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Biomedical subjects

Dominic S Lee

Publications and source records attributed to Dominic S Lee.

2 recordsLinked to original sources

Transient and steady-state euglycemic clamp validation of a model for glycemic control and insulin sensitivity testing.

BACKGROUND: There is an urgent need for a simple and accurate measure of insulin sensitivity to diagnose insulin resistance in the general population and quantify changes due to clinical intervention. A new physiological control model of glucose and insulin metabolism is validated with the euglycemic-hyperinsulinemic clamp during steady and transient states. METHODS: The data consist of n = 60 (15 lean, 15 overweight, 15 obese, and 15 morbidly obese) euglycemic-hyperinsulinemic clamp trials performed on normoglycemic insulin-resistant individuals. The glucose and insulin model is fitted using an integral-based method. Correlations between clamp-derived insulin sensitivity index (ISI) and the model's insulin sensitivity parameter (SI) are obtained during steady and transient states. Results are compared with log-homeostasis model assessment (HOMA), a widely used fasting surrogate for insulin sensitivity. RESULTS: Correlation between model-based insulin sensitivity, SI, and ISIG (ISI normalized by steady-state glucose) is r = 0.99 (n = 60) at steady state and r = 0.97 at transient state, respectively. Correlations did not significantly change across subgroups, with narrow 95% confidence intervals. Log-HOMA correlations are r=-0.72 to SI and r=-0.71 to ISIG for the overall population but are significantly lower in the subgroups, with wide 95% confidence intervals. CONCLUSIONS: The model-based insulin sensitivity parameter, SI, highly correlates to ISIG in all subgroups, even when only considering a transient state. The high correlation of SI offers the potential for a short, simple yet highly correlated, model-based assessment of insulin sensitivity that is not currently available.

Adult↗

A new model validation tool using kernel regression and density estimation.

In physiological system modelling for control or decision support, model validation is a critical element. A nonparametric approach for assessing the validity of deterministic dynamic models against empirical data is developed, based on kernel regression and kernel density estimation, yielding visual graphical assessment tools as well as numerical metrics of compatibility between the model and the data. Nonparametric regression has been suggested for assessing a parametric statistical model by constructing a confidence band for the proposed model and then checking whether the nonparametric regression curve lies within the band. However, for deterministic models, there is no confidence band that can be constructed. A reversal of roles is therefore suggested--construct a probability band for the nonparametric regression curve and check whether the proposed model lies within the band. This approach extends the utility of nonparametric regression for model assessment to deterministic models. Weighted kernel density estimation is incorporated to derive a density profile for the regression curve, creating a local graphical validation tool. In addition, the density profile is used to define and compute two numerical measures--average normalized density (AND) and relative average normalized density (RAND), representing global statistical validity measures. These tools are demonstrated using a biomedical system model for agitation-sedation and sedation management control.

Humans↗