Community Volunteer Responder Programs in Cardiac Arrest: The Horse Has Bolted, It's Time to Optimize

Logistic and cognitive-emotional barriers experienced by first responders when alarmed to get dispatched to out-of-hospital cardiac arrest events: a region-wide survey

Out-of-hospital cardiac arrest (OHCA) is a major public health concern with low survival rates. First responders (FRs) and public access defibrillation (PAD) programs can significantly improve survival, although barriers to response activation persist. The Emilia Romagna region in Italy has introduced a new system, the DAE RespondER App, to improve the efficiency of FR dispatch in response to OHCA. The study aimed to evaluate the association between different logistic factors, FRs' perceptions, and their decision to accept or decline dispatch to an OHCA scene using the DAE RespondER App. A cross-sectional web survey was conducted, querying 14,518 registered FRs using the DAE RespondER app in Emilia Romagna. The survey explored logistic and cognitive-emotional perceptions towards barriers in responding to OHCAs. Statistical analysis was conducted, with responses adjusted using non-response weights. 4,644 responses were obtained (32.0% response rate). Among these, 1,824 (39.3%) had received at least one dispatch request in the past year. Multivariable logistic regression showed that being male, having previous experience with OHCA situations, and having an automated external defibrillator (AED) available at the moment of the call were associated with a higher probability of accepting the dispatch. Regarding FRs' perceptions, logistic obstacles were associated with mission rejection, while higher scores in cognitive-emotional obstacles were associated with acceptance. The study suggests that both logistical and cognitive-emotional factors are associated with FRs' decision to accept a dispatch. Addressing these barriers and further refining the DAE RespondER App can enhance the effectiveness of PAD programs, potentially improving survival rates for OHCA. The insights from this study can guide the development of interventions to improve FR participation and enhance overall OHCA response systems.

Volunteer Responder Interventions in Out-of-Hospital Cardiac Arrest in Urban, Suburban, and Rural Areas

BACKGROUND

Patients with out-of-hospital cardiac arrest (OHCA) in rural areas experience longer emergency response times and have lower survival rates compared with patients in urban areas. Volunteer responders might improve care and outcomes for patients with OHCA specifically in rural areas. Therefore, we investigated volunteer responder interventions based on the degree of urbanization.

METHODS AND RESULTS

We included 1310 OHCAs from 3 different regions in Denmark where volunteer responders had arrived at the OHCA location. The location was classified as urban, suburban, or rural according to the Eurostat Degree of Urbanization Tool. A logistic regression model was used to examine associations between the degree of urbanization and volunteer responder arrival before emergency medical services, cardiopulmonary resuscitation, or defibrillation. We found the odds for volunteer responder arrival before emergency medical services more than doubled in rural areas (odds ratio [OR], 2.60 [95% CI, 1.91-3.53]) and suburban areas (OR, 2.05 [95% CI, 1.56-2.69]) compared with urban areas. In OHCA cases where volunteer responders arrived first, odds for bystander cardiopulmonary resuscitation was tripled in rural areas (OR, 3.83 [95% CI, 1.64-8.93]) and doubled in suburban areas (OR, 2.27 [95% CI, 1.17-4.41]) compared with urban areas. Bystander defibrillation was more common in suburban areas (OR, 1.53 [95% CI, 1.02-2.31]), where almost 1 out of 4 patients received bystander defibrillation, compared with urban areas.

CONCLUSIONS

Volunteer responders are significantly more likely to arrive before emergency medical services in rural and suburban areas than in urban areas. Patients with OHCA received more cardiopulmonary resuscitation in rural and suburban areas and more defibrillation in suburban areas than in urban areas.

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation

BACKGROUND

Smartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched.

OBJECTIVE

This study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert.

METHODS

We developed and compared 4 analytical models to predict VFRs' response behaviors based on emergency event characteristics, volunteers' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder's previous behavior.

RESULTS

The highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy.

CONCLUSIONS

VFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.