A 2-year prospective study carried out on ventilator-associated pneumonia (VAP) patients in the intensive care unit at a tertiary care hospital, Hail, Kingdom of Saudi Arabia (KSA), revealed a high prevalence of extremely drug-resistant (XDR) . About a 9% increase in the incidence rate of occurred in the VAP patients between 2019 and 2020 (21.4% to 30.7%). In 2019, the isolates were positive for IMP-1 and VIM-2 (31.1% and 25.7%, respectively) as detected by PCR. In comparison, a higher proportion of isolates produced NDM-1 in 2020. Here, we observed a high proportion of resistant ICU isolates towards the most common antibiotics in use. Colistin sensitivity dropped to 91.4% in the year 2020 as compared to 2019 (100%). Thus, the finding of this study has a highly significant clinical implementation in the clinical management strategies for VAP patients. Furthermore, strict implementation of antibiotic stewardship policies, regular surveillance programs for antimicrobial resistance monitoring, and screening for genes encoding drug resistance phenotypes have become imperative.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9690950 | PMC |
http://dx.doi.org/10.3390/healthcare10112210 | DOI Listing |
Surg Infect (Larchmt)
January 2025
Department of Surgery, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
It is unclear why differences in patient location change organisms causing ventilator-associated pneumonia (VAP). We investigated VAP organisms in three geographically separate trauma intensive care units (TICUs). A retrospective review of organisms causing VAP (bronchoalveolar lavage [BAL] performed ≤7 d after admission and growing ≥10 cfu/mL) in three geographically separate TICUs was conducted.
View Article and Find Full Text PDFSyst Rev
January 2025
Department of Neurosurgery, Pingxiang People's Hospital, Pingxiang, Jiangxi Province, 337000, China.
Background: A systematic appraisal of the comparative efficacy and safety profiles of naso-intestinal tube versus gastric tube feeding in the context of enteral nutrition for mechanically ventilated (MV) patients is imperative. Such an evaluation is essential to inform clinical practice, ensuring that the chosen method of nutritional support is both optimal and safe for this patient population.
Methods: We executed an exhaustive search across PubMed et al.
Chest
January 2025
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. Electronic address:
Background: Ventilator-associated pneumonia (VAP) rates are higher in low- and middle-income countries (LMICs) than in high-income countries (HICs).
Research Question: Could differences in ventilator bundle adherence, ventilation practices, and critical care staffing be driving variations in VAP risk between LMICs and HICs?
Study Design And Methods: This secondary analysis of the multicenter, international CERTAIN study included mechanically ventilated patients at risk for VAP from eleven LMICs and five HICs. We included oral care, head-of-bed elevation, spontaneous breathing assessments, and sedation breaks in the ventilator bundle.
Crit Care Med
January 2025
Department of Surgery, University of Southern California, Los Angeles, CA.
Objectives: To explore practice variations in the rate and timing of tracheostomy and gastrostomy for adolescent with severe traumatic brain injury (TBI) across trauma center types.
Design: Retrospective cohort study.
Setting: Trauma centers participating in the American College of Surgeons Trauma Quality Improvement Program (2017-2021) included adult (ATC), mixed (MTC), and pediatric trauma centers (PTC).
PLoS One
January 2025
Department of Anesthesiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Background: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital mortality in VAP patients. This study aims to develop an interpretable machine learning (ML) prediction model to enhance the assessment of in-hospital mortality risk in VAP patients.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!