Resuscitation
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Multicenter Study
Less is More: Detecting Clinical Deterioration in the Hospital with Machine Learning Using Only Age, Heart Rate, and Respiratory Rate.
We sought to develop a machine learning analytic (eCART Lite) for predicting clinical deterioration using only age, heart rate, and respiratory data, which can be pulled in real time from patient monitors and updated continuously without need for additional inputs or cumbersome electronic health record integrations. ⋯ Using only three inputs, we developed a tool for predicting clinical deterioration that is similarly or more accurate than commonly-used algorithms, with potential for use in inpatient settings with limited resources or in scenarios where low-cost tools are needed.
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To identify, appraise and synthesize all available clinical evidence to evaluate the diagnostic role of transoesophageal echocardiography (TEE) during resuscitation of in-hospital (IHCA) and out-of-hospital cardiac arrest (OHCA) in the identification of reversible causes of cardiac arrest and cardiac contractility. ⋯ Due to heterogeneity of studies, small sample size and inconsistent reference standard, the evidence for TEE in cardiac arrest resuscitation is of low certainty and is affected by a high risk of bias. Further studies are needed to better understand the true diagnostic accuracy of TEE in identifying reversible causes of arrest and cardiac contractility.