Digital tools for monitoring and therapeutic intensification

Abstract

The evolution of insulin infusion systems, and particularly automated insulin delivery systems (AIDs), has substantially increased the amount and complexity of information available for clinical decision-making. In this context, the challenge lies not only in incorporating technology but also in developing systematic strategies that allow for the efficient interpretation of this data and its translation into clinically relevant interventions.

Digital monitoring platforms allow for the integration of information from continuous glucose monitoring (CGM) with data related to insulin administration and user interaction with the system. A structured evaluation can begin by establishing whether the configured glycemic targets are appropriate according to individual clinical characteristics. Subsequently, an initial quantitative approach should consider metrics such as time in range (TIR), time above and below range (TAR and TBR), coefficient of variation (CV), glucose management indicator (GMI), total daily insulin dose, and basal/bolus distribution.

A second level of analysis should incorporate user behavior. Anticipating and omitting bolus doses, as well as using exercise prompts, provide relevant information for interpreting the interaction between the patient and the technology and contextualizing the observed glycemic patterns.

Subsequent analysis should focus on identifying reproducible patterns, particularly postprandial excursions and hypoglycemia, differentiating them from isolated events. In cases of recurrent postprandial hyperglycemia, it is necessary to integrate the timing of bolus administration, the insulin-to-carbohydrate ratio, and counting skills. For hypoglycemia, its magnitude, temporal distribution, and relationship to the established glycemic targets should be considered.

This sequential approach allows for optimizing consultation time and determining whether the findings justify modifications to therapeutic parameters or primarily require educational and/or behavioral intervention. Finally, each evaluation should conclude with the definition of a specific and re-evaluable target.

In clinical practice, maximizing the benefits of technology involves going beyond simply visualizing data: it requires prioritizing, contextualizing, and transforming that data into individualized therapeutic decisions.