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Associations Among Various Physical Parameters and the Axillary-to-Rectal Temperature Difference in Dogs and Cats
Cristian Martinez Alvarez1, Britt A L Thevelein1, Katie M Hodges1
1Department of Small Animal Medicine and Surgery, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA.
Summary
Axillary temperature (AT) measurement in dogs and cats showed less bias in smaller animals with lower body condition scores and shorter haircoats. Machine learning models can improve AT accuracy for predicting rectal temperature (RT).
Area of Science:
- Veterinary Medicine
- Animal Physiology
- Comparative Thermoregulation
Background:
- Accurate body temperature measurement is crucial for diagnosing and monitoring health in animals.
- Rectal temperature (RT) is considered the gold standard, but axillary temperature (AT) offers a less invasive alternative.
- Understanding the factors influencing AT accuracy is essential for its clinical application.
Purpose of the Study:
- To compare axillary temperature (AT) measurements with rectal temperature (RT) in dogs and cats.
- To investigate the impact of physical parameters (species, weight, haircoat, body condition score) on the difference between AT and RT.
- To evaluate the utility of a machine learning (ML) model in improving AT-based RT prediction.
Main Methods:
- Prospective study conducted at a university teaching hospital from 2022-2024.
- Involved 106 dogs and 101 cats aged ≥4 months.
- Contemporaneous AT and RT measurements using veterinary-specific and medical thermometers, respectively, followed by Bland-Altman analysis and ML modeling.
Main Results:
- Dogs showed a bias of 1.01°C between AT and RT, while cats had a bias of 0.23°C.
- Lower bias was observed in animals weighing <10 kg, with body condition scores <5, and shorter haircoats.
- The ML model improved AT prediction of RT, with biases of -0.57°C in dogs and -0.84°C in cats.
Conclusions:
- Axillary temperature measurement is a viable, less invasive alternative to rectal temperature in dogs and cats.
- Factors like weight, body condition score, and haircoat length influence AT accuracy.
- Machine learning algorithms can enhance the predictive accuracy of axillary temperature measurements for rectal temperature.
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