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Background: Extracorporeal cardiopulmonary resuscitation (ECPR) can improve survival for refractory out-of-hospital cardiac arrest (OHCA). We sought to assess the feasibility of a proposed ECPR programme in Scotland, considering both in-hospital and pre-hospital implementation scenarios., Methods: We included treated OHCAs in Scotland aged 16-70 between August 2018 and March 2022. We defined those clinically eligible for ECPR as patients where the initial rhythm was ventricular fibrillation, ventricular tachycardia, or pulseless electrical activity, and where pre-hospital return of spontaneous circulation was not achieved. We computed the call-to-ECPR access time interval as the amount of time from emergency medical service (EMS) call reception to either arrival at an ECPR-ready hospital or arrival of a pre-hospital ECPR crew. We determined the number of patients that had access to ECPR within 45 min, and estimated the number of additional survivors as a result., Results: A total of 6,639 OHCAs were included in the geospatial modelling, 1,406 of which were eligible for ECPR. Depending on the implementation scenario, 52.9-112.6 (13.8-29.4%) OHCAs per year had a call-to-ECPR access time within 45 min, with pre-hospital implementation scenarios having greater and earlier access to ECPR for OHCA patients. We further estimated that an ECPR programme in Scotland would yield 11.8-28.2 additional survivors per year, with the pre-hospital implementation scenarios yielding higher numbers., Conclusion: An ECPR programme for OHCA in Scotland could provide access to ECPR to a modest number of eligible OHCA patients, with pre-hospital ECPR implementation scenarios yielding higher access to ECPR and higher numbers of additional survivors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Systematic automated external defibrillator(AED) placement in schools may improve pediatric out-of-hospital cardiac arrest(OHCA) survival. To estimate their utility, we identified school-located pediatric and adult OHCAs to estimate the potential utilization of school-located AEDs. Further, we identified all OHCAs within an AED-retrievable distance of the school by walking, biking, and driving., Methods: We used prospectively collected data from the British Columbia(BC) Cardiac Arrest Registry(2013-2020), and geo-plotted all OHCAs and schools(n = 824) in BC. We identified adult and pediatric(age < 18 years) OHCAs occurring in schools, as well as nearby OHCAs for which a school-based externally-placed AED could be retrieved by a bystander prior to emergency medical system(EMS) arrival., Results: Of 16,409 OHCAs overall in the study period, 28.6 % occurred during school hours. There were 301 pediatric OHCAs. 5(1.7 %) occurred in schools, of whom 2(40 %) survived to hospital discharge. Among both children and adults, 28(0.17 %) occurred in schools(0.0042/school/year), of whom 21(75 %) received bystander resuscitation, 4(14 %) had a bystander AED applied, and 14(50 %) survived to hospital discharge. For each AED, an average of 0.29 OHCAs/year(95 % CI 0.21-0.37), 0.93 OHCAs/year(95 % CI 0.69-1.56) and 1.69 OHCAs/year(95 % CI 1.21-2.89) would be within the potential retrieval distance of a school-located AED by pedestrian, cyclist and automobile retrieval, respectively, using the median EMS response times., Conclusion: While school-located OHCAs were uncommon, outcomes were favourable. 11.1% to 60.9% of all OHCAs occur within an AED-retrievable distance to a school, depending on retrieval method. Accessible external school-located AEDs may improve OHCA outcomes of school children and in the surrounding community., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Introduction: Drone-delivered automated external defibrillators (AEDs) may reduce delays to defibrillation for out-of-hospital cardiac arrests (OHCAs). We sought to determine how integration of drones and selection of drone bases between emergency service stations (i.e., paramedic, fire, police) would affect 9-1-1 call-to-arrival intervals., Methods: We identified all treated OHCAs in southern Vancouver Island, British Columbia, Canada from Jan. 2014 to Dec. 2020. We developed mathematical models to select 1-5 optimal drone base locations from each of: paramedic stations, fire stations, police stations, or an unrestricted grid-based set of points to minimize drone travel time to OHCAs. We evaluated models on the estimated first response interval assuming that drones were integrated with existing OHCA response. We compared median response intervals with historical response, as well as across drone base locations., Results: A total of 1610 OHCAs were included in the study with a historical median response interval of 6.4 minutes (IQR 5.0-8.6). All drone-integrated response systems significantly reduced the median response interval to 4.2-5.4 minutes (all P < 0.001), with grid-based stations using 5 drones resulting in the lowest response interval (4.2 minutes). Median response times between drone base location types differed by 6-16 seconds, all comparisons of which were statistically significant (all P < 0.02)., Conclusion: Integrating drone-delivered AEDs into OHCA response may reduce first response intervals, even with a small quantity of drones. Implementing drone response with only one emergency service resulted in similar response metrics regardless of the emergency service hosting the drone base and was competitive with unrestricted drone base locations., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: The optimal locations and cost-effectiveness of placing automated external defibrillators (AEDs) for out-of-hospital cardiac arrest (OHCAs) in urban residential neighbourhoods are unclear., Methods: We used prospectively collected data from 2016 to 2018 from the British Columbia OHCA Registry to examine the utilization and cost-effectiveness of hypothetical AED deployment in municipalities with a population of over 100 000. We geo-plotted OHCA events using seven hypothetical deployment models where AEDs were placed at the exteriors of public schools and community centers and fetched by bystanders. We calculated the "radius of effectiveness" around each AED within which it could be retrieved and applied to an individual prior to EMS arrival, comparing automobile and pedestrian-based retrieval modes. For each deployment model, we estimated the number of OHCAs within the "radius of effectiveness"., Results: We included 4017 OHCAs from ten urban municipalities. The estimated radius of effectiveness around each AED was 625 m for automobile and 240 m for pedestrian retrieval. With AEDs placed outside each school and community center, 2567 (64%) and 605 (15%) of OHCAs fell within the radii of effectiveness for automobile and pedestrian retrieval, respectively. For each AED, there was an average of 1.20-2.66 and 0.25-0.61 in-range OHCAs per year for automobile retrieval and pedestrian retrieval, respectively, depending on the deployment model. All of our proposed surpassed the cost-effectiveness threshold of 0.125 OHCA/AED/year provided > 5.3-11.6% in-range AEDs were brought-to-scene., Conclusions: The systematic deployment of AEDs at schools and community centers in urban neighbourhoods may result in increased application and be a cost-effective public health intervention., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Although several Utstein variables are known to independently improve survival, how they moderate the effect of emergency medical service (EMS) response times on survival is unknown., Objectives: To quantify how public location, witnessed status, bystander CPR, and bystander AED shock individually and jointly moderate the effect of EMS response time delays on OHCA survival., Methods: This retrospective cohort study was a secondary analysis of the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database (December 2005 to June 2015). We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCAs from eleven sites across the US and Canada. We trained a logistic regression model with standard Utstein control variables and interaction terms between EMS response time and the four aforementioned OHCA characteristics., Results: 102,216 patients were included. Three of the four characteristics - witnessed OHCAs (OR = 0.962), bystander CPR (OR = 0.968) and public location (OR = 0.980) - increased the negative effect of a one-minute delay on the odds of survival. In contrast, a bystander AED shock decreased the negative effect of a one-minute response time delay on the odds of survival (OR = 1.064). The magnitude of the effect of a one-minute delay in EMS response time on the odds of survival ranged from 1.3% to 9.8% (average: 5.3%), depending on the underlying OHCA characteristics., Conclusions: Delays in EMS response time had the largest reduction in survival odds for OHCAs that did not receive a bystander AED shock but were witnessed, occurred in public, and/or received bystander CPR. A bystander AED shock appears to be protective against a delay in EMS response time., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Aim: Quantifying the ratio describing the difference between "true route" and "straight-line" distances from out-of-hospital cardiac arrests (OHCAs) to the closest accessible automated external defibrillator (AED) can help correct likely overestimations in AED coverage. Furthermore, we aimed to examine to what extent the closest AED based on true route distance differed from the closest AED using "straight-line"., Methods: OHCAs (1994-2016) and AEDs (2016) in Copenhagen, Denmark and in Toronto, Canada (2007-2015 and 2015, respectively) were identified. Three distances were calculated between OHCA and target AED: 1) the straight-line distance ("straight-line") to the closest AED, 2) the corresponding true route distance to the same AED ("true route"), and 3) the closest AED based only on true route distance ("shortest true route"). The ratio between "true route" and "straight-line" distance was calculated and differences in AED coverage (an OHCA ≤ 100 m of an accessible AED) were examined., Results: The "straight-line" AED coverage of 100 m was 24.2% (n = 2008/8295) in Copenhagen and 6.9% (n = 964/13916) in Toronto. The corresponding "true route" distance reduced coverage to 9.5% (n = 786) and 3.8% (n = 529), respectively. The median ratio between "true route" and "straight-line" distance was 1.6 in Copenhagen and 1.4 in Toronto. In 26.1% (n = 2167) and 22.9% (n = 3181) of all Copenhagen and Toronto OHCAs respectively, the closest AED in "shortest true route" was different than the closest AED initially found by "straight-line"., Conclusions: Straight-line distance is not an accurate measure of distance and overestimates the actual AED coverage compared to a more realistic true route distance by a factor 1.4-1.6., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Mathematical optimization can be used to place automated external defibrillators (AEDs) in locations that maximize coverage of out-of-hospital cardiac arrests (OHCAs). We sought to determine whether optimization can improve alignment between AED locations and OHCA counts across levels of socioeconomic deprivation., Methods: All suspected OHCAs and registered AEDs in Scotland between Jan. 2011 and Sept. 2017 were included and mapped to a corresponding socioeconomic deprivation level using the Scottish Index of Multiple Deprivation (SIMD). We used mathematical optimization to determine optimal locations for placing 10%, 25%, 50%, and 100% additional AEDs, as well as locations for relocating existing AEDs. For each AED placement policy, we examined the impact on AED distribution and OHCA "coverage" (suspected OHCA occurring within 100 m of AED) with respect to SIMD quintiles., Results: We identified 49,432 suspected OHCAs and 1532 AEDs. The distribution of existing AED locations across SIMD quintiles significantly differed from the distribution of suspected OHCAs (P < 0.001). Optimization-guided AED placement increased coverage of suspected OHCAs compared to existing AED locations (all P < 0.001). Optimization resulted in more AED placements and increased OHCA coverage in areas of greater socioeconomic deprivation, such that resulting distributions across SIMD quintiles matched the shape of the OHCA count distribution. Optimally relocating existing AEDs achieved similar OHCA coverage levels to that of doubling the number of total AEDs., Conclusions: Mathematical optimization results in AED locations and suspected OHCA coverage that more closely resembles the suspected OHCA distribution and results in more equitable coverage across levels of socioeconomic deprivation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Drone-delivered defibrillators have the potential to significantly reduce response time for out-of-hospital cardiac arrest (OHCA). However, optimal policies for the dispatch of such drones are not yet known. We sought to develop dispatch rules for a network of defibrillator-carrying drones., Methods: We identified all suspected OHCAs in Peel Region, Ontario, Canada from Jan. 2015 to Dec. 2019. We developed drone dispatch rules based on the difference between a predicted ambulance response time to a calculated drone response time for each OHCA. Ambulance response times were predicted using linear regression and neural network models, while drone response times were calculated using drone specifications from recent pilot studies and the literature. We evaluated the dispatch rules based on response time performance and dispatch decisions, comparing them to two baseline policies of never dispatching and always dispatching drones., Results: A total of 3573 suspected OHCAs were included in the study with median and mean historical ambulance response times of 5.8 and 6.2 min. All machine learning-based dispatch rules significantly reduced the median response time to 3.9 min and mean response time to 4.1-4.2 min (all P < 0.001) and were non-inferior to universally dispatching drones (all P < 0.001) while reducing the number of drone flights by up to 30%. Dispatch rules with more drone flights achieved higher sensitivity but lower specificity and accuracy., Conclusion: Machine learning-based dispatch rules for drone-delivered defibrillators can achieve similar response time reductions as universal drone dispatch while substantially reducing the number of trips., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Introduction: The COVID-19 pandemic has led to closures of non-essential businesses and buildings. The impact of such closures on automated external defibrillator (AED) accessibility compared to changes in foot traffic levels is unknown., Methods: We identified all publicly available online AED registries in Canada last updated May 1, 2019 or later. We mapped AED locations to location types and classified each location type as completely inaccessible, partially inaccessible, or unaffected based on government-issued closure orders as of May 1, 2020. Using location and mobility data from Google's COVID-19 Community Mobility Reports, we identified the change in foot traffic levels between February 15-May 1, 2020 (excluding April 10-12) compared to the baseline of January 3-February 1, 2020, and determined the discrepancy between foot traffic levels and AED accessibility., Results: We identified four provincial and two municipal AED registries containing a total of 5848 AEDs. Of those, we estimated that 69.9% were completely inaccessible, 18.8% were partially inaccessible, and 11.3% were unaffected. Parks, retail and recreation locations, and workplaces experienced the greatest reduction in AED accessibility. The greatest discrepancies between foot traffic levels and AED accessibility occurred in parks, retail and recreation locations, and transit stations., Conclusion: A majority of AEDs became inaccessible during the COVID-19 pandemic due to government-mandated closures. In a substantial number of locations across Canada, the reduction in AED accessibility was far greater than the reduction in foot traffic., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Aims: To determine if mathematical optimization of in-hospital defibrillator placements can reduce in-hospital cardiac arrest-to-defibrillator distance compared to existing defibrillators in a single hospital., Methods: We identified treated IHCAs and defibrillator placements in St. Michael's Hospital in Toronto, Canada from Jan. 2013 to Jun. 2017 and mapped them to a 3-D computer model of the hospital. An optimization model identified an equal number of optimal defibrillator locations that minimized the average distance between IHCAs and the closest defibrillator using a 10-fold cross-validation approach. The optimized and existing defibrillator locations were compared in terms of average distance to the out-of-sample IHCAs. We repeated the analysis excluding intensive care units (ICUs), operating theatres (OTs), and the emergency department (ED). We also re-solved the model using fewer defibrillators to determine when the average distance matched the performance of existing defibrillators., Results: We identified 433 treated IHCAs and 53 defibrillators. Of these, 167 IHCAs and 31 defibrillators were outside of ICUs, OTs, and the ED. Optimal defibrillator placements reduced the average IHCA-to-defibrillator distance from 16.1 m to 2.7 m (relative decrease of 83.0%; P = 0.002) compared to existing defibrillator placements. For non-ICU/OT/ED IHCAs, the average distance was reduced from 24.4 m to 11.9 m (relative decrease of 51.3%; P = 0.002. 8-9 optimized defibrillator locations were sufficient to match the average IHCA-to-defibrillator distance of existing defibrillator placements., Conclusions: Optimization-guided placement of in-hospital defibrillators can reduce the distance from an IHCA to the closest defibrillator. Equivalently, optimization can match existing defibrillator performance using far fewer defibrillators., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Introduction: Although out-of-hospital cardiac arrest (OHCA) is thought of as a sudden event, recent studies suggest that many patients have symptoms or have sought medical attention prior to their arrest. Our objective was to evaluate patterns of healthcare utilization before OHCA., Methods: We conducted a population-based cohort study in Ontario, Canada, which included all patients ≥20 years, who suffered out-of-hospital cardiac arrest and transferred to an emergency department (ED) from 2007 to 2018. Measurements included emergency room assessments, hospitalizations and physician visits prior to arrest., Results: The cohort comprised 38,906 patients, their mean age was 66.5 years, and 32.7% were women. Rates of ED assessments and hospital admissions were relatively constant until 90 days prior to arrest where they markedly increased to the time before arrest. Within 90 days, rates of ED assessment, hospitalization, and primary care physician visit were 29.5%, 16.4%, and 70.1%, respectively. Cardiovascular conditions were diagnosed in 14.4% of ED visits, and 33.7% of hospitalizations in this time period. The largest age-difference was the mental and behavioural disorders within 90 days of OHCA in the ED, where rates were 12.2% among patients <65 years vs. 1.9% for patients ≥65 years., Conclusions: In contrast to the conventional wisdom that OHCA occurs without prior contacts to the health care system, we found that more than 1 in 4 patients were assessed in the ED prior within 90 days of their arrest. Identification of warning signs of OHCA may allow future development of prevention strategies., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Aims: Mathematical optimization of automated external defibrillator (AED) placements has the potential to improve out-of-hospital cardiac arrest (OHCA) coverage and reverse the negative effects of limited AED accessibility. However, the generalizability of optimization approaches has not yet been investigated. Our goal is to examine the performance and generalizability of a spatiotemporal AED placement optimization methodology, initially developed for Toronto, Canada, to the new study setting of Copenhagen, Denmark., Methods: We identified all public OHCAs (1994-2016) and all registered AEDs (2016) in Copenhagen, Denmark. We calculated the coverage loss associated with limited temporal accessibility of registered AEDs, and used a spatiotemporal optimization model to quantify the potential coverage gain of optimized AED deployment. Coverage gain of spatiotemporal deployment over a spatial-only solution was quantified through 10-fold cross-validation. Statistical testing was performed using χ 2 and McNemar's tests., Results: We found 2149 public OHCAs and 1573 registered AED locations. Coverage loss was found to be 24.4% (1104 OHCAs covered under assumed 24/7 coverage, and 835 OHCAs under actual coverage). The coverage gain from using the spatiotemporal model over a spatial-only approach was 15.3%. Temporal and geographical trends in coverage gain were similar to Toronto., Conclusions: Without modification, a previously developed spatiotemporal AED optimization approach was applied to Copenhagen, resulting in similar OHCA coverage findings as Toronto, despite large geographic and cultural differences between the two cities. In addition to reinforcing the importance of temporal accessibility of AEDs, these similarities demonstrate the generalizability of optimization approaches to improve AED placement and accessibility., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Cities worldwide have underground or above-ground enclosed walkway systems for pedestrian travel, representing unique environments for studying out-of-hospital cardiac arrests (OHCAs). The characteristics and outcomes of OHCAs that occur in such systems are unknown., Objective: To determine whether OHCAs occurring in enclosed pedestrian walkway systems have differing demographics, prehospital intervention, and survival outcomes compared to the encompassing city, by examining the PATH walkway system in Toronto., Methods: We identified all atraumatic, public-location OHCAs in Toronto from April 2006 to March 2016. Exclusion criteria were obvious death, existing DNR, and EMS-witnessed OHCAs. OHCAs were classified into mutually exclusive location groups: Toronto, Downtown, and PATH-accessible. PATH-accessible OHCAs were those that occurred within the PATH system between the first basement and third floor. We analyzed demographic, prehospital intervention, and survival data using t-tests and chi-squared tests., Results: We identified 2172 OHCAs: 1752 Toronto, 371 Downtown, and 49 PATH-accessible. Compared to Toronto, a significantly higher proportion of PATH-accessible OHCAs was bystander-witnessed (62.6% vs 83.7%, p=0.003), had bystander CPR (56.6% vs 73.5%, p=0.019), bystander AED use (11.0% vs 42.6%, p<0.001), shockable initial rhythm (45.5% vs 72.9%, p<0.001), and overall survival (18.5% vs 33.3%, p=0.009). Similar significant differences were observed when compared to Downtown., Conclusions: This study suggests that OHCAs in enclosed pedestrian walkway systems are uniquely different from other public settings. Bystander resuscitation efforts are significantly more frequent and survival rates are significantly higher. Urban planners in similar infrastructure systems worldwide should consider these findings when determining AED placement and public engagement strategies., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Public access defibrillation with automated external defibrillators (AEDs) can improve survival from out-of-hospital cardiac arrests (OHCA) occurring in public. Increasing the effective range of AEDs may improve coverage for public location OHCAs., Objective: To quantify the relationship between AED effective range and public location cardiac arrest coverage., Methods: This was a retrospective cohort study using the Resuscitation Outcomes Consortium Epistry database. We included all public-location, atraumatic, EMS-attended OHCAs in Toronto, Canada between December 16, 2005 and July 15, 2010. We ran a mathematical model for AED placement that maximizes coverage of historical public OHCAs given pre-specified values of AED effective range and the number of locations to place AEDs. Locations of all non-residential buildings were obtained from the City of Toronto and used as candidate sites for AED placement. Coverage was evaluated for range values from 10 to 300 m and number of AED locations from 10 to 200, both in increments of 10, for a total of 600 unique scenarios. Coverage from placing AEDs in all public buildings was also measured., Results: There were 1310 public location OHCAs during the study period, with 25,851 non-residential buildings identified as candidate sites for AED placement. Cardiac arrest coverage increased with AED effective range, with improvements in coverage diminishing at higher ranges. For example, for a deployment of 200 AED locations, increasing effective range from 100 m to 200 m covered an additional 15% of cardiac arrests, whereas increasing range further from 200 m to 300 m covered an additional 10%. Placing an AED in each of the 25,851 public buildings resulted in coverage of 50% and 95% under assumed effective ranges of 50 m and 300 m, respectively., Conclusion: Increasing AED effective range can improve cardiac arrest coverage. Mathematical models can help evaluate the potential impact of initiatives which increase AED range., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)