Epidemiology, Molecular Characteristics, and Clinical Risk Factors of Klebsiella pneumoniae Bloodstream Infections: A Nine-Year Retrospective Study (2016–2024)
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background: Klebsiella pneumoniae bloodstream infections (KP-BSIs) are increasingly associated with carbapenem resistance and hypervirulence, posing serious challenges to clinical management. This study aimed to investigate the dynamic trends, molecular characteristics, and clinical risk factors associated with dominant serotypes and sequence types (STs) of KP-BSIs over a nine-year period. Methods: A retrospective study of 933 non-duplicate KP-BSIs isolates from 2016 to 2024 was conducted. Multiplex PCR was used to detect major capsular serotypes (K1, K2, K47, K64), sequence types (ST11, ST23, ST65, ST86), and virulence genes ( rmpA, rmpA2, iutA, iroN ). Clinical data were analyzed to identify independent risk factors using multivariate logistic regression. Results: The ST11-K64 clone was the most prevalent (21.1% of isolates) and increased markedly after 2019. It was strongly and independently associated with higher mortality (OR 3.11, P < 0.001), ICU admission (OR 2.9, P < 0.001), and healthcare exposures (OR 0.032, P < 0.001). In contrast, ST23 was linked to the K1 serotype and a community-acquired profile. Distinct clinical syndromes were associated with specific serotypes: K64 with healthcare-associated risks, K1 with chronic renal insufficiency, and K47 with malignancy and diabetes. Furthermore, individual virulence genes demonstrated niche-specific associations; rmpA2 was an independent predictor for invasive procedures, while iroN was associated with immunocompromised hosts. Conclusion: The epidemiology of KP-BSIs in our region is dominated by the high-risk ST11-K64 clone, signaling a concerning convergence of virulence and resistance. Molecular typing (STs and serotypes) is a powerful predictor of clinical outcomes and patient risk profiles. Integrating these markers into clinical practice is essential for precision management and improving patient outcomes.