Trial reportScientific reports2019
Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms.
Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.World journal of urology · 2024Pooled it
- Evaluation of diagnostic accuracy of urine neutrophil gelatinase-associated lipocalin in patients with symptoms of urinary tract infections: a meta-analysis.Frontiers in pediatrics · 2024Pooled it
- A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo.Urolithiasis · 2023Trial
- Peptidomic Profiling Reveals Extracellular Matrix Remodeling Signatures Discriminative of Multiple Myeloma.Proteomics · 2026Article
- Catheter-Associated Candiduria: Aggregates, Microscopy, and CFU Variability.Mycopathologia · 2026Article
- From the Petri dish to the patient's bedside: interpretingFrontiers in immunology · 2026Article
- Prediction of symptomatic and asymptomatic bacteriuria in spinal cord injury patients using machine learning.Microbiome · 2025Article
- Application of Machine Learning Algorithms in Urinary Tract Infections Diagnosis Based on Non-Microbiological Parameters.Pathogens (Basel, Switzerland) · 2025Article
- Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.BMC medical informatics and decision making · 2025Article
- Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines.Bioinformatics advances · 2025Article
- A novel method and classification criteria for analyzing urine turbidity and its relationship with urine dry chemical parameters.PloS one · 2025Observational
- Predictive value of machine learning model based on CT values for urinary tract infection stones.iScience · 2024Article
- Analysis of the urine flow characteristics inside catheters for intermittent catheter selection.Scientific reports · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Urine biomarkers individually and as a consensus model show high sensitivity and specificity for detecting UTIs.BMC infectious diseases · 2024Article
- Smart Diagnosis of Urinary Tract Infections: is Artificial Intelligence the Fast-Lane Solution?Current urology reports · 2024Review
- The Prevalence and Association of Different Uropathogens Detected by M-PCR with Infection-Associated Urine Biomarkers in Urinary Tract Infections.Research and reports in urology · 2024Article
- Urogenital microbiota-driven virulence factor genes associated with recurrent urinary tract infection.Frontiers in microbiology · 2024Article
- Inflammatory biomarker signatures in post-surgical drain fluid may detect anastomotic leaks within 48 hours of colorectal resection.Techniques in coloproctology · 2023Article
- Immune defenses in the urinary tract.Trends in immunology · 2023Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Women with uncomplicated urinary tract infection (UTI) symptoms are commonly treated with empirical antibiotics, resulting in overuse of antibiotics, which promotes antimicrobial resistance. Available diagnostic tools are either not cost-effective or diagnostically sub-optimal. Here, we identified clinical and urinary immunological predictors for UTI diagnosis. We explored 17 clinical and 42 immunological potential predictors for bacterial culture among women with uncomplicated UTI symptoms using random forest or support vector machine coupled with recursive feature elimination. Urine cloudiness was the best performing clinical predictor to rule out (negative likelihood ratio [LR-] = 0.4) and rule in (LR+ = 2.6) UTI. Using a more discriminatory scale to assess cloudiness (turbidity) increased the accuracy of UTI prediction further (LR+ = 4.4). Urinary levels of MMP9, NGAL, CXCL8 and IL-1β together had a higher LR+ (6.1) and similar LR- (0.4), compared to cloudiness. Varying the bacterial count thresholds for urine culture positivity did not alter best clinical predictor selection, but did affect the number of immunological predictors required for reaching an optimal prediction. We conclude that urine cloudiness is particularly helpful in ruling out negative UTI cases. The identified urinary biomarkers could be used to develop a point of care test for UTI but require further validation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.