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Predicting the optimal basal insulin infusion pattern in children and adolescents on insulin pumps
Paul-Martin Holterhus1, Jessica Bokelmann, Felix Riepe
1Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics,Christian-Albrechts-University of Kiel, University Hospital of Schleswig-Holstein, Campus Kiel, Kiel, Germany. holterhus@pediatrics.uni-kiel.de
Insights
This study identified four key basal insulin infusion patterns in children with type 1 diabetes on continuous subcutaneous insulin infusion therapy (CSII). These patterns help predict optimal insulin needs for better CSII management.
Area of Science:
- Pediatric Endocrinology
- Biomedical Engineering
- Computational Biology
Background:
- Type 1 diabetes (T1D) management in children requires precise insulin delivery.
- Continuous subcutaneous insulin infusion (CSII) therapy offers a flexible approach to insulin management.
- Understanding circadian basal insulin needs is crucial for optimizing CSII outcomes in pediatric patients.
Purpose of the Study:
- To develop and cross-validate a mathematical model for predicting optimal basal insulin infusion patterns in children with T1D using CSII.
- To identify distinct circadian basal rate (BR) patterns in a large cohort of pediatric CSII users.
Main Methods:
- Utilized the German/Austrian DPV-Wiss database, analyzing data from 6,063 pediatric CSII patients (<20 years).
- Employed unsupervised clustering to identify basal rate (BR) patterns and logistic regression to predict pattern probabilities.
- Cross-validated the developed prediction model using independent datasets.
Main Results:
- Identified four major circadian BR patterns in 5,903 (97.8%) of the analyzed patients.
- The biphasic dawn-dusk pattern (BC) was prevalent in older children (mean age 12.8 years), while a single evening peak (F) was observed in younger children (mean age 6.3 years).
- Age, diabetes duration, and sex were significant predictors of BR patterns; cross-validation showed high consistency for BC and F patterns.
Conclusions:
- The reconfirmed four key BR patterns are realistic approximations of insulin needs in pediatric T1D patients on CSII.
- A priori prediction of optimal basal insulin patterns can enhance CSII initiation and clinical management.
- These identified patterns provide valuable data for advanced insulin-infusion algorithms in closed-loop CSII systems.
Objective:
We 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 Methods:
We 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.
Results:
Of 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.
Conclusions:
Reconfirmation 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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