ArticleScientific reports2023
Comprehensive prediction of urolithiasis based on clinical factors, blood chemistry and urinalysis: UROLITHIASIS score.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Beyond Binary Cutoffs: An Explainable Machine Learning Framework for Individualized Diagnostic Reasoning in Suspected Urolithiasis.Diagnostics (Basel, Switzerland) · 2026Article
- Retrospective study of 353 confirmed cases of urolithiasis in dogs and cats treated at veterinary clinics in the northern region of Pará, Brazil.Brazilian journal of veterinary medicine · 2025Article
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2 authors at 1 institution in 1 country.
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Abstract
Comprehensive prediction of urolithiasis using available factors obtained in the emergency department may aid in patient-centered diagnostic imaging decisions. This retrospective study analyzed the clinical factors, blood chemistry and urine parameters of patients who underwent nonenhanced urinary computed tomography for suspected urolithiasis. A scoring system was developed from a logistic regression model and was tested using the area under the curve (AUC). The prevalence of urolithiasis and important possible causes in the three risk subgroups were determined. Finally, the scoring model was validated. In the derivation cohort (n = 673), 566 patients were diagnosed with urolithiasis. Age > 35 years, history of urolithiasis, pain duration < 8 h, nausea/vomiting, costovertebral angle tenderness, serum creatinine ≥ 0.92 mg/dL, erythrocytes ≥ 10/high power field, no leukocytes ≤ + , and any crystalluria were retained in the final multivariable model and became part of the score. This scoring model demonstrated good discrimination (AUC 0.808 [95% CI, 0.776-0.837]). In the validation cohort (n = 336), the performance was similar (AUC 0.803 [95% CI, 0.756-0.844]), surpassing that of the STONE score (AUC 0.654 [95% CI, 0.601-0.705], P < 0.001). This scoring model successfully stratified patients according to the probability of urolithiasis. Further validation in various settings is needed.
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