Hypoglycemia alarm enhancement using data fusion

Abstract

Background: The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features.

Methods: Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions.

Results: The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels.

Conclusion: Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms.

Figure 1.

Example of probabilistic simulation of CGMS data using an overnight BG level profile of a patient experiencing nocturnal hypoglycemia. The interpolated YSI profile is shown in red, with 10 probable CGMS traces (cyan). CGMS traces were simulated by adding three error components to YSI data. Errors were realistic of CGMS accuracy as described by Wentholt and colleagues: vertical shift (calibration bias) of BG level profile by ± SD = 19.8 mg/dl (1.1 mmol/liter), temporal delay of CGMS trace from BG level profile by a mean time of 7.1 minutes ± SD = 5.5 minutes, and measurement error offsets of BG level data points by ± SD = 4.5 mg/dl (0.25 mmol/liter).

Figure 2.

Simulated CGMS alarm performance on training data showing the sensitivity and specificity of hypoglycemic alarms as a function of threshold of CGMS reading. Even though the error band was not applied during algorithm training, it was applied in this evaluation in order to allow comparison with the test performance in Figure 3. At the optimal threshold of 72 mg/dl determined during algorithm training, training performance with the error band applied was 85% in terms of sensitivity and 81% in terms of specificity.

Figure 3.

Simulated CGMS alarm performance on test data showing the sensitivity and specificity of hypoglycemic alarms as a function of threshold of CGMS reading. At the optimal threshold of 72 mg/dl (4.0 mmol/liter) determined during algorithm training, the test performance was 87% in terms of sensitivity and 85% in terms of specificity.

Figure 4.

Alarm performance of fused data algorithm on test data as a function of CGMS threshold. The addition of ANS response features to CGMS data increases the sensitivity from 87 to 97%, which significantly reduces the number of missed hypoglycemic episodes from 13 to 3%.