AI-powered rapid detection of multidrug-resistant Klebsiella pneumoniae with informative peaks of MALDI-TOF MS

Abstract

Motivation Klebsiella pneumoniae is a highly virulent superbug with rising antibiotic resistance worldwide. While matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has transformed microbial identification, its application to antimicrobial resistance prediction remains underexplored, particularly for large clinical cohorts. In this study, we developed machine-learning models with feature-level interpretability using MALDI-TOF MS data to rapidly predict resistance to ciprofloxacin (CIP), cefuroxime (CXM), and ceftriaxone (CRO) in K. pneumoniae. Results Using more than 28 000 isolates from two hospitals, the best-performing models reached an independent test accuracy of 0.7858, with sensitivity of 0.7289 and specificity of 0.8127. Several resistance-associated m/z signals—including 3657, 4341, 4519, 4709, 5070, 5409, 5921, 5939, and 6516—were consistently enriched in resistant isolates, offering interpretable spectral markers linked to resistance. Performance remained stable in timebased validation but declined across hospitals, suggesting sensitivity to geographic variability in resistance profiles. Overall, this study demonstrates that combining MALDI-TOF MS with machine learning enables rapid and interpretable prediction of resistance to commonly used fluoroquinolone and cephalosporins in K. pneumoniae. These findings highlight the clinical potential of such models for supporting empiric therapy and emphasize the importance of incorporating local data or adaptive strategies to improve generalizability across healthcare settings. Availability and implementation Data available on request from the authors.