Biological variation of 16 biochemical analytes estimated from a large clinicopathologic database of dogs and cats

• Published in: Veterinary clinical pathology 2024-06, Vol.53 (2), p.218-228
• Main Authors: Tamamoto, Takashi, Miki, Yohei, Sakamoto, Mei, Yoshii, Maiko, Yamada, Megumi, Sudo, Daisuke, Fusato, Yusuke, Ozawa, Junko, Satake, Chikara
• Format: Article
• Language: English
• Subjects: between‐subject variation; large database; reference change value; within‐subject variation
• ISSN: 0275-6382; 1939-165X
• DOI: 10.1111/vcp.13357

Background Biochemical measurements are commonly evaluated using population‐based reference intervals; however, there is a growing trend toward reassessing results with within‐subject variation (CVI). Objectives We aimed to estimate the CVI of 16 biochemical analytes using a large database of dogs and cats, which refers to the results of routine health checkups. Methods Pairs of sequential results for 16 analytes were extracted from a database of adult patients. The second result was divided by the first result to produce the ratio of sequential results (rr), and the frequency distribution of rr was plotted. From the plots, the coefficient of variation (CVrr) was calculated. Analytical variation (CVA) was calculated using quality control data, and CVI was estimated as follows: CVI=CVrr/21/22−CVA21/2. Estimated CVI was compared with previously reported CVI using the Bland–Altman plot analysis. Results From the database, 9078 data points from 3610 dogs and 3743 data points from 1473 cats were extracted, with 5468 data pairs for dogs and 2270 for cats. Sampling intervals ranged from 10 to 1970 days (median 366) for dogs and 23 to 1862 days (median 365) for cats. Bland–Altman analysis showed most CVI plots fell within the limits of agreement; however, positive fixed biases were observed in both dogs and cats. Conclusions Our study introduces a novel approach of estimating CVI using routine health checkup data in dogs and cats. Despite biases, our method holds promise for clinical application in assessing the significance of measurement result differences.