A Web-Based Dynamic Nomogram to Predict the Risk of Methicillin-Resistant Staphylococcal Infection in Patients with Pneumonia.

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 trach

SUBMITTER: Duong-Thi-Thanh V 

PROVIDER: S-EPMC10969305 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature