Kernel density-encoded MALDI-TOF spectra with LightGBM machine learning for multiplex resistance prediction in...

Antimicrobial resistance(AMR) in Klebsiella pneumoniae is an urgent clinical challenge. Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry(MALDI-TOF-MS) is routinely used for species identification, and reusing these spectra for same-day AMR prediction could accelerate targeted therapy. We developed and validated Light Gradient Boosting Machine(LightGBM) models to predict K. pneumoniae resistance directly from routine Bruker MALDI-TOF MS spectra of single bacterial colonies obtained on routine Bruker instruments, using a kernel density-encoded feature representation. Raw spectra were processed to extract peak-level attributes, including m/z, signal-to-noise ratio, peak area and intensity. The m/z axis was intervalized using a kernel density-guided strategy to preserve local spectral density and ordering, and peak attributes within each interval were aggregated into a structured high-dimensional matrix. A total of 424 isolates were included, and balanced binary datasets were constructed for 12 antibiotics, with an average of 98 ± 8 susceptible and 98 ± 8 resistant isolates per antibiotic; intermediate isolates were not included. The dataset included both environmental and human-derived isolates, and antimicrobial susceptibility labels were determined by broth microdilution for amikacin, aztreonam, ciprofloxacin, meropenem, piperacillin-tazobactam, cefepime, cefmetazole, cefoperazone-sulbactam, cefotaxime, ceftazidime, imipenem, and ceftazidime-avibactam, with stronger agreement for cefotaxime and comparatively lower agreement for imipenem. In internal hold-out validation, model accuracy ranged from 0.81 to 0.92 across 12 evaluable antibiotic-specific models, with AUROC values ranging from 0.88 to 0.96. These findings suggest that kernel density-encoded MALDI-TOF MS spectra combined with LightGBM may provide a proof-of-concept framework for AMR prediction, although independent external validation is required before clinical application can be considered.