aceVancomycin treatment outcomes and machine learning-based identification of heterogeneous vancomycin-intermediate Staphylococcus aureus infections
Objectives Heterogeneous vancomycin-intermediate Staphylococcus aureus (hVISA) infections remain underdiagnosed with unclear clinical impact. This multicenter study evaluated vancomycin treatment outcomes and developed a machine learning (ML)-based diagnostic model. Methods We prospectively enrolled 200 patients with MRSA infections from 20 Chinese hospitals between February 2012 and July 2021. Population analysis profile-area under the curve (PAP-AUC) testing was performed on frozen isolates to confirm hVISA ph…
A LASSO-logistic regression model built on clinical predictors identified hVISA infection in MRSA patients with a test-set AUC of 0.834, which could support earlier detection and tailored vancomycin dosing.
In adults on vancomycin monotherapy, hVISA infection was linked to much higher treatment failure than VSSA infection, even though hVISA patients received higher doses and still reached lower PK/PD indices.
The model's test-set confidence interval is fairly wide (0.711-0.930), and the study reports prediction performance rather than evidence that model-guided dosing improves outcomes.
Evidence
- Peer-reviewedInternational Journal of Antimicrobial Agents2026-10-05
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Truvace Impact Record TRV-2026-1854, v1: “Vancomycin treatment outcomes and machine learning-based identification of heterogeneous vancomycin-intermediate Staphylococcus aureus infections.” Truvace, 2026-10-18. /record/TRV-2026-1854 (accessed at citation time). sha256 1beeb03b97a21a76…
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