Key result
Regularized autoregressive models using smoothed continuous glucose monitoring data accurately predicted subcutaneous glucose concentrations 30 minutes ahead with an average root-mean-squared error of 0.1 mmol/l and a negligible time lag of 0.2 minutes.
Why the study?
Does a regularized least-squares autoregressive model using smoothed data improve the accuracy and reduce the time lag of near-future subcutaneous glucose concentration predictions in patients with type 1 diabetes compared to unregularized models?
Does a regularized least-squares autoregressive model using smoothed data improve the accuracy and reduce the time lag of near-future subcutaneous glucose concentration predictions in patients with type 1 diabetes compared to unregularized models?
Smoothing raw continuous glucose monitoring data and regularizing autoregressive model coefficients enables accurate near-future glucose predictions with clinically acceptable time lags.
May aid real-time glucose forecasting in type 1 diabetes; leaves open clinical utility pending prospective validation.
The combination of predictive data-driven models with frequent glucose measurements may provide for an early warning of impending glucose excursions and proactive regulatory interventions for diabetes patients. However, from a modeling perspective, before the benefits of such a strategy can be attained, we must first be able to quantitatively characterize the behavior of the model coefficients as well as the model predictions as a function of prediction horizon. We need to determine if the model coefficients reflect viable physiologic dependencies of the individual glycemic measurements and whether the model is stable with respect to small changes in noise levels, leading to accurate near-future predictions with negligible time lag. We assessed the behavior of linear autoregressive data-driven models developed under three possible modeling scenarios, using continuous glucose measurements of nine subjects collected on a minute-by-minute basis for approximately 5 days. Simulation results indicated that stable and accurate models for near-future glycemic predictions (< 60 min) with clinically acceptable time lags are attained only when the raw glucose measurements are smoothed and the model coefficients are regularized. This study provides a starting point for further needed investigations before real-time deployment can be considered.
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Gani et al. (2008) studied Type 1 diabetes (n=9). Regularized autoregressive (AR) data-driven models using smoothed data (Scenario III) vs. Unregularized AR models using raw or smoothed data (Scenarios I and II) was evaluated on Root-mean-squared error (RMSE) and time lag for 30-minute-ahead glucose concentration predictions. Regularized autoregressive models using smoothed continuous glucose monitoring data accurately predicted subcutaneous glucose concentrations 30 minutes ahead with an average root-mean-squared error of 0.1 mmol/l and a negligible time lag of 0.2 minutes.
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