Clinical chemical diagnosis of diseases assisted by logistic regression illustrated by diagnosis of canine primary and secondary hepatobiliary diseases

A Multistep Approach in the Cytologic Evaluation of Liver Biopsy Samples of Dogs with Hepatic Diseases

Cytologic criteria were evaluated for their diagnostic value in liver disease in dogs. Therefore, histopathologic and cytologic examination was performed on liver biopsy samples of 73 dogs with liver diseases and 28 healthy dogs. Logistic regression analysis was used to select the measured parameters to be included in a multistep approach. With the logistic regression method, different characteristic cytologic parameters could be defined for each histopathologic diagnosis. In malignant lymphoma of the liver, the presence of large numbers of lymphoblasts with a minimum of 5% of all cells was found. Clusters of epithelial cells with several cytologic characteristics of malignancy intermixed with normal hepatocytes were indicative of metastatic carcinoma or cholangiocellular carcinoma. Liver cells in hepatocellular carcinoma were characterized by a high nucleus/cytoplasm ratio, large cell diameters, increased numbers of nucleoli per nuclei, small numbers of cytoplasmic vacuoles, and frequently, small numbers of lymphocytes. Extrahepatic cholestasis was characterized by excessive extracellular bile pigment in the form of biliary casts, an increased number of nucleoli within hepatocytes, decreased hepatic cell size, and low numbers of lymphocytes. In destructive cholangiolitis, increased numbers of neutrophils and a small mean nuclear size within hepatocytes were seen. Acute and nonspecific reactive hepatitis are diagnosed based on the presence of moderate reactive nuclear patterns, including more pronounced chromatin, prominent nucleoli, increased numbers of inflammatory cells, excluding lymphocytes, and the absence of increased numbers of bile duct cell clusters. Increased number of mast cells also was indicative of nonspecific reactive hepatitis. Important cytologic criteria for the diagnosis of liver cirrhosis, in addition to chronic hepatitis, are intracellular bile accumulation and increased numbers of bile duct cell clusters. In summary, the stepwise approach based on logistic regression presented in this study might be helpful in the objective cytologic diagnosis of hepatic diseases.

Application of probability techniques to the objective interpretation of veterinary clinical biochemistry data

Methods for the interpretation of veterinary clinical biochemistry have not developed as rapidly as biochemical technology. However, the results of clinical biochemistry tests are only of value when they are interpreted appropriately. A retrospective study was undertaken to investigate the equine biochemistry data which had been stored in a veterinary hospital database. By applying percentile analysis and Bayesian probability methods to the clinical biochemistry and corresponding diagnosis data, a novel method for the interpretation of clinical biochemistry data has been developed. The method allows clinicians to determine whether a biochemistry value is abnormal, its degree of abnormality, and the most likely associated diagnoses. The method could be used to investigate a practice-based population and may have significant implications for the interpretation of clinical biochemistry data in veterinary medicine in the future.

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/l 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/l, 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 clinical to aid in the recognition, treatment and prevention of disease in the veterinary domain.