Related Experiment Video
Updated: Jun 29, 2025
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
Published on: April 6, 2021
A Web-Based Dynamic Nomogram to Predict the Risk of Methicillin-Resistant Staphylococcal Infection in Patients with
Van Duong-Thi-Thanh1,2, Binh Truong-Quang1,3, Phu Tran-Nguyen-Trong2,4
1Faculty of Medicine, University of Medicine and Pharmacy, Ho Chi Minh 700000, Vietnam.
Abstract:
The aim of this study was to create a dynamic web-based tool to predict the risks of methicillin-resistant Staphylococcus spp. (MRS) infection in patients with pneumonia. We conducted an observational study of patients with pneumonia at Cho Ray Hospital from March 2021 to March 2023. The Bayesian model averaging method and stepwise selection were applied to identify different sets of independent predictors. The final model was internally validated using the bootstrap method. We used receiver operator characteristic (ROC) curve, calibration, and decision curve analyses to assess the nomogram model's predictive performance. Based on the American Thoracic Society, British Thoracic Society recommendations, and our data, we developed a model with significant risk factors, including tracheostomies or endotracheal tubes, skin infections, pleural effusions, and pneumatoceles, and used 0.3 as the optimal cut-off point. ROC curve analysis indicated an area under the curve of 0.7 (0.63-0.77) in the dataset and 0.71 (0.64-0.78) in 1000 bootstrap samples, with sensitivities of 92.39% and 91.11%, respectively. Calibration analysis demonstrated good agreement between the observed and predicted probability curves. When the threshold is above 0.3, we recommend empiric antibiotic therapy for MRS. The web-based dynamic interface also makes our model easier to use.
Insights
This study developed a web tool to predict methicillin-resistant Staphylococcus spp. (MRS) infection risk in pneumonia patients. Key predictors include tracheostomies and skin infections, guiding empiric antibiotic therapy decisions.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Clinical Prediction Modeling
Background:
- Pneumonia poses significant risks, with methicillin-resistant Staphylococcus spp. (MRS) presenting a growing challenge.
- Accurate prediction of MRS infection is crucial for timely and appropriate patient management.
Purpose of the Study:
- To develop a dynamic, web-based tool for predicting MRS infection risk in pneumonia patients.
- To identify key clinical predictors associated with MRS infection in this population.
Main Methods:
- An observational study was conducted with pneumonia patients over two years.
- Bayesian model averaging and stepwise selection identified independent predictors.
- The model was validated using bootstrap resampling and assessed with ROC, calibration, and decision curve analyses.
Main Results:
- A predictive model was developed, identifying tracheostomies/endotracheal tubes, skin infections, pleural effusions, and pneumatoceles as significant risk factors.
- The model achieved an Area Under the Curve (AUC) of 0.70 (0.63-0.77) in the dataset and 0.71 (0.64-0.78) in bootstrap samples.
- An optimal cut-off of 0.3 was determined, with a recommended threshold for empiric antibiotic therapy.
Conclusions:
- The developed nomogram model effectively predicts MRS infection risk in pneumonia patients.
- The web-based tool provides a user-friendly interface for clinical application.
- Empiric antibiotic therapy is recommended for patients with a predicted risk above 0.3.
More Related Videos
09:17A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii
Published on: January 2, 2017
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
