TY - JOUR

T1 - Clinical Chemical Diagnosis of Diseases Assisted by Logistic Regression Illustrated by Diagnosis of Canine Primary and Secondary Hepatobiliary Diseases

AU - JENSEN, A. L.

AU - HØIER, R.

PY - 1993

Y1 - 1993

N2 - Objective: The purpose of the present study was to demonstrate the use of logistic regression models in the prediction of diseases using the prediction of canine primary and secondary hepatobiliary diseases as an example. Briefly, in a logistic regression model independent variables (i.e. the analytical results) are combined in a linear equation that is used to estimate the logarithm of the odds (logit) of an event (i. e. having primary or secondary hepatobiliary disease). From the estimated logit given by the logistic regression model, a conditional probability of the event (i. e. having primary or secondary hepatobiliary disease) can be calculated. Study Design: Twenty‐six dogs with verified primary and secondary hepatobiliary diseases and 19 dogs, initially suspected to have hepatobiliary diseases, but with apparently other diseases, were included in the study. The following clinical chemical parameters were measured: alanine aminotransferase (ALAT), aspartate aminotransferase (ASAT), alkaline phosphatase (AP), bilirubinTotal (TB), urea, glucose, retention of bromosulphthalein (BSP), fasting and postprandial total serum bile acid concentration (FSBA and PSBA). Logistic regression analysis, using the CATMOD procedure in SAS®, was used to select which of the measured parameters should be included in the model, and to derive a logistic regression model using the selected parameters. To observe more closely the potential of the logistic regression model, the model was also used to classify a test group consisting of 13 dogs (6 dogs with hepatobiliary diseases and 7 dogs with other diseases). Results: By logistic regression analysis, ASAT and PSBA were selected to be included in the final model, and the final logistic regression model was Y = −3.194 + 0.044 · PSBA + 3.251 · ASAT. The logistic regression model classified correctly 38 (84%) of 45 dogs in the present study. Specifically, 21 (81%) of 26 dogs with verified primary or secondary hepatobiliary diseases and 17 (90%) of 19 dogs with various other diseases were correctly classified by the logistic regression model. When the model was used on the test group, 5 (83%) of 6 dogs with hepatobiliary diseases and 7 dogs (100%) of 7 dogs with other diseases were correctly classified. Conclusions: Eventhough the logistic regression model derived in the present study only serves as an example, thus reducing the practical usefulness of the derived logistic regression model, the present study indicates a great potential of logistic models for the diagnosis of diseases.

AB - Objective: The purpose of the present study was to demonstrate the use of logistic regression models in the prediction of diseases using the prediction of canine primary and secondary hepatobiliary diseases as an example. Briefly, in a logistic regression model independent variables (i.e. the analytical results) are combined in a linear equation that is used to estimate the logarithm of the odds (logit) of an event (i. e. having primary or secondary hepatobiliary disease). From the estimated logit given by the logistic regression model, a conditional probability of the event (i. e. having primary or secondary hepatobiliary disease) can be calculated. Study Design: Twenty‐six dogs with verified primary and secondary hepatobiliary diseases and 19 dogs, initially suspected to have hepatobiliary diseases, but with apparently other diseases, were included in the study. The following clinical chemical parameters were measured: alanine aminotransferase (ALAT), aspartate aminotransferase (ASAT), alkaline phosphatase (AP), bilirubinTotal (TB), urea, glucose, retention of bromosulphthalein (BSP), fasting and postprandial total serum bile acid concentration (FSBA and PSBA). Logistic regression analysis, using the CATMOD procedure in SAS®, was used to select which of the measured parameters should be included in the model, and to derive a logistic regression model using the selected parameters. To observe more closely the potential of the logistic regression model, the model was also used to classify a test group consisting of 13 dogs (6 dogs with hepatobiliary diseases and 7 dogs with other diseases). Results: By logistic regression analysis, ASAT and PSBA were selected to be included in the final model, and the final logistic regression model was Y = −3.194 + 0.044 · PSBA + 3.251 · ASAT. The logistic regression model classified correctly 38 (84%) of 45 dogs in the present study. Specifically, 21 (81%) of 26 dogs with verified primary or secondary hepatobiliary diseases and 17 (90%) of 19 dogs with various other diseases were correctly classified by the logistic regression model. When the model was used on the test group, 5 (83%) of 6 dogs with hepatobiliary diseases and 7 dogs (100%) of 7 dogs with other diseases were correctly classified. Conclusions: Eventhough the logistic regression model derived in the present study only serves as an example, thus reducing the practical usefulness of the derived logistic regression model, the present study indicates a great potential of logistic models for the diagnosis of diseases.

UR - http://www.scopus.com/inward/record.url?scp=84995066645&partnerID=8YFLogxK

U2 - 10.1111/j.1439-0442.1993.tb00606.x

DO - 10.1111/j.1439-0442.1993.tb00606.x

M3 - Journal article

C2 - 8480458

AN - SCOPUS:84995066645

VL - 40

SP - 102

EP - 110

JO - Journal of Veterinary Medicine A

JF - Journal of Veterinary Medicine A

SN - 0931-184X

IS - 1-10

ER -