Articles: critical-care.
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Critically ill patients with severe pancreatitis exhibit substantial muscle wasting, which limits in-hospital and post-hospital outcomes. Survivors of critical illness undergo extensive recovery processes. Previous studies have explored pancreatic function, quality of life, and costs post-hospitalization for AP patients, but none have comprehensively quantified muscle loss and recovery post-discharge. By applying an AI-based automated segmentation tool, we aimed to quantify muscle mass recovery in ICU patients after discharge. ⋯ Muscle recovery in ICU patients suffering from severe AP is highly variable, with only about one third of patients recovering to their initial physical status. Opportunistic screening of post-ICU patient recovery using clinically indicated imaging and AI-based segmentation tools enables precise quantification of patients' muscle status and can be employed to identify individuals who fail to recover and would benefit from secondary rehabilitation. Understanding the dynamics of muscle atrophy may improve prognosis and support personalized patient care.
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The aim of this study was to develop a consensus-based set of indicators of high-quality acute moderate to severe traumatic brain injury (msTBI) clinical management that can be used to measure structure, process, and outcome factors that are likely to influence patient outcomes. This is the first stage of the PRECISION-TBI program, which is a prospective cohort study that aims to identify and promote optimal clinical management of msTBI in Australia. ⋯ This study identified a set of 32 quality indicators that can be used to structure data collection to drive quality improvement in the clinical management of msTBI. They will also be used to guide feedback to PRECISION-TBI's participating sites.
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Curr Opin Crit Care · Sep 2024
Advances in critical care nephrology through artificial intelligence.
This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology. ⋯ The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.
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Healthcare is awash with numbers, and figuring out what knowledge these numbers might hold is worthwhile in order to improve patient care. Numbers allow for objective mathematical analysis of the information at hand, but while mathematics is objective by design, our choice of mathematical approach in a given situation is not. ⋯ With increasingly more advanced research questions and research designs, traditional statistical approaches are often inadequate, and being able to properly merge statistical competence with clinical knowhow is essential in order to arrive at not only correct, but also valuable and usable research results. By marrying clinical knowhow with rigorous statistical analysis we can accelerate the field of prehospital and critical care.