Integrating forecast probabilities in antibiograms: a way to guide antimicrobial prescriptions more reliably?

Antimicrobial susceptibility testing (AST) assigns pathogens to "susceptible" or "resistant" clinical categories based on clinical breakpoints (CBPs) derived from MICs or inhibition zone diameters and indicates the likelihood for therapeutic success. AST reports do not provide quantitative measures for the reliability of such categorization. Thus, it is currently impossible for clinicians to estimate the technical forecast uncertainty of an AST result regarding clinical categorization. AST error rates depend on the localization of pathogen populations in relation to CBPs. Bacterial species are, however, not homogeneous, and subpopulations behave differently with respect to AST results. We addressed how AST reporting errors differ between isolates with and without acquired drug resistance determinants. Using as an example the beta-lactams and their most important resistance mechanisms, we analyzed different pathogen populations for their individual reporting error probabilities. Categorization error rates were significantly higher for bacterial populations harboring resistance mechanisms than for the wild-type population. Reporting errors for amoxicillin-clavulanic acid and piperacillin-tazobactam in Escherichia coli infection cases were almost exclusively due to the presence of broad-spectrum- and extended-spectrum-beta-lactamase (ESBL)-producing microorganisms (79% and 20% of all errors, respectively). Clinicians should be aware of the significantly increased risk of erroneous AST reports for isolates producing beta-lactamases, particularly ESBL and AmpC. Including probability indicators for interpretation would improve AST reports.