Related Experiment Video

Updated: Jun 23, 2026

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

Recalibration and Validation of a Risk Scoring Tool to Predict Multidrug-Resistant Pseudomonas aeruginosa

Nhu Le1, Hyunuk Seung2, Megan E Dunning3

  • 1University of Maryland School of Medicine, Baltimore, Maryland, USA.

Open Forum Infectious Diseases
|June 22, 2026
PubMed

Insights

A new risk score helps identify difficult-to-treat resistant *Pseudomonas aeruginosa* in intensive care units. While specific, the score needs local validation due to moderate accuracy and low sensitivity for detecting this challenging bacteria.

Area of Science:

  • Infectious Diseases
  • Critical Care Medicine
  • Microbiology

Background:

  • *Pseudomonas aeruginosa* is a significant cause of hospital-acquired infections.
  • Difficult-to-treat resistant strains pose a growing clinical challenge.
  • Accurate identification of resistant pathogens is crucial for effective treatment.

Purpose of the Study:

  • To recalibrate and evaluate a risk score for identifying difficult-to-treat resistant *Pseudomonas aeruginosa*.
  • To assess the performance of the risk score in an intensive care unit (ICU) population.
  • To provide data for clinical decision-making in managing *Pseudomonas aeruginosa* infections.

Main Methods:

  • Retrospective analysis of 197 ICU patients with *Pseudomonas aeruginosa* bloodstream or respiratory infections.
  • Recalibration of an existing risk score.
  • Evaluation of model discrimination (c-statistic), sensitivity, and specificity.

Main Results:

  • The prevalence of difficult-to-treat resistant *Pseudomonas aeruginosa* was 6%.
  • The recalibrated risk score demonstrated moderate discrimination (c-statistic 0.71-0.74).
  • The model exhibited low sensitivity (18%) but high specificity (94%-96%).

Conclusions:

  • The developed risk score shows potential for identifying difficult-to-treat resistant *Pseudomonas aeruginosa*.
  • The low sensitivity necessitates caution and suggests the score is better for ruling out resistance.
  • Local validation and careful consideration of performance trade-offs are essential before clinical implementation.
Keywords:
Pseudomonas aeruginosaantimicrobial resistancedifficult-to-treat resistance (DTR)model calibration and validation

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