Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples.

Autor: Cain JL; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA. Electronic address: jennifer.cain@uky.edu., Slusarewicz P; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA; MEP Equine Solutions, 3905 English Oak Circle, Lexington, KY, USA., Rutledge MH; Department of Statistics, University of Kentucky, Lexington, KY, USA., McVey MR; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA., Wielgus KM; College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN, USA., Zynda HM; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA., Wehling LM; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA., Scare JA; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA., Steuer AE; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA., Nielsen MK; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
Jazyk: angličtina
Zdroj: Veterinary parasitology [Vet Parasitol] 2020 Aug; Vol. 284, pp. 109199. Date of Electronic Publication: 2020 Aug 07.
DOI: 10.1016/j.vetpar.2020.109199
Abstrakt: Fecal egg counts are the cornerstone of equine parasite control programs. Previous work led to the development of an automated, image-analysis-based parasite egg counting system. The system has been further developed to include an automated reagent dispenser unit and a custom camera (CC) unit that generates higher resolution images, as well as a particle shape analysis (PSA) algorithm and machine learning (ML) algorithm. The first aim of this study was to conduct a comprehensive comparison of method precision between the original smartphone (SP) unit with the PSA algorithm, CC/PSA, CC/ML, and the traditional McMaster (MM) and Wisconsin (MW) manual techniques. Additionally, a Bayesian analysis was performed to estimate and compare sensitivity and specificity of all five methods. Feces were collected from horses, screened with triplicate Mini-FLOTAC counts, and placed into five categories: negative (no eggs seen), > 0 - ≤ 200 eggs per gram (EPG), > 200 - ≤ 500 EPG, > 500 - ≤ 1000 EPG, and > 1000 EPG. Ten replicates per horse were analyzed for each technique. Technical variability for samples > 200 EPG was significantly higher for MM than CC/PSA and CC/ML (p <  0.0001). Biological variability for samples> 0 was numerically highest for CC/PSA, but with samples > 200 EPG, MM had a significantly lower CV than MW (p =  0.001), MW had a significantly lower CV than CC/PSA (p <  0.0001), CC/ML had a significantly lower CV than both MW and SP/PSA (p <  0.0001, p =  0.0003), and CC/PSA had a significantly lower CV than CC/SP (p =  0.0115). Sensitivity was> 98 % for all five methods with no significant differences. Specificity, however, was significantly the highest for CC/PSA, followed numerically by SP/PSA, MM, CC/ML, and finally MW. Overall, the automated counting system is a promising new development in equine parasitology. Continued refinement to the counting algorithms will help improve precision and specificity, while additional research in areas such as egg loss, analyst variability at the counting step, and accuracy will help create a complete picture of its impact as a new fecal egg count method.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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