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

Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines

OBJECTIVE

Bloodstream infection (BSI) is a significant cause of mortality in patients with hematologic malignancies(HMs), particularly amid rising antibiotic resistance. This study aimed to analyze pathogen distribution, drug-resistance patterns and develop a novel predictive model for 30-day mortality in HM patients with BSIs.

METHODS

A retrospective analysis of 231 HM patients with positive blood cultures was conducted. Logistic regression identified risk factors for 30-day mortality. Th1/Th2 cytokines were collected at BSI onset, with LASSO regression and restricted cubic spline analysis used to refine predictors. Seven machine learning(ML) algorithm (XGBoost, Logistic Regression, LightGBM, RandomForest, AdaBoost, GBDT and GNB) were trained using 10-fold cross-validation and model performance was evaluated with the ROC, calibration plots, decision and learning curves and the Shapley Additive Explanations (SHAP) analysis. The predictive model was developed by integrating Th1/Th2 cytokines with clinical features, aiming to enhance the accuracy of 30-day mortality prediction.

RESULTS

Among the cohort, acute myeloid leukemia (38%) was the most common HM, while gram negative bacteria (64%) were the predominant pathogens causing BSI. Age, polymicrobial BSI, IL-4, IL-6 and AST levels were significant predictors of 30-day mortality. The Logistic Regression model achieved AUCs of 0.802, 0.792, and 0.822 in training, validation, and test cohorts, respectively, with strong calibration and clinical benefit shown in decision curves. SHAP analysis highlighted IL-4 and IL-6 as key predictors.

CONCLUSIONS

This study introduces a novel ML-based model integrating Th1/Th2 cytokines and clinical features to predict 30-day mortality in HM patients with BSIs, demonstrating strong performance and clinical applicability.

Investigation of methicillin, beta lactam, carbapenem, and multidrug resistant bacteria from blood cultures of septicemia suspected patients in Northwest Ethiopia

The presence of microorganisms in the bloodstream can result in severe, potentially life-threatening conditions, which are a significant cause of morbidity and mortality worldwide. The rise of antimicrobial-resistant strains further exacerbates these outcomes. However, the data concerning bacterial profiles and resistance to antimicrobials, particularly against extended-spectrum beta-lactams and carbapenems, are limited. Aimed to characterize pathogens isolated from positive blood cultures, including bacterial profiles and antibiotic susceptibility patterns, and to identify predictors of blood culture positivity in septicemia-suspected patients at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia. A hospital-based cross-sectional study was conducted from February 15 to May 30, 2023. The study involved 341 patients suspected of having septicaemia who were selected consecutively through a convenience sampling technique. Blood samples were collected aseptically from each patient (10 ml from adults, 5 ml from children, and 1 ml from neonates) and inoculated into bottles containing tryptic soy broth in volumes appropriate for the patient's age. The samples were incubated at 35-37 °C for up to 7 days to detect bacterial growth. Positive blood cultures were subcultured onto various media, including chocolate agar, blood agar, modified Thayer-Martin agar, MacConkey agar, and mannitol salt agar, and incubated again at 35-37 °C for 24 h. The suspected bacteria were identified on the basis of colony morphology, Gram staining, and biochemical tests. Antimicrobial susceptibility testing was conducted via both the Kirby-Bauer and modified Kirby-Bauer disk diffusion methods. Resistance to methicillin, extended-spectrum beta-lactams, and carbapenems was determined via the cefoxitin disc test, combined-disk diffusion method, and modified carbapenem inactivation method, respectively. The data were entered into Epi-Data version 4.6 and analysed via SPSS version 25. Binary logistic regression analyses were employed to identify factors associated with bloodstream infections (BSI), with a P value of less than 0.05 considered statistically significant. Out of 341 patients suspected of septicemia, 196 (57.5%) were male and 145 (42.5%) were female, with a mean (± SD) age of 16.5 (± 7.5) years. Bloodstream infection was identified in 87 (25.5%) patients (95% CI: 21.1-30.4). Among these positive cases, 67 (77%) were from ward patients, while 20 (23%) were from those visiting outpatient departments. The primary gram-positive bacteria identified included S. aureus 27 (31.0%), CoNS 14 (16.1%), S. viridans 8 (9.2%), and S. agalactiae 4 (4.6%). The gram-negative isolates were predominantly K. pneumoniae 11 (12.6%), followed by E. coli 9 (10.3%), E. cloacae 6 (6.9%), Acinetobacter spp. 3 (3.5%), N. meningitidis 3 (3.5%), and P. aeruginosa 2 (2.3%). Methicillin resistance was detected in 17/27 (63.0%) S. aureus strains and 2/14 (14.3%) CoNS strains. Multidrug resistance was detected in 63/87 (72.4%, 95% CI: 67.2-84.7%) of the isolates. Extended-spectrum beta-lactamase and carbapenemase production were observed in 12/31 (38.7%) and 5/31 (16.1%) of isolates, respectively. The factors associated with BSI were the presence of wounds and burns (AOR = 2.103, 95% CI: 1.365-3.241, P = 0.041), length of hospital stay (≥ 5) (AOR = 2.209, 95% CI: 1.122-4.347, P = 0.022), and prior medical procedures (AOR = 1.982, 95% CI: 1.125-3.492, P = 0.018). Bloodstream infection was identified in 25.5% of suspected septicemia cases, with multidrug-resistant bacteria present in 72.4% of isolates. Gram-positive bacteria, particularly S. aureus, and gram-negative bacteria like K. pneumoniae and E. coli were predominant. High rates of methicillin, beta-lactam, and carbapenem resistance were observed, emphasizing the magnitude of antimicrobial resistance. Risk factors such as wounds, extended hospital stays, and prior medical procedures significantly increased the likelihood of culture positivity. This suggests the need for regular antimicrobial susceptibility testing to guide antibiotic selection and track resistance trends, proper wound care and medical device usage to reduce the risk of BSI in healthcare settings.

Geographical mapping and temporal trends of Acinetobacter baumannii carbapenem resistance: A comprehensive meta-analysis

BACKGROUND

Carbapenem-resistant Acinetobacter baumannii (CRAB) is of critical concern in healthcare settings, leading to limited treatment options. In this study, we conducted a comprehensive meta-analysis to assess the prevalence of CRAB by examining temporal, geographic, and bias-related variations.

METHODS

We systematically searched prominent databases, including Scopus, PubMed, Web of Science, and EMBASE. Quality assessment was performed using the JBI checklist. Subgroup analyses were performed based on the COVID-19 timeframes, years, countries, continents, and bias levels, antimicrobial susceptivity test method and guidelines.

RESULTS

Our comprehensive meta-analysis, which included 795 studies across 80 countries from 1995 to 2023, revealed a surge in carbapenem resistance among A. baumannii, imipenem (76.1%), meropenem (73.5%), doripenem (73.0%), ertapenem (83.7%), and carbapenems (74.3%). Temporally, 2020-2023 witnessed significant peaks, particularly in carbapenems (81.0%) and meropenem (80.7%), as confirmed by meta-regression, indicating a steady upward trend.

CONCLUSION

This meta-analysis revealed an alarmingly high resistance rate to CRAB as a global challenge, emphasizing the urgent need for tailored interventions. Transparency, standardized methodologies, and collaboration are crucial for the accurate assessment and maintenance of carbapenem efficacy.

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.