Abstract

The aim of this study was to assess the accuracy of anesthetic risk evaluation in dogs by using the ASA-classification system (ASA = American Society of Anesthesiologists). In human medicine, several studies criticize ranking patients prior to anesthesia via the ASA-classification due to its subjectivity and substantial variance. This study intends to detect and analyze possible comparable effects when applying the ASA-classification system to dogs. An online survey was conducted among small animal practitioners throughout Germany. Participants were asked to answer questions concerning their professional background. In addition, they received a questionnaire containing information on 15 selected patients. This included a brief introduction of the patient, the medical history, findings of the preanesthetic examination, the results of blood analysis and biochemistry as well as the reason for planned general anesthesia. Participants were asked to classify the patients according to the ASA-classification scheme. The results were analyzed using an independent t-test, univariate ANOVA and Fleiss' Kappa (κ). The level of significance was set at 5 %. Overall, only weak consistency of the assigned ASA-classes (κ = 0.33) was evident. Each of the 15 patients was ranked in at least 3 different ASA-classes and 4 patients received assignment to all 5 possible classes. No effect of gender or clinical experience of the veterinarian could be detected on ranking patients correctly. There was also no effect of how confident veterinarians felt in applying the ASA-classification system on the accuracy of evaluating these 15 patients. This study provides further evidence for a certain subjectivity as well as considerable variance when applying the ASA-classification system to dogs. The ASA-classification system is a widespread tool for rapid and easy preanesthetic risk classification of a veterinary patient. Nevertheless, the inclusion of criteria of increased objectivity in the preanesthetic evaluation is warranted in order to obtain precise and reproducible data.

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