Comparison and validation of different models and variable selection methods for predicting survival after canine parvovirus infection.

Abstract

BACKGROUND: Canine parvovirus (CPV) represents one of the major infections in dogs. While supportive therapy significantly reduces mortality, other approaches have been reported to provide significant benefits. Unfortunately, the high cost of these treatments is typically a limiting factor. Consequently, a reliable prognostic tool allowing for an informed therapeutic approach would be of great interest. However, current methods are essentially based on 'a priori' selection of predictive variables, which could limit their predictive potential. METHODS: In the present study, the predictive performances in terms of CPV enteritis survival likelihood of an operator-validated logistic regression were compared with those of more flexible methods featured by automatic variable selection. Several anamnestic, clinical, haematological and biochemical parameters were collected from 134 dogs at admission in a veterinary practice. Animal status was monitored until dismissal or death (mortality=21.6%). RESULTS: The best automatic variable selection method (random forest) showed excellent discriminatory capabilities (AUC=0.997, sensitivity=0.941 and specificity=1) compared with the logistic regression model (AUC=0.831, sensitivity=0.882 and specificity=0.652), when evaluated on a fully independent test data set. The implemented approaches allowed to identify antithrombin, serum aspartate aminotransferase, serum lipase, monocyte and lymphocyte count as the clinical parameter combination with the highest predictive capability, thus limiting the panel of required tests. CONCLUSION: The model validated in the present study allows prompt prediction of disease severity at admission and provides objective and reliable criteria to support the clinician in selection of the therapeutic approach.