Abstract: Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias).

Abstract: In an embodiment, a system for of preventively treating hypoglycemia includes a continuous glucose monitoring (CGM) sensor system configured to generate measurements associated with a current glucose level of a patient. The system further includes one or more memories comprising executable instructions and one or more processors in data communication with the one or more memories. The one or more processors are configured to execute the executable instructions to receive, from the CGM sensor system, one or more measurements associated with the current glucose level of the patient and compute a sequence of preventive hypoglycemia treatments over a future time period based on the one or more measurements and a prediction of glucose control to be produced by the sequence. The one or more processors are further configured to prompt the patient with a first preventive hypoglycemia treatment in the sequence of preventive hypoglycemia treatments.