Prediction of KPC-producing Klebsiella pneumoniae by MALDI-TOF MS, ensemble learning, and spectral peak annotation

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Sensitivity 72%, Specificity >95%

• Primary Outcome: Detection of KPC-producing K. pneumoniae using MALDI-TOF MS with ensemble learning

• Nnt Or Sensitivity: Sensitivity 72%, Specificity >95% (significantly higher specificity than MALDI Biotyper KPC module)

• Confidence Interval: Not reported

Clinical Application

High - MALDI-TOF MS widely available in Australian laboratories, though machine learning implementation may require technical expertise Relevant for Australian laboratories given increasing carbapenem resistance; could support TGA antimicrobial stewardship initiatives and NHMRC infection control guidelines Clinical microbiology laboratories processing K. pneumoniae isolates for carbapenem resistance screening

Abstract

The increasing prevalence of carbapenem-resistant Klebsiella pneumoniae represents a serious global health challenge. Rapid and accurate detection methods are essential to inform early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of resistant isolates. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been evaluated for resistance detection, but its clinical application remains limited. In this study, MALDI-TOF MS was integrated with machine learning (ML) to improve detection of Klebsiella pneumoniae carbapenemase (KPC) among K. pneumoniae isolates harboring KPC carbapenemase. Ensemble learning strategies were applied to 435 clinical isolates from different geographic locations (Spain, United States, South America, and Asia), with validation performed using external data sets. By constructing two peak matrices and applying four ML algorithms, as well as combinations of two and three in ensemble models, 92 different classifiers were tested. Ensemble combinations increased the specificity of classifiers to over 95%, while sensitivity reached 72%, being significantly higher than that of the MALDI Biotyper KPC module. Low sensitivity may be affected by technical variability during spectral acquisition. The first annotated MALDI-TOF MS spectrum for K. pneumoniae by in silico prediction of protein masses was developed to enable peak identification, including peaks potentially related to antimicrobial resistance. Overall, the results of this study show that combining MALDI-TOF MS with ensemble learning can enhance KPC detection performance.IMPORTANCERapid and accurate detection of Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae informs early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of antimicrobial resistance. In this study, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and ensemble machine learning were employed to detect KPC-producing isolates, using diverse isolate collections, obtaining excellent specificity. An annotated K. pneumoniae MALDI-TOF MS profile is provided with peak annotation, providing a resource for peak analysis.

References

1. 1.Rodriguez-Temporal, D., Gutiérrez-Pareja, M., Gordy, G. G., Nahkala, E. M., Rodríguez-Sánchez, B., & Patel, R. (2026). Prediction of KPC-producing Klebsiella pneumoniae by MALDI-TOF MS, ensemble learning, and spectral peak annotation. Journal of Clinical Microbiology. https://doi.org/10.1128/jcm.01466-25