A new index to optimally design and compare continuous glucose monitoring glucose prediction algorithms

Abstract Background: Continuous glucose monitoring (CGM) data can be exploited to prevent hypo-/hyperglycemic events in real time by forecasting future glucose levels. In the last few years, several glucose prediction algorithms have been proposed, but how to compare them (e.g., methods based on polynomial rather than autoregressive time-series models) and even how to determine the optimal parameter set for a given method (e.g., prediction horizon and forgetting) are open problems. Methods: A new index, J, is proposed to optimally design a prediction algorithm by taking into account two key components: the regularity of the predicted profile and the time gained thanks to prediction. Effectiveness of J is compared with previously proposed criteria such as the root mean square error (RMSE) and continuous glucose-error grid analysis (CG-EGA) on 20 Menarini (Florence, Italy) Glucoday® CGM data sets. Results: For a given prediction algorithm, the new index J is able to suggest a more consistent and better parameter set (e.g., prediction horizon and forgetting factor of choice) than RMSE and CG-EGA. In addition, the minimization of J can reliably be used as a selection criterion in comparing different prediction methods. Conclusions: The new index can be used to compare different prediction strategies and to optimally design their parameters.