Brit J Hosp Med
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Aims/Background The Geriatric Nutritional Risk Index (GNRI) is an effective tool for identifying malnutrition, and helps monitor the prognosis of patients undergoing maintenance hemodialysis. However, the association between the GNRI and cardiovascular or all-cause mortality in hemodialysis patients remains unclear. Therefore, this study investigated the correlation of the GNRI with all-cause and cardiovascular mortality in patients undergoing maintenance hemodialysis. ⋯ ROC curve analysis revealed that GNRI, age, and serum creatinine had moderate predictive value for mortality, with GNRI indicating an area under the curve (AUC) of 0.605 for all-cause mortality and 0.565 for cardiovascular mortality. Moreover, the N2 and N3 groups had a significantly reduced risk of cardiovascular mortality compared to the N1 group. Conclusion A lower GNRI is closely associated with a higher risk of all-cause and cardiovascular mortality in hemodialysis patients.
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Aims/Background Patients receiving kidney transplant experience immunosuppression, which increases the risk of bacterial, viral, fungal, and parasitic infections. Q fever is a potentially fatal infectious disease that affects immunocompromised renal transplant recipients and has implications in terms of severe consequences for the donor's kidney. Case Presentation A patient with acute Q fever infection following kidney transplantation was admitted to the Tsinghua Changgung Hospital in Beijing, China, in March 2021. ⋯ Comprehensive data on clinical symptoms, blood tests, chest computed tomography (CT), NGS, Immunoglobulin G (IgG) antibody titer, and therapeutic efficacy associated with Q fever infection following renal transplantation in this patient were gathered. Conclusion This is the first reported case of acute Q fever occurring in a Chinese renal transplant recipient detected using metagenomic NGS. This case underscores the need to consider acute Q fever as a possible differential diagnosis in kidney transplant recipients with fever of unknown origin.
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Aims/Background Hypertension (HT) is a prevalent medical condition showing an increasing incidence rate in various populations over recent years. Long-term hypertension increases the risk of the occurrence of hypertensive nephropathy (HTN), which is also a health-threatening disorder. Given that very little is known about the pathogenesis of HTN, this study was designed to identify disease biomarkers, which enable early diagnosis of the disease, through the utilization of high-throughput untargeted metabolomics strategies. ⋯ LASSO regression analysis results indicated that 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid are potential biomarkers for HTN or HT. Conclusion With untargeted metabolomics analysis, we successfully identified differential metabolites in HTN. A further LASSO regression analysis revealed that four key metabolites, namely 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid, hold promise for the diagnosis of early-stage HTN.
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Aims/Background Lobar pneumonia is an acute inflammation with increasing incidence globally. Delayed treatment can lead to severe complications, posing life-threatening risks. Thus, it is crucial to determine effective treatment methods to improve the prognosis of children with lobar pneumonia. ⋯ However, 7 days after treatment, the CD3+, CD4+, and CD4+/CD8+ levels increased significantly in the observation group compared to the control group (p < 0.001). Additionally, there was no significant difference in the incidence of adverse reactions in both groups (p > 0.05). Conclusion Pidotimod-assisted erythromycin treatment can significantly improve the treatment efficiency in children with lobar pneumonia, improving clinical signs and symptoms and enhancing the cellular immune function without increasing the risk of adverse drug reactions.
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Aims/Background Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, and reduce the subjectivity of the operating physician, which may help to achieve standardization and normalization of colonoscopy. In this study, we aimed to explore the value of using an AI-driven intestinal image recognition model to evaluate intestinal preparation before colonoscopy. Methods In this retrospective analysis, we analyzed the clinical data of 98 patients who underwent colonoscopy in Nantong First People's Hospital from May 2023 to October 2023. ⋯ The incidence of adverse reactions in the AI group (3.92%) was lower than that in the Regular group (10.64%), but the difference was not statistically significant (p > 0.05). The satisfaction rate of intestinal preparation in the AI group (96.08%) was comparable to that of the Regular group (82.98%) (p > 0.05). Conclusion Compared with the assessment based solely on the intestinal preparation map and the last fecal characteristics, the application of AI intestinal image recognition model in intestinal preparation before colonoscopy can shorten the time of colonoscopy and improve intestinal cleanliness, but with comparable patient satisfaction and safety.