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Introduction:For diagnosing acute pyelonephritis, a comprehensive patient history is collected to identify symptoms such as dysuria, frequent or urgent urination, flank pain, or costovertebral angle (CVA) tenderness that may suggest a kidney infection.Physical ExaminationDuring the physical examination, CVA tenderness is assessed. This involves gentle percussion over the costovertebral angle, where tenderness often indicates a kidney infection.Diagnostic TestsUrinalysis: Used to identify white...
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Pyelonephritis is a bacterial infection that primarily affects the renal parenchyma and collecting system, including the renal pelvis, tubules, and interstitial tissue of one or both kidneys. It can be classified as either acute—a sudden, severe infection—or chronic, which refers to long-term or recurrent kidney infections.The primary cause of acute pyelonephritis (APN) is bacterial infection, with Escherichia coli accounting for approximately 70-80% of cases. Other bacteria, such...
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Mouse Models of Epididymitis Induced by Pathogen-Associated Molecular Patterns
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Novel algorithm for management of acute epididymitis.

Hiroshi Hongo1, Eiji Kikuchi1, Kazuhiro Matsumoto1

  • 1Department of Urology, Keio University School of Medicine, Tokyo, Japan.

International Journal of Urology : Official Journal of the Japanese Urological Association
|October 8, 2016
PubMed
Summary

This study identified key factors like age, diabetes, and elevated lab values that predict severe epididymitis. A new algorithm accurately classifies patients into risk groups, aiding clinical management decisions for acute epididymitis.

Keywords:
acute epididymitisdiagnosisseverity

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Area of Science:

  • Urology
  • Clinical Medicine
  • Predictive Analytics

Background:

  • Epididymitis is a common condition requiring effective management strategies.
  • Identifying predictive factors for severity is crucial for optimizing patient care.
  • Current management guidelines may benefit from data-driven decision support tools.

Purpose of the Study:

  • To pinpoint factors predicting the severity of epididymitis.
  • To develop a predictive algorithm for guiding the clinical management of epididymitis.
  • To assess the algorithm's efficacy in classifying disease risk and aiding treatment decisions.

Main Methods:

  • Retrospective analysis of 160 epididymitis patients at Keio University Hospital.
  • Classification of cases into severe and non-severe groups based on clinical and laboratory data.
  • Development and external validation of a predictive algorithm using a cohort of 96 patients from Tokyo Medical Center.
  • Efficacy assessment via decision curve analysis.

Main Results:

  • Older age, history of diabetes mellitus, fever, elevated white blood cell count, C-reactive protein, and blood urea nitrogen were independently associated with severe epididymitis.
  • The developed algorithm effectively classified patients into high-, intermediate-, and low-risk groups for severe disease.
  • High specificity (100% and 98.8%) in predicting severe epididymitis was observed in both study cohorts.
  • Decision curve analysis confirmed the algorithm's high clinical utility.

Conclusions:

  • The validated predictive algorithm demonstrates significant potential in aiding clinical decision-making for acute epididymitis management.
  • Risk stratification using this algorithm can help tailor treatment strategies for patients with epididymitis.
  • Further prospective studies can confirm the algorithm's impact on patient outcomes.