Internal and emergency medicine
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Review Meta Analysis
Bleeding and thrombotic events in atrial fibrillation patients with cancer: a systematic review and meta-analysis.
Atrial fibrillation (AF) and cancer are frequently coexisting in elderly patients. Pooled metanalytic data on the impact of cancer on clinical outcomes in AF patients are lacking. We performed a systematic review and meta-regression analysis of clinical studies retrieved from Medline (PubMed) and Cochrane (CENTRAL) databases according to PRISMA guidelines. Bleeding endpoints included any, major, gastrointestinal (GI) bleeding and intracranial haemorrhage (ICH). ⋯ Patients with AF and cancer were less likely to suffer from IS/SE (HR 0.91, 95% CI 0.89-0.94). Cancer complicates the clinical history of AF patients, mainly increasing the risk of bleeding. Further analyses according to the type and stage of cancer are necessary to better stratify bleeding risk in these patients.
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Obesity is a serious and global health problem. The multiple complications of obesity reduce quality of life and increase mortality. Bariatric surgery is one of the best treatment options for obesity management. ⋯ The most frequent surgical options are Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). There is controversy regarding changes in food preferences and selection after bariatric surgery. In this review, we aim to outline the changes in food intake and selection, clarify the behavior changes in food intake, and assess the potential mechanisms responsible for these changes in patients after bariatric surgery.
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In-hospital cardiac arrest (IHCA) in the emergency department (ED) is not uncommon but often fatal. Using the machine learning (ML) approach, we sought to predict ED-based IHCA (EDCA) in patients presenting to the ED based on triage data. We retrieved 733,398 ED records from a tertiary teaching hospital over a 7 year period (Jan. 1, 2009-Dec. 31, 2015). ⋯ Although the differences between each of ML models and LR (AUC: 0.905, 95% CI 0.882-0.926) were not significant, all constructed ML models performed significantly better than using the NEWS2 scoring system (AUC 0.678, 95% CI 0.635-0.722). Our ML models showed excellent discriminatory performance to identify EDCA based only on the triage information. This ML approach has the potential to reduce unexpected resuscitation events if successfully implemented in the ED information system.