ArticleJournal of clinical microbiology2024
Predictive performance of urinalysis for urine culture results according to causative microorganisms: an integrated analysis with artificial intelligence.
Article in Journal of clinical microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Construction and validation of a machine learning model for predicting urine culture positivity in children with suspected urinary tract infections.Annals of medicine · 2026Article
- Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.BJUI compass · 2026Article
- Urinary bacterial count as a predictor of concomitant bacteremia in adults with community-acquired Enterobacterales urinary tract infections.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Predictive Factors for Upper Urinary Tract Stones Combined with Carbapenem-Resistant Enterobacteriaceae Associated Urinary Tract Infections.Infection and drug resistance · 2026Article
- Development of a prediction model for postoperative urinary tract infection in ureteral stone patients based on automated machine learning models.Frontiers in physiology · 2026Article
- Application of Machine Learning Algorithms in Urinary Tract Infections Diagnosis Based on Non-Microbiological Parameters.Pathogens (Basel, Switzerland) · 2025Article
- Use of Artificial Intelligence Methods for Improved Diagnosis of Urinary Tract Infections and Urinary Stone Disease.Journal of clinical medicine · 2025Review
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Authors and funding
8 authors.
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Abstract
Urinary tract infections (UTIs) are pervasive and prevalent in both community and hospital settings. Recent trends in the changes of the causative microorganisms in these infections could affect the effectiveness of urinalysis (UA). We aimed to evaluate the predictive performance of UA for urinary culture test results according to the causative microorganisms. In addition, UA results were integrated with artificial intelligence (AI) methods to improve the predictive power. A total of 360,376 suspected UTI patients were enrolled from two university hospitals and one commercial laboratory. To ensure broad model applicability, only a limited range of clinical data available from commercial laboratories was used in the analyses. Overall, 53,408 (14.8%) patients were identified as having a positive urine culture. Among the UA tests, the combination of leukocyte esterase and nitrite tests showed the highest area under the curve (AUROC, 0.766; 95% CI, 0.764-0.768) for predicting urine culture positivity but performed poorly for Gram-positive bacteriuria (0.642; 0.637-0.647). The application of an AI model improved the predictive power of the model for urine culture results to an AUROC of 0.872 (0.870-0.875), and the model showed superior performance metrics not only for Gram-negative bacteriuria (0.901; 0.899-0.902) but also for Gram-positive bacteriuria (0.745; 0.740-0.749) and funguria (0.872; 0.865-0.879). As the prevalence of non-
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