment Recommendation

Tailoring treatment for severely ill patients is crucial yet challenging to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations. However, they often rely solely on a patient's current physiological state, which may not accurately represent the true health status of the patient. This limitation hampers policy learning and evaluation, undermining the effectiveness of the treatment. In this study, we propose Deep Attention Q-Network for personalized treatment recommendation, leveraging the Transformer architecture within a deep reinforcement learning framework to efficiently integrate historical observations of patients. We evaluated our proposed method on two real-world datasets: sepsis and acute hypotension patients, demonstrating its superiority over state-of-the-art methods. The source code for our model is available at https://github.com/stevenmsm/RL-ICU-DAQN.

Using the Delphi process to determine the minimum clinically important effect size for the Balanced-2 randomised controlled trial

BACKGROUND

The sample size calculation is an important step in designing randomised controlled trials. For a trial comparing a control and an intervention group, where the outcome is binary, the sample size calculation requires choosing values for the anticipated event rates in both the control and intervention groups (the effect size), and the error rates. The Difference ELicitation in TriAls guidance recommends that the effect size should be both realistic, and clinically important to stakeholder groups. Overestimating the effect size leads to sample sizes that are too small to reliably detect the true population effect size, which in turn results in low achieved power. In this study, we use the Delphi approach to gain consensus on what the minimum clinically important effect size is for Balanced-2, a randomised controlled trial comparing processed electroencephalogram-guided 'light' to 'deep' general anaesthesia on the incidence of postoperative delirium in older adults undergoing major surgery.

METHODS

Delphi rounds were conducted using electronic surveys. Surveys were administered to two stakeholder groups: specialist anaesthetists from a general adult department in Auckland City Hospital, New Zealand (Group 1), and specialist anaesthetists with expertise in clinical research, identified from the Australian and New Zealand College of Anaesthetist's Clinical Trials Network (Group 2). A total of 187 anaesthetists were invited to participate (81 from Group 1 and 106 from Group 2). Results from each Delphi round were summarised and presented in subsequent rounds until consensus was reached (>70% agreement).

RESULTS

The overall response rate for the first Delphi survey was 47% (88/187). The median minimum clinically important effect size was 5.0% (interquartile range: 5.0-10.0) for both stakeholder groups. The overall response rate for the second Delphi survey was 51% (95/187). Consensus was reached after the second round, as 74% of respondents in Group 1 and 82% of respondents in Group 2 agreed with the median effect size. The combined minimum clinically important effect size across both groups was 5.0% (interquartile range: 3.0-6.5).

CONCLUSIONS

This study demonstrates that surveying stakeholder groups using a Delphi process is a simple way of defining a minimum clinically important effect size, which aids the sample size calculation and determines whether a randomised study is feasible.

Heterogeneity in defining multiple trauma: a systematic review of randomized controlled trials

INTRODUCTION

While numerous randomized controlled trials (RCTs) have been conducted in the field of trauma, a substantial portion of them are yielding negative results. One potential contributing factor to this trend could be the lack of agreement regarding the chosen definitions across different trials. The primary objective was to identify the terminology and definitions utilized for the characterization of multiple trauma patients within randomized controlled trials (RCTs).

METHODS

A systematic review of the literature was performed in MEDLINE, EMBASE and clinicaltrials.gov between January 1, 2002, and July 31, 2022. RCTs or RTCs protocols were eligible if they included multiple trauma patients. The terms employed to characterize patient populations were identified, and the corresponding definitions for these terms were extracted. The subsequent impact on the population recruited was then documented to expose clinical heterogeneity.

RESULTS

Fifty RCTs were included, and 12 different terms identified. Among these terms, the most frequently used were "multiple trauma" (n = 21, 42%), "severe trauma" (n = 8, 16%), "major trauma" (n = 4, 8%), and trauma with hemorrhagic shock" (n = 4, 8%). Only 62% of RCTs (n = 31) provided a definition for the terms used, resulting a total of 21 different definitions. These definitions primarily relied on the injury severity score (ISS) (n = 15, 30%), displaying an important underlying heterogeneity. The choice of the terms had an impact on the study population, affecting both the ISS and in-hospital mortality. Eleven protocols were included, featuring five different terms, with "severe trauma" being the most frequent, occurring six times (55%).

CONCLUSION

This systematic review uncovers an important heterogeneity both in the terms and in the definitions employed to recruit trauma patients within RCTs. These findings underscore the imperative of promoting the use of a unique and consistent definition.

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model

BACKGROUND

Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality).

METHODS

This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate.

RESULTS

A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6.

CONCLUSION

The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

[Individualized treatment in anesthesiology and intensive care medicine]

BACKGROUND

Individualized medicine uses data on biological characteristics of individual patients in order to tailor treatment planning to their unique constitution. With respect to the practice of anesthesiology and intensive care medicine, it bears the potential to systematize the often complex medical care of critically ill patients and to improve outcomes.

