Optimising trial design for cardiogenic shock: insights from the sixth Critical Care Clinical Trialists Workshop.

Despite substantial advancements in the management of cardiogenic shock, mortality rates remain greater than 40%. Trials have shown that increasing survival rates in cardiogenic shock is challenging. Even the most successful trials show 5-15% reductions in mortality, and gains have been restricted to acute myocardial infarction cardiogenic shock, representing approximately 5% of the population with cardiogenic shock.

Evaluating inter-and intra-rater reliability in the bronchoscopic grading of burn inhalation injury: The iBRONCH-BII study.

Background: The evidence that the severity of burn inhalation injury (BII) impacts clinical outcomes is inconsistent. This may be due to misclassification arising from the subjectivity in bronchoscopically grading BII using systems such as the Abbreviated Injury Score (AIS). This study aimed to evaluate inter- and intra-rater reliability in the grading of BII using the AIS.

Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trials.

Background: Cardiogenic shock (CS) is a heterogeneous clinical syndrome, making it challenging to predict patient trajectory and response to treatment. This study aims to identify biological/molecular CS subphenotypes, evaluate their association with outcome, and explore their impact on heterogeneity of treatment effect (ShockCO-OP, NCT06376318).

Methods: We used unsupervised clustering to integrate plasma biomarker data from two prospective cohorts of CS patients: CardShock (N = 205 [2010-2012, NCT01374867]) and the French and European Outcome reGistry in Intensive Care Units (FROG-ICU) (N = 228 [2011-2013, NCT01367093]) to determine the optimal number of classes.

Management of cardiogenic shock: state-of-the-art.

The management of cardiogenic shock is an ongoing challenge. Despite all efforts and tremendous use of resources, mortality remains high. Whilst reversing the underlying cause, restoring/maintaining organ perfusion and function are cornerstones of management.

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

Aims: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU.

Impact of goal-directed hemodynamic therapy on perioperative outcomes in head and neck free flap surgery: A before-and-after pilot study.

Background: Free flap reconstruction for head and neck cancer is associated with a high risk of perioperative complications. One of the modifiable risk factors associated with perioperative morbidity is intraoperative hypotension (IOH). The main aim of this pilot study is to determine if the intraoperative use of goal-directed hemodynamic therapy (GDHT) is associated with a reduction in the number of IOH events in this population.

Mechanical Ventilation, Past, Present, and Future.

Mechanical ventilation (MV) has played a crucial role in the medical field, particularly in anesthesia and in critical care medicine (CCM) settings. MV has evolved significantly since its inception over 70 years ago and the future promises even more advanced technology. In the past, ventilation was provided manually, intermittently, and it was primarily used for resuscitation or as a last resort for patients with severe respiratory or cardiovascular failure.

An international RAND/UCLA expert panel to determine the optimal diagnosis and management of burn inhalation injury.

Background: Burn inhalation injury (BII) is a major cause of burn-related mortality and morbidity. Despite published practice guidelines, no consensus exists for the best strategies regarding diagnosis and management of BII. A modified DELPHI study using the RAND/UCLA (University of California, Los Angeles) Appropriateness Method (RAM) systematically analysed the opinions of an expert panel.

Validation of reference gene stability for miRNA quantification by reverse transcription quantitative PCR in the peripheral blood of patients with COVID-19 critical illness.

The COVID-19 pandemic has created an urgency to study the host gene response that leads to variable clinical presentations of the disease, particularly the critical illness response. miRNAs have been implicated in the mechanism of host immune dysregulation and thus hold potential as biomarkers and/or therapeutic agents with clinical application. Hence, further analyses of their altered expression in COVID-19 is warranted.

Cardiogenic shock: a major challenge for the clinical trialist.

Purpose Of Review: Cardiogenic shock (CS) results in persistently high short-term mortality and a lack of evidence-based therapies. Several trials of novel interventions have failed to show an improvement in clinical outcomes despite promising preclinical and physiologic principles. In this review, we highlight the challenges of CS trials and provide suggestions for the optimization and harmonization of their design.

Advances in the Staging and Phenotyping of Cardiogenic Shock: Part 1 of 2.

Cardiogenic shock (CS) is a heterogeneous syndrome reflecting a broad spectrum of shock severity, diverse etiologies, variable cardiac function, different hemodynamic trajectories, and concomitant organ dysfunction. These factors influence the clinical presentation, management, response to therapy, and outcomes of CS patients, necessitating a tailored approach to care. To better understand the variability inherent to CS populations, recent algorithms for staging the severity of CS have been described and validated.

Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness: Part 2 of 2.

Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches.

Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival.

Background: Metabolic predictors and potential mediators of survival in sepsis have been incompletely characterized. We examined whether machine learning (ML) tools applied to the human plasma metabolome could consistently identify and prioritize metabolites implicated in sepsis survivorship, and whether these methods improved upon conventional statistical approaches.

Methods: Plasma gas chromatography-liquid chromatography mass spectrometry quantified 411 metabolites measured ≤ 72 h of ICU admission in 60 patients with sepsis at a single center (Brigham and Women's Hospital, Boston, USA).

Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort.

Background: Late mortality risk in sepsis-survivors persists for years with high readmission rates and low quality of life. The present study seeks to link the clinical sepsis-survivors heterogeneity with distinct biological profiles at ICU discharge and late adverse events using an unsupervised analysis.

Methods: In the original FROG-ICU prospective, observational, multicenter study, intensive care unit (ICU) patients with sepsis on admission (Sepsis-3) were identified (N = 655).

Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.

Background: Proposed phenotypes have recently been identified in cardiogenic shock (CS) populations using unsupervised machine learning clustering methods. We sought to validate these phenotypes in a mixed cardiac intensive care unit (CICU) population of patients with CS.

Methods: We included Mayo Clinic CICU patients admitted from 2007 to 2018 with CS.