Articles: cations.
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There is a clinical need for treatments that can slow or prevent the growth of an abdominal aortic aneurysm, not only to reduce the need for surgery, but to provide a means to treat those who cannot undergo surgery. ⋯ The strong association of metformin with slower abdominal aortic aneurysm growth highlights the importance of the ongoing clinical trials assessing the effectiveness of metformin with regard to the prevention of abdominal aortic aneurysm growth and/or rupture. The association of angiotensin-converting enzyme inhibitors, angiotensin II receptor antagonists, and diuretics with slower abdominal aortic aneurysm growth points to the possibility that optimization of cardiovascular risk management as part of abdominal aortic aneurysm surveillance may have the secondary benefit of also reducing abdominal aortic aneurysm growth rates.
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The 2022 National Institute for Health and Care Excellence melanoma guideline update made significant changes to follow-up. The aim of this study was to assess the impact these changes will have on a national melanoma cohort over a 5-year follow-up interval. ⋯ Melanoma follow-up guideline changes will result in a substantial reduction in the number of clinical follow-up appointments, but a significant additional burden to radiological services. The overall cost of follow-up at a national level will be reduced.
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J Neurosurg Anesthesiol · Jan 2024
Evaluating a Novel EEG-Based Index for Stroke Detection Under Anesthesia During Mechanical Thrombectomy.
The rapid identification of acute stroke (AS) during and after anesthesia might lead to early interventions and improved outcomes. We investigated a novel 2-channel electroencephalogram (EEG)-based marker for stroke detection-the lateral interconnection ratio (LIR)-in AS patients having endovascular thrombectomy (EVT) with general anesthesia (GA) or sedation. The LIR in 2 reference groups of patients without postoperative neurological complications was used for comparison. ⋯ We demonstrated the utility of using AS patients undergoing EVT as a platform for assessing a novel EEG marker for the identification of stroke during anesthesia. Further, large-scale studies in AS patients during EVT and in patients undergoing different surgeries and anesthesia are required to validate the LIR.
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Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. ⋯ Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.