OBJECTIVE

The aim of this narrative review is to provide an overview of the possible applications of the principles of individualized medicine in anesthesiology and intensive care medicine.

MATERIAL AND METHODS

Based on a search in MEDLINE, CENTRAL and Google Scholar, the results of previous studies and systematic reviews are narratively synthesized and the implications for the scientific and clinical practice are presented.

RESULTS AND DISCUSSION

There are possibilities for individualization and an increase in precision of patient care in most if not all problems in anesthesiology and symptoms in intensive medical care. Even now, all practicing physicians can initiate measures to individualize treatment at different timepoints throughout the course of treatment. Individualized medicine can supplement and be integrated into protocols. Plans for future applications of individualized medicine interventions should consider the feasibility in a real-world setting. Clinical studies should contain process evaluations in order to create ideal preconditions for a successful implementation. Quality management, audits and feedback should become a standard procedure to ensure sustainability. In the long run, individualization of care, especially in the critically ill, should be enshrined in guidelines and become an integral part of clinical practice.

Laying the Groundwork for Physiology-Guided Precision Medicine in the Critically Ill

Canonical critical care syndromes such as sepsis and acute respiratory distress syndrome (ARDS) include patients with markedly heterogeneous biology.1 This, paired with decades of randomized controlled trials (RCTs) that were traditionally viewed as "negative," has stalled progress in improving patient outcomes.2 However, emerging awareness of sub-phenotypes based on differences in biomarker profiles and resulting heterogeneous treatment effects have led to calls for precision medicine in which therapies are targeted to those most likely to benefit.3.

Upregulation of alveolar fluid clearance is not sufficient for Na+,K+-ATPase β subunit-mediated gene therapy of LPS-induced acute lung injury in mice

Acute Lung Injury/Acute Respiratory Distress Syndrome (ALI/ARDS) is characterized by diffuse alveolar damage and significant edema accumulation, which is associated with impaired alveolar fluid clearance (AFC) and alveolar-capillary barrier disruption, leading to acute respiratory failure. Our previous data showed that electroporation-mediated gene delivery of the Na⁺, K⁺-ATPase β1 subunit not only increased AFC, but also restored alveolar barrier function through upregulation of tight junction proteins, leading to treatment of LPS-induced ALI in mice. More importantly, our recent publication showed that gene delivery of MRCKα, the downstream effector of β1 subunit-mediated signaling towards upregulation of adhesive junctions and epithelial and endothelial barrier integrity, also provided therapeutic potential for ARDS treatment in vivo but without necessarily accelerating AFC, indicating that for ARDS treatment, improving alveolar capillary barrier function may be of more benefit than improving fluid clearance. In the present study, we investigated the therapeutical potential of β2 and β3 subunits, the other two β isoforms of Na⁺, K⁺-ATPase, for LPS-induced ALI. We found that gene transfer of either the β1, β2, or β3 subunits significantly increased AFC compared to the basal level in naïve animals and each gave similar increased AFC to each other. However, unlike that of the β1 subunit, gene transfer of the β2 or β3 subunit into pre-injured animal lungs failed to show the beneficial effects of attenuated histological damage, neutrophil infiltration, overall lung edema, or increased lung permeability, indicating that β2 or β3 gene delivery could not treat LPS induced lung injury. Further, while β1 gene transfer increased levels of key tight junction proteins in the lungs of injured mice, that of either the β2 or β3 subunit had no effect on levels of tight junction proteins. Taken together, this strongly suggests that restoration of alveolar-capillary barrier function alone may be of equal or even more benefit than improving AFC for ALI/ARDS treatment.

Personalized ventilatory strategy based on lung recruitablity in COVID-19-associated acute respiratory distress syndrome: a prospective clinical study

BACKGROUND

Heterogeneity is an inherent nature of ARDS. Recruitment-to-inflation ratio has been developed to identify the patients who has lung recruitablity. This technique might be useful to identify the patients that match specific interventions, such as higher positive end-expiratory pressure (PEEP) or prone position or both. We aimed to evaluate the physiological effects of PEEP and body position on lung mechanics and regional lung inflation in COVID-19-associated ARDS and to propose the optimal ventilatory strategy based on recruitment-to-inflation ratio.

METHODS

Patients with COVID-19-associated ARDS were consecutively enrolled. Lung recruitablity (recruitment-to-inflation ratio) and regional lung inflation (electrical impedance tomography [EIT]) were measured with a combination of body position (supine or prone) and PEEP (low 5 cmH₂O or high 15 cmH₂O). The utility of recruitment-to-inflation ratio to predict responses to PEEP were examined with EIT.

