Bacteriological Profile and Antimicrobial Susceptibility Patterns of Gram-Negative Bloodstream Infection and Risk Factors Associated with Mortality and Drug Resistance: A Retrospective Study from Shanxi, China

Characterization of the Pathogen Distribution and Drug Resistance in Bloodstream Infections During COVID-19 Pandemic in a Tertiary Hospital in Eastern China: Comparison with the Pre-Pandemic Period

Purpose

To explore the characteristics of the pathogen distribution and drug resistance in bloodstream infections (BSIs) during the COVID-19 pandemic in a tertiary hospital in eastern China, and to compare them with those before the pandemic.

Patients and Methods

Non-repetitive strain data of BSIs were retrospectively obtained before the COVID-19 pandemic (Pre-Pandemic, n=2698) and during the COVID-19 pandemic (Pandemic, n=2922), the distribution of pathogens and drug resistance were compared between the two groups.

Results

The main pathogens of BSIs were Gram-negative bacteria (57.91%), followed by Gram-positive bacteria (32.58%), fungi and anaerobic bacteria accounting for 5.48% and 3.39%, respectively. Escherichia coli, Klebsiella pneumoniae and Staphylococcus aureus were the top 3 isolates. The proportion of Serratia marcescens, Enterobacter aerogenes, Enterococcus faecium, Enterococcus faecalis and Candida tropicalis were significantly increased, while those of Pseudomonas aeruginosa, Streptococcus sanguinis and Streptococcus pneumoniae were significantly decreased when compared to the Pre-Pandemic (P<0.05). Carbapenem-resistant Enterobacterales (CRE) significantly elevated during the Pandemic (17.4% vs 14.4%, P=0.041); the detection of carbapenem-resistant Pseudomonas aeruginosa (CRPA) significantly ascended (39.0% vs 24.4%, P=0.016); and the proportion of carbapenem-resistant Acinetobacter baumannii (CRAB) maintained stable (78.8%). Gram-positive bacteria had the lowest resistance to linezolid, vancomycin and tigecycline, which remained a stable trend with the Pre-Pandemic (<5.0%). The isolate rates of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) were 38.9% and 1.0%, respectively. Staphylococcus aureus showed a decrease in the isolation rate of vancomycin minimum inhibitory concentration (MIC) ≤ 0.5 μg/mL (χ2=7.676, P=0.006) and an increase with vancomycin MIC=1 μg/mL (χ2=9.008, P=0.003).

Conclusion

The pathogen distribution and drug resistance of BSIs during the COVID-19 pandemic were transformed from Pre-Pandemic and accompanied by increasing bacterial resistance. Clinical management of antibiotic application and infection control should be strengthened.

Antibiogram Profiling of Antibiotics in Ear, Nose, and Throat Infections in Tertiary Healthcare Settings

Introduction Antibiotic resistance is an emerging threat in tertiary healthcare settings, with increased usage of antibiotics on patients having ear, nose, and throat (ENT) infections, the bacterial strains are becoming resistant to its treatment causing antibiotic resistance and ineffective treatment. This study focuses on the antibiogram profiling of bacterial pathogens by the conventional disc diffusion method in a tertiary healthcare setting and the recent method using a matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF) to identify bacterial strains isolated from infections of the ENT. Materials and methods Swab samples were collected from patients with ENT infections and were subjected to bacteriological and proteomic studies to assess the status of drug-resistant pathogens. About 125 samples were subjected to an antimicrobial susceptibility test by disc diffusion, and the bacterial isolates were screened on MALDI-TOF for identification. Result The study identified beta-hemolytic Streptococci as the most prevalent bacterial species, followed by Pseudomonas aeruginosa and Staphylococcus aureus. MALDI-TOF analysis yielded high identification accuracy for beta-hemolytic Streptococcus pyogenes, and the antibiogram profile of bacterial isolates indicated that most of the bacteria are resistant to penicillin, amoxicillin, and chloramphenicol.  Conclusion  The study emphasized the importance of appropriate antibiotic selection in treating ENT infections, considering local antibiograms and understanding antibiotic resistance patterns. This shall aid clinicians in choosing effective antibiotics, reducing treatment failure, and preventing the emergence of antibiotic resistance. Overall, the research provides valuable insights into antibiotic resistance in ENT infections.

Establishment and Validation of a Risk Prediction Model for Mortality in Patients with Acinetobacter baumannii Infection: A Retrospective Study

Purpose

This study aims to establish a valuable risk prediction model for mortality in patients with Acinetobacter baumannii (A. baumannii).

