ArticleDiabetes care2013
Predicting the optimal basal insulin infusion pattern in children and adolescents on insulin pumps.
Article in Diabetes care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- A Randomized Controlled Study of an Insulin Dosing Application That Uses Recognition and Meal Bolus Estimations.Journal of diabetes science and technology · 2017Trial
- Toward a Personalized Basal Tuner for Detecting Basal Rate Inaccuracies in Type 1 Diabetes Mellitus Without Meal Data: Algorithm Development and Retrospective Validation Study.JMIR diabetes · 2025Article
- ISPAD Clinical Practice Consensus Guidelines 2022: Managing diabetes in preschoolers.Pediatric diabetes · 2022Article
- Systems Chronotherapeutics.Pharmacological reviews · 2017Review
- Continuous subcutaneous insulin infusion in diabetes: patient populations, safety, efficacy, and pharmacoeconomics.Diabetes/metabolism research and reviews · 2016Review
- The artificial pancreas: is it important to understand how the β cell controls blood glucose?Journal of diabetes science and technology · 2013Review
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Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed at developing and cross-validating a mathematical prediction model for an optimal basal insulin infusion pattern for children with type 1 diabetes on continuous subcutaneous insulin infusion therapy (CSII). RESEARCH DESIGN AND
methodsWe used the German/Austrian DPV-Wiss database for quality control and scientific surveys in pediatric diabetology and retrieved all CSII patients <20 years of age (November 2009). A total of 1,248 individuals from our previous study were excluded (dataset 1), resulting in 6,063 CSII patients (dataset 2) (mean age 10.6 ± 4.3 years). Only the most recent basal insulin infusion rates (BRs) were considered. BR patterns were identified and corresponding patients sorted by unsupervised clustering. Logistic regression analysis was applied to calculate the probabilities for each BR pattern. Equations were based on both independent datasets separately, and probabilities for BR patterns were cross-validated using typical test patients.
resultsOf the 6,063 children, 5,903 clustered in one of four major circadian BR patterns, confirming our previous study. The oldest age-group (mean age 12.8 years) was represented by 2,490 patients (42.18%) with a biphasic dawn-dusk pattern (BC). A broad single insulin maximum at 9-10 p.m. (F) was unveiled by 853 patients (14.45%) (mean age 6.3 years). Logistic regression analysis revealed that age, to a lesser extent duration of diabetes, and partly sex predicted BR patterns. Cross-validation revealed almost identical probabilities for BR patterns BC and F in the two datasets but some variation in the remaining two BR patterns.
conclusionsReconfirmation of four key BR patterns in two very large independent cohorts supports that these patterns are realistic approximations of the circadian distribution of insulin needs in children with type 1 diabetes. Prediction of an optimal pattern a priori can improve initiation and clinical follow-up of CSII in children and adolescents. In addition, these BR patterns represent valuable information for insulin-infusion algorithms in closed-loop CSII.
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