RESULTS

Forty-three patients were included. Recruitment-to-inflation ratio was 0.68 (IQR 0.52-0.84), separating high recruiter versus low recruiter. Oxygenation was the same between two groups. In high recruiter, a combination of high PEEP with prone position achieved the highest oxygenation and less dependent silent spaces in EIT (vs. low PEEP in both positions) without increasing non-dependent silent spaces in EIT. In low recruiter, low PEEP in prone position resulted in better oxygenation (vs. both PEEPs in supine position), less dependent silent spaces (vs. low PEEP in supine position) and less non-dependent silent spaces (vs. high PEEP in both positions). Recruitment-to-inflation ratio was positively correlated with the improvement in oxygenation and respiratory system compliance, the decrease in dependent silent spaces, and was inversely correlated with the increase in non-dependent silent spaces, when applying high PEEP.

CONCLUSIONS

Recruitment-to-inflation ratio may be useful to personalize PEEP in COVID-19-associated ARDS. Higher PEEP in prone position and lower PEEP in prone position decreased the amount of dependent silent spaces (suggesting lung collapse) without increasing the amount of non-dependent silent spaces (suggesting overinflation) in high recruiter and in low recruiter, respectively.

The Uncertainty Associated with Moving Early

Some of the most common questions asked of clinicians caring for patients with critical illness are about prognosis: Will my loved one survive? If they do survive, at what cost to their quality of life? While some consolation can be provided that mortality rates for common conditions have fallen,1 patients who survive the initial intensive care unit admission face an uncertain future, with the potential for significant morbidity, poor mental health, cognitive dysfunction, frequent hospital readmissions, and poor functional recovery.2,3.

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.

Personalized medicine targeting different ARDS phenotypes: The future of pharmacotherapy for ARDS?

INTRODUCTION

Acute respiratory distress syndrome (ARDS) still represents a major challenge with high mortality rates and altered quality of life. Many well-designed studies have failed to improve ARDS outcomes. Heterogeneity of etiologies, mechanisms of lung damage, different lung mechanics, and different treatment approaches may explain these failures. At the era of personalized medicine, ARDS phenotyping is not only a field of research, but a bedside consideration when implementing therapy. ARDS has moved from being a simple syndrome to a more complex area of subgrouping. Intensivists must understand these phenotypes and therapies associated with a better outcome.

AREAS COVERED

After a brief sum-up of the different type of ARDS phenotypes, we will present some relevant therapy that may be impacted by phenotyping. A focus on pharmacotherapy will be realized before a section on non-pharmaceutical strategies. Eventually, we will highlight the limits of our knowledge of phenotyping and the pitfalls of personalized medicine.

EXPERT OPINION

Biological and morphological ARDS phenotypes are now well studied. The future of ARDS therapy will go through phenotyping that allows a personalized medication for each patient. However, a better assessment of these phenotypes is required, and clinical trials should be conducted with an ad-hoc phenotyping before randomization.

Management of Severe and Critical COVID-19 Infection with Immunotherapies

Following the reduction in mortality demonstrated by dexamethasone treatment in severe COVID-19, many targeted immunotherapies have been investigated. Thus far, inhibition of IL-6 and JAK pathways have the most robust data and have been granted Emergency Use Authorization for treatment of severe disease. However, it must be noted that critically ill patients comprised a relatively small proportion of most of the trials of COVID-19 therapeutics, despite bearing a disproportionate burden of morbidity and mortality. Furthermore, the rapidity and fluidity with which clinical trials have been conducted in the pandemic setting have contributed to difficulty in extrapolating available trial data to critically ill patients. The exclusion of many patients requiring invasive mechanical ventilation, preponderance of ordinal scale based endpoints, and frequent lack of blinding are particular challenges. More data is needed to identify beneficial treatments in the complex milieu of critical illness from COVID-19 infection.

Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels

Atrial fibrillation (AF) is the most common arrhythmia found in the intensive care unit (ICU), and is associated with many adverse outcomes. Effective handling of AF and similar arrhythmias is a vital part of modern critical care, but obtaining knowledge about both disease burden and effective interventions often requires costly clinical trials. A wealth of continuous, high frequency physiological data such as the waveforms derived from electrocardiogram telemetry are promising sources for enriching clinical research. Automated detection using machine learning and in particular deep learning has been explored as a solution for processing these data. However, a lack of labels, increased presence of noise, and inability to assess the quality and trustworthiness of many machine learning model predictions pose challenges to interpretation. In this work, we propose an approach for training deep AF models on limited, noisy data and report uncertainty in their predictions. Using techniques from the fields of weakly supervised learning, we leverage a surrogate model trained on non-ICU data to create imperfect labels for a large ICU telemetry dataset. We combine these weak labels with techniques to estimate model uncertainty without the need for extensive human data annotation. AF detection models trained using this process demonstrated higher classification performance (0.64-0.67 F1 score) and improved calibration (0.05-0.07 expected calibration error).

