Objective interpretation of bovine clinical biochemistry data: application of Bayes law to a database model

With the advent of animal-side biochemistry analysers in veterinary practice, the requirement for ready access to reliable means for interpretation of the results is of increasing importance. At the University of Glasgow Veterinary School (GUVS), a large computerised hospital database containing extensive clinical, laboratory, and pathological information has been maintained. A retrospective study was undertaken to investigate plasma biochemistry results and corresponding post mortem diagnosis data from 754 unwell cattle which had presented to GUVS over the study period. Initial analysis of the clinical biochemistry data from this unwell population revealed that the parameters did not follow a normal distribution. This finding suggested that the accepted reference range method for the interpretation of clinical biochemistry data may provide limited information about the unwell animal. By applying a combination of percentile analysis and conditional probability techniques to the hospital data, the development of a means of clinical biochemistry interpretation was developed whereby a clinician could determine whether a value was abnormal, the degree of abnormality, and the most likely associated diseases. For example, a urea value of 30 mmol/1 lay within the top 5% of results, and one of the most common diseases associated with this urea value was pyelonephritis. Furthermore, a Bayesian approach allowed the quantification of the relationship between any plasma biochemistry value and disease through the generation of a ratio termed the ‘biochemical factor’. Using the same example, given a urea value of 30 mmol/1, pyelonephritis was eight times more likely than before any biochemistry information was known. The results from the study were used to form the basis of a software system which may ultimately be used by the clinician to aid in the recognition, treatment and prevention of disease in the veterinary domain.