ArticleAntimicrobial agents and chemotherapy2014
Mathematical model to quantify the effects of risk factors on carbapenem-resistant Acinetobacter baumannii.
Article in Antimicrobial agents and chemotherapy, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors at 3 institutions in 2 countries.
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Abstract
Carbapenem-resistant Acinetobacter baumannii (CRAB) infections are increasing, and they are associated with an increased risk of mortality in hospitalized patients. Linear regression is commonly used to identify concurrent trends, but it cannot quantify the relationship between risk factors and resistance. We developed a model to quantify the impact of antibiotic consumption on the prevalence of CRAB over time. Data were collected from January 2007 to June 2013 from our institution. Quarterly antibiotic consumption was expressed as defined daily dose/1,000 inpatient days. Six-month prevalence of CRAB was expressed as a percentage of all nonrepeat A. baumannii isolates tested. Individual trends were identified using linear regression. Antibiotic consumption from 2007 to 2011 was input as a step function in a relationship with CRAB. Model fit was evaluated by visual inspection and the residual sum of squares. The final model was validated using the best-fit (95% confidence interval) parameter estimates and antibiotic consumption to predict CRAB prevalence from January 2012 to June 2013. Cefepime, ertapenem, and piperacillin-tazobactam consumption and CRAB prevalence increased significantly over time. CRAB prevalence was best correlated to ertapenem (use sensitive; r2=0.76), and accounting for additional concurrent antibiotic use did not significantly improve model fit. Prospective validation with ertapenem consumption correlated well with CRAB observations, suggesting good predicting ability of the model. Our model provided the quantitative impact of antibiotic consumption on CRAB. We plan to further refine this model to account for multiple risk factors. Interventions should focus on controlling risk factors with the highest impact on resistance.
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