Nanocarrier-based approaches to combat chronic obstructive pulmonary disease

Abnormalities in airway mucus lead to chronic disorders in the pulmonary system such as asthma, fibrosis and chronic obstructive pulmonary disease (COPD). Among these, COPD is more prominent worldwide. Various conventional approaches are available in the market for the treatment of COPD, but the delivery of drugs to the target site remains a challenge with conventional approaches. Nanocarrier-based approaches are considered the best due to their sustained release properties to the target site, smaller size, high surface-to-volume ratio, patient compliance, overcoming airway defenses and improved pharmacotherapy. This article provides updated information about the treatment of COPD along with nanocarrier-based approaches as well as the potential of gene therapy and stem cell therapy to combat the COPD.

Factors to be Considered in Advancing Pediatric Critical Care Across the World

This article reviews the many factors that have to be taken into account as we consider the advancement of pediatric critical care (PCC) in multiple settings across the world. The extent of PCC and the range of patients who are cared for in this environment are considered. Along with a review of the ongoing treatment and technology advances in the PCC setting, the structures and systems required to support these services are also considered. Finally the question of how PCC can be made sustainable in a volatile world with the impacts of global crises such as climate change is addressed.

White blood cell count trajectory and mortality in septic shock: a historical cohort study

PURPOSE

Septic shock is associated with a mortality of 20-40%. The white blood cell count (WBC) at hospital admission correlates with prognosis in septic shock. Here, we explore whether the trajectory of WBC after admission provides further information about outcomes. We aimed to identify groups of patients with different WBC trajectories and the association of WBC trajectory with mortality.

METHODS

We included adult patients with septic shock in two academic intensive care units (ICU) in Winnipeg, MB, Canada between 2006 and 2012. We used group-based trajectory analysis to group patients according to their WBC patterns over the first seven days in the ICU. Our primary analysis was the association of WBC trajectory group on 30-day mortality using multivariable Cox proportional hazards regression.

RESULTS

We included 917 patients with septic shock. The final model identified seven distinct WBC trajectories. The rising WBC trajectory was independently associated with increased mortality (hazard ratio, 3.41; 95% confidence interval, 1.86 to 6.26; P < 0.001) compared with the stable WBC trajectory.

CONCLUSION

In patients with septic shock, distinct and clinically relevant groups can be identified by analyzing WBC trajectories. A rising WBC trajectory was associated with higher mortality.

Biomarkers for the Prediction and Judgement of Sepsis and Sepsis Complications: A Step towards precision medicine?

Sepsis and septic shock are a major public health concern and are still associated with high rates of morbidity and mortality. Whilst there is growing understanding of different phenotypes and endotypes of sepsis, all too often treatment strategies still only employ a “one-size-fits-all” approach. Biomarkers offer a unique opportunity to close this gap to more precise treatment approaches by providing insight into clinically hidden, yet complex, pathophysiology, or by individualizing treatment pathways. Predicting and evaluating systemic inflammation, sepsis or septic shock are essential to improve outcomes for these patients. Besides opportunities to improve patient care, employing biomarkers offers a unique opportunity to improve clinical research in patients with sepsis. The high rate of negative clinical trials in this field may partly be explained by a high degree of heterogeneity in patient cohorts and a lack of understanding of specific endotypes or phenotypes. Moving forward, biomarkers can support the selection of more homogeneous cohorts, thereby potentially improving study conditions of clinical trials. This may finally pave the way to a precision medicine approach to sepsis, septic shock and complication of sepsis in the future.

Mesenchymal stromal cells-derived extracellular vesicles reprogramme macrophages in ARDS models through the miR-181a-5p-PTEN-pSTAT5-SOCS1 axis

RATIONALE

A better understanding of the mechanism of action of mesenchymal stromal cells (MSCs) and their extracellular vesicles (EVs) is needed to support their use as novel therapies for acute respiratory distress syndrome (ARDS). Macrophages are important mediators of ARDS inflammatory response. Suppressor of cytokine signalling (SOCS) proteins are key regulators of the macrophage phenotype switch. We therefore investigated whether SOCS proteins are involved in mediation of the MSC effect on human macrophage reprogramming.

METHODS

Human monocyte-derived macrophages (MDMs) were stimulated with lipopolysaccharide (LPS) or plasma samples from patients with ARDS (these samples were previously classified into hypo-inflammatory and hyper-inflammatory phenotype) and treated with MSC conditioned medium (CM) or EVs. Protein expression was measured by Western blot. EV micro RNA (miRNA) content was determined by miRNA sequencing. In vivo: LPS-injured C57BL/6 mice were given EVs isolated from MSCs in which miR-181a had been silenced by miRNA inhibitor or overexpressed using miRNA mimic.

