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- Predicting stone composition based on signalment, rads, UA and culture
Predicting stone composition based on signalment, rads, UA and culture
JAVMA 2024
Ryan Appleby
April 10, 2024
Iris To DVM, Allyson C. Berent DVM, DACVIM, Chick W. Weisse VMD, DACVS, Anjile An MPH, Brett Harling DVM, Danny Sack DVM, Robert Ciardullo DVM, Dennis J. Slade DVM, DACVIM, Douglas A. Palma DVM, DACVIM, Antonia A. DeJesus DVM, DACVR, and Anthony J. Fischetti DVM, DACVR
Background
The study addresses the challenge of accurately predicting the composition of canine urocystoliths—stones formed in the urinary bladder of dogs. Proper determination of stone composition is crucial for selecting the appropriate treatment strategy, which may include surgical removal or medical dissolution depending on the type of urolith. The research aimed to evaluate the effectiveness of four preoperative parameters (signalment, urinalysis, urine microbiological culture, and digital radiography) in predicting urocystolith composition. Additionally, it sought to compare prediction accuracies between evaluators of different clinical experiences and a mobile application, proposing a novel algorithm to enhance prediction accuracy.
Methods
A prospective experimental study was conducted on 175 client-owned dogs diagnosed with urocystoliths between January 1, 2012, and July 31, 2020. The study involved three rounds of evaluations by six blinded stone evaluators of varying experience levels, with the evaluations including baseline knowledge, post-teaching lecture, and following a novel algorithm. The predictions were also compared against those from the Minnesota Urolith Center mobile application.
Results
The accuracy of stone composition predictions varied with evaluator experience but improved significantly with both the teaching lecture and the use of the novel algorithm. The novel algorithm, in particular, resulted in high accuracy (93-96%) across all evaluators, outperforming the mobile application, which had an accuracy of 74%. The most common misidentified stones were those of mixed composition.
Limitations
The study was limited by a small number of evaluators and a potential selection bias due to its retrospective nature and the inclusion criteria, which may not reflect the broader population of dogs with urolithiasis.
Conclusions
The use of four preoperative parameters, in conjunction with a novel algorithm, significantly improves the accuracy of predicting canine urocystolith composition. This approach surpasses the accuracy achievable with baseline clinical knowledge or the current mobile application, potentially guiding more effective patient management. The study suggests that such an algorithm can be a valuable tool for clinicians in the preoperative evaluation of dogs with suspected urocystolithiasis, although further validation with a broader range of evaluators and in different clinical settings is recommended.
Accuracy (percentage of correct predictions out of 175 total canine urolith cases) of the 6 evaluators and the MN Urolith mobile phone application (red dot). Evaluators consisted of 2 rotating interns, 2 board-certified internists, and 2 board-certified radiologists, who were each tested at 3 time points with 2 weeks between each time point: baseline knowledge (round 1), training lecture provided (round 2), and using our novel algorithm (round 3; Supplementary Figure S1). In each round, they predicted the urolith type in 175 client-owned dogs that presented to the Schwarzman Animal Medical Center between January 1, 2012, and July 31, 2020. Evaluators were given signalment, urinalysis, urine culture data and a lateral abdominal radiograph for each dog. The same case data were input into the MN Urolith mobile application to determine its accuracy (red dot). A prediction was considered correct if it matched the quantitative laboratory stone analysis of the dog.
Citation: Journal of the American Veterinary Medical Association 2024; 10.2460/javma.23.12.0686
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