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Theriogenology. 2006 Jul 15;66(2):198-206. Epub 2005 Dec 20.

Differentiation between pyometra and cystic endometrial hyperplasia/mucometra in bitches by prostaglandin F2alpha metabolite analysis.

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  • 1Department of Small Animal Clinical Sciences, Faculty of Veterinary Medicine, Swedish University of Agricultural Sciences, Box 7037, SE-75007 Uppsala, Sweden.


Bitches with pyometra are potential emergency cases which may be clinically difficult to differentiate from cases of cystic endometrial hyperplasia (CEH) in combination with mucometra. In the present study plasma prostaglandin F(2alpha), as measured by its main metabolite 15-keto-13,14-dihydro-PGF(2alpha) (PG-metabolite) concentrations, blood biochemical and hematological parameters were measured in 59 bitches with pyometra, 10 bitches with CEH and nine controls to determine if PG-metabolite could differentiate between the three uterine conditions. Bitches with pyometra had significantly higher plasma levels of PG-metabolite than bitches with CEH (P=0.002) and the controls (P=0.002). PG-metabolite analysis alone had a high sensitivity (98.3%) and a high specificity (80.0%) for the differentiation of pyometra versus CEH in bitches where fluid in the uterus was diagnosed. When a combination of PG-metabolite and percentage band neutrophils (PBN) was used for differentiation of the two diagnoses, a sensitivity of 100% and specificity of 90.0% was obtained. This means that the combination of PG-metabolite and PBN analysis allows for differentiation between cases of pyometra and CEH. If the PG-metabolite level in a bitch is >or=4,524 pmol l(-1), there is a 99% probability of the diagnosis pyometra versus CEH. Levels of PG-metabolite >or=3,054 pmol l(-1), >or=2,388 pmol l(-1) or>or=1,666 pmol l(-1) indicates a 95%, 90% or 80% probability of pyometra, respectively. At high PG-metabolite levels (above about 3,000 pmol l(-1)), PG-metabolite alone is enough for differentiation of pyometra versus CEH. The results of the present study showed that PG-metabolite analysis is valuable in the diagnosis and prediction of severity of uterine diseases.

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