Introduction: Management of ventilator-associated pneumonia (VAP), the most common infection in patients on mechanical ventilation, should be tailored to local microbiological data. The aim of this study was to determine susceptibility patterns of organisms causing VAP to develop a treatment algorithm based on these findings and evidence from the literature.
Materials And Methods: This is a retrospective analysis of the microbiological etiology of VAP in the intensive care unit (ICU) of a Lebanese tertiary care hospital from July 2015 to July 2016. We reviewed the latest clinical practice guidelines on VAP and tried to adapt these recommendations to our setting.
Results: In all, 43 patients with 61 VAP episodes were identified, and 75 bacterial isolates caused VAP. Extensively drug-resistant (XDR) was the most common organism (37%), and it had occurred endemically throughout the year. was the next most common organism (31%), and 13% were XDR. Enterobacteriaceae (15%) and (12%) shared similar incidences. Our algorithm was based on guidelines, in addition to trials, systematic reviews, and meta-analyses that studied the effectiveness of available antibiotics in treating VAP.
Conclusion: Knowing that resistance can rapidly develop within a practice environment, more research is needed to identify the best strategy for the management of VAP.
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http://dx.doi.org/10.2147/IDR.S145827 | DOI Listing |
Cancer Cell Int
December 2024
Department of Plastic and Aesthetic Surgery, the Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.
Background: Cutaneous melanoma is one of the most invasive and lethal skin malignant tumors. Compared to primary melanoma, metastatic melanoma (MM) presents poorer treatment outcomes and a higher mortality rate. The tumor microenvironment (TME) plays a critical role in MM progression and immunotherapy resistance.
View Article and Find Full Text PDFBMC Res Notes
December 2024
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
This dataset contains demographic, morphological and pathological data, endoscopic images and videos of 191 patients with colorectal polyps. Morphological data is included based on the latest international gastroenterology classification references such as Paris, Pit and JNET classification. Pathological data includes the diagnosis of the polyps including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory and Adenocarcinoma with Dysplasia Grade & Differentiation.
View Article and Find Full Text PDFBMC Public Health
December 2024
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
View Article and Find Full Text PDFBMC Infect Dis
December 2024
Infectious Disease Hospital of Heilongjiang Province, No. 1 Jian She Street, Hulan District, Harbin, Heilongjiang, 150500, China.
Background: Tuberculosis (TB) remains a significant global health issue. Drug-resistant TB and comorbidities exacerbate its burden, influencing treatment outcomes and healthcare utilization. Despite the growing prevalence of TB comorbidities, research often focuses on single comorbidities rather than comorbidity patterns.
View Article and Find Full Text PDFAcad Radiol
December 2024
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China (Y.T., Y.W., Y.Y., X.Q., Y.H., J.L.); Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021, Guangxi Zhuang Autonomous Region, PR China (J.L.). Electronic address:
Rationale And Objectives: To develop a radiomics nomogram based on clinical and magnetic resonance features to predict lymph node metastasis (LNM) in endometrial cancer (EC).
Materials And Methods: We retrospectively collected 308 patients with endometrial cancer (EC) from two centers. These patients were divided into a training set (n=155), a test set (n=67), and an external validation set (n=86).
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