1Department of Clinical Sciences, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran.
2Department of pathobiology, Faculty of Veterinary Medicine, Science, and Research Branch, Islamic Azad University, Tehran, Iran.
3Department of Clinical Sciences, Faculty of Veterinary Medicine, Science, and Research Branch, Islamic Azad University, Tehran, Iran.
4Department of Pathobiology, Faculty of Veterinary Medicine, Science, and Research Branch, Islamic Azad University, Tehran, Iran.
چکیده
The feline infectious peritonitis (FIP) is a fatal disease caused by the feline infectious peritonitis virus (FIPV). The characteristic etiopathogenesis of FIP makes the diagnosis difficult. The objective of this study was to investigate the determination of the sensitivity and specificity of ELISA compared to histopathologic findings as the gold standard. Samples from 25 cats suspected of feline infectious peritonitis (FIP) (16 cats with the signs of wet FIP and 9 cats with signs of dry FIP) were collected from 8 clinics in north-west Tehran from 2013-2015. In addition, the sensitivities and specificities of biochemistry parameters (albumin, albumin to globulin ratio, AST, ALT, total bilirubin, total protein) were determined. ELISA test were performed on the serum and abdominal cavity samples. Statistical analyses were performed on the obtained data by ROC analysis, Youden index, and Mann-Whitney U test. Sensitivity, specificity, positive and negative predictive values were calculated as 100% in 13 cats with wet FIP and 7 cats with dry FIP as compared to the gold standard. The area under the curve (AUC) was calculated as 1, which shows the high diagnostic value of the ELISA test. The ODs in the positive cats and the negative control group didn't show a significant difference between the effusive and non-effusive forms of FIP. In conclusion, ELISA can be used to diagnose FIP effusive form with high clinical accuracy. It may be helpful to quantify the clinical accuracy of the other tests used to diagnose FIP to develop a more accurate logistic regression model.