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Replication Dynamics for Six Gram-Negative Bacterial Species during Bloodstream Infection.

ABSTRACT: Bloodstream infections (BSI) are a major public health burden due to high mortality rates and the cost of treatment. The impact of BSI is further compounded by a rise in antibiotic resistance among Gram-negative species associated with these infections. Escherichia coli, Serratia marcescens, Klebsiella pneumoniae, Enterobacter hormaechei, Citrobacter freundii, and Acinetobacter baumannii are all common causes of BSI, which can be recapitulated in a murine model. The objective of this study was to characterize infection kinetics and bacterial replication rates during bacteremia for these six pathogens to gain a better understanding of bacterial physiology during infection. Temporal observations of bacterial burdens of the tested species demonstrated varied abilities to establish colonization in the spleen, liver, or kidney. K. pneumoniae and S. marcescens expanded rapidly in the liver and kidney, respectively. Other organisms, such as C. freundii and E. hormaechei, were steadily cleared from all three target organs throughout the infection. In situ replication rates measured by whole-genome sequencing of bacterial DNA recovered from murine spleens demonstrated that each species was capable of sustained replication at 24 h postinfection, and several species demonstrated <60-min generation times. The relatively short generation times observed in the spleen were in contrast to an overall decrease in bacterial burden for some species, suggesting that the rate of immune-mediated clearance exceeded replication. Furthermore, bacterial generation times measured in the murine spleen approximated those measured during growth in human serum cultures. Together, these findings provide insight into the infection kinetics of six medically important species during bacteremia. IMPORTANCE Bloodstream infections are a global public health problem. The goal of this work was to determine the replication characteristics of Gram-negative bacterial species in the host following bloodstream infection. The number of bacteria in major organs is likely determined by a balance between replication rates and the ability of the host to clear bacteria. We selected a cohort of six species from three families that represent common causative agents of bloodstream infections in humans and determined their replication rates in a murine bacteremia model. We found that the bacteria grow rapidly in the spleen, demonstrating that they can obtain the necessary nutrients for growth in this environment. However, the overall number of bacteria decreased in most cases, suggesting that killing of bacteria outpaces their growth. Through a better understanding of how bacteria replicate during bloodstream infections, we aim to gain insight into future means of combating these infections.

SUBMITTER: Anderson MT

PROVIDER: S-EPMC8406280 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Extensively drug-resistant Gram-negative bacterial (XDR-GNB) bloodstream infection (BSI) is difficult to treat and is associated with a high mortality rate in patients with hematological diseases. The aim of this study is to investigate the predisposing risk factors and the efficacy of the antibiotic treatment in these patients, including exploration of efficacy and adverse effects of high-dose tigecycline. Methods:Between January 2013 and December 2017, 27 XDR-GNB BSI patients with hematological diseases were diagnosed and retrospectively reviewed in the current study. Results:Clinical response in patients with severe complications (such as severe neutropenia >10 days, grade III-IV acute graft-versus-host disease (aGVHD), and concurrent pneumonia) was significantly lower than in patients without or with only mild complications (P=0.033). The efficacy rate was 62.5% (10/16) in patients with tigecycline-based combination therapy regimen, 77.8% (7/9) with a high-dose tigecycline regimen, and 42.9% (3/7) with a standard-dose tigecycline regimen (P=0.36). The 30-day survival rates of patients undergoing high-dose or standard-dose tigecycline treatment were 66.7% (95% CI: 28.2-87.8) and 57.1% (95% CI: 17.2-83.7), respectively, (P=0.603). Patients with mild complications were associated with superior 30-day survival rates than patients with severe complications (93.8% vs 36.4%, P=0.001), >10 days of neutropenia (90.9% vs 33.3%, P=0.012), severe aGVHD (100% vs 40%, P=0.049), and concurrent pneumonia (84.6% vs 57.1%, P=0.048). Conclusion:Our study indicated that XDR-GNB BSI in patients of hematological diseases with severe complications, such as long duration of neutropenia (>10 days) and severe aGVHD were associated with poor clinical response and short survival. We first indicated that these patients undergoing high-dose tigecycline treatment had an improved clinical response and an increased 30-day survival rate compared with the standard-dose group, although the differences were not statistically significant. This might be due to more severe complicated patients enrolled in high-dose group and the limited number size in our study.

Project description:BackgroundBacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters.MethodsData for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model.ResultsAfter feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible.ConclusionThe present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

Project description:BACKGROUND:Clusters of bloodstream infections caused by Burkholderia cepacia and Stenotrophomonas maltophilia are uncommon, but have been previously identified in hemodialysis centers that reprocessed dialyzers for reuse on patients. We investigated an outbreak of bloodstream infections caused by B cepacia and S maltophilia among hemodialysis patients in clinics of a dialysis organization. STUDY DESIGN:Outbreak investigation, including matched case-control study. SETTING & PARTICIPANTS:Hemodialysis patients treated in multiple outpatient clinics owned by a dialysis organization. PREDICTORS:Main predictors were dialyzer reuse, dialyzer model, and dialyzer reprocessing practice. OUTCOMES:Case patients had a bloodstream infection caused by B cepacia or S maltophilia; controls were patients without infection dialyzed at the same clinic on the same day as a case; results of environmental cultures and organism typing. RESULTS:17 cases (9 B cepacia and 8 S maltophilia bloodstream infections) occurred in 5 clinics owned by the same dialysis organization. Case patients were more likely to have received hemodialysis with a dialyzer that had been used more than 6 times (matched OR, 7.03; 95% CI, 1.38-69.76) and to have been dialyzed with a specific reusable dialyzer (Model R) with sealed ends (OR, 22.87; 95% CI, 4.49-?). No major lapses during dialyzer reprocessing were identified that could explain the outbreak. B cepacia was isolated from samples collected from a dialyzer header-cleaning machine from a clinic with cases and was indistinguishable from a patient isolate collected from the same clinic, by pulsed-field gel electrophoresis. Gram-negative bacteria were isolated from 2 reused Model R dialyzers that had undergone the facility's reprocessing procedure. LIMITATIONS:Limited statistical power and overmatching; few patient isolates and dialyzers available for testing. CONCLUSIONS:This outbreak was likely caused by contamination during reprocessing of reused dialyzers. Results of this and previous investigations demonstrate that exposing patients to reused dialyzers increases the risk for bloodstream infections. To reduce infection risk, providers should consider implementing single dialyzer use whenever possible.

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Replication Dynamics for Six Gram-Negative Bacterial Species during Bloodstream Infection.

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