AvaLife-A New Multi-Disciplinary Approach Supported by Accident and Field Test Data to Optimize Survival Chances in Rescue and First Aid of Avalanche Patients.

Snow sports in the backcountry have seen a steep increase in popularity, and therefore preparedness for efficient companion and organized rescue is important. While technical rescue skills are widely taught, there is a lack of knowledge regarding first aid for avalanche patients. The stressful and time-critical situation for first responders requires a rule-based decision support tool. AvaLife has been designed from scratch, applying mathematical and statistical approaches including Monte Carlo simulations. New analysis of retrospective data and large prospective field test datasets were used to develop evidence-based algorithms exclusively for the avalanche rescue environment. AvaLife differs from other algorithms as it is not just a general-purpose CPR algorithm which has been slightly adapted for the avalanche patient. The sequence of actions, inclusion of the ≥150 cm burial depth triage criterion, advice to limit CPR duration for normothermic patients to 6 min in case of multiple burials and shortage of resources, criteria for using recovered subjects as a resource in the ongoing rescue, the adapted definition of "injuries incompatible with life", reasoning behind the utmost importance of rescue breaths, as well as the updated BLS-iCPR algorithm make AvaLife useful in single and multiple burial rescue. AvaLife is available as a companion rescue basic life support (BLS) version for the recreational user and an advanced companion and organized rescue BLS version for guides, ski patrols and mountain rescuers. AvaLife allows seamless interoperability with advanced life support (ALS) qualified medical personnel arriving on site.

A concept for optimizing avalanche rescue strategies using a Monte Carlo simulation approach.

Recent technical and strategical developments have increased the survival chances for avalanche victims. Still hundreds of people, primarily recreationists, get caught and buried by snow avalanches every year. About 100 die each year in the European Alps-and many more worldwide. Refining concepts for avalanche rescue means to optimize the procedures such that the survival chances are maximized in order to save the greatest possible number of lives. Avalanche rescue includes several parameters related to terrain, natural hazards, the people affected by the event, the rescuers, and the applied search and rescue equipment. The numerous parameters and their complex interaction make it unrealistic for a rescuer to take, in the urgency of the situation, the best possible decisions without clearly structured, easily applicable decision support systems. In order to analyse which measures lead to the best possible survival outcome in the complex environment of an avalanche accident, we present a numerical approach, namely a Monte Carlo simulation. We demonstrate the application of Monte Carlo simulations for two typical, yet tricky questions in avalanche rescue: (1) calculating how deep one should probe in the first passage of a probe line depending on search area, and (2) determining for how long resuscitation should be performed on a specific patient while others are still buried. In both cases, we demonstrate that optimized strategies can be calculated with the Monte Carlo method, provided that the necessary input data are available. Our Monte Carlo simulations also suggest that with a strict focus on the "greatest good for the greatest number", today's rescue strategies can be further optimized in the best interest of patients involved in an avalanche accident.