ΔCt-informed, calibrated logistic regression accurately attributes <i>mecA</i> in <i>Staphylococcus aureus</i> -positive wound specimens

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 <i>Staphylococcus aureus</i> and <i>mecA</i> does not reliably resolve whether methicillin resistance originates from <i>S. aureus</i> or coagulase-negative staphylococci in polymicrobial specimens. Our approach is not a stand-alone methicillin-resistant <i>S. aureus</i> (MRSA) screen; it functions as a post-analytic attribution layer that probabilistically disambiguates the source of <i>mecA</i> 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 <i>S. aureus</i> 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.