This study aimed to differentiate hypervirulent (hvKp) Klebsiella pneumoniae (hvKp) from classical (cKp) strains, particularly those with acquired antimicrobial resistance. Researchers analyzed 49 K. pneumoniae strains, categorizing them as hvKp (N=16) or cKp (N=33) using a murine infection model. They evaluated various biomarkers, including siderophore production, mucoviscosity, and virulence plasmid characteristics, to identify the most accurate predictors of hvKp. Both logistic regression and a machine-learning model (CART) were used to determine the most predictive factors. The biomarker count alone was the strongest predictor, with a high accuracy of 94% for correctly classifying hvKp strains. A count of ≥4 biomarkers showed 100% sensitivity but lower specificity. The presence of all five biomarkers (iucA, iroB, peg-344, rmpA, and rmpA2) was most accurate for identifying hvKp. These findings suggest that the presence of these biomarkers is a key factor in distinguishing hvKp from cKp. The study also highlights the importance of accurately identifying hvKp for clinical management, as these strains can cause severe infections in otherwise healthy individuals. The results provide a framework for developing diagnostic tests and improving surveillance and research on hvKp. However, the study acknowledges limitations, including the need for further research to understand the full range of virulence factors and the impact of antimicrobial resistance on virulence. The study underscores the importance of accurate identification of hvKp for optimal patient care and infection control.This study aimed to differentiate hypervirulent (hvKp) Klebsiella pneumoniae (hvKp) from classical (cKp) strains, particularly those with acquired antimicrobial resistance. Researchers analyzed 49 K. pneumoniae strains, categorizing them as hvKp (N=16) or cKp (N=33) using a murine infection model. They evaluated various biomarkers, including siderophore production, mucoviscosity, and virulence plasmid characteristics, to identify the most accurate predictors of hvKp. Both logistic regression and a machine-learning model (CART) were used to determine the most predictive factors. The biomarker count alone was the strongest predictor, with a high accuracy of 94% for correctly classifying hvKp strains. A count of ≥4 biomarkers showed 100% sensitivity but lower specificity. The presence of all five biomarkers (iucA, iroB, peg-344, rmpA, and rmpA2) was most accurate for identifying hvKp. These findings suggest that the presence of these biomarkers is a key factor in distinguishing hvKp from cKp. The study also highlights the importance of accurately identifying hvKp for clinical management, as these strains can cause severe infections in otherwise healthy individuals. The results provide a framework for developing diagnostic tests and improving surveillance and research on hvKp. However, the study acknowledges limitations, including the need for further research to understand the full range of virulence factors and the impact of antimicrobial resistance on virulence. The study underscores the importance of accurate identification of hvKp for optimal patient care and infection control.