The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity. (September 2022)

• Record Type: Journal Article
• Authors: Lam, Nicholas; Murray, Rua; Docherty, Paul D.; Te Morenga, Lisa; Chase, J. Geoffrey
• Is Part Of: Journal of diabetes science and technology. Volume 16:Number 5(2022)
• Page Start: 1196
• Page End: 1207
• Publication Date: 2022-09
• Subjects: glycemic modelling -- least squares estimation -- outlier data -- numerical optimization; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/19322968211021602
• Languages: English
• ISSNs: 1932-2968

Abstract

Background: The identification of insulin sensitivity in glycemic modelling can be heavily obstructed by the presence of outlying data or unmodelled effects. The effect of data indicative of local mixing is especially problematic with models assuming rapid mixing of compartments. Methods such as manual removal of data and outlier detection methods have been used to improve parameter ID in these cases, but modelling data with more compartments is another potential approach. Methods: This research compares a mixing model with local depot site compartments with an existing, clinically validated insulin sensitivity test model. The Levenberg-Marquardt (LM) parameter identification method was implemented alongside a modified version (aLM) capable of operator-independent omission of outlier data in accordance with the 3 standard deviation rule. Three cases were tested: LM where data points suspected to be affected by incomplete mixing at the depot site were removed, aLM, and LM with the more complex mixing model. Results: While insulin parameters identified in the mixing model differed greatly from those in the DISST model, there were strong Spearman correlations of approximately 0.93 for the insulin sensitivity values identified across all 3 methods. The 2 models also showed comparable identification stability in insulin sensitivity estimation through a Monte Carlo analysis. However, the mixing model required modifications to the identification process to improve convergence, and still failed to converge to feasible parameters on 5 of the 212 trials. Conclusions: The mixing compartment model effectively captured the dynamics of mixing behavior, but with no significant improvement in insulin sensitivity identification.