AEDs at your fingertips: automated external defibrillators on college campuses and a novel approach for increasing accessibility

Characteristics and outcomes of cardiac arrests reported in the national collegiate emergency medical services foundation data registry

OBJECTIVE

The objective of this study was to identify out-of-hospital cardiac arrest characteristics for patients treated by collegiate-based emergency medical services (CBEMS) organizations.

PARTICIPANTS

CBEMS organizations provided data via the National Collegiate EMS Foundation Cardiac Arrest Data Registry.

METHODS

CBEMS organization details, patient demographics, cardiac arrest characteristics and treatments, and prehospital outcomes for cases spanning October 2007 to May 2020 were analyzed with descriptive statistics.

RESULTS

There were 65 OHCA entries. The majority were for male patients (82%) and a notable number of cases occurred in patients 45 years of age or younger (41%). Cases were frequently witnessed (71%) with high rates of bystander cardiopulmonary resuscitation (57%) and defibrillation (29%) prior to EMS arrival. Almost half of the patients (48%) had achieved return of spontaneous circulation until care was transferred to a provider of equal/higher level.

CONCLUSIONS

CBEMS organizations may be well situated to respond rapidly to on-campus OHCAs.

New Concepts in Sudden Cardiac Arrest to Address an Intractable Epidemic: JACC State-of-the-Art Review

Sudden cardiac arrest (SCA) is one of the largest causes of mortality globally, with an out-of-hospital survival below 10% despite intense research. This document outlines challenges in addressing the epidemic of SCA, along the framework of respond, understand and predict, and prevent. Response could be improved by technology-assisted orchestration of community responder systems, access to automated external defibrillators, and innovations to match resuscitation resources to victims in place and time. Efforts to understand and predict SCA may be enhanced by refining taxonomy along phenotypical and pathophysiological "axes of risk," extending beyond cardiovascular pathology to identify less heterogeneous cohorts, facilitated by open-data platforms and analytics including machine learning to integrate discoveries across disciplines. Prevention of SCA must integrate these concepts, recognizing that all members of society are stakeholders. Ultimately, solutions to the public health challenge of SCA will require greater awareness, societal debate and focused public policy.