A ten-year surveillance of P aeruginosa bloodstream infections in a tertiary care hospital: Trends and risk factors for mortality

Clinical characteristics and prognosis analysis of pseudomonas aeruginosa bloodstream infection in adults: a retrospective study

Pseudomonas aeruginosa bloodstream infections (PA BSIs) in adults, especially those complicated by sepsis, are associated with high rates of morbidity and mortality. Early identification of risk factors for both mortality and sepsis-induced coagulopathy (SIC) is critical to optimizing patient management and improving outcomes. We conducted a retrospective analysis of 118 adult patients diagnosed with PA BSIs at the Affiliated Hospital of Xuzhou Medical University from January 2022 to February 2024. Univariate analysis was employed to identify significant clinical factors, followed by multivariate stepwise logistic regression to determine independent predictors of mortality and SIC. Based on these findings, nomogram models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), Bootstrap resampling, and calibration plots to assess model performance. Empiric sensitive antibiotic therapy (ESAT) (OR = 0.039, P < 0.001), coronary artery disease (CAD) (OR = 10.315, P = 0.010), and invasive mechanical ventilation (OR = 3.926, P = 0.020) emerged as significant predictors of mortality. In contrast, elevated C-reactive protein (CRP) (OR = 1.011, P = 0.003), procalcitonin (PCT) (OR = 1.030, P = 0.005), and lower hemoglobin levels (OR = 0.963, P = 0.004) were independently associated with SIC. The AUC of mortality prediction model is 0.908, while the SIC prediction model yielded an AUC of 0.817. The predictive models developed in this study demonstrate early identification of mortality rates and SIC risk in PA BSI patients, which may have the potential to guide timely therapeutic interventions and improve clinical outcomes in this high-risk population.

Antibody-Mediated Serum Resistance Protects Pseudomonas aeruginosa During Bloodstream Infections

BACKGROUND

Pseudomonas aeruginosa is a frequent pathogen isolated from bacterial bloodstream infection (BSI) and is associated with high mortality. To survive in the blood, P aeruginosa must resist the bactericidal action of complement (ie, serum killing). Antibodies usually promote serum killing through the classical complement pathway; however, "cloaking antibodies" (cAbs) have been described, which paradoxically protect bacteria from serum killing. The relevance of cAbs in P aeruginosa BSI is unknown.

METHODS

Serum and P aeruginosa were collected from a cohort of 100 patients with BSI. Isolates were tested for sensitivity to healthy control serum (HCS). cAb prevalence was determined in sera. Patient sera were mixed with HCS to determine if killing of the matched isolate was inhibited.

RESULTS

Overall, 36 patients had elevated titers of cAbs, and 34 isolates were sensitive to HCS killing. Fifteen patients had cAbs and HCS-sensitive isolates; of these patients, 14 had serum that protected their matched bacteria from HCS killing. Patients with cAbs were less likely to be neutropenic or have comorbidities.

CONCLUSIONS

cAbs are prevalent in patients with P aeruginosa BSI and allow survival of otherwise serum-sensitive bacteria in the bloodstream. Generation of cAbs may be a risk factor for the development of BSI.

Incidence, antimicrobial resistance and mortality of Pseudomonas aeruginosa bloodstream infections among hospitalized patients in China: a retrospective observational multicenter cohort study from 2017 to 2021

Background

Pseudomonas aeruginosa (P. aeruginosa) accounts for high antimicrobial resistance and mortality rates of bloodstream infections (BSIs). We aim to investigate incidence, antimicrobial resistance and risk factors for mortality of P. aeruginosa BSIs among inpatients.

Methods

A retrospective cohort study were conducted at two tertiary hospitals in 2017-2021. Medical and laboratory records of all inpatients diagnosed with P. aeruginosa BSIs were reviewed. A generalized linear mixed model was used to identify risk factors for mortality.

Results

A total of 285 patients with P. aeruginosa BSIs were identified. Incidence of P. aeruginosa BSIs fluctuated between 2.37 and 3.51 per 100,000 patient-days over the study period. Out of 285 P. aeruginosa isolates, 97 (34.04%) were carbapenem-resistant (CR) and 75 (26.32%) were multidrug-resistant (MDR). These isolates showed low resistance to aminoglycosides (9.51-11.62%), broad-spectrum cephalosporins (17.19-17.61%), fluoroquinolones (17.25-19.43%), and polymyxin B (1.69%). The crude 30-day mortality rate was 17.89% (51/285). Healthcare costs of patients with MDR/CR isolates were significantly higher than those of patients with non-MDR/CR isolates (P < 0.001/=0.002). Inappropriate definitive therapy [adjusted odds ratio (aOR) 4.47, 95% confidence interval (95% CI) 1.35-14.77; P = 0.014], ICU stay (aOR 2.89, 95% CI: 1.26-6.63; P = 0.012) and corticosteroids use (aOR 2.89, 95% CI: 1.31-6.41; P = 0.009) were independently associated with 30-day mortality.

Conclusion

Incidence of P. aeruginosa BSIs showed an upward trend during 2017-2020 but dropped in 2021. MDR/CR P. aeruginosa BSIs are associated with higher healthcare costs. Awareness is required that patients with inappropriate definitive antimicrobial therapy, ICU stay and corticosteroids use are at higher risk of death from P. aeruginosa BSIs.

A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa

Pseudomonas aeruginosa is a ubiquitous opportunistic bacterial pathogen, which is a leading cause of nosocomial pneumonia. Early identification of the risk factors is urgently needed for severe infection patients with P. aeruginosa. However, no detailed relevant investigation based on machine learning has been reported, and little research has focused on exploring relationships between key risk clinical variables and clinical outcome of patients. In this study, we collected 571 severe infections with P. aeruginosa patients admitted to the Xijing Hospital of the Fourth Military Medical University from January 2010 to July 2021. Basic clinical information, clinical signs and symptoms, laboratory indicators, bacterial culture, and drug related were recorded. Machine learning algorithm of XGBoost was applied to build a model for predicting mortality risk of P. aeruginosa infection in severe patients. The performance of XGBoost model (AUROC = 0.94 ± 0.01, AUPRC = 0.94 ± 0.03) was greater than the performance of support vector machine (AUROC = 0.90 ± 0.03, AUPRC = 0.91 ± 0.02) and random forest (AUROC = 0.93 ± 0.03, AUPRC = 0.89 ± 0.04). This study also aimed to interpret the model and to explore the impact of clinical variables. The interpretation analysis highlighted the effects of age, high-alert drugs, and the number of drug varieties. Further stratification clarified the necessity of different treatment for severe infection for different populations.