Articles: critical-illness.
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J Clin Monit Comput · Apr 2024
Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.
This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. ⋯ The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.
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Pediatr Crit Care Me · Apr 2024
ReviewThe Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.
Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. ⋯ Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.
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Acta Anaesthesiol Scand · Apr 2024
Observational StudyBeta-lactam antibiotic concentrations in critically ill patients with standard and adjusted dosages: A prospective observational study.
Antibiotic concentration target attainment is known to be poor in critically ill patients. Dose adjustment is recommended in patients with altered clearance, obesity and those with bacterial species with intermediate susceptibility. The aim of this study was to investigate the variation of antibiotic concentration in critically ill patients with standard or adjusted dosing regimens. ⋯ Beta-lactam antibiotics concentration vary widely in critically ill patients. The current standard dosing regimens employed during the study were not sufficient to reach 100% ƒT > MIC in approximately a quarter of the patients. In patients where dose adjustment was performed, the group with increased dose also had low target attainment, as opposed to patients with dose reduction, who all reached target. This suggests the need for further individualization of dosing where therapeutic drug monitoring can be an alternative to further increase target attainment.
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Recent large-scale randomized controlled trials (RCTs) challenged current beliefs about the potential role of micronutrients to attenuate the inflammatory response and improve clinical outcomes of critically ill patients. The purpose of this narrative review is to provide an overview and critical discussion about most recent clinical trials, which evaluated the clinical significance of a vitamin C, vitamin D, or selenium administration in critically ill patients. ⋯ Current data received from most recent large-scale RCTs could not demonstrate clinically meaningful effects of an intervention with either vitamin C, vitamin D, or selenium in critically ill patients. More attention is needed to carefully identify potential confounding factors and to better evaluate the role of timing, duration, and combined strategies.