Association Between Management of Continuous Subcutaneous Basal Insulin Administration and HbA1C

Abstract

Background: While we expect that patients who adjust their insulin delivery algorithms between clinic visits to have better glucose control compared to those who do not, this effect has not been quantified.

Method: This is a single-center retrospective cohort study including pediatric and adult patients with type 1 diabetes evaluating insulin pump self-management behaviors. Basal insulin dose information was obtained from the Glooko-Diasend database, and used to quantify the frequency and magnitude of basal insulin daily dose adjustments within the 90-day window preceding HbA1c measurement. We use a linear mixed-effects model to analyze associations between frequency/magnitude of daily basal insulin changes and HbA1c.

Results: We present data on 114 adult (44 ± 17 years, 60% female) and 212 pediatric (12 ± 4 years, 50% female) patients. Individuals changed their basal insulin dose on 72%-94% (interquartile range [IQR]) of observed days relative to the previous day. These changes varied 0.6%-2.4% IQR from the previous day's value. In pediatric patients, lower HbA1c was associated with more frequent daily profile adjustments, while controlling for rate of hypoglycemia (z = -3.2, P = .001). In adults, there was no relationship between HbA1c and magnitude or frequency of basal profile adjustments.

Conclusions: Pediatric patients who frequently modify their basal insulin exhibit somewhat better clinical outcomes, although the magnitude by which their basal amount is changed does not contribute to this effect.

Keywords: basal insulin; insulin pump therapy; patient treatment management; type 1 diabetes.

Figure 1.

Illustrated calculation of pump metrics. Frequency of change is calculated as the proportion of days with a different total basal value from the previous day, while magnitude of change is the median percent difference from the previous day across all pairs of days. Days with the same total basal value as the previous day were not included in the median calculation.

Figure 2.

Patient inclusion flowchart, pediatric and adult. The majority of exclusions in both populations (74% pediatrics, 89% adults) were due to a lack of information in the Diasend database. Most of the remaining exclusions (16% pediatrics, 10% adults) were due to a lack of treatment adherence.

Figure 3.

Results for pediatric population. (a) Distribution of frequency of basal profile changes: for most windows, the majority of days have different total basal values from the previous. (b) Distribution of magnitude of basal profile changes (across days with any changes). Most median changes are of fairly low magnitude. (c) Distribution of daily basal amount changes; magnitude vs frequency of change. (z = 6.4, P < .0001). (d) HbA1c vs frequency of basal profile change. Patients who change their basal profiles more frequently tend to have lower HbA1c (z = -3.4, P = .001). (e) HbA1c vs magnitude of basal profile change. There is no linear association (z = -1.2, P = .23).

Figure 4.

Results for adult population. (a) Distribution of frequency of basal profile changes: for most windows, the majority of days have different total basal values from the previous. (b) Distribution of magnitude of basal profile changes (across days with any changes). Most median changes are of fairly low magnitude. (c) Distribution of daily basal amount changes; magnitude vs frequency of change (z = 1.6, P = .18). (d) HbA1c vs frequency of basal profile change. There is no linear association (z = -1.5, P = .14). (e) HbA1c vs magnitude of basal profile change. There is no linear association (z = -0.76, P = .45).

Figure 5.

Proportion of time hypoglycemic vs frequency of basal profile change in the pediatric cohort. There is no linear association (z = 0.88, P = .38).