Journal of critical care
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Journal of critical care · Dec 2020
The wave of very old people in the intensive care unit-A challenge in decision-making.
In this paper the authors express the opinion that there is much to be learned about the 80+ year old age group as it relates to critical care and end-of-life matters. We need to learn how to better predict outcome, we need to learn our limitations and deal with uncertainties, we need to better communicate with our elderly patients and their caregivers and we need to engage with our colleagues in Geriatrics. There is a wave of very old people arriving in the intensive care unit and we have much to do to prepare for it and for the ethical, fair and appropriate care of these critically ill, but elderly, patients.
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Journal of critical care · Dec 2020
Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).
Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on patient health record data for the early prediction of ARDS. ⋯ Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset.
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Journal of critical care · Dec 2020
Review Meta AnalysisWhat factors predict length of stay in the intensive care unit? Systematic review and meta-analysis.
Studies have shown that a small percentage of ICU patients have prolonged length of stay (LoS) and account for a large proportion of resource use. Therefore, the identification of prolonged stay patients can improve unit efficiency. In this study, we performed a systematic review and meta-analysis to understand the risk factors of ICU LoS. ⋯ This work suggested a list of risk factors that should be considered in prediction models for ICU LoS, as follows: severity scores, mechanical ventilation, hypomagnesemia, delirium, malnutrition, infection, trauma, red blood cells, and PaO2:FiO2. Our findings can be used by prediction models to improve their predictive capacity of prolonged stay patients, assisting in resource allocation, quality improvement actions, and benchmarking analysis.