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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Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services. ⋯ An automated methodology was validated to accurately compute CPR metrics in large and heterogeneous OHCA datasets. Automated processing of defibrillator files and the associated clinical annotations enables the aggregation and analysis of CPR data from multiple sources.
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Multicenter Study
Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest.
To develop and validate a model for the early prediction of long-term neurological outcome in patients with non-traumatic out-of-hospital cardiac arrest (OHCA). ⋯ The prediction tool containing detailed in-hospital information showed good performance for predicting neurological outcome at 90 days immediately after ROSC in patients with OHCA.