Development and validation of a multivariate predictive model to estimate serum ionized calcium concentration from serum biochemical profile results in cats

Background: Measurement of serum ionized calcium is not always available in practice. Total calcium (tCa) might not be reliable for determination of calcium status in cats. Objectives: To predict serum ionized calcium concentration from signalment, biochemistry profile and T4, and compare predicted ionized calcium (piCa) to tCa. Animals: A total of 1701 cats from two hospitals. Methods: Cross-sectional study. Cats with serum ionized calcium, biochemistry profile and T4 available were screened over 6 years and included in the training set (569 cats) to create a multivariate adaptive regression splines model to calculate piCa. Diagnostic performances of tCa and piCa and its prediction interval (PI) were compared in 652 cats from the same institution (test set) and 480 cats from a different hospital (external set). Results: The final model included tCa, chloride, albumin, cholesterol, creatinine, BUN, body condition score, GGT, age, and potassium. For hypercalcemia, piCa was highly specific (test set: 99.8%; confidence interval [CI]: 99.5-100; external set: 97%; CI: 95.3-98.7) but poorly sensitive (test set: 30.4%; CI: 18.3-42.4; external set: 42.5%; CI: 31.7-53.3). For hypocalcemia, piCa was also highly specific (test set: 81.6%; CI: 78-85; external set: 99.6%; CI: 99-100) but poorly sensitive (test set: 57.6%; CI: 50.6-64.6; external set: 0%). These diagnostic performances were comparable to those of tCa. The upper and lower limits of piCa PI had high sensitivity for detecting ionized hypercalcemia and hypocalcemia, respectively. Conclusions and clinical importance: Predicted ionized calcium is useful to confirm suspected hypercalcemia in cats and screen for hypercalcemia and hypocalcemia.