Brit J Hosp Med
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Aims/Background The background for establishing and verifying a dehydration prediction model for elderly patients with post-stroke dysphagia (PSD) based on General Utility for Latent Process (GULP) is as follows: For elderly patients with PSD, GULP technology is utilized to build a dehydration prediction model. This aims to improve the accuracy of dehydration risk assessment and provide clinical intervention, thereby offering a scientific basis and enhancing patient prognosis. This research highlights the innovative application of GULP technology in constructing complex medical prediction models and addresses the special health needs of elderly stroke patients. ⋯ In the validation set, the AUC was 0.867 with a standard error of 0.025 and a 95% CI of 0.694 to 0.934. The optimal cutoff value here was 0.66, with a sensitivity of 80.16% and a specificity of 85.94%. Conclusion This study successfully established and validated a GULP-based dehydration prediction model for elderly patients with dysphagia following a stroke, demonstrating high application value.
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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 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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The contribution of health care to environmental and climate crises is significant, under-addressed, and with consequences for human health. This editorial is a call to action. Focusing on pharmaceuticals as a major environmental threat, we examine pharmaceutical impacts across their lifecycle, summarising greenhouse gas emissions, pollution, and biodiversity loss, and outlining challenges and opportunities to reduce this impact. We urge health care decision-makers and providers to urgently consider environmental factors in their decision-making relating to both policy, and practice, promoting actions such as rational prescribing, non-pharmaceutical interventions, and research and advocacy for sustainable production, procurement, and use.
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Predictive algorithms have myriad potential clinical decision-making implications from prognostic counselling to improving clinical trial efficiency. Large observational (or "real world") cohorts are a common data source for the development and evaluation of such tools. There is significant optimism regarding the benefits and use cases for risk-based care, but there is a notable disparity between the volume of clinical prediction models published and implementation into healthcare systems that drive and realise patient benefit. Considering the perspective of a clinician or clinical researcher that may encounter clinical predictive algorithms in the near future as a user or developer, this editorial: (1) discusses the ways in which prediction models built using observational data could inform better clinical decisions; (2) summarises the main steps in producing a model with special focus on key appraisal factors; and (3) highlights recent work driving evolution in the ways that we should conceptualise, build and evaluate these tools.