Intensive care medicine
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Intensive care medicine · Nov 2024
Practice GuidelineEuropean Society of Intensive Care Medicine guidelines on end of life and palliative care in the intensive care unit.
The European Society of Intensive Care Medicine (ESICM) has developed evidence-based recommendations and expert opinions about end-of-life (EoL) and palliative care for critically ill adults to optimize patient-centered care, improving outcomes of relatives, and supporting intensive care unit (ICU) staff in delivering compassionate and effective EoL and palliative care. An international multi-disciplinary panel of clinical experts, a methodologist, and representatives of patients and families examined key domains, including variability across countries, decision-making, palliative-care integration, communication, family-centered care, and conflict management. Eight evidence-based recommendations (6 of low level of evidence and 2 of high level of evidence) and 19 expert opinions were presented. ⋯ Methods for enhancing family-centeredness of care include structured family conferences and culturally sensitive interventions. Conflict-management protocols and strategies to prevent burnout among healthcare professionals are also considered. The work done to develop these guidelines highlights many areas requiring further research.
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Intensive care medicine · Nov 2024
From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.
Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benefits is necessary to improve neonatal and pediatric care for critically ill patients. This systematic review seeks to assess the maturity of AI models in neonatal and pediatric intensive care unit (NICU and PICU) treatment, and their risk of bias and objectives. ⋯ The majority of AI models remain within the testing and prototyping phase and have a high risk of bias. Bridging the gap between designing and clinical implementation of AI models is needed to warrant safe and trustworthy AI models. Specific guidelines and approaches can help improve clinical outcome with usage of AI.