Development of a Multivariate Predictive Model to Estimate Ionized Calcium Concentration from Serum Biochemical Profile Results in Dogs

Background: Ionized calcium concentration is the gold standard to assess calcium status in dogs, but measurement is not always available. Objectives: (1) To predict ionized calcium concentration from biochemical results and compare the diagnostic performance of predicted ionized calcium concentration (piCa) to those of total calcium concentration (tCa) and 2 corrected tCa formulas; and (2) to study the relationship between biochemical results and variation of measured ionized calcium concentration (miCa). Animals: A total of 1,719 dogs with both miCa and biochemical profile results available. Methods: Cross-sectional study. Using 1,200 dogs, piCa was determined using a multivariate adaptive regression splines model. Its accuracy and performance were tested on the remaining 519 dogs. Results: The final model included creatinine, albumin, tCa, phosphorus, sodium, potassium, chloride, alkaline phosphatase, triglycerides, and age, with tCa, albumin, and chloride having the highest impact on miCa variation. Measured ionized calcium concentration was better correlated with piCa than with tCa and corrected tCa and had higher overall diagnostic accuracy to diagnose hypocalcemia and hypercalcemia, but not significantly for hypercalcemia. For hypercalcemia, piCa was as sensitive (64%) but more specific (99.6%) than tCa and corrected tCa. For hypocalcemia, piCa was more sensitive (21.8%) and as specific (98.4%) as tCa. Positive and negative predictive values of piCa were high for both hypercalcemia (90% and 98%, respectively) and hypocalcemia (70.8% and 87.7%, respectively). Conclusions and clinical importance: Predicted ionized calcium concentration can be obtained from readily available biochemical and patient results and seems more useful than tCa and corrected tCa to assess calcium disorders in dogs when miCa is unavailable. Validation on external data, however, is warranted.