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New AED Distribution Model Could Save Countless Lives

Posted by Thomas Oliver on 9/16/2014

By using geographic optimization modeling, a group of academics and clinicians in Paris has created a new model that can accurately predict the optimal location and number of automated external defibrillators (AED) necessary for a greater rate of lives saved during cardiac arrest.

This revolutionary approach utilizes a scientific and mathematically-backed process of determining the optimal number of AEDs required in different urban areas. Researchers conducted an analysis of all Paris hospital cardiac arrest locations from 2009 to 2010 and evaluated how beneficial AEDs could have been from varying distances ranging from 200 to 2000 meters.

The researchers also extended the study to simulate scenarios in public venues such as subway stations, post offices, pharmacies, and bike-sharing stations. Using road network information and a geographic information system (GIS), they calculated the median distance between out-of-hospital cardiac arrests (OHCAs) and potential AED locations. 

According to the findings of the model, Paris alone requires 350 AEDs located in public places in order to adequately prevent complete cardiac arrest in OHCA situations.

Early defibrillation and CPR are the only way to save victims who are suffering from cardiac arrest, meaning that the availability of AEDs is critical. According to findings, every minute of delay prior to defibrillation decreases survival by 10%.

As this is quite a significant percentage, it is clear that the use of AEDs is crucial. The incidence of OHCAs ranges from 50 to 100 out of 100,000 per year in North America and Europe, making it a major public health issue.

Cardiac arrest is primarily fatal. Previous research has shown that the availability and proper use of AEDs can improve the rate of survival by 100% 

Since the publication of the findings in Paris, other research communities in the United States and the U.K. have begun similar methodical testing in additional communities. These teams have come to the same conclusions regarding the usefulness and practicality of the model and the absolute necessity of its use in determining strategic locations for AEDs.

The geographic optimization model could be adopted and put into practice in any urban area. After taking specific demographic information into account, such as population density, population movements, among others, the model can be tailored to any environment. 

The effectiveness of AEDs could be dramatically improved with the implementation of this innovative distribution model. Policy-makers are concerned, though, with the high expected cost of purchase (1,000 Euros) and maintenance of so many AEDs, especially if laymen in the public do not employ them during times of need.

In order for this program to be successful, the public needs to be aware of how to use and be unafraid to utilize the machines. First, however, potential rescuers must know where they are located.

The new approach should address both this and concerns regarding the cost-effectiveness of deploying and maintaining so many of these devices.

Researchers will continue to publicize and reaffirm the findings of their work, as this could be the advancement required for reducing the number of fatalities due to out-of-hospital cardiac arrests.

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