Christine P. Shen, Sanjeev P Bhavnani, John D Rogers
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Mortality from sudden cardiac arrest remains high despite increased awareness and advancements in emergency resuscitation efforts. Various gaps exist in bystander resuscitation, automated external defibrillators, and access. Significant racial, gender, and geographic disparities have also been found. A myriad of recent innovations in sudden cardiac arrest uses new machine learning algorithms with high levels of performance. These have been applied to a broad range of efforts to identify individuals at high risk, recognize emergencies, and diagnose high-risk cardiac arrhythmias. Such technological advancements must be coupled to novel public health approaches to best implement these innovations in an equitable way. The authors propose a data-driven, technology-enabled system of care within a public health system of care to ultimately improve sudden cardiac arrest outcomes.
期刊介绍:
US Cardiology Review (USC) is an international, US-English language, peer-reviewed journal that is published bi-annually and aims to assist time-pressured physicians to stay abreast of key advances and opinion in the area of cardiovascular disease. The journal comprises balanced and comprehensive review articles written by leading authorities. The journal provides updates on a range of salient issues to support physicians in developing their knowledge and effectiveness in day-to-day clinical practice. The journal endeavours to support the continuous medical education of specialist and general cardiologists and disseminate knowledge of the field to the wider cardiovascular community.