ΔCt-informed, calibrated logistic regression accurately attributes mecA in Staphylococcus aureus-positive wound...

In wound specimens, co-detection of mecA and Staphylococcus aureus by PCR does not necessarily indicate methicillin-resistant S. aureus (MRSA) because coagulase-negative staphylococci (CoNS) frequently harbor mecA. We evaluated a ΔCt-informed, biologically gated, calibrated logistic regression model to attribute mecA to S. aureus versus CoNS. Using paired culture/antimicrobial susceptibility test and multi-target real-time PCR cycle-threshold values (internal n = 93; external n = 47), we trained fivefold cross-validated models in the culture-positive S. aureus subset (n = 36) and applied an S. aureus PCR gate (no attribution when S. aureus PCR is negative). The primary model achieved sensitivity 90.9% and specificity 92.0% for MRSA attribution with AUC 0.931 (out-of-fold). Decision curve analysis showed positive net benefit across clinically relevant thresholds; at the prespecified 50% cutoff, the model achieved a net benefit of 0.222 compared with negative benefit for a treat-all strategy. In an external cohort, S. aureus detection by PCR versus culture showed 92.3% sensitivity and 97.1% specificity; within S. aureus PCR-positives (n = 12), MRSA attribution reached 100% sensitivity and 87.5% specificity (accuracy = 91.7%). This framework improves mecA interpretability in polymicrobial specimens tested by panel-based molecular assays and can reduce unnecessary MRSA-directed antibiotics.

This work addresses a practical diagnostic gap in routine wound infection testing using panel-based PCR. In some CAP/CLIA laboratories using multiplex or panel-based PCR, detection of Staphylococcus aureus and mecA does not reliably resolve whether methicillin resistance originates from S. aureus or coagulase-negative staphylococci in polymicrobial specimens. Our approach is not a stand-alone methicillin-resistant S. aureus (MRSA) screen; it functions as a post-analytic attribution layer that probabilistically disambiguates the source of mecA within existing PCR workflows. By leveraging routinely available cycle-threshold data within a biologically gated logistic framework, this method provides transparent and auditable MRSA/methicillin-susceptible S. aureus attribution without additional instrumentation or workflow changes. Accurate attribution reduces biologically implausible MRSA calls while preserving detection of probable MRSA, supporting more rational antimicrobial stewardship in polymicrobial wound infections.