Weighted-Incidence Syndromic Combination Antibiogram

Clinicians initiating empirical antimicrobial therapy must select a regimen before culture results are available. The traditional cumulative antibiogram, as standardised by the Clinical and Laboratory Standards Institute (CLSI) in its M39 guideline, presents susceptibility percentages for individual organism–antibiotic pairs. This format has two principal limitations in the empirical setting: it does not inform the clinician about the relative frequency of different causative organisms for a given syndrome, and it cannot evaluate multi-drug regimens in a single metric. Additional challenges arise when sample sizes are small, as simple proportions such as 5 out of 10 susceptible (50%) carry far greater uncertainty than 500 out of 1000 (also 50%), yet both are displayed identically on a traditional antibiogram. == Methodology ==

Basic principle A WISCA estimates empirical coverage for a specific infection syndrome by combining two components: the relative incidence of each pathogen within the syndrome, and the susceptibility of each pathogen to the regimen under evaluation. For a regimen r and pathogens i = 1, ..., K, the coverage is calculated as: :\text{Coverage}_r = \sum_{i=1}^{K} p_i \cdot \theta_{i,r} where pi is the proportion of the syndrome caused by pathogen i and θi,r is the probability that pathogen i is susceptible to regimen r. This approach allows stratified coverage estimates for specific patient subgroups while borrowing strength across strata. == Clinical applications ==

WISCA has been applied to a variety of infection syndromes across different clinical settings. Urinary tract infections Hebert et al. (2012) demonstrated the method for urinary tract infections (UTIs) and abdominal-biliary infections, comparing WISCA with a traditional antibiogram. Prosthetic joint infections A 2025 study applied WISCA to prosthetic joint infections, demonstrating its utility for guiding both empirical and definitive antibiotic selection in culture-negative cases. == Advantages over traditional antibiograms ==

Traditional cumulative antibiograms and WISCA yield the same point estimate for monotherapy coverage when applied to the same population, because weighting pathogen incidence against per-pathogen susceptibility algebraically simplifies to the overall proportion of susceptible isolates. The principal advantages of WISCA are therefore: • Uncertainty quantification: the Bayesian approach provides a credibility interval alongside each coverage estimate, making the reliability of the estimate transparent, which is especially important when the number of available isolates is small. • Combination therapy evaluation: WISCA provides a standardised, validated method for computing the joint coverage of multi-drug regimens, accounting for the fact that an isolate is covered if susceptible to at least one agent. • Syndrome specificity: results can be stratified by clinical syndrome and patient subgroup. == Limitations ==

WISCA assumes that the microbiological data used as input are representative of the target population. It does not model changes in resistance over time, does not account for pharmacokinetic factors such as tissue penetration, and does not incorporate clinical outcomes. Small sample sizes remain a challenge; although the Bayesian framework handles low counts more gracefully than simple proportions, the resulting credibility intervals may be too wide to support clinical decision-making. == Software ==

WISCA is implemented in the open-source AMR R package which provides the function wisca() for computing Bayesian WISCA estimates from microbiological data, including support for combination regimens, stratification, and multiple languages. == See also ==