Key result
A neural network-based clinical decision support system achieved an acceptable consistency of 58.9-66.3% with physician recommendations for initial insulin pump settings in children with T1D.
Why the study?
Does a neural network based CDSS provide acceptable agreement with physician expert opinion for initial insulin pump settings in children with T1D?
Observational (n=2,885)
Does a neural network based CDSS provide acceptable agreement with physician expert opinion for initial insulin pump settings in children with T1D?
A neural network-based CDSS demonstrates acceptable consistency with physician expert opinion for determining initial insulin pump settings in children with T1D.
Supports further CDSS development for pediatric T1D pump initiation; leaves open prospective outcome validation before practice change.
BACKGROUND: Despite existing recommendations for the initial calculation of insulin pump settings, the process is largely subjective and depends on the physician’s personal experience. AIM: Development of a clinical decision support system (CDSS) that determines the initial settings of the insulin pump, which would have satisfactory agreement with the expert opinion of physicians. MATERIALS AND METHODS: Neural network model developed using data (continuous subcutaneous insulin infusion (CSII) settings, age, weight, total daily dose, and HbA1c) from 2850 children with T1D who were switched to CSII and achieved optimal glycemic control according to glucose levels. CDSS utilizing the model implemented as a computer program in Python. A prospective assessment of the agreement between the recommendations of the CDSS and the physician conducted on 35 data sets of children with T1D (median age 9.3 years [6.4, 11.5]), and 840 points for decisions were analyzed. 4 degrees of agreement were used: complete consistency, when the physicians agreed with the CDSS recommendations; partial consistency, when the physicians didn’t agree with the CDSS recommendations, but the difference was in the range of ±15%; complete inconsistency — the difference more than ±15%; acceptable consistency is the sum of full and partial consistency (± 15% error is clinically acceptable). The null hypothesis of the study was the absence of difference in consistency/inconsistency between physicians and the CDSS. RESULTS: The frequency of full consistency between CDSS and physician recommendations for initiating insulin pump therapy is 29.8-43.8%, and inconsistency is 33.7-41.1%. Acceptable consistency is 58.9–66.3%. There were no significant differences in mean insulin pump parameters between CDSS and physicians. CONCLUSION: The results obtained are consistent with previous studies. Proposed model demonstrates acceptable performance regarding initial CSII settings, without significant deviations between various parameters.
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Лаптев et al. (2025) conducted an observational in Type 1 Diabetes (n=2,885). Neural network based clinical decision support system (CDSS) vs. Physician recommendations was evaluated on Acceptable consistency (± 15% error) between CDSS and physician recommendations. A neural network-based clinical decision support system achieved an acceptable consistency of 58.9-66.3% with physician recommendations for initial insulin pump settings in children with T1D.
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