Current opinion in critical care
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The purpose of this review is to provide an update for critical care clinicians and providers on the recent developments in patient and healthcare professional (HCP) resuscitation education. ⋯ Frequent resuscitation education and training is critical to improving cardiac arrest patient outcomes. Recent evidence shows the effectiveness of technological developments to improve access to training and outcomes.
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Curr Opin Crit Care · Jun 2022
ReviewNew developments in the understanding of right ventricular function in acute care.
Right ventricular dysfunction has an important impact on the perioperative course of cardiac surgery patients. Recent advances in the detection and monitoring of perioperative right ventricular dysfunction will be reviewed here. ⋯ Perioperative right ventricular function monitoring is based on echocardiographic and extra-cardiac flow evaluation. In addition to imaging modalities, hemodynamic evaluation using various types of pulmonary artery catheters can be achieved to track changes rapidly and quantitatively in right ventricular function perioperatively. These monitoring techniques can be applied during and after surgery to increase the detection rate of right ventricular dysfunction. All this to improve the treatment of patients presenting early signs of right ventricular dysfunction before systemic organ dysfunction ensue.
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Cardiac arrest centres (CACs) may play a key role in providing postresuscitation care, thereby improving outcomes in out-of-hospital cardiac arrest (OHCA). There is no consensus on CAC definitions or the optimal CAC transport strategy despite advances in research. This review provides an updated overview of CACs, highlighting evidence gaps and future research directions. ⋯ Real-world study designs evaluating CAC transport strategies are needed. OHCA patients with underlying culprit lesions, such as those with ST-elevation myocardial infarction (STEMI) or initial shockable rhythms, will likely benefit the most from CACs.
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To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI). ⋯ Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging.