Assessing feasibility of proposed extracorporeal cardiopulmonary resuscitation programmes for out-of-hospital cardiac arrest in Scotland via geospatial modelling.

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.

Combinations of First Responder and Drone Delivery to Achieve 5-Minute AED Deployment in OHCA.

Background: Defibrillation in the critical first minutes of out-of-hospital cardiac arrest (OHCA) can significantly improve survival. However, timely access to automated external defibrillators (AEDs) remains a barrier. Objectives: The authors estimated the impact of a statewide program for drone-delivered AEDs in North Carolina integrated into emergency medical service and first responder (FR) response for OHCA. Methods: Using Cardiac Arrest Registry to Enhance Survival registry data, we included 28,292 OHCA patients ≥18 years of age between 1 January 2013 and 31 December 2019 in 48 North Carolina counties. We estimated the improvement in response times (time from 9-1-1 call to AED arrival) achieved by 2 sequential interventions: 1) AEDs for all FRs; and 2) optimized placement of drones to maximize 5-minute AED arrival within each county. Interventions were evaluated with logistic regression models to estimate changes in initial shockable rhythm and survival. Results: Historical county-level median response times were 8.0 minutes (IQR: 7.0-9.0 minutes) with 16.5% of OHCAs having AED arrival times of <5 minutes (IQR: 11.2%-24.3%). Providing all FRs with AEDs improved median response to 7.0 minutes (IQR: 6.2-7.8 minutes) and increased OHCAs with <5-minute AED arrival to 22.3% (IQR: 16.4%-30.9%). Further incorporating optimized drone networks (326 drones across all 48 counties) improved median response to 4.8 minutes (IQR: 4.3-5.2 minutes) and OHCAs with <5-minute AED arrival to 56.3% (IQR: 46.9%-64.2%). Survival rates were estimated to increase by 34% for witnessed OHCAs with estimated drone arrival <5 minutes and ahead of FR and emergency medical service. Conclusions: Deployment of AEDs by FRs and optimized drone delivery can improve AED arrival times which may lead to improved clinical outcomes. Implementation studies are needed.

Constrained Optimization for Decision Making in Health Care Using Python: A Tutorial.

HIGHLIGHTS: This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.

Factors affecting public access defibrillator placement decisions in the United Kingdom: A survey study.

Aim: This study aimed to understand current community PAD placement strategies and identify factors which influence PAD placement decision-making in the United Kingdom (UK). Methods: Individuals, groups and organisations involved in PAD placement in the UK were invited to participate in an online survey collecting demographic information, facilitators and barriers to community PAD placement and information used to decide where a PAD is installed in their experiences. Survey responses were analysed through descriptive statistical analysis and thematic analysis. Results: There were 106 included responses. Distance from another PAD (66%) and availability of a power source (63%) were most frequently used when respondents are deciding where best to install a PAD and historical occurrence of cardiac arrest (29%) was used the least. Three main themes were identified influencing PAD placement: (i) the relationship between the community and PADs emphasising community engagement to create buy-in; (ii) practical barriers and facilitators to PAD placement including securing consent, powering the cabinet, accessibility, security, funding, and guardianship; and (iii) 'risk assessment' methods to estimate the need for PADs including areas of high footfall, population density and type, areas experiencing health inequalities, areas with delayed ambulance response and current PAD provision. Conclusion: Decision-makers want to install PADs in locations that maximise impact and benefit to the community, but this can be constrained by numerous social and infrastructural factors. The best location to install a PAD depends on local context; work is required to determine how to overcome barriers to optimal community PAD placement.

Incremental gains in response time with varying base location types for drone-delivered automated external defibrillators.

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.

Pediatric and adult Out-of-Hospital cardiac arrest incidence within and near public schools in British Columbia: Missed opportunities for Systematic AED deployment strategies.

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.

Utilization and cost-effectiveness of school and community center AED deployment models in Canadian cities.

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.

Socioeconomically equitable public defibrillator placement using mathematical optimization.

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.

Public defibrillator accessibility and mobility trends during the COVID-19 pandemic in Canada.

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.

Machine learning-based dispatch of drone-delivered defibrillators for out-of-hospital cardiac arrest.

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.

The association of pH values during the first 24 h with neurological status at hospital discharge and futility among patients with out-of-hospital cardiac arrest.

STUDY OBJECTIVE: Post-resuscitation prognostic biomarkers for out-of-hospital cardiac arrest (OHCA) outcomes have not been fully elucidated. We examined the association of acid-base blood values (pH) with patient outcomes and calculated the pH test performance to predict prognosis. METHODS: This was a post-hoc analysis of data from the continuous chest compression trial, which enrolled non-traumatic adult emergency medical system-treated OHCA in Canada and the United States. We examined cases who survived a minimum of 24 h post hospital arrival. The independent variables of interest were initial pH, final pH, and the change in pH (δpH). The primary outcome was neurological status at hospital discharge, with favorable status defined as modified Rankin Scale (mRS) ≤ 3. We reported adjusted odds ratios for favorable neurological outcome using multivariable logistic regression models. We calculated the test performance of increasing pH thresholds in 0.1 increments to predict unfavorable neurological status (defined as mRS >3) at hospital discharge. RESULTS: We included 4189 patients. 32% survived to hospital discharge with favorable neurological status. In the adjusted analysis, higher initial pH (OR 6.82; 95% CI 3.71-12.52) and higher final pH (OR 7.99; 95% CI 3.26-19.62) were associated with higher odds of favorable neurological status. pH thresholds with highest positive predictive values were initial pH < 6.8 (92.5%; 95% CI 86.2 %-98.8%) and final pH < 7.0 (100%; 95% CI 95.2 %-100%). CONCLUSION: In patients with OHCA, pH values were associated with patients' subsequent neurological status at hospital discharge. Final pH may be clinically useful to predict unfavorable neurological status at hospital discharge.

Optimal in-hospital defibrillator placement.

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.

A drone delivery network for antiepileptic drugs: a framework and modelling case study in a low-income country.

BACKGROUND: In urbanized, low-income cities with high rates of congestion, delivery of antiepileptic drugs (AEDs) by unmanned aerial vehicles (drones) to people with epilepsy for both emergency and non-urgent distribution may prove beneficial. METHODS: Conakry is the capital of the Republic of Guinea, a low-income sub-Saharan African country (2018 per capita gross national income US$830). We computed the number of drones and delivery times to distribute AEDs from a main urban hospital to 27 pre-identified gas stations, mosques and pharmacies and compared these to the delivery times of a personal vehicle. RESULTS: We predict that a single drone could serve all pre-identified delivery locations in Conakry within a 20.4-h period. In an emergency case of status epilepticus, 8, 20 and 24 of the 27 pre-identified destinations can be reached from the hub within 5, 10 and 15 min, respectively. Compared with the use of a personal vehicle, the response time for a drone is reduced by an average of 78.8% across all times of the day. CONCLUSIONS: Drones can dramatically reduce the response time for both emergency and routine delivery of lifesaving medicines. We discuss the advantages and disadvantages of such a drone delivery model with relevance to epilepsy. However, the commissioning of a trial of drones for drug delivery in related diseases and geographies is justified.