Articles: intensive-care-units.
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Clostridioides difficile infection (CDI) causes considerable morbidity, mortality, and economic cost. Advanced age, prolonged stay in healthcare facility, and exposure to antibiotics are leading risk factors for CDI. Data on CDI clinical outcomes in the very elderly patients are limited. ⋯ In our cohort, the duration of hospital stay seemed to be shorter in the very elderly with no increase of in-hospital and post-discharge mortality. Although admitted less frequently to ICU, the in-hospital survival of the very elderly was not adversely affected compared to the elderly, suggesting that very advanced age per se should not be a major factor to consider in determining the prognosis of a patient with CDI.
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While serum Ca has proven to be a reliable predictor of mortality across various diseases, its connection with the clinical outcomes of ischemic stroke (IS) remains inconclusive. Our research aimed to explore the relationships between serum total Ca (tCa) and serum ionized Ca (iCa) and mortality among acute IS (AIS) patients. ⋯ Our findings suggest that serum iCa, rather than tCa, is linked to ischemic stroke prognosis. Both high and low serum iCa levels are associated with poor short-term prognosis, while only low serum iCa is associated with poor long-term prognosis in AIS patients.
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Journal of anesthesia · Apr 2024
ReviewMachine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.
Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for prediction and detection poses a challenge. This study aims to provide a comprehensive literature review on the application of machine learning (ML) algorithms for predicting and detecting new-onset atrial fibrillation (NOAF) in ICU-treated patients. ⋯ Notably, CatBoost exhibited superior performance in NOAF prediction, while the support vector machine excelled in NOAF detection. Machine learning algorithms emerge as promising tools for predicting and detecting NOAF in ICU patients. The incorporation of these algorithms in clinical practice has the potential to enhance decision-making and the overall management of NOAF in ICU settings.