Critical care : the official journal of the Critical Care Forum
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Observational Study
The pleural gradient does not reflect the superimposed pressure in patients with class III obesity.
The superimposed pressure is the primary determinant of the pleural pressure gradient. Obesity is associated with elevated end-expiratory esophageal pressure, regardless of lung disease severity, and the superimposed pressure might not be the only determinant of the pleural pressure gradient. The study aims to measure partitioned respiratory mechanics and superimposed pressure in a cohort of patients admitted to the ICU with and without class III obesity (BMI ≥ 40 kg/m2), and to quantify the amount of thoracic adipose tissue and muscle through advanced imaging techniques. ⋯ In patients with class III obesity, the superimposed pressure does not approximate the pleural pressure gradient, which is higher than in patients with lower BMI. The quantity and distribution of subcutaneous and intrathoracic adiposity also contribute to increased pleural pressure gradients in individuals with BMI ≥ 40. This study introduces a novel physiological concept that provides a solid rationale for tailoring mechanical ventilation in patients with high BMI, where specific guidelines recommendations are lacking.
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Too high or too low patient volumes and work amounts may overwhelm health care professionals and obstruct processes or lead to inadequate personnel routine and process flow. We sought to evaluate, whether an association between current caseload, current workload, and outcomes exists in intensive care units (ICU). ⋯ In a system with comparably high intensive care resources and mandatory staffing levels, patients' survival chances are generally not affected by high intensive care unit caseload and workload. However, extraordinary circumstances, such as the COVID-19 pandemic, may lead to higher risk of death, if planned capacities are exceeded. High workload in ICUs in smaller hospitals with lower staffing levels may be associated with increased risk of death.
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In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.
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Exploring clinical trial data using alternative methods may enhance original study's findings and provide new insights. The SOAP II trial has been published more than 10 years ago; but there is still some speculation that some patients may benefit from dopamine administration for shock management. We aimed to reanalyse the trial under different approaches and evaluate for heterogeneity in treatment effect (HTE). ⋯ The harm associated with the use of dopamine for the management of shock appears to be present in both septic and cardiogenic shock patients. There was no suggestion of any subgroup in which dopamine was found to be favourable over norepinephrine.