MP69-19 NON-ANTIBIOTIC INTERVENTIONS TO PREVENT URINARY TRACT INFECTIONS: A NETWORK META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS
Bibliographic record
Abstract
You have accessJournal of UrologyInfections/Inflammation/Cystic Disease of the Genitourinary Tract: Kidney & Bladder I (MP69)1 May 2024MP69-19 NON-ANTIBIOTIC INTERVENTIONS TO PREVENT URINARY TRACT INFECTIONS: A NETWORK META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS Zeyu Han, Xianyanling Yi, Jin Li, Tianyi Zhang, and Jianzhong Ai Zeyu HanZeyu Han , Xianyanling YiXianyanling Yi , Jin LiJin Li , Tianyi ZhangTianyi Zhang , and Jianzhong AiJianzhong Ai View All Author Informationhttps://doi.org/10.1097/01.JU.0001008892.86171.de.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The guidelines updated in 2022 by the American Urological Association (AUA), Canadian Urological Association (CUA), and the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction (SUFU) indicate that, along with antibiotics, non-antibiotic measures such as cranberries, D-mannose, and probiotics serve as preventive options for urinary tract infections (UTIs). This study aimed to compare the efficacy and safety of various non-antibiotic interventions using network meta-analysis in preventing UTIs among individuals with a history of UTI or those presenting risk factors for such infections. METHODS: The authors systematically searched PubMed, EMBASE, Web of Science, and the Cochrane Library from their inception up to October 2023 without language restrictions. The inclusion criteria encompassed randomized controlled trials (RCTs) focusing on one or more non-antibiotic interventions for UTI prevention, with the incidence of UTIs being a key outcome measure. The methodological quality of studies was assessed using the risk of bias assessment tool from Cochrane Collaboration. RESULTS: This study incorporated a total of 32 articles, encompassing 5,474 subjects. 87.5% of RCTs employed double-blind or triple-blind designs. The vast majority of studies were of high methodological quality and had a low risk of bias. Nearly 80% of the participants were female, and most studies featured a follow-up period ranging from 6 to 12 months. The interventions encompassed nine measures, including cranberry, propolis plus cranberry, probiotics, vaccines, vitamins, etc. In both all-age and adult groups (25 RCTs), D-mannose, vaccine, cranberry, and a combination of cranberry, probiotics, and vitamins exhibited a significant reduction in UTI incidence compared to the placebo. The top three SUCRA rankings were for D-mannose, a combination including cranberries, probiotics and vitamins, and vaccines. No statistically significant differences in adverse event incidence were observed for the various interventions compared to the placebo group. All results were devoid of publication bias. CONCLUSIONS: In summary, D-mannose, vaccine, cranberry, and a combination of cranberry, probiotics, and vitamins present as a potentially effective regimen for UTI prevention. Download PPT Source of Funding: This study was supported by grants from the National Natural Science Foundation of China (82070784, 81702536) to J. A., a grant from Science & Technology Department of Sichuan Province, China (2022JDRC0040) to J. A © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e1126 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Zeyu Han More articles by this author Xianyanling Yi More articles by this author Jin Li More articles by this author Tianyi Zhang More articles by this author Jianzhong Ai More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".