Diabetes care
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The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as "diabetes." Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. ⋯ The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.
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Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and outcomes and to analyze the risk factors for in-hospital mortality of COVID-19 patients with diabetes. ⋯ COVID-19 patients with diabetes had worse outcomes compared with the sex- and age-matched patients without diabetes. Older age and comorbid hypertension independently contributed to in-hospital death of patients with diabetes.
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To examine trends in uninsured rates between 2012 and 2016 among low-income adults aged <65 years and to determine whether the Patient Protection and Affordable Care Act (ACA), which expanded Medicaid, impacted insurance coverage in the Diabetes Belt, a region across 15 southern and eastern U.S. states in which residents have high rates of diabetes. ⋯ ACA-driven Medicaid expansion was more significantly associated with reduced uninsured rates in Diabetes Belt than in non-Belt counties. Initial disparities in uninsured rates between Diabetes Belt and non-Belt counties have not existed since 2014 among expansion states. Future studies should examine whether and how Medicaid expansion may have contributed to an increase in the use of health services in order to prevent and treat diabetes in the Diabetes Belt.
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Patients with obesity are at increased risk of exacerbations from viral respiratory infections. However, the association of obesity with the severity of coronavirus disease 2019 (COVID-19) is unclear. We examined this association using data from the only referral hospital in Shenzhen, China. ⋯ In this study, obese patients had increased odds of progressing to severe COVID-19. As the severe acute respiratory syndrome coronavirus 2 may continue to spread worldwide, clinicians should pay close attention to obese patients, who should be carefully managed with prompt and aggressive treatment.
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Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.
We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms. ⋯ Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.