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Hospital Length of Stay and 30-Day Mortality Prediction in Stroke: A Machine Learning Analysis of 17,000 ICU Admissions in Brazil
ConclusionsHospital length of stay and 30-day mortality of patients admitted to the ICU with stroke were accurately predicted through machine learning methods, even in the absence of stroke-specific data, such as the National Institutes of Health Stroke Scale score or neuroimaging findings. The proposed methods using general intensive care databases may be used for resource use allocation planning and performance assessment of ICUs treating stroke. More detailed acute neurological and management data, as well as long-term functional outcomes, may improve the accuracy and applicability of future machine-learning-based prediction algorithms.
Source: Neurocritical Care - April 6, 2022 Category: Neurology Source Type: research

A Pilot Study of the Fluctuating Mental Status Evaluation: A Novel Delirium Screening Tool for Neurocritical Care Patients
ConclusionsIn this pilot study, the FMSE achieved a high sensitivity and specificity in detecting delirium. Follow-up validation studies in a larger more diverse cohort of neurocritical care patients will use score cutoffs of  ≥ 1 as “possible” delirium and ≥ 2 as “probable” delirium.
Source: Neurocritical Care - October 14, 2022 Category: Neurology Source Type: research