Mapping data to virtual patients in type 1 diabetes. (October 2020)

• Record Type: Journal Article
• Authors: Colmegna, Patricio; Wang, Ke; Garcia-Tirado, Jose; Breton, Marc D.
• Is Part Of: Control engineering practice. Volume 103(2020)
• Publication Date: 2020-10
• Subjects: Type 1 diabetes -- Personalized therapy -- Precision medicine -- Model identification -- Uncertainty -- Intraday variability; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104605
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: In this work, a new approach to represent dynamic variations in type 1 diabetes (T1D) leveraging field-collected data (glucose monitor, insulin pump and meal records) is presented. The proposed methodology consists in identifying a time-invariant model structure of the glucose metabolism along with a variability component (VC) that captures daily changes in insulin sensitivity (IS). Performance is tested using both synthetic and real data, evidencing the ability of the methodology to replicate and predict outcomes under diverse conditions. This reconstruction-replay framework lays the foundation for a viable replay engine that can represent a powerful tool for precision medicine in T1D. Highlights: Personalization of the UVA/Padova type 1 diabetes model using field-collected data. Most impacting parameters are determined via practical identifiability analysis. Changes in insulin sensitivity are expressed as a truncated Fourier series. The methodology is evaluated with synthetic and real data. The UVA/Padova simulator is equipped with a new variability library.