Impact of Retrospective Calibration Algorithms on Hypoglycemia Detection in Newborn Infants Using Continuous Glucose Monitoring

Abstract

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. 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. 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. 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.