当前位置： SCI文献检索 > Biomedical Engineering Online期刊下所有文献 > 3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation.

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation.

Abstract：

BACKGROUND:The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- and intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm. Lately, studies have proven the ability to provide safe, effective control for lower, normoglycaemic, ranges, using model-based computerised methods. Such methods usually identify patient-specific physiological parameters to personalize titration of insulin and/or nutrition. The Stochastic-Targeted (STAR) glycaemic control framework uses patient-specific insulin sensitivity and a stochastic model of its future variability to directly account for both inter- and intra-patient variability in a risk-based insulin-dosing approach. RESULTS:In this study, a more personalized and specific 3D version of the stochastic model used in STAR is compared to the current 2D stochastic model, both built using kernel-density estimation methods. Fivefold cross validation on 681 retrospective patient glycaemic control episodes, totalling over 65,000 h of control, is used to determine whether the 3D model better captures metabolic variability, and the potential gain in glycaemic outcome is assessed using validated virtual trials. Results show that the 3D stochastic model has similar forward predictive power, but provides significantly tighter, more patient-specific, prediction ranges, showing the 2D model over-conservative > 70% of the time. Virtual trial results show that overall glycaemic safety and performance are similar, but the 3D stochastic model reduced median blood glucose levels (6.3 [5.7, 7.0] vs. 6.2 [5.6, 6.9]) with a higher 61% vs. 56% of blood glucose within the 4.4-6.5 mmol/L range. CONCLUSIONS:This improved performance is achieved with higher insulin rates and higher carbohydrate intake, but no loss in safety from hypoglycaemia. Thus, the 3D stochastic model developed better characterises patient-specific future insulin sensitivity dynamics, resulting in improved simulated glycaemic outcomes and a greater level of personalization in control. The results justify inclusion into ongoing clinical use of STAR.

journal_name

Biomed Eng Online

journal_title

Biomedical engineering online

authors

Uyttendaele V,Knopp JL,Davidson S,Desaive T,Benyo B,Shaw GM,Chase JG

doi

10.1186/s12938-019-0720-8

subject

Has Abstract

pub_date

2019-10-22 00:00:00

pages

102

issue

issn

1475-925X

pii

10.1186/s12938-019-0720-8

journal_volume

pub_type

杂志文章

在线工具

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation.