Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos

The 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 dia...

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Autores:: Ospitia Forero, Miguel Angel

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2023

Institución:: Universidad Antonio Nariño

Repositorio:: Repositorio UAN

Idioma:: spa

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dc.title.es_ES.fl_str_mv	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
title	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
spellingShingle	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos Variabilidad glucémica data gaps métricas precisión análisis 621.52 O839 Glycemic variability data gaps metrics accuracy analysis
title_short	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
title_full	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
title_fullStr	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
title_full_unstemmed	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
title_sort	Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos
dc.creator.fl_str_mv	Ospitia Forero, Miguel Angel
dc.contributor.advisor.spa.fl_str_mv	León, Fabian
dc.contributor.author.spa.fl_str_mv	Ospitia Forero, Miguel Angel
dc.subject.es_ES.fl_str_mv	Variabilidad glucémica data gaps métricas precisión análisis
topic	Variabilidad glucémica data gaps métricas precisión análisis 621.52 O839 Glycemic variability data gaps metrics accuracy analysis
dc.subject.ddc.es_ES.fl_str_mv	621.52 O839
dc.subject.keyword.es_ES.fl_str_mv	Glycemic variability data gaps metrics accuracy analysis
description	The 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.
publishDate	2023
dc.date.issued.spa.fl_str_mv	2023-11-23
dc.date.accessioned.none.fl_str_mv	2024-01-24T19:26:20Z
dc.date.available.none.fl_str_mv	2024-01-24T19:26:20Z
dc.type.spa.fl_str_mv	Trabajo de grado (Pregrado y/o Especialización)
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dc.identifier.bibliographicCitation.spa.fl_str_mv	Danne, 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.html MathWorks. (2023). MathWorks. Retrieved from Two-sample F-test for equal variances: https://www.mathworks.com/help/stats/vartest2.html Monnier, 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.
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identifier_str_mv	Danne, 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.html MathWorks. (2023). MathWorks. Retrieved from Two-sample F-test for equal variances: https://www.mathworks.com/help/stats/vartest2.html Monnier, 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 Nariño reponame:Repositorio Institucional UAN repourl:https://repositorio.uan.edu.co/
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dc.publisher.spa.fl_str_mv	Universidad Antonio Nariño
dc.publisher.program.spa.fl_str_mv	Ingeniería Mecatrónica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería Mecánica, Electrónica y Biomédica
dc.publisher.campus.spa.fl_str_mv	Bogotá - Sur
institution	Universidad Antonio Nariño
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spelling	Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2León, FabianOspitia Forero, Miguel Angel114819158692024-01-24T19:26:20Z2024-01-24T19:26:20Z2023-11-23http://repositorio.uan.edu.co/handle/123456789/9043Danne, 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/The 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.Ingeniero(a) Mecatrónico(a)PregradoPresencialInvestigaciónspaUniversidad Antonio NariñoIngeniería MecatrónicaFacultad de Ingeniería Mecánica, Electrónica y BiomédicaBogotá - SurVariabilidad glucémicadata gapsmétricasprecisiónanálisis621.52 O839Glycemic variabilitydata gapsmetricsaccuracyanalysisAnálisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datosTrabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85GeneralORIGINAL2023_MiguelAngelOspitiaForero_Autorización.pdf2023_MiguelAngelOspitiaForero_Autorización.pdfAutorización autoresapplication/pdf613599https://repositorio.uan.edu.co/bitstreams/bb3a2c86-38f7-4c0a-8c3c-8dfb60e39663/download928963a09a112945d3bc4287749be87bMD512023_MiguelAngelOspitiaForero.pdf2023_MiguelAngelOspitiaForero.pdfTrabajo de gradoapplication/pdf1253214https://repositorio.uan.edu.co/bitstreams/2374d8a2-aad7-48d7-ab93-41bba776eb2a/download49fbee1faf0abc1f581bb33120aeb734MD522023_MiguelAngelOspitiaForero_Acta.pdf2023_MiguelAngelOspitiaForero_Acta.pdfActa de sustentaciónapplication/pdf263092https://repositorio.uan.edu.co/bitstreams/6e903c70-994f-45d6-998f-d45849c7ee60/download33b7b79b46373043223383e44048208cMD53CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.uan.edu.co/bitstreams/f4f99cb0-14c8-4640-8a13-e6fd2d26946b/download9868ccc48a14c8d591352b6eaf7f6239MD54123456789/9043oai:repositorio.uan.edu.co:123456789/90432024-10-09 23:07:17.009https://creativecommons.org/licenses/by-nc-nd/4.0/Acceso abiertorestrictedhttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co

Análisis del impacto en la precisión del cálculo de métricas de variabilidad glucémica en registros de glucosa que presentan pérdida de datos

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