Data in the literature on the use of ceftazidime-avibactam (CAZ-AVI) in carbapenem-resistant Klebsiella pneumoniae bloodstream infections (CRKP-BSIs) are limited especially in OXA-48 (Oxacillinase-48) predominant regions. Our study aimed to evaluate the effect of CAZ-AVI use on outcomes in CRKP-BSIs in Turkey, where OXA-48 is endemic. A multicenter retrospective observational study was conducted between January 2017 and September 2021. The effects of clinical and treatment characteristics on 30-day mortality and relapse in CRKP-BSIs were analyzed. Predictors of outcomes were detected using a Cox regression model. The study enrolled 106 adults with CAZ-AVI-sensitive CRKP-BSIs who received CAZ-AVI for at least 72 h. Patients who received CAZ-AVI as initial therapy had lower mortality rates when compared to those who switched from last resort regimens [14.3% (n = 3/21) vs. 37.7% (n = 32/85), p = 0.04]. In multivariate analysis, older age and severe neutropenia were detected to be associated with higher mortality, significantly. Initiation of CAZ-AVI on the day of blood culture was obtained, was found to be significantly associated with lower mortality (HR: 0.25, CI: 0.07–0.84, p = 0.025). CAZ-AVI monotherapy is an important treatment option for CRKP-BSIs in OXA-48 endemic areas. Early initiation of CAZ-AVI should be preferred rather than switching from a last-resort regimen as it profoundly improves the survival rates.

Background: Ceftazidime–avibactam (CAZ-AVI) is recommended as first-line treatment for Oxacillinase-48 (OXA-48) β-Lactamase-producing carbapenem-resistant Enterobacterales (CRE) infections, while polymyxin-based combination therapies (PBCTs) are used as a last resort when CAZ-AVI is unavailable. Research comparing the effectiveness of CAZ-AVI and PBCT in CRE blood stream infections (CRE-BSIs) is limited, mostly focusing on Klebsiella pneumoniae carbapenemase (KPC)-producing isolates. In Turkey, OXA-48 is endemic and OXA-48-Like is common. Therefore, our study aimed to compare the impact of these treatments on 30-day mortality in patients with CRE-BSIs in endemic regions. Methods: Retrospective data from January 2019 to May 2023 were collected from four tertiary healthcare centers in Istanbul. Demographic, clinical, and outcome data of ICU patients treated with CAZ-AVI monotherapy or PBCT for CRE-BSIs were analyzed. The effect on 30-day survival was evaluated using Cox regression analysis post propensity score matching (PSM). Results: Out of 151 patients, 44.4% (n: 67) received CAZ-AVI and 55.6% (n: 84) received PBCT. All-cause mortality rates were 20% (n: 13) with CAZ-AVI and 36.9% (n: 31) with PBCT. Cox regression analysis post PSM indicated CAZ-AVI monotherapy significantly reduced the mortality risk compared to PBCT (HR: 0.16, 95%CI: 0.07–0.37, p < 0.001), while age increased the risk (HR: 1.02 per year, 95% CI 1.0–1.04, p: 0.01). Conclusions: In OXA-48-predominant areas, CAZ-AVI demonstrated significantly lower mortality in patients with CRE-BSIs compared to PBCT. The results were attributed to the pharmacokinetic and pharmacodynamic disadvantages of polymyxins compared to CAZ-AVI, and the impact of age-related physical conditions. Therefore, CAZ-AVI should be the preferred treatment for CRE-BSIs in OXA-48-endemic regions.

Background: Managing Pseudomonas aeruginosa bloodstream infections (BSIs) is challenging due to increasing antimicrobial resistance, limited therapeutic options, and high mortality rates. In this study, we aimed to identify 30-day mortality risk factors and assess infectious diseases consultants’ preferences for combination or monotherapy. Methods: The study was conducted in four hospitals in Istanbul, Turkey, involving 140 adult ICU beds and 336,780 ICU-bed-days between 1 January 2014, and 31 December 2021. A total of 157 patients were included in the study. Cox proportional hazard regression was performed to assess the factors on 30-day mortality. Results: The 30-day mortality rate was 44.6% (70/157). Higher Charlson Comorbidity Index (CCI) score, severe sepsis, primary bloodstream infection, being in COVID-19 pandemic period, and infection caused by MDR strain were associated with higher hazard of 30-day mortality. Combination therapy was more commonly used in patients with BSIs with MDR or DTR (difficult-to-treat) strains but did not significantly improve the hazard of 30-day mortality. Conclusions: Targeted interventions and vigilant management strategies are crucial for patients with defined risk factors. While infectious disease consultants tended to favor combination therapy, particularly for drug-resistant strains, our analysis revealed no significant impact on 30-day mortality hazard. The increased incidence of P. aeruginosa BSIs during the pandemic emphasizes the need for infection control measures and appropriate antibiotic prescribing practices.

Background/Objectives: Studies attempting to predict the development of severe respiratory failure in patients with a COVID-19 infection using machine learning algorithms have yielded different results due to differences in variable selection. We aimed to predict the development of severe respiratory failure, defined as the need for high-flow oxygen support, continuous positive airway pressure, or mechanical ventilation, in patients with COVID-19, using machine learning algorithms to identify the most important variables in achieving this prediction. Methods: This retrospective, cross-sectional study included COVID-19 patients with mild respiratory failure (mostly receiving oxygen through a mask or nasal cannula). We used XGBoost, support vector machines, multi-layer perceptron, k-nearest neighbor, random forests, decision trees, logistic regression, and naïve Bayes methods to accurately predict severe respiratory failure in these patients. Results: A total of 320 patients (62.1% male; average age, 54.67 ± 15.82 years) were included in this study. During the follow-ups of these cases, 114 patients (35.6%) required high-level oxygen support, 67 (20.9%) required intensive care unit admission, and 43 (13.4%) died. The machine learning algorithms with the highest accuracy values were XGBoost, support vector machines, k-nearest neighbor, logistic regression, and multi-layer perceptron (0.7395, 0.7395, 0.7291, 0.7187, and 0.75, respectively). The method that obtained the highest ROC-AUC value was logistic regression (ROC-AUC = 0.7274). The best predictors of severe respiratory failure were a low lymphocyte count, a high computed tomography score in the right and left upper lung zones, an elevated neutrophil count, a small decrease in CRP levels on the third day of admission, a high Charlson comorbidity index score, and a high serum procalcitonin level. Conclusions: The development of severe respiratory failure in patients with COVID-19 could be successfully predicted using machine learning methods, especially logistic regression, and the best predictors of severe respiratory failure were the lymphocyte count and the degree of upper lung zone involvement.

Streptococcus pneumoniae,community-acquired pneumonia,adult