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

Content Provider	Springer Nature : BioMed Central
Author	Uyttendaele, Vincent Knopp, Jennifer L. Davidson, Shaun Desaive, Thomas Benyo, Balazs Shaw, Geoffrey M. Chase, J. Geoffrey
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.
Related Links	https://biomedical-engineering-online.biomedcentral.com/counter/pdf/10.1186/s12938-019-0720-8.pdf
Ending Page	18
Page Count	18
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s12938-019-0720-8
Journal	BioMedical Engineering OnLine
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2019-10-22
Access Restriction	Open
Subject Keyword	Biomedical Engineering and Bioengineering Biomaterials Biotechnology Biomedical Engineering Glycaemic control Hyperglycaemia Blood glucose Insulin Insulin sensitivity Kernel density Biomedical Engineering/Biotechnology
Content Type	Text
Resource Type	Article
Subject	Biomaterials Radiological and Ultrasound Technology Biomedical Engineering Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.5/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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