Preoperative parameters (signalment, digital radiography, urinalysis, urine microbiological culture) and novel algorithm improve prediction of canine urocystolith composition.

Autor:	To I; 1Schwarzman Animal Medical Center, New York, NY., Berent AC; 1Schwarzman Animal Medical Center, New York, NY., Weisse CW; 1Schwarzman Animal Medical Center, New York, NY., An A; 2Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY., Harling B; 1Schwarzman Animal Medical Center, New York, NY., Sack D; 1Schwarzman Animal Medical Center, New York, NY., Ciardullo R; 1Schwarzman Animal Medical Center, New York, NY., Slade DJ; 1Schwarzman Animal Medical Center, New York, NY., Palma DA; 1Schwarzman Animal Medical Center, New York, NY., DeJesus AA; 1Schwarzman Animal Medical Center, New York, NY., Fischetti AJ; 1Schwarzman Animal Medical Center, New York, NY.
Jazyk:	angličtina
Zdroj:	Journal of the American Veterinary Medical Association [J Am Vet Med Assoc] 2024 Apr 05; Vol. 262 (8), pp. 1039-1046. Date of Electronic Publication: 2024 Apr 05 (Print Publication: 2024).
DOI:	10.2460/javma.23.12.0686
Abstrakt:	Objective: To determine the accuracy of 4 preoperative parameters (signalment, urinalysis, urine microbiological culture, and digital radiography) in predicting urocystolith composition, compare accuracy between evaluators of varying clinical experience and a mobile application, and propose a novel algorithm to improve accuracy. Animals: 175 client-owned dogs with quantitative analyses of urocystoliths between January 1, 2012, and July 31, 2020. Methods: Prospective experimental study. Canine urocystolith cases were randomly presented to 6 blinded "stone evaluators" (rotating interns, radiologists, internists) in 3 rounds, each separated by 2 weeks: case data alone, case data with a urolith teaching lecture, and case data with a novel algorithm. Case data were also entered into the Minnesota Urolith Center mobile application. Prediction accuracy was determined by comparison to quantitative laboratory stone analysis results. Results: Prediction accuracy of evaluators varied with experience when shown case data alone (accuracy, 57% to 82%) but improved with a teaching lecture (accuracy, 76% to 89%) and further improved with a novel algorithm (accuracy, 93% to 96%). Mixed stone compositions were the most incorrectly predicted type. Mobile application accuracy was 74%. Clinical Relevance: Use of the 4 preoperative parameters resulted in variable accuracy of urocystolith composition predictions among evaluators. The proposed novel algorithm improves accuracy for all clinicians, surpassing accuracy of the mobile application, and may help guide patient management.
Databáze:	MEDLINE
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