Cut-off values of serum potassium and core temperature at hospital admission for extracorporeal rewarming of avalanche victims in cardiac arrest: A retrospective multi-centre study. (June 2019) - Brugger, Hermann; Bouzat, Pierre; Pasquier, Mathieu; Mair, Peter; Fieler, Julia; Darocha, Tomasz; Blancher, Marc; de Riedmatten, Matthieu; Falk, Markus; Paal, Peter; Strapazzon, Giacomo; Zafren, Ken; et al.
Abstract: Aim: Evidence of existing guidelines for the on-site triage of avalanche victims is limited and adherence suboptimal. This study attempted to find reliable cut-off values for the identification of hypothermic avalanche victims with reversible out-of-hospital cardiac arrest (OHCA) at hospital admission. This may enable hospitals to allocate extracorporeal life support (ECLS) resources more appropriately while increasing the proportion of survivors among rewarmed victims. Methods: All avalanche victims with OHCA admitted to seven centres in Europe capable of ECLS from 1995 to 2016 were included. Optimal cut-off values, for parameters identified by logistic regression, were determined by means of bootstrapping and exact binomial distribution and served to calculate sensitivity, rate of overtriage, positive and negative predictive values, and receiver operating curves. Results: In total, 103 avalanche victims with OHCA were included. Of the 103 patients 61 (58%) were rewarmed by ECLS. Six (10%) of the rewarmed patients survived whilst 55 (90%) died. We obtained optimal cut-off values of 7 mmol/L for serum potassium and 30 °C for core temperature. Conclusion: For in-hospital triage of avalanche victims admitted with OHCA, serum potassium accurately predicts survival. The combination of the cut-offs 7 mmol/L for serum potassium and 30 °C for core temperature achieved the lowest overtriage rate (47%) and the highest positive predictive value (19%), with a sensitivity of 100% for survivors. The presence of vital signs at extrication is strongly associated with survival. For further optimisation of in-hospital triage, larger datasets are needed to include additional parameters.