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Friday, September 24
Fri, Sep 24, 3:45 PM - 5:00 PM
Virtual
Digital Data, Endpoints, and Analyses: Regulatory Guidance for Clinical Trials

Considerations for Analysis and Trial Designs in Diabetes Studies Involving Continuous Blood Glucose Monitoring (CGM) Devices (303514)

*Anna Kettermann, US Food and Drug Administration 

Keywords: diabetes, HbA1c, CGM, and digital health technology

Continuous glucose monitoring (CGM) measures interstitial glucose in patients with diabetes. Use of CGM has become more prevalent in clinical practice since it provides instantaneous response to therapy decision, lifestyle modifications, and identification of patterns of hypoglycemia. Currently, HbA1c is considered the key surrogate marker for the progression of long-term complications in diabetes. This biomarker is based on the average blood glucose concentration over the preceding three months and thus does not provide information about acute glycemic changes (blood glucose variability). The instantaneous glucose values provided by CGM could provide addition evidence of a drug’s efficacy as well as trigger an immediate therapeutic action, such as to stem an impending need for glycemic rescue in hypoglycemia or to optimize management of pre- and postprandial hyperglycemia. Use of CGM to derive clinical trial endpoints also brings its own challenges, such as impact of instantaneous information on patients’ behavior (for example, reduction of insulin dose overnight and associated risk of nocturnal hypoglycemia) and therefore should be considered in the design of future clinical trials. Also, high frequency of measurements (288 measurements per day) brings the need for the analysis of very large datasets. To evaluate CGM output, researchers need to have sufficient information to appropriately interpret causes of missing data and impact of intercurrent events on raw and summary measures provided by CGM. Given the current COVID-19 pandemic, the role of decentralized clinical trials becomes more prominent. It allows remote access to CGM-derived measurements and has the potential to limit the number of required laboratory visits. Reductions in study visits also may be important for collection of research data from individuals with demanding work or academic schedules, as well as help to reduce missing data and minimize costs associated with conducting the trial.