RESULTS

EVs were the key component of MSC CM responsible for anti-inflammatory modulation of human macrophages. EVs significantly reduced secretion of tumour necrosis factor-α and interleukin-8 by LPS-stimulated or ARDS plasma-stimulated MDMs and this was dependent on SOCS1. Transfer of miR-181a in EVs downregulated phosphatase and tensin homolog (PTEN) and subsequently activated phosphorylated signal transducer and activator of transcription 5 (pSTAT5) leading to upregulation of SOCS1 in macrophages. In vivo, EVs alleviated lung injury and upregulated pSTAT5 and SOCS1 expression in alveolar macrophages in a miR181-dependent manner. Overexpression of miR-181a in MSCs significantly enhanced therapeutic efficacy of EVs in this model.

CONCLUSION

miR-181a-PTEN-pSTAT5-SOCS1 axis is a novel pathway responsible for immunomodulatory effect of MSC EVs in ARDS.

Feasibility of Extracting Meaningful Patient Centered Outcomes From the Electronic Health Record Following Critical Illness in the Elderly

Background

Meaningful patient centered outcomes of critical illness such as functional status, cognition and mental health are studied using validated measurement tools that may often be impractical outside the research setting. The Electronic health record (EHR) contains a plethora of information pertaining to these domains. We sought to determine how feasible and reliable it is to assess meaningful patient centered outcomes from the EHR.

Methods

Two independent investigators reviewed EHR of a random sample of ICU patients looking at documented assessments of trajectory of functional status, cognition, and mental health. Cohen's kappa was used to measure agreement between 2 reviewers. Post ICU health in these domains 12 month after admission was compared to pre- ICU health in the 12 months prior to assess qualitatively whether a patient's condition was “better,” “unchanged” or “worse.” Days alive and out of hospital/health care facility was a secondary outcome.

Results

Thirty six of the 41 randomly selected patients (88%) survived critical illness. EHR contained sufficient information to determine the difference in health status before and after critical illness in most survivors (86%). Decline in functional status (36%), cognition (11%), and mental health (11%) following ICU admission was observed compared to premorbid baseline. Agreement between reviewers was excellent (kappa ranging from 0.966 to 1). Eighteen patients (44%) remained home after discharge from hospital and rehabilitation during the 12- month follow up.

Conclusion

We demonstrated the feasibility and reliability of assessing the trajectory of changes in functional status, cognition, and selected mental health outcomes from EHR of critically ill patients. If validated in a larger, representative sample, these outcomes could be used alongside survival in quality improvement studies and pragmatic clinical trials.

One-year patient outcomes based on lung morphology in acute respiratory distress syndrome: secondary analysis of LIVE trial

Background

Acute respiratory distress syndrome (ARDS) has different phenotypes and distinct short-term outcomes. Patients with non-focal ARDS have a higher short-term mortality than focal ones. The aim of this study was to assess the impact of the morphological phenotypes of ARDS on long-term outcomes.

Methods

This was a secondary analysis of the LIVE study, a prospective, randomised control trial, assessing the usefulness of a personalised ventilator setting according to lung morphology in moderate-to-severe ARDS. ARDS was classified as focal (consolidations only in the infero-posterior part of the lungs) or non-focal. Outcomes were assessed using mortality and functional scores for quality of life at the 1-year follow-up.

Results

A total of 124 focal ARDS and 236 non-focal ARDS cases were included. The 1-year mortality was higher for non-focal ARDS than for focal ARDS (37% vs. 24%, p = 0.012). Non-focal ARDS (hazard ratio, 3.44; 95% confidence interval, 1.80–6.59; p < 0.001), age, McCabe score, haematological cancers, SAPS II, and renal replacement therapy were independently associated with 1-year mortality. This difference was driven by mortality during the first 90 days (28 vs. 16%, p = 0.010) but not between 90 days and 1 year (7 vs. 6%, p = 0.591), at which point only the McCabe score was independently associated with mortality. Morphological phenotypes had no impact on patient-reported outcomes.

Conclusion

Lung morphologies reflect the acute phase of ARDS and its short-term impact but not long-term outcomes, which seem only influenced by comorbidities.

Trial registration: NCT 02149589; May 29, 2014.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04036-7.

Recent Trends in Admission Diagnosis and Related Mortality in the Medically Critically Ill

PURPOSE

With decades of declining ICU mortality, we hypothesized that the outcomes and distribution of diseases cared for in the ICU have changed and we aimed to further characterize them.

STUDY DESIGN AND METHODS

A retrospective cohort analysis of 287,154 nonsurgical-critically ill adults, from 237 U.S. ICUs, using the manually abstracted Cerner APACHE Outcomes database from 2008 to 2016 was performed. Surgical patients, rare admission diagnoses (<100 occurrences), and low volume hospitals (<100 total admissions) were excluded. Diagnoses were distributed into mutually exclusive organ system/disease-based categories based on admission diagnosis. Multi-level mixed-effects negative binomial regression was used to assess temporal trends in admission, in-hospital mortality, and length of stay (LOS).

