Predicting the Optimal Basal Insulin Infusion Pattern in Children and Adolescents on Insulin Pumps

  1. the German/Austrian DPV-Initiative and the German Pediatric CSII Working Group
  1. 1Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, Christian-Albrechts-University of Kiel, University Hospital of Schleswig-Holstein, Campus Kiel, Kiel, Germany
  2. 2Division of Pediatric Endocrinology and Diabetes, Catholic Children’s Hospital Wilhelmstift, Hamburg, Germany
  3. 3Division of Pediatric Endocrinology and Diabetology, Department of Pediatrics, University of Luebeck, University Hospital of Schleswig-Holstein, Campus Luebeck, Luebeck, Germany
  4. 4Department of Pediatrics, Medical University of Vienna, Vienna, Austria
  5. 5Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
  6. 6Clinic of Paediatrics and Molecular Diabetes Research Group Experimental and Clinical Research Center, Berlin, Germany
  7. 7Diabetes Center, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University of Bochum, Bad Oeynhausen, Germany
  8. 8Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
  1. Corresponding author: Paul-Martin Holterhus, holterhus{at}pediatrics.uni-kiel.de.

Abstract

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.

Footnotes

  • Received August 22, 2012.
  • Accepted November 7, 2012.

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  1. Diabetes Care vol. 36 no. 6 1507-1511
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