Early detection of ampicillin susceptibility in Enterococcus faecium with MALDI-TOF/MS and machine learning

Enterococcus faecium can cause severe infections and is often resistant to the first-line antibiotic ampicillin. Consequently, clinicians usually prescribe broad-spectrum antibiotics, promoting the selection of multidrug-resistant bacteria. We investigated whether machine learning models can detect ampicillin susceptibility directly from MALDI-TOF/MS to enable earlier optimised treatment in ampicillin-susceptible E. faecium infections.

Two datasets of clinical E. faecium MALDI-TOF spectra and their resistance phenotype were analysed: our own Technical University of Munich (TUM) dataset and the publicly available MS-UMG dataset. We evaluated logistic regression (LR) and Light gradient boosting machine (GBM) models and explored transferability including a target-domain-adapted external validation. Discriminatory MALDI-TOF peaks were investigated using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

LightGBM slightly outperformed LR in identifying ampicillin-susceptible isolates in both datasets (area under the precision-recall curve 0.907 ± 0.016 vs. 0.902 ± 0.030 for TUM; 0.902 ± 0.029 vs. 0.899 ± 0.054 for MS-UMG). Target-domain-adapted training demonstrated good transferability of LightGBM models (area under the precision-recall curve of 0.869 ± 0.013 when trained on TUM plus 30% MS-UMG, tested on the remaining 70% MS-UMG). SHapley Additive exPlanations (SHAP) analysis consistently identified a MALDI-TOF spectral peak at m/z ≈ 5091 as the most discriminative, which LC-MS/MS analysis mapped to bacteriocin T8.

LightGBM and LR models can identify ampicillin-susceptible E. faecium isolates from MALDI-TOF spectra and generalise well to unseen datasets. Bacteriocin T8 serves as a key discriminatory feature associated with ampicillin resistance. While clinical implementation currently still requires confirmatory testing, the addition of larger datasets will support the development of more robust machine learning models.