Articles: critical-illness.
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Intensive care unit (ICU) mortality prediction helps to guide therapeutic decision making for critically ill patients. Several scoring systems based on statistical techniques have been developed for this purpose. In this study, we developed a machine-learning model to predict patient mortality in the very early stage of ICU admission. ⋯ The XGBoost model most accurately predicted ICU mortality and was superior to traditional scoring systems. Our results highlight the utility of machine learning for ICU mortality prediction in the Asian population.
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Observational Study
Emergency department use of an electronic differential diagnosis generator in the evaluation of critically ill patients.
Accurate diagnosis is an essential component of managing critically ill emergency department (ED) patients. Electronic diagnosis generators (EDGs) are software tools which assist clinicians in their diagnosis generation; however, they have not been evaluated for use for critical ED patients. We aimed to evaluate the use of an EDG for this population to determine its impact on diagnosis generation and diagnostic testing. ⋯ EDGs have some potential to improve diagnosis in critical EM patients by expanding the differential diagnosis and, to a lesser extent, altering diagnostic testing.
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Critical care medicine · Apr 2024
Randomized Controlled Trial Multicenter Study Pragmatic Clinical TrialA Comparison of High and Usual Protein Dosing in Critically Ill Patients With Obesity: A Post Hoc Analysis of an International, Pragmatic, Single-Blinded, Randomized, Clinical Trial.
Across guidelines, protein dosing for critically ill patients with obesity varies considerably. The objective of this analysis was to evaluate whether this population would benefit from higher doses of protein. ⋯ In critically ill patients with obesity, higher protein doses did not improve clinical outcomes, including those with higher nutritional and frailty risk.
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Moderate-severe traumatic brain injury (msTBI) carries high morbidity and mortality worldwide. Accurate neuroprognostication is essential in guiding clinical decisions, including patient triage and transition to comfort measures. Here we provide recommendations regarding the reliability of major clinical predictors and prediction models commonly used in msTBI neuroprognostication, guiding clinicians in counseling surrogate decision-makers. ⋯ These guidelines provide recommendations to clinicians on the formal reliability of individual predictors and prediction models of poor outcome when counseling surrogates of patients with msTBI and suggest broad principles of neuroprognostication.