Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)León, FabianOspitia Forero, Miguel Angel2024-01-242024-01-242023-11-23http://repositorio.uan.edu.co/handle/123456789/9043The international endocrine association has a consensus of metrics, which are used to assess glycemic variability from continuous glucose monitoring sensor measurements. Glucose monitoring records sample every 5 minutes and are useful for detecting episodes of hypo/hyperglycemia in patients with diabetes. Communication failures, device misuse and other reasons lead to data loss affecting the calculation of metrics.La asociación internacional de endocrinología cuenta con un consenso de métricas, las cuales son usadas para evaluar la variabilidad glucémica a partir de las mediciones de los sensores de monitoreo continuo de glucosa. Los registros de monitorización toman muestras cada 5 minutos y son útiles para la detección de episodios de hipo/hiperglucemia en pacientes con diabetes. Fallas en la comunicación, mal uso del dispositivo y otras razones llevan a pérdidas de datos (‘data gaps’) afectando el cálculo de las métricas.spaAcceso abiertoVariabilidad glucémicadata gapsmétricasprecisiónanálisis621.52 O839Trabajo de grado (Pregrado y/o Especialización)Glycemic variabilitydata gapsmetricsaccuracyanalysisinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Danne, T. (2017, November 10). International Consensus on Use of Continuous Glucose Monitoring. CONTINUOUS GLUCOSE MONITORING AND RISK OF HYPOGLYCEMIA.Fabian Mauricio León Vargas, M. G.-J. (2018). Different Indexes of Glycemic Variability as Identifiers of Patients with Risk of Hypoglycemia in Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 1007-1015.Maira A. García-Jaramillo, F. M. (2019). Impact of sensor-augmented pump therapy with predictive low-glucose management on hypoglycemia and glycemic control in patients with type 1 diabetes mellitus: 1-year follow-up. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2635-2631.Martina. Drecogna, e. a. (2021). Data Gap Modeling in Continuous Glucose Monitoring Sensor Data. 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Virtual Conference .MathWorks. (2023). MathWorks. Retrieved from One-sample Kolmogorov-Smirnov test: https://www.mathworks.com/help/stats/kstest.htmlMathWorks. (2023). MathWorks. Retrieved from Two-sample F-test for equal variances: https://www.mathworks.com/help/stats/vartest2.htmlMonnier, L. (2016). Toward Defining the Threshold Between Low and High Glucose Variability in Diabetes. CLINICAL CARE/EDUCATION/NUTRITION/PSYCHOSOCIAL RESEARCH, 832–838.Nathan. R. Hil, e. a. (2011). Normal Reference Range for Mean Tissue Glucose and Glycemic Variability Derived from Continuous Glucose Monitoring for Subjects Without Diabetes in Different Ethnic Groups. DIABETES TECHNOLOGY & THERAPEUTICS, nº 201 921-928.Peter. A. Baghurst, e. a. (2010). The Minimum Frequency of Glucose Measurements from Which Glycemic Variation Can Be Consistently Assessed. Journal of Diabetes Science and Technology, vol. IV, nº 6, 1382 - 1385.Rodbard, D. (2011). Glycemic Variability: Measurement and Utility in Clinical Medicine and Research—One Viewpoint. DIABETES TECHNOLOGY & THERAPEUTICS,1077-1080.Stephanie. J. Fonda, e. a. (2013). Minding the Gaps in Continuous Glucose Monitoring: A Method to Repair Gaps to Achieve More Accurate Glucometrics. Journal of Diabetes Science and Technology, vol. XII, 88-92.instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/