INTRODUCTION: Public access defibrillation (PAD) has shown potential to increase cardiac arrest survival rates. OBJECTIVES: To describe the geographic epidemiology of prehospital cardiac arrest in Singapore using geographic information systems (GIS) technology and assess the potential for deployment of a PAD program. METHODS: We conducted an observational prospective study looking at the geographic location of pre-hospital cardiac arrests in Singapore. Included were all patients with out-of-hospital cardiac arrest (OHCA) presented to emergency departments. Patient characteristics, cardiac arrest circumstances, emergency medical service (EMS) response and outcomes were recorded according to the Utstein style. Location of cardiac arrests was spot-mapped using GIS. RESULTS: From 1 October 2001 to 14 October 2004, 2428 patients were enrolled into the study. Mean age for arrests was 60.6 years with 68.0% male. 67.8% of arrests occurred in residences, with 54.5% bystander witnessed and another 10.5% EMS witnessed. Mean EMS response time was 9.6 min with 21.7% receiving prehospital defibrillation. Cardiac arrest occurrence was highest in the suburban town centers in the Eastern and Southern part of the country. We also identified communities with the highest arrest rates. About twice as many arrests occurred during the day (07:00-18:59 h) compared to night (19:00-06:59 h). The categories with the highest frequencies of occurrence included residential areas, in vehicles, healthcare facilities, along roads, shopping areas and offices/industrial areas. CONCLUSION: We found a definite geographical distribution pattern of cardiac arrest. This study demonstrates the utility of GIS with a national cardiac arrest database and has implications for implementing a PAD program, targeted CPR training, AED placement and ambulance deployment.
INTRODUCTION: Public access defibrillation (PAD) has shown potential to increase cardiac arrest survival rates. OBJECTIVES: To describe the geographic epidemiology of prehospital cardiac arrest in Singapore using geographic information systems (GIS) technology and assess the potential for deployment of a PAD program. METHODS: We conducted an observational prospective study looking at the geographic location of pre-hospital cardiac arrests in Singapore. Included were all patients with out-of-hospital cardiac arrest (OHCA) presented to emergency departments. Patient characteristics, cardiac arrest circumstances, emergency medical service (EMS) response and outcomes were recorded according to the Utstein style. Location of cardiac arrests was spot-mapped using GIS. RESULTS: From 1 October 2001 to 14 October 2004, 2428 patients were enrolled into the study. Mean age for arrests was 60.6 years with 68.0% male. 67.8% of arrests occurred in residences, with 54.5% bystander witnessed and another 10.5% EMS witnessed. Mean EMS response time was 9.6 min with 21.7% receiving prehospital defibrillation. Cardiac arrest occurrence was highest in the suburban town centers in the Eastern and Southern part of the country. We also identified communities with the highest arrest rates. About twice as many arrests occurred during the day (07:00-18:59 h) compared to night (19:00-06:59 h). The categories with the highest frequencies of occurrence included residential areas, in vehicles, healthcare facilities, along roads, shopping areas and offices/industrial areas. CONCLUSION: We found a definite geographical distribution pattern of cardiac arrest. This study demonstrates the utility of GIS with a national cardiac arrest database and has implications for implementing a PAD program, targeted CPR training, AED placement and ambulance deployment.
Authors: Benjamin W Chrisinger; Anne V Grossestreuer; Meredith C Laguna; Heather M Griffis; Charles C Branas; Douglas J Wiebe; Raina M Merchant Journal: Resuscitation Date: 2016-10-01 Impact factor: 5.262
Authors: Nicholas J Tierney; Antonietta Mira; H Jost Reinhold; Giuseppe Arbia; Samuel Clifford; Angelo Auricchio; Tiziano Moccetti; Stefano Peluso; Kerrie L Mengersen Journal: PLoS One Date: 2019-08-07 Impact factor: 3.240