Open AccessDiagnostic and Predictive Capability of Routine Laboratory Tests for the Diagnosis and Staging of Equine Inflammatory Disease
Author(s) -
Hooijberg E.H.,
Hoven R.,
Tichy A.,
Schwendenwein I.
Publication year - 2014
Publication title -
journal of veterinary internal medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.356
H-Index - 103
eISSN - 1939-1676
pISSN - 0891-6640
DOI - 10.1111/jvim.12404
Subject(s) - medicine , serum amyloid a , fibrinogen , receiver operating characteristic , gastroenterology , white blood cell , inflammation , logistic regression , area under the curve , diagnostic accuracy , c reactive protein , absolute neutrophil count , disease , horse , pathology , paleontology , biology , toxicity , neutropenia
Background A wide spectrum of laboratory tests is available to aid diagnosis and classification of equine inflammatory disease. Objectives To compare diagnostic efficacy and combined predictive capability of the myeloperoxidase index ( MPXI ), and plasma fibrinogen, iron and serum amyloid A ( SAA ) concentrations for the diagnosis of inflammation. Animals Twenty‐six hospitalized horses with systemic inflammation ( SI ), 114 with local inflammation ( LI ) and 61 healthy horses or those with noninflammatory disease ( NI ) were included. Methods A retrospective study was performed; clinicopathologic data from horses were compared between groups. Receiver‐operator characteristic ( ROC ) curves were used to evaluate diagnostic efficacy; classification and regression tree analysis ( CART ) and logistic regression analysis were used to generate diagnostic algorithms. Results Horses with SI had significantly higher SAA than horses with LI ( P = .007) and NI ( P < .001) and lower iron concentrations than horses with LI ( P < .001) and NI ( P < .001). Fibrinogen concentration was higher in horses with inflammation than in those without inflammation ( P = .002). There was no difference between the SI and LI groups. White blood cell count, neutrophil count and MPXI were similar between groups. SAA had the highest accuracy for diagnosing inflammation (area under ROC curve [ AUC ], 0.83 ± 0.06) and iron and SAA concentration had the highest accuracy for differentiating SI from LI ( AUC , 0.80 ± 0.09 and 0.73 ± 0.10 respectively). Predictive modeling failed to generate useful algorithms and classification of cases was moderate. Conclusions and Clinical Importance Very high SAA and low iron concentrations may reflect SI , but diagnostic guidelines based on quantitative results of inflammatory markers could not be formulated.