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基于模型的强化常规胰岛素治疗患者基础胰岛素供给个性化调整工具

Model-Based Tool for Personalized Adjustment of Basal Insulin Supply in Patients With Intensified Conventional Insulin Therapy.

作者信息

Vogt Lutz, Thomas Andreas, Fritzsche Gert, Heinke Peter, Kohnert Klaus-Dieter, Salzsieder Eckhard

机构信息

1 Diabetes Service Center Karlsburg, Karlsburg, Germany.

2 Medtronic GmbH Germany, Diabetes Division, Meerbusch, Germany.

出版信息

J Diabetes Sci Technol. 2019 Sep;13(5):928-934. doi: 10.1177/1932296818823020. Epub 2019 Jan 19.

Abstract

BACKGROUND

The decisive factor in successful intensive insulin therapy is the ability to deliver need-based-adjusted nutrition-independent insulin dosages at the closest possible approximation to the physiological insulin level. Because this basal insulin requirement is strongly influenced by the patient's lifestyle, its subtlety is of great importance. This challenge is very different between patients with type 1 diabetes and those with insulin-dependent type 2 diabetes. Furthermore, it is more difficult to finetune a basal insulin dosage with intensified conventional insulin therapy (ICT), due to delayed insulin delivery, compared to insulin pump therapy, which provides continuous delivery of small doses of exclusively short-acting insulin. In all cases, the goal is to achieve an optimal basal delivery rate.

METHOD

We hypothesized that this goal could be achieved with a modeling tool that determined the optimal basal insulin supply based on the patient's anamnestic data and monitored glucose values. This type of modeling tool has been used in health insurance programs in Germany to improve insulin control in patients that receive ICT.

RESULTS

Our retrospective data analysis showed that this modeling tool provided a significant improvement in metabolic control, significant reductions in HbA1c and Q scores, and improved time-in-range values, with reduced daily insulin levels.

CONCLUSION

The model-based basal rate test could provide additional data of the actual effect of the basal insulin adjustment in intensified insulin treated diabetes to the physician or treatment team.

摘要

背景

强化胰岛素治疗成功的决定性因素是能够根据需求调整营养独立的胰岛素剂量,尽可能接近生理胰岛素水平。由于基础胰岛素需求受患者生活方式的强烈影响,其细微差别非常重要。1型糖尿病患者和胰岛素依赖型2型糖尿病患者面临的这一挑战差异很大。此外,与胰岛素泵疗法相比,强化常规胰岛素治疗(ICT)因胰岛素输送延迟,更难微调基础胰岛素剂量,胰岛素泵疗法可连续输送小剂量的短效胰岛素。在所有情况下,目标都是实现最佳基础输送率。

方法

我们假设可以通过一种建模工具实现这一目标,该工具根据患者的既往数据和监测的血糖值确定最佳基础胰岛素供应。这种建模工具已在德国的医疗保险项目中用于改善接受ICT治疗患者的胰岛素控制。

结果

我们的回顾性数据分析表明,这种建模工具显著改善了代谢控制,显著降低了糖化血红蛋白(HbA1c)和Q评分,改善了血糖达标时间值,同时降低了每日胰岛素水平。

结论

基于模型的基础率测试可为医生或治疗团队提供强化胰岛素治疗糖尿病时基础胰岛素调整实际效果的额外数据。

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