Articles: respiratory-distress-syndrome.
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Am. J. Respir. Crit. Care Med. · Nov 2024
Randomized Controlled Trial Multicenter StudyLung Protective Mechanical Ventilation in Severe Acute Brain Injured Patients: A Multicenter, Randomized Clinical Trial (PROLABI).
Rationale: Lung-protective strategies using low Vt and moderate positive end-expiratory pressure (PEEP) are considered best practice in critical care, but interventional trials have never been conducted in patients with acute brain injuries because of concerns about carbon dioxide control and the effect of PEEP on cerebral hemodynamics. Objectives: To test the hypothesis that ventilation with lower VT and higher PEEP compared to conventional ventilation would improve clinical outcomes in patients with acute brain injury. Methods: In this multicenter, open-label, controlled clinical trial, 190 adult patients with acute brain injury were assigned to receive either a lung-protective or a conventional ventilatory strategy. ⋯ Conclusions: In patients with acute brain injury without ARDS, a lung-protective ventilatory strategy, as compared with a conventional strategy, did not reduce mortality, percentage of patients weaned from mechanical ventilation, or incidence of ARDS and was not beneficial in terms of neurological outcomes. Because of the early termination, these preliminary results require confirmation in larger trials. Clinical trial registered with www.clinicaltrials.gov (NCT01690819).
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Critical care medicine · Nov 2024
ReviewMachine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction.
Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. ⋯ This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.
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Intensive care medicine · Nov 2024
Fluid responsiveness in acute respiratory distress syndrome patients: a post hoc analysis of the HEMOPRED study.
Optimal fluid management in patients with acute respiratory distress syndrome (ARDS) is challenging due to risks associated with both circulatory failure and fluid overload. The performance of dynamic indices to predict fluid responsiveness (FR) in ARDS patients is uncertain. ⋯ Performance of dynamic indices to predict FR appears preserved in ARDS patients, albeit with distinct thresholds. Hypovolemia, indicated by a > 32% increase in aortic VTI during PLR, rather than FR, was associated with ICU mortality in this population.
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The airway epithelium (AE) fulfils multiple functions to maintain pulmonary homeostasis, among which ensuring adequate barrier function, cell differentiation and polarization, and actively transporting immunoglobulin A (IgA), the predominant mucosal immunoglobulin in the airway lumen, via the polymeric immunoglobulin receptor (pIgR). Morphological changes of the airways have been reported in ARDS, while their detailed features, impact for mucosal immunity, and causative mechanisms remain unclear. Therefore, this study aimed to assess epithelial alterations in the distal airways of patients with ARDS. ⋯ The airway epithelium from patients with ARDS exhibits multifaceted alterations leading to altered mucociliary differentiation, compromised defense functions and increased permeability with pneumoproteinemia.