Prediction model for rectal temperature in cats with different baseline characteristics using a non-contact infrared thermometer.

BACKGROUND AND AIM

Body temperature is the most useful clinical parameter for evaluating animal health. In clinical practice, rectal temperature is the gold standard for assessing body temperature, but rectal temperature measurement is not convenient and can cause stress in animals. The non-contact infrared thermometer is considered an alternative method for skin temperature measurements in animals. Many biological factors may influence the response of body regions to thermal challenges; thus, the identification of these variables is essential for accurate infrared temperature measurements. This study aimed to estimate the relationship between the physiological factors of cats and their body temperature measured across various body positions, as well as to propose a model for predicting rectal temperature using an infrared thermometer.

MATERIALS AND METHODS

A total of 184 client-owned cats were included in this study. The infrared temperature (°F) was measured using a non-contact infrared thermometer at five body positions: maxillary canine gingival margin (GCT), anal skin (ANS), inguinal canal (ING), ear canal (EC), and palmar pad. The five biological factors (age, body condition score [BCS], gender, hair type, and hair color) were recorded and analyzed to adjust predictive factors for rectal temperature prediction. All statistical analyses were performed using multivariable linear regression. The rectal temperature prediction model was then designed using the forward stepwise selection method.

RESULTS

Based on multivariable linear regression analysis of infrared temperature results, the pre-prediction model showed significant correlations with rectal temperature for ANS, GCT, and EC (p = 0.0074, 0.0042, and 0.0118, respectively). Moreover, the combination of infrared temperatures on ANS and ING was the most appropriate parameter for predicting rectal temperature (p = 0.0008). All models were adjusted according to the baseline characteristics of the cats. However, the adjusted R-squared values of the pre-prediction model of the infrared temperature on the ANS, GCT, and EC and the final prediction model by the infrared temperature on the ANS combined with the ING were low (8.7%, 8.9%, 7.3%, and 12.8%, respectively).

CONCLUSION

The prediction model of rectal temperature of cats by infrared temperature from a non-contact infrared thermometer in ANS combined with ING and adjusted by age, BCS, hair type, and hair color may be applicable for use in clinical practice. This study found that the adjusted R-squared values of all models were low; the predictive model will need to be developed and used to test validity and reliability with an external study group for assessing their practical usefulness.