Impact of retrospective calibration algorithms on hypoglycemia detection in newborn infants using continuous glucose monitoring

Abstract

Background: Neonatal hypoglycemia is common and may cause serious brain injury. Diagnosis is by blood glucose (BG) measurements, often taken several hours apart. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing the number of BG measurements. Calibration algorithms convert sensor signals into CGM output. Thus, these algorithms directly affect measures used to quantify hypoglycemia. This study was designed to quantify the effects of recalibration and filtering of CGM data on measures of hypoglycemia (BG <2.6 mmol/L) in neonates.

Subjects and methods: CGM data from 50 infants were recalibrated using an algorithm that explicitly recognized the high-accuracy BG measurements available in this study. CGM data were analyzed as (1) original CGM output, (2) recalibrated CGM output, (3) recalibrated CGM output with postcalibration median filtering, and (4) recalibrated CGM output with precalibration median filtering. Hypoglycemia was classified by number of episodes, duration, severity, and hypoglycemic index.

Results: Recalibration increased the number of hypoglycemic events (from 161 to 193), hypoglycemia duration (from 2.2% to 2.6%), and hypoglycemic index (from 4.9 to 7.1 μmol/L). Median filtering postrecalibration reduced hypoglycemic events from 193 to 131, with little change in duration (from 2.6% to 2.5%) and hypoglycemic index (from 7.1 to 6.9 μmol/L). Median filtering prerecalibration resulted in 146 hypoglycemic events, a total duration of hypoglycemia of 2.6%, and a hypoglycemic index of 6.8 μmol/L.

Conclusions: Hypoglycemia metrics, especially counting events, are heavily dependent on CGM calibration BG error, and the calibration algorithm. CGM devices tended to read high at lower levels, so when high accuracy calibration measurements are available it may be more appropriate to recalibrate the data.

FIG. 1.

Distribution of errors between continuous glucose monitoring (CGM) and blood glucose (BG) measurements with median (dashed vertical line) and interquartile range (solid vertical lines), for different glucose levels. Color images available online at www.liebertonline.com/dia

FIG. 2.

Comparison of a section of continuous glucose monitoring (CGM) trace containing hypoglycemia for the original CGM, recalibrated CGM, recalibrated and filtered CGM, and filtered valid electrical current detected by the monitor from the sensor (I_SIG) and then recalibrated CGM. Hypoglycemia is defined as one or more consecutive CGM measurement(s) <2.6 mmol/L, surrounded by one or more CGM measurement(s) >2.6 mmol/L. Note that recalibrating increases the number of hypoglycemic events from one to four; then filtering reduces it back to one in this example. Color images available online at www.liebertonline.com/dia

FIG. 3.

The continuous glucose monitoring (CGM) trace that had the largest change in hypoglycemia metrics after recalibration. The original CGM trace (dashed line) contains a long period of hypoglycemia in the first 12 h of monitoring. However, all four calibration blood glucose (BG) measurements in this period were >2.6 mmol/L, and consequently the recalibrated CGM trace (solid line) had no hypoglycemia. Color images available online at www.liebertonline.com/dia

FIG. 4.

Comparison of ranked hypoglycemic (Hypo) index for 43 patients (3 days) for original continuous glucose monitoring (CGM), recalibrated CGM, recalibrated and Filtered CGM, and filtered electrical current detected by the monitor from the sensor (I_SIG) and then recalibrated. The curved solid line repeats the ranked distribution as determined by the original CGM data. The integral index captures the overall area of each panel for a single comparator value. Color images available online at www.liebertonline.com/dia