Imputation Model for Glucose Values Above the Upper Detection Limit for Continuous Glucose Monitors.

Objective: All continuous glucose monitors (CGMs) have an upper detection limit, typically of 22.2 mmol/L. This might bias CGM metrics. We aimed to develop and validate a statistical model for imputing values above this limit. Methods: We analyzed CGM data from 85 inpatients with type 2 diabetes, 705 outpatients with type 1 diabetes, and 27 outpatients with type 2 diabetes. A Bayesian nonparametric latent Gaussian process regression model was applied to the CGM data intentionally right censored for the top 5%, 10%, 20%, and 30% and compared with the uncensored CGM data by the bias, mean squared error (MSE), and coefficient of determination (R²) of mean glucose, standard deviation (SD), and coefficient of variation (CV). Results: In hospitalized patients with diabetes, outpatients with type 1 diabetes, and outpatients with type 2 diabetes for 5% to 30% right censoring, respectively, the bias on mean glucose after imputation ranged from -0.012 to 0.362, -0.018 to 0.485, and -0.008 to 0.130, respectively. Bias on SD ranged from -0.024 to 0.226, -0.033 to 0.381, and -0.016 to 0.138, respectively. Bias on CV ranged from -0.207 to 1.543, -0.316 to 2.609, and -0.222 to 1.721, respectively. Similar results indicating good performance of the imputation model were observed for MSE and R². Conclusions: An imputation model for glucose values above the upper detection limit of CGMs was developed and validated in various populations. This enables a more unbiased quantification of CGM metrics for patients with severe hyperglycemia.