A novel kernel-based method provides individualized estimates of insulin action using home-monitored data, accounting for nonlinear behavior across the glucose range.
May enable personalized insulin dosing from routine data; leaves open prospective validation before clinical adoption.
To improve glycemic control in insulin-dependent diabetes, estimates of the glucose- lowering effect of insulin-the insulin action (IA)-and the dynamics thereof are essential. The current practice relies on generic estimates of the dynamic shape of the IA and rules-of-thumb to assess the total glucose-lowering effect. In this paper we present individualized estimates based on home-monitored data, incorporating a kernel-based estimate that also accounts for the nonlinear behavior of the IA across the glucose range. The method is successfully validated on 29 patients on continuous insulin pump therapy.
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Ståhl et al. (2015) studied this question.
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