JMIR Publications

ABSTRACT

Background:

Basal rates (BR) adjustment is crucial for managing Type 1 Diabetes Mellitus (T1DM), accounting for 30% to 50% of Total Daily Insulin (TDI) needs. All current Closed Loop systems revert to the user’s usual pump BR (known as manual mode) in the event of closed-loop failure. Further, those in low and middle-income countries (LMICs) and those without suitable health insurance, access to Closed Loop remains relatively low. Accurately adjusting the BR remains challenging, leading to hyperglycaemia or hypoglycaemia, and research on optimizing the BR is limited.

Objective:

This study proposes an adaptive algorithm that utilizes continuous glucose monitoring (CGM) data to identify BR inaccuracies without requiring meal intake information.

Methods:

The OhioT1DM dataset formed the basis for implementing this methodology. Each composite day was generated by excluding bolus insulin profiles lacking meal intake information and by calculating hourly blood glucose (BG) relative levels along with their corresponding reliability measures, enabling assessment of deviations from the recommended BR (i.e., a BG relative change of 0 mg/dL). Both a non-inferiority analysis and a classification precision metric were performed to assess the practicality of this approach compared to using meal data.

Results:

Data from 12 participants (n=12) showed non-inferiority to the meal-based method, with all participants meeting the predefined non-inferiority margin (10% difference threshold in absolute BG relative-level deviation; P < .001 to .007). Classification precision was 74% (≈139 out of 188 meals correctly classified on average per participant; 95% CI, 67.2%–79.7%). The daily cumulative BG average was 200.6 mg/dL (11.1 mmol/L), with peak values reaching 270.15 mg/dL (14.99 mmol/L). Furthermore, 286 of the 288 BG relative values (99.3%; 95% CI, 97.5%–99.8%) that were unaffected by external factors were associated with incorrect BR settings, with deviations ranging from −25.5 to 46 mg/dL (−1.58 to 2.59 mmol/L).

Conclusions:

Current strategies to optimize BR settings are inadequate, and our approach of a Personalised Basal Tuner (PBT) helps to better analysis BR without relying on meal intake information. Indeed, without an optimally set BR, in the event of Closed Loop reverting to manual mode, patients may be exposed to persistent hypo- or hyperglycaemia leading to safety and efficacy issues. Future work will focus on generating BR recommendations through the application of this algorithm in clinical practice to assist clinicians in setting BR in LMICs where Closed Loops systems are not prevalent to help increase time in range (TIR).