To I, Berent AC, Weisse CW, An A, Harling B, Sack D, Ciardullo R, Slade DJ, Palma DA, DeJesus AA, Fischetti AJ. Preoperative parameters (signalment, digital radiography, urinalysis, urine microbiological culture) and novel algorithm improve prediction of canine urocystolith composition.
J Am Vet Med Assoc 2024:1-8. [PMID:
38579782 DOI:
10.2460/javma.23.12.0686]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/26/2024] [Indexed: 04/07/2024]
Abstract
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.
Collapse