This study is a retrospective observation study using a computerized virtual simulator. We included all OHCA cases registered in Korea OHCA Registry (KOHCAR) from 2013 to 2016, transported by emergency medical services(EMS) across South Korea. The Korea Disease Control and Prevention Agency (KDCA) approved use of all data, and the study was approved by the Institutional Review Board of the study site.
The KOHCAR is a nationwide database including all cardiac arrest patients transported by EMS ambulances operated by fire departments across South Korea since 2006. This database registers prehospital information written by emergency medical technicians of National Fire Agency. Trained medical record reviewers of KCDC visited the hospitals, in which the OHCA patients were transported by National Fire Agency. They collected in-hospital information and outcome of OHCA victims. Then, prehospital and in-hospital data of each case was merged using the Utstein guidelines. (16, 17)
To develop a UAV-AED flight simulation, we initially extracted data on topography and altitude of Seoul city from Google maps. We constructed a geographic information database including height of buildings by combining the data about altitudes of each terrain with the altitude of all facilities supported by Ministry of Government Administration and Home Affairs. We also added meteorological information including wind speed, precipitation, snowfall, temperature, visibility and weather phenomenon, which were hourly recorded on Korea Meteorological Administration database. These data were added for scenario models of limited UAV-AED flight due to extreme weather condition.
Seoul has a population of 9.7 million and covers a total surface area of 605.2 km2. Seoul is a metropolitan city with many high-rise buildings and mountains. The EMS system of Seoul is a two-tiered public service model with service level of EMT-intermediate. Seoul is divided into 25 districts. Each district has 3 to 8 fire stations with ambulance vehicles and there are 116 fire stations in total. (18) Each ambulance vehicle is usually staffed with 3 EMTs. (2, 19) One dispatch center covers all EMS calls across Seoul(20). The PAD installation is mandatory on public health offices, ambulances, airport, train, and apartments with more than 500 households. Approximately 8,000 AEDs are installed in Seoul. (21)
All OHCA cases in Seoul from January 1, 2013 to December 31, 2016 with age of ≥ 9 years at cardiac arrest recognition time were eligible for this study. Patients with OHCA occurrence in the ambulance during EMS transport was excluded. Moreover, we excluded pediatric OHCA cases aged under 8 years. This is because pediatric cases aged under 8 were recommended for dose attenuator usage to optimize defibrillation energy. (22) Cases with missing AED attach time or cardiac arrest recognition time were also excluded.
We used the Utstein variables from KOHCAR database such as gender, age, witnessed status, location of event (private vs public vs unknown), bystander CPR, initial electrocardiogram (ECG) rhythm, and EMS defibrillation. (23) We collected the EMS time profiles including EMS call time, EMS arrival at the scene time, EMS departure from the scene time, EMS hospital arrival time, call to cardiac arrest recognition time and call to AED attach time.
The address recorded on KOHCAR was used as the place of cardiac arrest. Geo-coding for the place of cardiac arrest was performed using Google Maps APIs (Google, California, United States). Regarding the weather-related variables, we collected hourly wind speed, precipitation, snowfall, temperature, visibility and weather phenomenon. Daytime and nighttime of OHCA occurrence was divided by 6AM and 6PM.
Development of UAV-AED flight simulation
UAV-AED station allocation
All of 116 fire stations in Seoul were used as the candidates for UAV-AED installation. Among the 116 stations, the optimal location for each number of stations increased by 5 (i.e. 5, 10, 15, etc.) was selected for simulation from 5 to 116 stations. A multicriteria evaluation was conducted for selecting optimal combinations of possible UAV-AED installed stations to reduce call to AED attach time. We generated an OHCA occurrence layer by heat-map analysis of the OHCA occurrence location from 2013 to 2016 in Seoul (Appendix. 1). The EMS call to scene arrival time was analyzed by inverse distance weight interpolation to obtain EMS-response time layer. (Appendix. 2) The OHCA risk map was calculated by adding 1: 1 weighting of OHCA occurrence layer and EMS call to scene arrival time layer. GIS analysis was performed using qGIS 3.4. The OHCA risk map was constructed with a lattice with a resolution of 50 m x 50 m. For optimal number of UAV-AED stations, the estimated coverage of each UAV-AED station was obtained by allocating a 3km circle for each station. The optimal location for each number of stations, increased by 5, was selected according to the location with maximum score of OHCA risk map, which was calculated using the genetic algorithm. (24) (Appendix. 3)
UAV-AED flight simulation
UAV-AED flight simulation consists of environment information of Seoul and drone flight operation. Environmental information of Seoul was constructed by combining topographic information of natural terrain and facility information including location and height of high-rise buildings. (25) The UAV-AED topographic flight pathway was defined by 3 components. The first component was for the UAV-AED to take off vertically from the UAV-AED allocated station above the maximal altitude of natural terrain or high-rise buildings between UAV-AED station and OHCA site (Fig. 1). The second was horizontal flight to the OHCA site according to Euclidean distance. The final component was vertical landing of UAV-AED to the OHCA site. The entire flight pathway including take-off, horizontal flight and landing from UAV-AED station to OHCA site was divided by 3-dimensional virtual blocks of 10 m × 10 m × 10 m. The flight time of UAV-AED was defined as the sum of time required for passing all blocks in the flight pathway. (Fig 1.) The flight time was computed by simulating the passage time of each block by HackflightSim. (26) The flight performance of UAV used for simulation was carried out based on performance of a Huesin Blueye 1k model (Huins Inc., Gyunggi-do, South Korea) weighting 1.2 kg and moving up to 50 km/h. (27) The UAV flight simulation tests were performed using the dynamic simulator of drone transfer simulator on the reference webpage. (28)
UAV-AED simulation scenarios according to topographic and weather conditions
Based on meteorological information of Seoul during study period, we simulated 4 scenarios according to flight performance of the UAV regarding weather and visibility. The first scenario was basic UAV model. In this model, flight of UAV-AED was restricted if EMS call for OHCA occurred during extreme weather conditions, which were defined as strong winds of 10km/h or higher, rain, snow, and temperature below 0℃. Also, if the call was made during nighttime or if the sight distance was less than 1km, flight of UAV in the basic model was not permitted. The second model, control advanced UAV could fly regardless of time or limited visibility during flight. However, it was prohibited for use during extreme weather conditions. The third model, flight advanced UAV could fly in extreme weather conditions, but it could not fly in situations of poor visibility. Lastly, the flight and control advanced UAV model could fly whenever during the study period regardless of weather conditions or poor visibility. We simulated these 4 types of UAV model scenarios for 2 different flight pathways. The first flight pathway is the direct flight route through Euclidean distance from UAV-AED station to OHCA site. The second topographic flight pathway was generated by UAV-AED flight simulation developed in this study using topographic information.
Primary outcome was call to AED attach time. Call to AED attach time profiles by current EMS practice was measured by time profiles in the KOHCAR database; and time profiles by UAV-AED was measured by profiles derived from UAV-AED simulation. Secondary outcome was success rate of call to AED attach within 5 minutes or 10 minutes, and pre-arrival rate of UAV-AED before current EMS based AED delivery.
The paired Wilcoxon rank sum test was used to compare the call to AED attach time between current practice and UAV-AED program. Call to AED attach success rate within 5 or 10 minutes before and after UAV-AED program implementation was compared using McNemar test. Call to AED attach time was compared according to the 4 drone flight simulation scenarios in both Euclidean distance pathway and topographic simulation pathway using the paired Wilcoxon rank-sum test. We used SAS 9.4.(NC, USA) for statistical analysis.