mBio · 2024 · 119 citations · 41 references
Life SciencesInfectious DiseasesMicrobiologyAntimicrobial ResistanceDiagnostic MicrobiologyMedical MicrobiologyAcquired ResistanceAntibiotic ResistanceAntibiotic Resistance MechanismsPathogen DetectionResistance GenesResistance SurveillanceAntimicrobial Resistance PatternsBiomarker CorrelationKlebsiella Pneumoniae ResistanceLogistic RegressionMachine Learning Prediction ModelsPhenotypic Resistance ProfilingResistance Biomarker DiscoveryResistance Phenotype Prediction
Hypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all five of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which, in turn, would assist in optimizing patient care.
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