Critical care : the official journal of the Critical Care Forum
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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. ⋯ 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.
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Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored. ⋯ Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.
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Triggers have been developed internationally to identify intensive care patients with palliative care needs. Due to their work, nurses are close to the patient and their perspective should therefore be included. In this study, potential triggers were first identified and then a questionnaire was developed to analyse their acceptance among German intensive care nurses. ⋯ Intensive care nurses see various triggers, with interprofessional collaboration and the patient's prognosis playing a major role. The questionnaire can be used for further surveys, e.g. interprofessional triggers could be developed.
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Acute respiratory distress syndrome (ARDS) represents a life-threatening inflammatory reaction marked by refractory hypoxaemia and pulmonary oedema. Despite advancements in treatment perspectives, ARDS still carries a high mortality rate, often due to systemic inflammatory responses leading to multiple organ dysfunction syndrome (MODS). Indeed, the deterioration and associated mortality in patients with acute lung injury (LI)/ARDS is believed to originate alongside respiratory failure mainly from the involvement of extrapulmonary organs, a consequence of the complex interaction between initial inflammatory cascades related to the primary event and ongoing mechanical ventilation-induced injury resulting in multiple organ failure (MOF) and potentially death. ⋯ This review aims to elucidate the complex interplay between lung and gut in patients with LI/ARDS. We will examine various factors, including systemic inflammation, epithelial barrier dysfunction, the effects of mechanical ventilation (MV), hypercapnia, and gut dysbiosis. Understanding these factors and their interaction may provide valuable insights into the pathophysiology of ARDS and potential therapeutic strategies to improve patient outcomes.