The work was performed at the University of Veterinary Medicine, Vienna, Austria.
Diagnostic and Predictive Capability of Routine Laboratory Tests for the Diagnosis and Staging of Equine Inflammatory Disease
Article first published online: 23 JUL 2014
Copyright © 2014 by the American College of Veterinary Internal Medicine
Journal of Veterinary Internal Medicine
Volume 28, Issue 5, pages 1587–1593, September/October 2014
How to Cite
Hooijberg, E.H., van den Hoven, R., Tichy, A. and Schwendenwein, I. (2014), Diagnostic and Predictive Capability of Routine Laboratory Tests for the Diagnosis and Staging of Equine Inflammatory Disease. Journal of Veterinary Internal Medicine, 28: 1587–1593. doi: 10.1111/jvim.12404
A part of this article, with preliminary data and a smaller sample size, was presented at the European Society of Veterinary Clinical Pathology Annual Congress, Berlin, November 2013, as a short oral abstract.
- Issue published online: 1 OCT 2014
- Article first published online: 23 JUL 2014
- Manuscript Accepted: 29 MAY 2014
- Manuscript Revised: 6 MAY 2014
- Manuscript Received: 28 NOV 2013
- Classification and regression tree;
- Myeloperoxidase index;
- Serum amyloid A
A wide spectrum of laboratory tests is available to aid diagnosis and classification of equine inflammatory disease.
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.
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.
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.
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.