ArticleAnnals of medicine2025
Early detection of positive urine culture in patients with urolithiasis: a machine learning model with dynamic online nomogram.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Early Identification of Endometrial Malignancy in Postmenopausal Women with Asymptomatic Endometrial Thickening: A Novel Explainable Machine Learning Model.International journal of medical sciences · 2026Article
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5 authors.
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Abstract
backgroundPositive urine cultures are common in urinary stone patients, yet tools for early infection prediction are limited. To address this gap, a user-friendly, dynamic online nomogram was developed to predict the incidence of positive urine cultures in patients with urolithiasis.
methodsA retrospective study was conducted with 3,641 patients with urinary stones at the Second Hospital of Tianjin Medical University. The cohort was split into training and validation sets. Key variables were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, while Random Forest and SHapley Additive exPlanations (SHAP) methods were applied to assess their importance. Online nomograms were developed and evaluated for performance through metrics such as area under the curve (AUC), calibration curve, decision curve analysis (DCA), probability density function (PDF), and clinical utility curve (CUC).
resultsMultivariate logistic analysis identified four significant predictors-bacteria (BACT), C-reactive protein (CRP), nitrite, and leukocyte esterase (LEU)-which were integrated into the nomogram. The AUC values for the overall, training, and validation sets were 90.53, 91.22, and 89.06%, respectively. Calibration curves confirmed the nomogram's accuracy, and DCA demonstrated its superior performance over individual metrics. The PDF/CUC method revealed a threshold of 0.168, which effectively distinguished 88.54% of negatives from 78.70% of positives.
conclusionsThis dynamic online nomogram accurately predicts positive urine cultures in patients with urolithiasis, helping clinicians identify high-risk individuals , optimize antibiotic use, and improve patient outcomes. Further validation and biomarker exploration are needed to enhance its generalizability.
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