RESULTS

The number of ICU admissions remained unchanged (IRR 0.99, 0.98-1.003) while certain organ system/disease groups increased (toxicology [25%], hematologic/oncologic [55%] while others decreased (gastrointestinal [31%], pulmonary [24%]). Overall risk-adjusted in-hospital mortality was unchanged (IRR 0.98, 0.96-1.0004). Risk-adjusted ICU LOS (Estimate -0.06 days/year, -0.07 to -0.04) decreased. Risk-adjusted mortality varied significantly by disease.

CONCLUSION

Risk-adjusted ICU mortality rate did not change over the study period, but there was evidence of shifting disease burden across the critical care population. Our data provides useful information regarding future ICU personnel and resource needs.

Gene Therapy for Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a devastating clinical syndrome that leads to acute respiratory failure and accounts for over 70,000 deaths per year in the United States alone, even prior to the COVID-19 pandemic. While its molecular details have been teased apart and its pathophysiology largely established over the past 30 years, relatively few pharmacological advances in treatment have been made based on this knowledge. Indeed, mortality remains very close to what it was 30 years ago. As an alternative to traditional pharmacological approaches, gene therapy offers a highly controlled and targeted strategy to treat the disease at the molecular level. Although there is no single gene or combination of genes responsible for ARDS, there are a number of genes that can be targeted for upregulation or downregulation that could alleviate many of the symptoms and address the underlying mechanisms of this syndrome. This review will focus on the pathophysiology of ARDS and how gene therapy has been used for prevention and treatment. Strategies for gene delivery to the lung, such as barriers encountered during gene transfer, specific classes of genes that have been targeted, and the outcomes of these approaches on ARDS pathogenesis and resolution will be discussed.

Six-month and 12-month patient outcomes based on inflammatory subphenotypes in sepsis-associated ARDS: secondary analysis of SAILS-ALTOS trial

BACKGROUND

Prior acute respiratory distress syndrome (ARDS) trials have identified hypoinflammatory and hyperinflammatory subphenotypes, with distinct differences in short-term outcomes. It is unknown if such differences extend beyond 90 days or are associated with physical, mental health or cognitive outcomes.

METHODS

568 patients in the multicentre Statins for Acutely Injured Lungs from Sepsis trial of rosuvastatin versus placebo were included and assigned a subphenotype. Among 6-month and 12-month survivors (N=232 and 219, respectively, representing 243 unique survivors), subphenotype status was evaluated for association with a range of patient-reported outcomes (eg, mental health symptoms, quality of life). Patient subsets also were evaluated with performance-based tests of physical function (eg, 6 min walk test) and cognition.

FINDINGS

The hyperinflammatory versus hypoinflammatory subphenotype had lower overall 12-month cumulative survival (58% vs 72%, p<0.01); however, there was no significant difference in survival beyond 90 days (86% vs 89%, p=0.70). Most survivors had impairment across the range of outcomes, with little difference between subphenotypes at 6-month and 12-month assessments. For instance, at 6 months, in comparing the hypoinflammatory versus hyperinflammatory subphenotypes, respectively, the median (IQR) patient-reported SF-36 mental health domain score was 47 (33-56) vs 44 (35-56) (p=0.99), and the per cent predicted 6 min walk distance was 66% (48%, 80%) vs 66% (49%, 79%) (p=0.76).

INTERPRETATION

Comparing the hyperinflammatory versus hypoinflammatory ARDS subphenotype, there was no significant difference in survival beyond 90 days and no consistent findings of important differences in 6-month or 12-month physical, cognitive and mental health outcomes. These findings, when considered with prior results, suggest that inflammatory subphenotypes largely reflect the acute phase of illness and its short-term impact.

COVID-19, corticosteroids and public health: a reappraisal

OBJECTIVES

To assess whether regulatory guidance on the use of dexamethasone in hospitalised COVID-19 patients is applicable to the larger population of COVID-19 cases. The surge in worldwide demand for dexamethasone suggests that the guidance, although correct, has not emphasised the danger of its wider use.

STUDY DESIGN

Data from the Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial and the World Health Organisation (WHO) prospective meta-analysis have been deconstructed and analysed.

METHODS

To provide context, relevant publications were identified in PubMed using the following keywords: COVID-19, RECOVERY trial, WHO meta-analysis, variants, immunity, public health.

RESULTS

The WHO guidance 'Corticosteroids for COVID-19' was based on their prospective meta-analysis. This meta-analysis was weighted by data from the RECOVERY trial.

