Evidence map›Paper›PMID 39888618›Full record

ArticleJAMA network open2025

Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection.

Noah Jones, Ming-Chieh Shih, Elizabeth Healey, Chen Wen Zhai, Sonali Advani, Aaron Smith-McLallen, David Sontag, Sanjat Kanjilal

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Noah JonesMIT Media Lab, Massachusetts Institute of Technology, Cambridge.
Ming-Chieh ShihCollege of Life Sciences and Medicine, National Tsing Hua University, Hsinchu, Taiwan.
Elizabeth HealeyProgram in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge.
Chen Wen ZhaiOperations Research Center, Massachusetts Institute of Technology, Cambridge.
Sonali AdvaniDivision of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina.
Aaron Smith-McLallenIndependence Blue Cross, Philadelphia, Pennsylvania.
David SontagInstitute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge.
Sanjat KanjilalDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts.

Funding

Duke KURe ProgramK12DK100024 · NIDDK · DUKE UNIVERSITY · PI Cindy Amundsen · 2013 to 2026
$8.4M
AHRQ HHS K08 HS027841NIDDK NIH HHS K12 DK100024
6 · The paper itself

Abstract

Importance: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published in 2011, but the extent to which they align with current practices, patient diversity, and pathogen biology, all of which have evolved greatly in the time since their publication, is not fully known. Objective: To reevaluate the effectiveness and adverse event profile for first-line antibiotics, fluoroquinolones, and oral β-lactams for treating uncomplicated UTI in contemporary clinical practice. Design, Setting, and Participants: This retrospective, population-based cohort study used a claims dataset from Independence Blue Cross, which contains inpatient, outpatient, laboratory, and pharmacy claims that occurred between 2012 and 2021, formatted into the Observational Medical Outcomes Partnership (OMOP) common data model. Participants were nonpregnant female individuals aged 18 years or older with a diagnosis of uncomplicated, nonrecurrent UTI at an outpatient setting. Patients must also have been treated with first-line (nitrofurantoin or trimethoprim-sulfamethoxazole), fluoroquinolone (ciprofloxacin, levofloxacin, or ofloxacin), or oral β-lactam (amoxicillin-clavulanate, cefadroxil, or cefpodoxime) antibiotics. Data analysis was performed from November 2021 to August 2024. Exposures: Patients exposed to first-line antibiotics were assigned to the treatment group, and those exposed to fluoroquinolone or β-lactam treatments were assigned to control groups. Main Outcomes and Measures: The primary outcome was a composite end point for treatment failure, defined as outpatient or inpatient revisit within 30 days for UTI, pyelonephritis, or sepsis. Secondary outcomes were the risk of 4 common antibiotic-associated adverse events: gastrointestinal symptoms, rash, kidney injury, and Clostridium difficile infection. Results: There were 57 585 episodes of UTI among 49 037 female patients (mean [SD] age, 51.7 [20.1]) years), with prescriptions for first-line antibiotics in 35 018 episodes (61%), fluoroquinolones in 21 140 episodes (37%), and β-lactams in 1427 episodes (2%). After adjustment, receipt of first-line therapies was associated with an absolute risk difference of -1.78% (95% CI, -2.37% to -1.06%) for having a revisit for UTI within 30 days of diagnosis vs fluoroquinolones. First-line therapies were associated with an absolute risk difference of -6.40% (95% CI, -10.14% to -3.24%) for 30-day revisit compared with β-lactam antibiotics. Differences in adverse events were similar between all comparators. Results were identical for models built with an automated OMOP feature extraction package. Conclusions and Relevance: In this cohort study of patients with uncomplicated UTI derived from a large regional claims dataset, national treatment guidelines published almost 14 years ago continue to recommend optimal treatments. These results also provide proof-of-principle that automated feature extraction methods for OMOP formatted data can emulate manually curated models, thereby promoting reproducibility and generalizability.

Indexed as

Anti-Bacterial AgentsMachine LearningUrinary Tract InfectionsAdultAgedbeta-LactamsFemaleFluoroquinolonesHumansMiddle AgedRetrospective StudiesAnti-Bacterial Agentsbeta-LactamsFluoroquinolones

Identifiers

PMID39888618
PMCPMC11786233

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Registered trials

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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.