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Discover 14,244 clinical trials near Texas. Find research studies in your area.
Showing 4641-4660 of 14,244 trials
NCT04230694
Systematic continuous glucose monitoring (CGM) is commonly provided as a treatment option to patients with diabetes in ambulatory care settings yet is rarely provided during hospitalization. CGM of inpatients has the potential to be the care delivery innovation that is feasible, cost effective and can improve glucose control, especially by reducing hypoglycemic events. Studies of CGM use in the ICU setting have been found to be helpful for reducing hypoglycemia in some studies while less so in others, however, these studies were performed with earlier generation glucose monitoring devices(5). ICU studies have confirmed accuracy of CGM measurements compared with capillary glucose even in settings with use of vasopressors and large-volume resuscitation. A limited number of studies have evaluated glycemic outcomes in the inpatient non-ICU setting. Studies of non-ICU patients (6-10) are limited by very small sample size, short study duration, and use of older CGM devices. There is, therefore, a critical need to systematically investigate the use of CGM in the inpatient care of patients with diabetes mellitus who are receiving care in a hospital setting that is typical of inpatient care.
NCT05835024
Acorai is developing a non-invasive monitoring system for the estimation of intracardiac hemodynamic parameters in patients with suspected or confirmed heart failure, and/or pulmonary hypertension, who require hemodynamic assessment. The device will be intended as a companion test or clinical decision support tool to be used and interpreted by qualified healthcare professionals to aid standard-of-care clinical assessment in identifying hemodynamic congestion and supporting personalized treatment of heart failure and pulmonary congestion. This study is part of the development of a non-invasive monitoring system for the estimation of intracardiac hemodynamic parameters. It will be conducted to collect the data needed to train the machine learning models retrospectively.