Patients and Methods

The 622 patients with A. baumannii infection from the First Affiliated Hospital of Anhui Medical University were enrolled as the study cohort. Univariate and multivariate logistic regression analysis was used to preliminarily screen the independent risk factors of death caused by A. baumannii infection, followed by LASSO regression analysis to determine the risk factors. According to the calculated regression coefficient, the Nomogram death prediction model is established. The area under the curve (AUC) and decision curve analysis (DCA) of the operating characteristic (ROC) curve of the subjects are used to evaluate the discrimination of the established prediction model. The calibration degree of the prediction model is represented by a calibration chart. A validation cohort that consisted of 477 patients admitted to the 901st Hospital was also included.

Results

Our results revealed that the source of infection, carbapenem-resistant A. baumannii, mechanical ventilation, serum albumin value, and Charlson comorbidity index were independent risk factors for death caused by A. baumannii infection. The AUC value of ROC curves of study cohort and validation cohort were 0.76 and 0.69, respectively. The probability range (30-80%) indicated a high net income of the modified model and strong capacity of discrimination. The calibration curve obtained by analysis swings up and down around the 45 diagonal line, which shows that the calibration degree of the prediction model is very high.

Conclusion

In this study, we have reconstructed a risk prediction model for mortality in patients with A. baumannii infections. This model provides useful information to predict the risk of death in patients with A. baumannii infection, but the specificity is not optimistic. If this prediction model is wanted to be applied to clinical practice, more analysis and research are necessary.

Microbiology and prognostic prediction model of bloodstream infection in patients with hematological malignancies

Background

In recent years, with the continuous development of treatments for hematological malignancies (HMs), the remission and survival rates of patients with HMs have been significantly improved. However, because of severe immunosuppression and long-term recurrent neutropenia during treatment, the incidence and mortality of bloodstream infection (BSI) were all high in patients with HMs. Therefore, we analyzed pathogens' distribution and drug-resistance patterns and developed a nomogram for predicting 30-day mortality in patients with BSIs among HMs.

Methods

In this retrospective study, 362 patients with positive blood cultures in HMs were included from June 2015 to June 2020 at West China Hospital of Sichuan University. They were randomly divided into the training cohort (n = 253) and the validation cohort (n = 109) by 7:3. A nomogram for predicting 30-day mortality after BSIs in patients with HMs was established based on the results of univariate and multivariate logistic regression. C-index, calibration plots, and decision curve analysis were used to evaluate the nomogram.

Results

Among 362 patients with BSIs in HMs, the most common HM was acute myeloid leukemia (48.1%), and the most common pathogen of BSI was gram-negative bacteria (70.4%). The final nomogram included the septic shock, relapsed/refractory HM, albumin <30g/l, platelets <30×10⁹/l before BSI, and inappropriate empiric antibiotic treatment. In the training and validation cohorts, the C-indexes (0.870 and 0.825) and the calibration plots indicated that the nomogram had a good performance. The decision curves in both cohorts showed that the nomogram model for predicting 30-day mortality after BSI was more beneficial than all patients with BSIs or none with BSIs.

Conclusion

In our study, gram-negative bacterial BSIs were predominant in patients with HMs. We developed and validated a nomogram with good predictive ability to help clinicians evaluate the prognosis of patients.

Antimicrobial Activity of Apis mellifera Bee Venom Collected in Northern Peru

Due to the emergence of microorganisms resistant to antibiotics and the failure of antibiotic therapies, there is an urgent need to search for new therapeutic options, as well as new molecules with antimicrobial potential. The objective of the present study was to evaluate the in vitro antibacterial activity of Apis mellifera venom collected in the beekeeping areas of the city of Lambayeque in northern Peru against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. Bee venom extraction was performed by electrical impulses and separated using the Amicon ultra centrifugal filter. Subsequently, the fractions were quantified by spectrometric 280 nm and evaluated under denaturant conditions in SDS-PAGE. The fractions were pitted against Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 29213, and Pseudomonas aeruginosa ATCC 27853. A purified fraction (PF) of the venom of A. mellifera and three low molecular weight bands of 7 KDa, 6 KDa, and 5 KDa were identified that showed activity against E. coli with a MIC of 6.88 µg/mL, while for P. aeruginosa and S. aureus, it did not present a MIC. No hemolytic activity at a concentration lower than 15.6 µg/mL and no antioxidant activity. The venom of A. mellifera contains a potential presence of peptides and a predilection of antibacterial activity against E. coli.