Outcomes of the Dulce Digital-COVID Aware (DD-CA) discharge texting platform for US/Mexico border Hispanic individuals with diabetes.

BACKGROUND: Hispanic individuals have higher type 2 diabetes (T2D) prevalence, poorer outcomes, and are disproportionately affected by COVID-19. Culturally-tailored, diabetes educational text messaging has previously improved HbA1c in this population. METHODS: During the pandemic, hospitalized Hispanic adults with T2D (N = 172) were randomized to receive Dulce Digital-COVID Aware ("DD-CA") texting platform upon discharge plus diabetes transition service (DTS) or DTS alone. DD-CA includes diabetes educational messaging with additional COVID-safe messaging (e.g., promoting masking; social distancing; vaccination). FINDINGS: Among adults with poorly-controlled diabetes (Mean HbA1c = 9.6 ± 2.2 %), DD-CA did not reduce 30- or 90-day readmissions compared to standard care (28 % vs 15 %, p = .06; 37 % vs 35 %, p = .9, respectively). However, the improvement in HbA1c was larger among those in the DD-CA compared to DTS at 3 months (n = 56; -2.69 % vs. -1.45 %, p = .0496) with reduced effect at 6 months (n = 64; -2.03 % vs -0.91 %, p = .07). Low follow-up completion rates and the addition of covariates (to control for baseline group differences that existed despite randomization) impacted statistical power. INTERPRETATION: During the pandemic, DD-CA offered an alternative digital approach to diabetes and COVID education and support for a high-risk Hispanic population and achieved trends toward improvement in glycemic control despite relatively low engagement and not reducing hospital readmissions.

Real-Time Continuous Glucose Monitoring in the Hospital: A Real-World Experience.

BACKGROUND: Glycemic control in the hospital setting is imperative for improving outcomes among patients with diabetes. Bedside point-of-care (POC) glucose monitoring has remained the gold standard for decades, while only providing momentary glimpses into a patient's glycemic control. Continuous glucose monitoring (CGM) has been shown to improve glycemic control in the ambulatory setting. However, a paucity of inpatient experience and data remains a barrier to US Food and Drug Administration (FDA) approval and expanded/non-research use in the hospital setting. METHOD: Amid the COVID-19 pandemic, the FDA exercised its enforcement discretion to not object to the use of CGM systems for the treatment of patients in hospital settings to support COVID-19 health care-related efforts to reduce viral exposure of health care workers. Following this announcement, Scripps Health, a large not-for-profit health care system in San Diego, California, implemented CGM as the new "standard of care" (CGM as SOC) for glucose monitoring and management in the hospital. RESULTS: The present report serves to (1) detail the implementation procedures for employing this new SOC; (2) describe the patients receiving CGM as SOC, their glycemic control, and hospital outcomes; and (3) share lessons learned over two years and nearly 900 hospital encounters involving CGM. CONCLUSIONS: Here, we conclude that CGM is feasible in the hospital setting by using a dedicated diabetes care team and the CGM technology with remote monitoring.

The Effect of Hydration on Urine Color Objectively Evaluated in CIE L*a*b* Color Space.

Urine color has been shown to be a viable marker of hydration status in healthy adults. Traditionally, urine color has been measured using a subjective color scale. In recent years, tristimulus colorimetry developed by the International Commission on Illumination (CIE L*a*b*) has been widely adopted as the reference method for color analysis. In the L*a*b* color space, L* indicates lightness ranging from 100 (white) to 0 (black), while a* and b* indicate chromaticity. a* and b* are color directions: -a* is the green axis, +a* is the red axis, -b* is the blue axis, and +b* is the yellow axis. The L*a*b* color space model is only accurately represented in three-dimensional space. Considering the above, the purpose of the current study was to evaluate urine color during different hydration states, with the results expressed in CIE L*a*b* color space. The study included 28 healthy participants (22 males and 6 females) ranging between the age of 20 and 67 years (28.6 ± 11.3 years). One hundred and fifty-one urine samples were collected from the subjects in various stages of hydration, including morning samples after 7-15 h of water deprivation. Osmolality and CIE L*a*b* parameters were measured in each sample. As the urine osmolality increased, a significant linear increase in b* values was observed as the samples became more pronouncedly yellow (τb = 0.708). An increase in dehydration resulted in darker and significantly more yellow urine, as L* values decreased in lightness and b* values increased along the blue-yellow axis. However, as dehydration increased, a notable polynomial trend in color along the green-red axis was observed as a* values initially decreased, indicating a green hue in slightly dehydrated urine, and then increased as urine became more concentrated and thus more dehydrated. It was determined that 74% of the variance seen in urine osmolality was due to CIE L*a*b* variables. This newfound knowledge about urine color change along with the presented regression model for predicting urine osmolality provides a more detailed and objective perspective on the effect of hydration on urine color, which to our knowledge has not been previously researched.