615-P: Developing Self-Monitored Blood Glucose Metrics Correlated to Glycated Hemoglobin to Improve Real-World Evidence Generation
| العنوان: | 615-P: Developing Self-Monitored Blood Glucose Metrics Correlated to Glycated Hemoglobin to Improve Real-World Evidence Generation |
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| المؤلفون: | Wei Lu, Roberta James, Eric S. Meadows, Felicia Gelsey, Magaly Perez-Nieves, Bimal R. Shah, Ludi Fan |
| المصدر: | Diabetes. 70 |
| بيانات النشر: | American Diabetes Association, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Actuarial science, Leverage (finance), Endocrinology, Diabetes and Metabolism, Real world evidence, Glucose management, chemistry.chemical_compound, chemistry, Shareholder, Spouse, Internal Medicine, Glycated hemoglobin, Psychology, Stock (geology), Remote management |
| الوصف: | Background: Self-monitoring blood glucose (SMBG) values have been used to estimate A1c (eA1c) using the A1c-Derived Average Glucose (ADAG) equation with modifications causing limitations with the results. Our study9s objective was to develop new glucose management metrics leveraging real-world SMBG values and checking patterns correlated to lab A1c values. Methods: We conducted a retrospective analysis of data from the Livongo remote management Program for Diabetes. Spearman rank was used to measure the correlation between lab A1c and mean SMBG values by relationship to meals during a 90-day time-period. Using the ADAG equation, mean SBMG metrics were used to generate eA1c values for comparison. Results: There were 815 participants with 906 lab A1c and SMBG values with mean age of 56 years, 54% male, and 10% type 1. Three metrics in Table 1 most correlated (r >= 0.70) to lab A1c had differences of eA1c from -0.5% to -0.6%. Post-meal SMBG had slightly lower correlation (0.66) but with a mean eA1c more similar to the laboratory value. Conclusions: Though real-world SMBG values are correlated to lab A1c values, checking patterns impact the ability of the ADAG equation to estimate A1c. Future work will develop a new linear transformation equation based on meal distribution checking patterns to improve A1c estimation and the ability to leverage SMBG data for real-world evidence generation. Disclosure M. Perez-nieves: Employee; Self; Eli Lilly and Company. W. Lu: Employee; Self; Teladoc Health. E. Meadows: Employee; Self; Eli Lilly and Company, Employee; Spouse/Partner; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. F. Gelsey: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company. L. Fan: Employee; Self; Eli Lilly and Company, Stock/Shareholder; Self; Eli Lilly and Company, Stock/Shareholder; Spouse/Partner; Eli Lilly and Company. R. James: Employee; Self; Livongo, Teladoc Health, Stock/Shareholder; Self; Livongo, Teladoc Health. B. Shah: Employee; Self; Teladoc Health, Stock/Shareholder; Self; Teladoc Health. |
| تدمد: | 1939-327X 0012-1797 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::caf6da5202ec15031fa1a0607c3b0190Test https://doi.org/10.2337/db21-615-pTest |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi...........caf6da5202ec15031fa1a0607c3b0190 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1939327X 00121797 |
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