Recombinase-Aided Amplification Assay for Rapid Detection of Hypervirulent Klebsiella pneumoniae (hvKp) and Characterization of the hvKp Pathotype

Clinical characteristics and prognosis of Klebsiella pneumoniae meningitis in adults

Background

Klebsiella pneumoniae is a causative agent of bacterial meningitis in adults. However, there is little information regarding this infection. Therefore, this study comprehensively analyzed the clinical characteristics and prognosis of Klebsiella pneumoniae meningitis (KPM) patients.

Methods

The clinical data of adult hospitalized patients with KPM were retrospectively collected from January 2015 to December 2022. The clinical characteristics and antibiotic resistance of KPM were evaluated. Meanwhile, a set of logistic regression models was constructed to identify prognostic factors for death. These prognostic factors were subsequently combined to develop a nomogram for predicting the risk of in-hospital mortality in individual patients. Finally, the receiver operating characteristic curve and calibrate plot were utilized to verify the performance of the nomogram.

Results

This study included 80 adult patients with KPM, 58 (72.5%) of whom were males. The mortality rate was 45%. Among them, 74 (92.5%) were diagnosed with healthcare-associated meningitis. Thirty-seven carbapenem-resistant Klebsiella pneumoniae (CRKP) strains were susceptible to tigecycline, polymyxin, and ceftazidime/avibactam. CRKP (OR = 9.825, 95%CI = 2.757-35.011, P < 0.001), length of stay (OR = 0.953, 95%CI = 0.921-0.986, P = 0.005), and C-reactive protein-to-prealbumin ratio (CRP/PA, OR = 3.053, 95%CI = 1.329-7.016, P = 0.009) were identified as predictive factors for mortality using multivariate logistic regression. Finally, a nomogram for death prediction was established. The area under the curve of this nomogram was 0.900 (95% CI = 0.828-0.971).

Conclusions

KPM is a fatal disease associated with high incidence of healthcare-associated infections and carbapenem resistance. Moreover, CRKP, length of stay, and CRP/PA were found to be independent predictors of mortality.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g., siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Furthermore, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N = 16) or cKp (N = 33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression, the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49), respectively. These findings can be used to inform the identification of hvKp.IMPORTANCEHypervirulent 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.

Hypervirulent Klebsiella pneumoniae detection methods: a minireview

Hypervirulent Klebsiella pneumoniae (hvKp), characterized by high virulence and epidemic potential, has become a global public health challenge. Therefore, improving the identification of hvKp and enabling earlier and faster detection in the community to support subsequent effective treatment and prevention of hvKp are an urgent issue. To address these issues, a number of assays have emerged, such as String test, Galleria mellonella infection test, PCR, isothermal exponential amplification, and so on. In this paper, we have collected articles on the detection methods of hvKp and conducted a retrospective review based on two aspects: traditional detection technology and biomarker-based detection technology. We summarize the advantages and limitations of these detection methods and discuss the challenges as well as future directions, hoping to provide new insights and references for the rapid detection of hvKp in the future. The aim of this study is to focus on the research papers related to Hypervirulent Klebsiella pneumoniae involving the period from 2012 to 2022. We conducted searches using the keywords "Hypervirulent Klebsiella pneumoniae, biomarkers, detection techniques" on ScienceDirect and Google Scholar. Additionally, we also searched on PubMed, using MeSH terms associated with the keywords (such as Klebsiella pneumoniae, Klebsiella Infections, Virulence, Biomarkers, diagnosis, etc.).