CONCLUSIONS

In terms of COVID-19, dexamethasone has value in a narrow indication, namely, in hospitalised patients requiring respiratory support. The media blitz likely resulted in the wider use of dexamethasone in outpatients and as a preventive medication. This is reflected in the surge in worldwide demand for dexamethasone. We ask whether the use of steroids, beyond regulatory indications, may be responsible for the recent increase in mortality and especially the emergence of mucormycosis? From the public health standpoint, the current guidance for use of dexamethasone in COVID-19 could benefit from clarification and the addition of a cautionary note.

A Bayesian analysis of mortality outcomes in multicentre clinical trials in critical care

BACKGROUND

Multicentre RCTs are widely used by critical care researchers to answer important clinical questions. However, few trials evaluating mortality outcomes report statistically significant results. We hypothesised that the low proportion of trials reporting statistically significant differences for mortality outcomes is plausibly explained by lower-than-expected effect sizes combined with a low proportion of participants who could realistically benefit from studied interventions.

METHODS

We reviewed multicentre trials in critical care published over a 10-yr period in the New England Journal of Medicine, the Journal of the American Medical Association, and the Lancet. To test our hypothesis, we analysed the results using a Bayesian model to investigate the relationship between the proportion of effective interventions and the proportion of statistically significant results for prior distributions of effect size and trial participant susceptibility.

RESULTS

Five of 54 trials (9.3%) reported a significant difference in mortality between the control and the intervention groups. The median expected and observed differences in absolute mortality were 8.0% and 2.0%, respectively. Our modelling shows that, across trials, a lower-than-expected effect size combined with a low proportion of potentially susceptible participants is consistent with the observed proportion of trials reporting significant differences even when most interventions are effective.

CONCLUSIONS

When designing clinical trials, researchers most likely overestimate true population effect sizes for critical care interventions. Bayesian modelling demonstrates that that it is not necessarily the case that most studied interventions lack efficacy. In fact, it is plausible that many studied interventions have clinically important effects that are missed.

Nanotherapeutics in the treatment of acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is a form of oxygenation failure primarily characterized by rapid inflammation resulting from a direct pulmonary or indirect systemic insult. ARDS has been a major cause of death in the recent COVID-19 outbreak wherein asymptomatic respiratory tract infection progresses to ARDS from pneumonia have emphasized the need for a reliable therapy for the disease. The disease has a high mortality rate of approximately 30-50%. Despite the high mortality rate, a dearth of effective pharmacotherapy exists that demands extensive research in this area. The complex ARDS pathophysiology which remains to be understood completely and the multifactorial etiology of the disease has led to the poor diagnosis, impeded drug-delivery to the deeper pulmonary tissues, and delayed treatment of the ARDS patients. Besides, critically ill patients are unable to tolerate the off-target side effects. The vast domain of nanobiotechnology presents several drug delivery systems offering numerous benefits such as targeted delivery, prolonged drug release, and uniform drug-distribution. The present review presents a brief insight into the ARDS pathophysiology and summarizes conventional pharmacotherapies available to date. Furthermore, the review provides an updated report of major developments in the nanomedicinal approaches for the treatment of ARDS. We also discuss different nano-formulations studied extensively in the ARDS preclinical models along with underlining the advantages as well as challenges that need to be addressed in the future.

Data Analysis will not Result in Knowledge Production about Sepsis

How to cite this article: Sampath S. Data Analysis will not Result in Knowledge Production about Sepsis. Indian J Crit Care Med 2021;25(7):750-751.

Mesenchymal stromal cell extracellular vesicles rescue mitochondrial dysfunction and improve barrier integrity in clinically relevant models of ARDS

Alveolar epithelial–capillary barrier disruption is a hallmark of acute respiratory distress syndrome (ARDS). Contribution of mitochondrial dysfunction to the compromised alveolar-capillary barrier in ARDS remains unclear. Mesenchymal stromal cells-derived extracellular vesicles (MSC-EVs) are considered as a cell-free therapy for ARDS. Mitochondrial transfer was shown to be important for the therapeutic effects of MSCs and MSC-EVs. Here we investigated the contribution of mitochondrial dysfunction to the injury of alveolar epithelial and endothelial barriers in ARDS and the ability of MSC-EVs to modulate alveolar–capillary barrier integrity through mitochondrial transfer.

Primary human small airway epithelial and pulmonary microvascular endothelial cells and human precision cut lung slices (PCLSs) were stimulated with endotoxin or plasma samples from patients with ARDS and treated with MSC-EVs, barrier properties and mitochondrial functions were evaluated. Lipopolysaccharide (LPS)-injured mice were treated with MSC-EVs and degree of lung injury and mitochondrial respiration of the lung tissue were assessed.

Inflammatory stimulation resulted in increased permeability coupled with pronounced mitochondrial dysfunction in both types of primary cells and PCLSs. Extracellular vesicles derived from normal MSCs restored barrier integrity and normal levels of oxidative phosphorylation while an extracellular vesicles preparation which did not contain mitochondria was not effective. In vivo, presence of mitochondria was critical for extracellular vesicles ability to reduce lung injury and restore mitochondrial respiration in the lung tissue.

