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Predicting risk-adjusted incidence rates of methicillin-resistant Staphylococcus Aureus and Pseudomonas Aeruginosa in cystic fibrosis programs in the United States

Published:August 10, 2022DOI:https://doi.org/10.1016/j.jcf.2022.08.002

      Abstract

      Background

      Healthcare-associated transmission of methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa occurs for people with cystic fibrosis (CF), but CF programs lack a process to monitor incidence rates (IRs). We assessed predictors of incident infections and created a model to determine risk-adjusted IRs for CF programs.

      Methods

      Using the CF Foundation Patient Registry data for all patients from 2012 to 2015, coefficients for variables that predicted IRs were estimated. Hazard functions were then used to predict IRs of MRSA and P. aeruginosa for CF programs based on their patient and program characteristics. Predicted IRs were compared with observed IRs over multiple time intervals.

      Results

      Multiple patient and program characteristics were identified as predictors of observed IRs. Our model's predicted IRs closely aligned with observed IRs for most CF programs. Alarm values (defined as observed IR > 95% confidence interval of predicted IR) were found at 5.9%, 5.9%, 6.0% (adult, pediatric, affiliate) of programs for MRSA and 3.0%, 1.7%, 0.0% (adult, pediatric, affiliate) of programs for P. aeruginosa.

      Conclusions

      We found patient and program characteristics that predicted MRSA and P. aeruginosa IRs. Our model accurately predicted risk-adjusted IRs of MRSA and P. aeruginosa. CF programs could use our model to monitor their IRs and potentially improve infection prevention and control.

      Keywords

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