Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes.

Autor: Sebastian-Valles F; Universidad Autónoma de Madrid, Department of Endocrinology and Nutrition, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa, Madrid, Spain., Arranz Martin JA; Universidad Autónoma de Madrid, Department of Endocrinology and Nutrition, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa, Madrid, Spain., Martínez-Alfonso J; Department of Family and Community Medicine, Hospital La Princesa/Centro de Salud Daroca, Madrid, Spain., Jiménez-Díaz J; Department of Endocrinology and Nutrition, Hospital Universitario Severo Ochoa, Leganés, Spain., Hernando Alday I; Department of Endocrinology and Nutrition, Hospital Universitario Basurto, Bilbao, Spain., Navas-Moreno V; Universidad Autónoma de Madrid, Department of Endocrinology and Nutrition, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa, Madrid, Spain., Armenta Joya T; Universidad Autónoma de Madrid, Department of Endocrinology and Nutrition, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa, Madrid, Spain., Del Fandiño García MDM; Department of Endocrinology and Nutrition, Hospital Universitario Severo Ochoa, Leganés, Spain., Román Gómez GL; Department of Endocrinology and Nutrition, Hospital Universitario Severo Ochoa, Leganés, Spain., Garai Hierro J; Department of Endocrinology and Nutrition, Hospital Universitario Basurto, Bilbao, Spain., Lander Lobariñas LE; Department of Endocrinology and Nutrition, Hospital Universitario Severo Ochoa, Leganés, Spain., González-Ávila C; Department of Neurology, Hospital Universitario Infanta Elena, Valdemoro, Spain., de Martinez de Icaya P; Department of Endocrinology and Nutrition, Hospital Universitario Severo Ochoa, Leganés, Spain., Martínez-Vizcaíno V; Health and Social Care Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain.; Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile., Sampedro-Nuñez MA; Universidad Autónoma de Madrid, Department of Endocrinology and Nutrition, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa, Madrid, Spain., Marazuela M; Universidad Autónoma de Madrid, Department of Endocrinology and Nutrition, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria de La Princesa, Madrid, Spain.
Jazyk: angličtina
Zdroj: Endocrinology, diabetes & metabolism [Endocrinol Diabetes Metab] 2025 Jan; Vol. 8 (1), pp. e70020.
DOI: 10.1002/edm2.70020
Abstrakt: 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.
(© 2024 The Author(s). Endocrinology, Diabetes & Metabolism published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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