In the ARDS environment, MSC-EVs improve alveolar–capillary barrier properties through restoration of mitochondrial functions at least partially via mitochondrial transfer.

This study demonstrates that mitochondrial dysfunction is an important mechanism of ARDS pathogenesis. Mitochondrial transfer is crucial for the ability of MSC extracellular vesicles to restore integrity of the alveolar–capillary barrier.https://bit.ly/2JuqoCY

Detection and Profiling of Human Coronavirus Immunoglobulins in Critically Ill Coronavirus Disease 2019 Patients

OBJECTIVES

Coronavirus disease 2019 continues to spread worldwide with high levels of morbidity and mortality. We performed anticoronavirus immunoglobulin G profiling of critically ill coronavirus disease 2019 patients to better define their underlying humoral response.

DESIGN

Blood was collected at predetermined ICU days to measure immunoglobulin G with a research multiplex assay against four severe acute respiratory syndrome coronavirus 2 proteins/subunits and against all six additionally known human coronaviruses.

SETTING

Tertiary care ICU and academic laboratory.

SUBJECTS

ICU patients suspected of being infected with severe acute respiratory syndrome coronavirus 2 had blood collected until either polymerase chain reaction testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until death or discharge if the patient tested polymerase chain reaction positive (coronavirus disease 2019 positive).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Age- and sex-matched healthy controls and ICU patients who were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well-balanced with the exception that coronavirus disease 2019 positive patients had greater body mass indexes, presented with bilateral pneumonias more frequently, and suffered lower Pao2:Fio2 ratios, when compared with coronavirus disease 2019 negative patients (p < 0.05). Mortality rate for coronavirus disease 2019 positive patients was 50%. On ICU days 1-3, anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G was significantly elevated in coronavirus disease 2019 positive patients, as compared to both healthy control subjects and coronavirus disease 2019 negative patients (p < 0.001). Weak severe acute respiratory syndrome coronavirus immunoglobulin G serologic responses were also detected, but not other coronavirus subtypes. The four anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G were maximal by ICU day 3, with all four anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G providing excellent diagnostic potential (severe acute respiratory syndrome coronavirus 2 Spike 1 protein immunoglobulin G, area under the curve 1.0, p < 0.0005; severe acute respiratory syndrome coronavirus receptor binding domain immunoglobulin G, area under the curve, 0.93-1.0; p ≤ 0.0001; severe acute respiratory syndrome coronavirus 2 Spike proteins immunoglobulin G, area under the curve, 1.0; p < 0.0001; severe acute respiratory syndrome coronavirus 2 Nucleocapsid protein immunoglobulin G area under the curve, 0.90-0.95; p ≤ 0.0003). Anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G increased and/or plateaued over 10 ICU days.

CONCLUSIONS

Critically ill coronavirus disease 2019 patients exhibited anti-severe acute respiratory syndrome coronavirus 2 immunoglobulin G, whereas serologic responses to non-severe acute respiratory syndrome coronavirus 2 antigens were weak or absent. Detection of human coronavirus immunoglobulin G against the different immunogenic structural proteins/subunits with multiplex assays may be useful for pathogen identification, patient cohorting, and guiding convalescent plasma therapy.

Emerging pharmacological therapies for ARDS: COVID-19 and beyond

ARDS, first described in 1967, is the commonest form of acute severe hypoxemic respiratory failure. Despite considerable advances in our knowledge regarding the pathophysiology of ARDS, insights into the biologic mechanisms of lung injury and repair, and advances in supportive care, particularly ventilatory management, there remains no effective pharmacological therapy for this syndrome. Hospital mortality at 40% remains unacceptably high underlining the need to continue to develop and test therapies for this devastating clinical condition. The purpose of the review is to critically appraise the current status of promising emerging pharmacological therapies for patients with ARDS and potential impact of these and other emerging therapies for COVID-19-induced ARDS. We focus on drugs that: (1) modulate the immune response, both via pleiotropic mechanisms and via specific pathway blockade effects, (2) modify epithelial and channel function, (3) target endothelial and vascular dysfunction, (4) have anticoagulant effects, and (5) enhance ARDS resolution. We also critically assess drugs that demonstrate potential in emerging reports from clinical studies in patients with COVID-19-induced ARDS. Several therapies show promise in earlier and later phase clinical testing, while a growing pipeline of therapies is in preclinical testing. The history of unsuccessful clinical trials of promising therapies underlines the challenges to successful translation. Given this, attention has been focused on the potential to identify biologically homogenous subtypes within ARDS, to enable us to target more specific therapies 'precision medicines.' It is hoped that the substantial number of studies globally investigating potential therapies for COVID-19 will lead to the rapid identification of effective therapies to reduce the mortality and morbidity of this devastating form of ARDS.