Impact of Different Types of Data Loss on Optimal Continuous Glucose Monitoring Sampling Duration

Aims: To determine if a longer duration of continuous glucose monitoring (CGM) sampling is needed to correctly assess the quality of glycemic control given different types of data loss.Materials and Methods: Data loss was generated in two different methods until the desired percentage of data loss (10-50%) was achieved with (1) eliminating random individual CGM values and (2) eliminating gaps of a predefined time length (1-5 h). For CGM metrics, days required to cross predetermined targets for median absolute percentage error (MdAPE) for the different data loss strategies were calculated and compared with current international consensus recommendation of >70% of optimal data sampling.Results: Up to 90 days of CGM data from 291 adults with type 1 diabetes were analyzed. MdAPE threshold crossing remained virtually constant for random CGM data loss up to 50% for all CGM metrics. However, the MdAPE crossing threshold increased when losing data with longer gaps. For all CGM metrics assessed in our study (%T70-180, %T < 70, %T < 54, %T > 180, and %T > 250), up to 50% data loss in a random manner did not cause any significant change on optimal sampling duration; however, >30% of data loss in gaps up to 5 h required longer optimal sampling duration.Conclusions: Optimal sampling duration for CGM metrics depends on percentage of data loss as well as duration of data loss. International consensus recommendation for 70% CGM data adequacy is sufficient to report %T70-180 with 2 weeks of data without large data gaps.

Automated summary

The study suggests that random CGM data loss up to 50% does not significantly affect the optimal sampling duration for CGM metrics, but losing data with longer gaps increases the MdAPE crossing threshold, requiring a longer optimal sampling duration.