Rational Antibiotic Escalation Applied to Specific Patient Groups

BackgroundClinicians commonly escalate empiric antibiotic therapy due to poor clinical progress, without microbiology guidance. When escalating, they should take account of how resistance to an initial antibiotic affects the probability of resistance to subsequent options. The term Escalation Antibiogram (EA) has been coined to describe this concept. One difficulty when applying the EA concept to clinical practice is understanding the uncertainty in results and how this changes for specific patient subgroups. MethodsA Bayesian model was developed to estimate antibiotic resistance rates in Gram-negative bloodstream infections based on phenotypic resistance data. It provides an expected value (posterior mean) with 95% credible interval to illustrate uncertainty, based on the size of the patient subgroup, and estimates probability of inferiority between two antibiotics. This model can be applied to specific patient groups where resistance rates and underlying microbiology may differ from the whole hospital population. ResultsRates of resistance to empiric first choice and potential escalation antibiotics were calculated for the whole hospitalised population based on 10,486 individual bloodstream infections, and for a range of specific patient groups, including ICU, haematology-oncology, and paediatric patients. Differences in optimal escalation antibiotic options between specific patient groups were noted. ConclusionsEA analysis informed by our Bayesian model is a useful tool to support empiric antibiotic switches, providing an estimate of local resistances rates, and a comparison of antibiotic options with a measure of the uncertainty in the data. We demonstrate that EAs calculated for the whole population cannot be assumed to apply to specific patient groups.