Plasma Kallikrein and Hageman Factor in Gram-Negative Bacteremia

1970 ◽  
Vol 73 (4) ◽  
pp. 545 ◽  
Author(s):  
JOHN W. MASON
1971 ◽  
Vol 26 (02) ◽  
pp. 325-331 ◽  
Author(s):  
J. W Mason ◽  
R. W Colman

SummaryThe human plasma kallikrein system was assayed in patients with disseminated intravascular coagulation (DIC) induced by gram negative bacteremia, neoplasia and severe liver disease. Only the patients with gram negative septicemia showed activation of plasma kallikrein with concomitant depletion of kallikreinogen and kallikrein inhibition. Since the activation of kallikrein is a function of activated Hageman factor, it is suggested that in DIC associated with gram negative septicemia, Hageman factor activation may be involved in the DIC. In DIC associated with neoplasia or liver disease lack of Hageman factor activation should be considered.


Author(s):  
Jamie L. Wagner ◽  
Kylie C. Markovich ◽  
Katie E. Barber ◽  
Kayla R. Stover ◽  
Lauren R. Biehle

2021 ◽  
Vol 34 (2) ◽  
Author(s):  
Caitlyn L. Holmes ◽  
Mark T. Anderson ◽  
Harry L. T. Mobley ◽  
Michael A. Bachman

SUMMARY Gram-negative bacteremia is a devastating public health threat, with high mortality in vulnerable populations and significant costs to the global economy. Concerningly, rates of both Gram-negative bacteremia and antimicrobial resistance in the causative species are increasing. Gram-negative bacteremia develops in three phases. First, bacteria invade or colonize initial sites of infection. Second, bacteria overcome host barriers, such as immune responses, and disseminate from initial body sites to the bloodstream. Third, bacteria adapt to survive in the blood and blood-filtering organs. To develop new therapies, it is critical to define species-specific and multispecies fitness factors required for bacteremia in model systems that are relevant to human infection. A small subset of species is responsible for the majority of Gram-negative bacteremia cases, including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. The few bacteremia fitness factors identified in these prominent Gram-negative species demonstrate shared and unique pathogenic mechanisms at each phase of bacteremia progression. Capsule production, adhesins, and metabolic flexibility are common mediators, whereas only some species utilize toxins. This review provides an overview of Gram-negative bacteremia, compares animal models for bacteremia, and discusses prevalent Gram-negative bacteremia species.


2012 ◽  
Vol 71 (3) ◽  
pp. 261-266 ◽  
Author(s):  
Laura L. Raynor ◽  
Jeffrey J. Saucerman ◽  
Modupeola O. Akinola ◽  
Douglas E. Lake ◽  
J. Randall Moorman ◽  
...  

Author(s):  
Julieta Madrid-Morales ◽  
Aditi Sharma ◽  
Kelly Reveles ◽  
Carolina Velez-Mejia ◽  
Teri Hopkins ◽  
...  

Background: Extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae are increasingly common; however, predicting which patients are likely to be infected with an ESBL pathogen is challenging, leading to increased use of carbapenems. To date, five prediction models have been developed to distinguish between patients infected with ESBL pathogens. The aim of this study was to validate and compare each of these models, to better inform antimicrobial stewardship. Methods: This was a retrospective cohort study of patients with gram-negative bacteremia treated at the South Texas Veterans Health Care System over 3 months from 2018 to 2019. We evaluated isolate, clinical syndrome, and score variables for the five published prediction models/scores: Italian “Tumbarello”, Duke, University of South Carolina (USC), Hopkins Clinical Decision Tree, and Modified Hopkins. Each model was assessed using the receiver-operating-characteristic curve (AUROC) and Pearson correlation. Results: 145 patients were included for analysis, of which 20 (13.8%) were infected with an ESBL E. coli or Klebsiella spp. The most common sources of infection were genitourinary (55.8%) and gastrointestinal/intraabdominal (24.1%) and the most common pathogen was E. coli (75.2%). The prediction model with the strongest discriminatory ability (AUROC) was Tumbarello (0.7556). Correlation between prediction model score and percent ESBL was strongest with Modified Hopkins (R2=0.74). Conclusions: In this veteran population, the Modified Hopkins and Duke prediction models were most accurate in discriminating between gram-negative bacteremia patients when considering both AUROC and correlation. However, given the moderate discriminatory ability, many patients with ESBL Enterobacteriaceae (at least 25%) may still be missed empirically.


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