Maximum expected survival rate model for public access defibrillator placement.

AIM: Mathematical optimization of automated external defibrillator (AED) placement has demonstrated potential to improve survival of out-of-hospital cardiac arrest (OHCA). Existing models mostly aim to improve accessibility based on coverage radius and do not account for detailed impact of delayed defibrillation on survival. We aimed to predict OHCA survival based on time to defibrillation and developed an AED placement model to directly maximize the expected survival rate. METHODS: We stratified OHCAs occurring in Singapore (2010-2017) based on time to defibrillation and developed a regression model to predict the Utstein survival rate. We then developed a novel AED placement model, the maximum expected survival rate (MESR) model. We compared the performance of MESR with a maximum coverage model developed for Canada that was shown to be generalizable to other settings (Denmark). The survival gain of MESR was assessed through 10-fold cross-validation for placement of 20 to 1000 new AEDs in Singapore. Statistical analysis was performed using χ2 and McNemar's tests. RESULTS: During the study period, 15,345 OHCAs occurred. The power-law approximation with R2 of 91.33% performed best among investigated models. It predicted a survival of 54.9% with defibrillation within the first two minutes after collapse that was reduced by more than 60% without defibrillation within the first 4 minutes. MESR outperformed the maximum coverage model with P-value < 0.05 (<0.0001 in 22 of 30 experiments). CONCLUSION: We developed a novel AED placement model based on the impact of time to defibrillation on OHCA outcomes. Mathematical optimization can improve OHCA survival.

myResponder Smartphone Application to Crowdsource Basic Life Support for Out-of-Hospital Cardiac Arrest: The Singapore Experience.

OBJECTIVE: The Singapore myResponder is a novel smartphone application developed by the Singapore Civil Defence Force (SCDF) that notifies volunteer first responders of a suspected out-of-hospital cardiac arrest (OHCA) case and locations of Automated External Defibrillators (AED) in the vicinity so that they can assist with resuscitation. We aimed to examine the performance of this application, challenges encountered, and future directions. Methods: We analyzed data from the myResponder app since its launch from April 2015 to July 2019. The number of installations, registered community first responders, suspected OHCA cases, notifications sent by the app, percentage of responders who accepted activation and arrived at scene were reviewed. A subgroup of taxi driving responders (within a 1.5-kilometer response radius) carrying an AED under a subsequent pilot program was also analyzed. Results: By July 2019, 46,689 responders were registered in the myResponder app. There were a total of 19,189 cases created for suspected OHCA, with a median of 358 cases per month (IQR 330-430), in which 10,073 responders accepted activation from myResponder and 4,955 arrived on-scene. A total of 135,599 notifications were sent for these cases, with a median of 7.1 notifications per case (IQR 4.3-8.7). In 2019, the percentages of responders who accepted notification and arrived on scene were 45.8% and 24.1%, respectively. 43% (1110/2581) of responders arrived before EMS crew. Conclusion: The myResponder mobile application is a feasible smart technology solution to improve community response to OHCA, and to increase bystander CPR and AED use. Future directions include increasing the number of active responders, improving response rates, app performance, and better data capture for quality improvement.