A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

The Impact of COVID-19 on Care Seeking Behavior of Patients at Tertiary Care Follow-up Clinics: A Cross-Sectional Telephone Survey. Addis Ababa, Ethiopia.

BackgroundCOVID-19, the disease caused by the new coronavirus SARS-CoV-2 is among the most obscure global pandemics resulting in diverse health and economic disruptions. It adversely affects the routine health care delivery and health service uptake by patients. However, its impact on care-seeking behaviour is largely unknown in Ethiopia. ObjectiveThis study was to determine the impact of the pandemic on care-seeking behaviour of patients with chronic health condition at Tikur Anbessa Specialized hospital in Addis Ababa. MethodsA cross-sectional hospital-based survey conducted between May and July 2020 on patients whose appointment was between March to June 2020. Sample of 750 patients were approached using phone call and data collection was done using a pretested questionnaire. After cleaning, the data entered in to IBM SPSS software package for analysis. ResultsA total of 644 patients with a median age of 25 years, and M: F ratio of 1:1.01 was described with a response rate of 86%. A loss to follow up, missed medication and death occurred in 70%, 12%, and 1.3% of the patients respectively. In the multivariable logistic regression analysis, patients above 60 years old were more likely to miss follow-up (OR-23.28 (9.32-58.15), P<001). Patients who reported fear of COVID-19 at the hospital were 19 times more likely to miss follow-up (adjusted OR=19.32, 95% CI:10.73-34.79, P<0.001), while patients who reported transportation problems were 6.5 times more likely to miss follow-up (adjusted OR=6.11, 95% CI:3.06-12.17, P<0.001). ConclusionsCOVID-19 pandemic affected the care-seeking behaviour of patients with chronic medical condition adversely and the impact was more pronounced among patients with severe disease, fear of COVID19 and with transportation problems. Education on preventive measures of COVID-19, use of phone clinic and improving chronic illness services at the local health institutions may reduce loss to follow-up among these patients. What is already known?O_LIAs a result of COVID-19, an essential maternal, newborn and child health (MNCH) services in Addis Ababa city showed that first antenatal attendance and under-five pneumonia treatment decreased by 12 and 35%. C_LIO_LIA drop in client flow was ascribed to fear of acquiring COVID-19 at health facilities, limited access due to movement restrictions, and dedication of health facilities as COVID-19 treatment centers. C_LI What are the new findings?O_LIA cross-sectional hospital-based telephone survey indicated that a loss to follow up, missed medication and death occurred in 70%, 12%, and 1.3% of patients with chronic medical conditions respectively. C_LI What do the new findings imply?O_LIFear of COVID-19 and transportation problems are the most commonly stated reasons thus, the finding implies that since health care services to patients with chronic medical conditions is concentrated in specialized referral hospitals mostly aggregated in big cities, patients who travel long distance to get the service are at high risk of Loss to follow up. C_LIO_LIStrengthening the chronic care service at a local health institutions, and promoting COVD-19 preventive measures, may help decrease the LTFU and associated complications. C_LI