Critical care medicine
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Critical care medicine · Nov 2020
Observational StudyEvaluation of the Risk Prediction Tools for Patients With Coronavirus Disease 2019 in Wuhan, China: A Single-Centered, Retrospective, Observational Study.
To evaluate and compare the efficacy of National Early Warning Score, National Early Warning Score 2, Rapid Emergency Medicine Score, Confusion, Respiratory rate, Blood pressure, Age 65 score, and quick Sepsis-related Organ Failure Assessment on predicting in-hospital death in patients with coronavirus disease 2019. ⋯ In this single-center study, the discrimination of National Early Warning Score/National Early Warning Score 2 for predicting mortality in patients with coronavirus disease 2019 admitted to the ward was found to be superior to Rapid Emergency Medicine Score, Confusion, Respiratory rate, Blood pressure, Age 65 score, and quick Sepsis-related Organ Failure Assessment. Peripheral oxygen saturation could independently predict in-hospital death in these patients. Further validation of our finding in multiple settings is needed to determine its applicability for coronavirus disease 2019.
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Critical care medicine · Nov 2020
ReviewSociety of Critical Care Medicine's International Consensus Conference on Prediction and Identification of Long-Term Impairments After Critical Illness.
After critical illness, new or worsening impairments in physical, cognitive, and/or mental health function are common among patients who have survived. Who should be screened for long-term impairments, what tools to use, and when remain unclear. ⋯ Beginning with an assessment of a patient's pre-ICU functional abilities at ICU admission, clinicians have a care coordination strategy to identify and manage impairments across the continuum. As hospital discharge approaches, clinicians should use brief, standardized assessments and compare these results to patient's pre-ICU functional abilities ("functional reconciliation"). We recommend serial assessments for post-intensive care syndrome-related problems continue within 2-4 weeks of hospital discharge, be prioritized among high-risk patients, using the identified screening tools to prompt referrals for services and/or more detailed assessments.
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Critical care medicine · Nov 2020
Multicenter Study Observational StudyAn Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.
Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019. ⋯ Explainable artificial intelligence sepsis predictor model achieves superior performance for predicting sepsis risk in a real-time way and provides interpretable information for understanding sepsis risk in ICU.