Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes
ABSTRACT Purpose To investigate the impact of clinical and socio‐economic factors on glycaemic control and construct statistical models to predict optimal glycaemic control (OGC) after implementing intermittently scanned continuous glucose monitoring (isCGM) systems. Methods This retrospective study...
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Wiley
2025-01-01
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| Series: | Endocrinology, Diabetes & Metabolism |
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| Online Access: | https://doi.org/10.1002/edm2.70020 |
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| author | Fernando Sebastian‐Valles Jose Alfonso Arranz Martin Julia Martínez‐Alfonso Jessica Jiménez‐Díaz Iñigo Hernando Alday Victor Navas‐Moreno Teresa Armenta Joya Maria del Mar delFandiño García Gisela Liz Román Gómez Jon Garai Hierro Luis Eduardo Lander Lobariñas Carmen González‐Ávila Purificación deMartinez de Icaya Vicente Martínez‐Vizcaíno Miguel Antonio Sampedro‐Nuñez Mónica Marazuela |
| author_facet | Fernando Sebastian‐Valles Jose Alfonso Arranz Martin Julia Martínez‐Alfonso Jessica Jiménez‐Díaz Iñigo Hernando Alday Victor Navas‐Moreno Teresa Armenta Joya Maria del Mar delFandiño García Gisela Liz Román Gómez Jon Garai Hierro Luis Eduardo Lander Lobariñas Carmen González‐Ávila Purificación deMartinez de Icaya Vicente Martínez‐Vizcaíno Miguel Antonio Sampedro‐Nuñez Mónica Marazuela |
| author_sort | Fernando Sebastian‐Valles |
| collection | DOAJ |
| description | ABSTRACT Purpose To investigate the impact of clinical and socio‐economic factors on glycaemic control and construct statistical models to predict optimal glycaemic control (OGC) after implementing intermittently scanned continuous glucose monitoring (isCGM) systems. Methods This retrospective study included 1072 type 1 diabetes patients (49.0% female) from three centres using isCGM systems. Clinical data and net income from the census tract were collected for each individual. OGC was defined as time in range > 70%, with time below 70 mg/dL < 4%. The sample was randomly split in two equal parts. Logistic regression models to predict OGC were developed in one of the samples, and the best model was selected using the Akaike information criterion and adjusted for Pearson's and Hosmer–Lemeshow's statistics. Model reliability was assessed via external validation in the second sample and internal validation using bootstrap resampling. Results Out of 2314 models explored, the most effective predictor model included annual net income per person, sex, age, diabetes duration, pre‐isCGM HbA1c, insulin dose/kg, and the interaction between sex and HbA1c. When applied to the validation cohort, this model demonstrated 72.6% specificity, 67.3% sensitivity, and an area under the curve (AUC) of 0.736. The AUC through bootstrap resampling was 0.756. Overall, the model's validity in the external cohort was 80.4%. Conclusions Clinical and socio‐economic factors significantly influence OGC in type 1 diabetes. The application of statistical models offers a reliable means of predicting the likelihood of achieving OGC following isCGM system implementation. |
| format | Article |
| id | doaj-art-35d4fa516c68475489be4b5b6385dac0 |
| institution | Kabale University |
| issn | 2398-9238 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Endocrinology, Diabetes & Metabolism |
| spelling | doaj-art-35d4fa516c68475489be4b5b6385dac02025-01-25T18:20:28ZengWileyEndocrinology, Diabetes & Metabolism2398-92382025-01-0181n/an/a10.1002/edm2.70020Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 DiabetesFernando Sebastian‐Valles0Jose Alfonso Arranz Martin1Julia Martínez‐Alfonso2Jessica Jiménez‐Díaz3Iñigo Hernando Alday4Victor Navas‐Moreno5Teresa Armenta Joya6Maria del Mar delFandiño García7Gisela Liz Román Gómez8Jon Garai Hierro9Luis Eduardo Lander Lobariñas10Carmen González‐Ávila11Purificación deMartinez de Icaya12Vicente Martínez‐Vizcaíno13Miguel Antonio Sampedro‐Nuñez14Mónica Marazuela15Universidad Autónoma de Madrid, Department of Endocrinology and Nutrition Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa Madrid SpainUniversidad Autónoma de Madrid, Department of Endocrinology and Nutrition Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa Madrid SpainDepartment of Family and Community Medicine Hospital La Princesa/Centro de Salud Daroca Madrid SpainDepartment of Endocrinology and Nutrition Hospital Universitario Severo Ochoa Leganés SpainDepartment of Endocrinology and Nutrition Hospital Universitario Basurto Bilbao SpainUniversidad Autónoma de Madrid, Department of Endocrinology and Nutrition Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa Madrid SpainUniversidad Autónoma de Madrid, Department of Endocrinology and Nutrition Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa Madrid SpainDepartment of Endocrinology and Nutrition Hospital Universitario Severo Ochoa Leganés SpainDepartment of Endocrinology and Nutrition Hospital Universitario Severo Ochoa Leganés SpainDepartment of Endocrinology and Nutrition Hospital Universitario Basurto Bilbao SpainDepartment of Endocrinology and Nutrition Hospital Universitario Severo Ochoa Leganés SpainDepartment of Neurology Hospital Universitario Infanta Elena Valdemoro SpainDepartment of Endocrinology and Nutrition Hospital Universitario Severo Ochoa Leganés SpainHealth and Social Care Research Center Universidad de Castilla‐La Mancha Cuenca SpainUniversidad Autónoma de Madrid, Department of Endocrinology and Nutrition Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa Madrid SpainUniversidad Autónoma de Madrid, Department of Endocrinology and Nutrition Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa Madrid SpainABSTRACT Purpose To investigate the impact of clinical and socio‐economic factors on glycaemic control and construct statistical models to predict optimal glycaemic control (OGC) after implementing intermittently scanned continuous glucose monitoring (isCGM) systems. Methods This retrospective study included 1072 type 1 diabetes patients (49.0% female) from three centres using isCGM systems. Clinical data and net income from the census tract were collected for each individual. OGC was defined as time in range > 70%, with time below 70 mg/dL < 4%. The sample was randomly split in two equal parts. Logistic regression models to predict OGC were developed in one of the samples, and the best model was selected using the Akaike information criterion and adjusted for Pearson's and Hosmer–Lemeshow's statistics. Model reliability was assessed via external validation in the second sample and internal validation using bootstrap resampling. Results Out of 2314 models explored, the most effective predictor model included annual net income per person, sex, age, diabetes duration, pre‐isCGM HbA1c, insulin dose/kg, and the interaction between sex and HbA1c. When applied to the validation cohort, this model demonstrated 72.6% specificity, 67.3% sensitivity, and an area under the curve (AUC) of 0.736. The AUC through bootstrap resampling was 0.756. Overall, the model's validity in the external cohort was 80.4%. Conclusions Clinical and socio‐economic factors significantly influence OGC in type 1 diabetes. The application of statistical models offers a reliable means of predicting the likelihood of achieving OGC following isCGM system implementation.https://doi.org/10.1002/edm2.70020continuous glucose monitoring systemsintermittently scanned continuous glucose monitoringoptimal controlpredictive modelstype 1 diabetes |
| spellingShingle | Fernando Sebastian‐Valles Jose Alfonso Arranz Martin Julia Martínez‐Alfonso Jessica Jiménez‐Díaz Iñigo Hernando Alday Victor Navas‐Moreno Teresa Armenta Joya Maria del Mar delFandiño García Gisela Liz Román Gómez Jon Garai Hierro Luis Eduardo Lander Lobariñas Carmen González‐Ávila Purificación deMartinez de Icaya Vicente Martínez‐Vizcaíno Miguel Antonio Sampedro‐Nuñez Mónica Marazuela Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes Endocrinology, Diabetes & Metabolism continuous glucose monitoring systems intermittently scanned continuous glucose monitoring optimal control predictive models type 1 diabetes |
| title | Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes |
| title_full | Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes |
| title_fullStr | Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes |
| title_full_unstemmed | Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes |
| title_short | Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes |
| title_sort | predicting time in range without hypoglycaemia using a risk calculator for intermittently scanned cgm in type 1 diabetes |
| topic | continuous glucose monitoring systems intermittently scanned continuous glucose monitoring optimal control predictive models type 1 diabetes |
| url | https://doi.org/10.1002/edm2.70020 |
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