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Classification and regression tree analysis for predicting prognosis in wildlife rehabilitation: a case study of leptospirosis in california sea lions (Zalophus californianus)
Whitmer, E.R.; Borremans, B.; Duignan, P.J.; Johnson, S.P.; Lloyd-Smith, J.O.; McClain, A.M.; Field, C.L.; Prager, K.C. (2021). Classification and regression tree analysis for predicting prognosis in wildlife rehabilitation: a case study of leptospirosis in california sea lions (Zalophus californianus). J. Zoo Wildl. Med. 52(1): 38-48. https://dx.doi.org/10.1638/2020-0111
In: Journal of Zoo and Wildlife Medicine. American Association of Zoo Veterinarians: Lawrence, Kan.. ISSN 1042-7260; e-ISSN 1937-2825
Peer reviewed article  
Beschikbaar in  Auteurs 
Trefwoorden
    Zalophus californianus (Lesson, 1828) [WoRMS]
    Marien/Kust
Auteurs  Top 
  • Whitmer, E.R.
  • Borremans, B.
  • Duignan, P.J.
  • Johnson, S.P.
  • Lloyd-Smith, J.O.
  • McClain, A.M.
  • Field, C.L.
  • Prager, K.C.
Abstract
    The spirochete bacterium Leptospira interrogans serovar Pomona is enzootic to California sea lions (CSL; Zalophus californianus) and causes periodic epizootics. Leptospirosis in CSL is associated with a high fatality rate in rehabilitation. Evidence-based tools for estimating prognosis and guiding early euthanasia of animals with a low probability of survival are critical to reducing the severity and duration of animal suffering. Classification and regression tree (CART) analysis of clinical data was used to predict survival outcomes of CSL with leptospirosis in rehabilitation. Classification tree outputs are binary decision trees that can be readily interpreted and applied by a clinician. Models were trained using data from cases treated from 2017 to 2018 at The Marine Mammal Center in Sausalito, CA, and tested against data from cases treated from 2010 to 2012. Two separate classification tree analyses were performed, one including and one excluding data from euthanized animals. When data from natural deaths and euthanasias were included in model-building, the best classification tree predicted outcomes correctly for 84.7% of cases based on four variables: appetite over the first 3 days in care, and blood urea nitrogen (BUN), creatinine, and sodium at admission. When only natural deaths were included, the best model predicted outcomes correctly for 87.6% of cases based on BUN and creatinine at admission. This study illustrates that CART analysis can be successfully applied to wildlife in rehabilitation to establish evidence-based euthanasia criteria with the goal of minimizing animal suffering. In the context of a large epizootic that challenges the limits of a facility's capacity for care, the models can assist in maximizing allocation of resources to those animals with the highest predicted probability of survival. This technique may be a useful tool for other diseases seen in wildlife rehabilitation.
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