Vancomycin treatment outcomes and machine learning-based identification of heterogeneous vancomycin-intermediate...

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.

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 phenotype, and confirmed strains underwent whole-genome sequencing. Vancomycin therapeutic drug monitoring and pharmacokinetic/pharmacodynamic (PK/PD) analysis was performed. A ML model was developed for hVISA prediction.

Of 200 MRSA-infected patients, 117 (58.5%) were identified with hVISA infections, including 98 (98/175, 56.0%) adults and 19 (19/25, 76.0%) pediatric patients. Despite higher vancomycin doses, adult patients with hVISA infection achieved lower PK/PD indices than vancomycin-susceptible S. aureus (VSSA). In adults receiving vancomycin monotherapy, hVISA infection was associated with significantly higher treatment failure than VSSA infection (34.6% vs. 5.0%, P = 0.028). The predominant hVISA clones were ST5, ST239 and ST764, with ST5 and ST239 showing higher treatment failure rates than ST764. Among 10 ML models, LASSO-logistic regression performed best with AUC of 0.834 (95% CI 0.711-0.930) on test set, identifying vancomycin MIC, tracheal intubation, and prior penicillin use as key predictors.

High prevalence of hVISA among MRSA isolates and its impact on vancomycin monotherapy warrant early identification. The ML model enables identification of high-risk patients, supporting a shift toward phenotype- and sequence type-guided personalized dosing.