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Related Concept Videos

Urinary Tract Infection I: Introduction01:26

Urinary Tract Infection I: Introduction

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Urinary tract infections (UTIs) impact various parts of the urinary system, including the kidneys, ureters, bladder, and urethra. These infections are generally bacterial, with Escherichia coli being the most common causative agent, often originating from the gastrointestinal tract. However, other bacteria, such as Staphylococcus saprophyticus, Klebsiella pneumoniae, and Proteus mirabilis, are also known to cause UTIs. The type, location, and underlying complexity of the UTI guide both...
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Urinary Tract Infection II: Pathophysiology01:25

Urinary Tract Infection II: Pathophysiology

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The pathophysiology of urinary tract infections (UTIs) encompasses several progressive stages, beginning with bacterial colonization and culminating in potential systemic complications if untreated. UTIs are primarily initiated by bacteria, such as Escherichia coli, which often originate from the gastrointestinal tract and migrate to the urinary system through the periurethral area. This migration can occur via several routes, including improper hygiene practices, sexual activity, or...
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Urinary Tract Infection IV: Nursing Management01:17

Urinary Tract Infection IV: Nursing Management

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In managing urinary tract infections (UTIs) in nursing, a comprehensive assessment is essential. Begin by gathering subjective data, such as the patient’s complaints of dysuria (painful urination), urinary frequency, urgency, suprapubic pain, and any lower abdominal discomfort. This information can be complemented by questions regarding previous UTIs, sexual activity, and personal hygiene practices, which can provide insight into risk factors. Objective assessment should focus on signs...
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Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care01:30

Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care

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A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
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Urinary Tract Calculi I: Introduction01:28

Urinary Tract Calculi I: Introduction

574
Renal calculi, or kidney stones, are solid deposits of minerals and salts formed inside the kidneys. In medical terminology, "calculus" refers to the stone itself, while "lithiasis" describes the process of stone formation. Depending on their location within the urinary system, these stones may be classified as either urolithiasis, when situated within the urinary tract, or nephrolithiasis, when located within the kidneys. Each term signifies the specific impact of the stone.Predisposition...
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Urinary Tract Calculi V: Nursing Management01:28

Urinary Tract Calculi V: Nursing Management

322
AssessmentSubjective Data: Obtain a detailed health history, including any recent or chronic urinary tract infections, periods of immobilization, previous episodes of renal calculi, and medical conditions such as gout, benign prostatic hyperplasia, or hyperparathyroidism. Review the medication history for drugs that may influence stone formation, including allopurinol, analgesics, loop diuretics, or thiazide diuretics. Document the use of long-term indwelling catheters and any past surgical...
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Machine Learning Tools for Predicting Pediatric Urinary Tract Infections Caused by ESBL-producing Bacteria.

Chen Hajaj1,2, Shani Alkoby1,2, Shai Ashkenazi3,4

  • 1From the Department of Industrial Engineering & Management, Ariel University, Ariel, Israel.

The Pediatric Infectious Disease Journal
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict pediatric urinary tract infections (UTIs) caused by extended-spectrum β-lactamases (ESBL)-producing bacteria. These tools help clinicians identify high-risk cases for appropriate antibiotic selection.

Keywords:
antibiotic resistanceantibiotic stewardshipchildrenmachine learningurinary tract infection

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Area of Science:

  • Pediatric infectious diseases
  • Medical informatics
  • Machine learning in healthcare

Background:

  • Increasing global prevalence of pediatric urinary tract infections (UTIs) caused by extended-spectrum β-lactamases (ESBL)-producing bacteria.
  • ESBL-UTIs necessitate specialized antibiotic treatment, often leading to delayed empirical therapy, increased ICU admissions, morbidity, and prolonged hospital stays.
  • Predicting ESBL-UTIs is challenging but crucial for timely and appropriate patient management.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting pediatric UTIs caused by ESBL-producing bacteria.
  • To assist pediatricians in identifying children at higher risk for ESBL-positive UTIs.
  • To enable earlier initiation of appropriate empiric antibiotic therapy for ESBL-UTIs.

Main Methods:

  • Retrospective analysis of electronic medical records for pediatric patients (1 month to 18 years) with confirmed UTIs from January 2010 to August 2020.
  • Data extraction included demographics, clinical information, and laboratory results.
  • Development of five ML models using available patient data at UTI presentation for predicting ESBL-positive bacterial infections.

Main Results:

  • The study analyzed 35,830 pediatric UTI events.
  • Factors significantly associated with ESBL-positive UTIs included age, sex, socioeconomic status, infection site, prior antibiotic use, previous ESBL-UTI history, and specific uropathogen.
  • The developed ML models demonstrated a high negative predictive value (~0.98), indicating strong performance in ruling out ESBL-positive UTIs.

Conclusions:

  • Machine learning models utilizing data available at UTI presentation can aid clinicians in assessing the probability of ESBL-producing bacterial UTIs in children.
  • These models show promise in supporting clinical decision-making for empiric antibiotic selection.
  • Further prospective studies are needed to refine model performance and evaluate their impact on clinical outcomes.