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Comparison and validation of different models and variable selection methods for predicting survival after canine parvovirus infection
  1. Giovanni Franzo1,
  2. Barbara Corso2,
  3. Claudia Maria Tucciarone1,
  4. Michele Drigo1,
  5. Marco Caldin3 and
  6. Mattia Cecchinato1
  1. 1 Animal Medicine, Production and Health, Università degli Studi di Padova, Scuola di Agraria e Medicina Veterinaria, Legnaro, Padova, Italy
  2. 2 Biomedical Sciences, Neuroscience Institute, National Research Council, Padova, Italy
  3. 3 San Marco Private Veterinary Clinic, Veggiano, Padova, Italy
  1. Correspondence to Dr Giovanni Franzo, Animal Medicine,Production and Health, Universita degli Studi di Padova Scuola di Agraria e Medicina Veterinaria, Legnaro 35020, Italy; giovanni.franzo{at}unipd.it

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.

  • machine learning
  • diagnostic
  • outcome prediction
  • lethality
  • CPV
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Footnotes

  • Funding This research was funded by a grant (CECC_SID16_01) from the Department of Animal Medicine, Production and Health, University of Padua.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data may be obtained from a third party and are not publicly available. All data were obtained from routine diagnostic or emergency room screening protocols and are not available for privacy reasons. Interested researchers can contact the corresponding author.

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