Clinically relevant hypoglycemia prediction metrics for event mitigation.
BACKGROUND
The purpose of this study was to develop a method to compare hypoglycemia prediction algorithms and choose parameter settings for different applications, such as triggering insulin pump suspension or alerting for rescue carbohydrate treatment.


METHODS
Hypoglycemia prediction algorithms with different parameter settings were implemented on an ambulatory dataset containing 490 days from 30 subjects with type 1 diabetes mellitus using the Dexcom™ (San Diego, CA) SEVEN™ continuous glucose monitoring system. The performance was evaluated using a proposed set of metrics representing the true-positive ratio, false-positive rate, and distribution of warning times. A prospective, in silico study was performed to show the effect of using different parameter settings to prevent or rescue from hypoglycemia.


RESULTS
The retrospective study results suggest the parameter settings for different methods of hypoglycemia mitigation. When rescue carbohydrates are used, a high true-positive ratio, a minimal false-positive rate, and alarms with short warning time are desired. These objectives were met with a 30-min prediction horizon and two successive flags required to alarm: 78% of events were detected with 3.0 false alarms/day and 66% probability of alarms occurring within 30 min of the event. This parameter setting selection was confirmed in silico: treating with rescue carbohydrates reduced the duration of hypoglycemia from 14.9% to 0.5%. However, for a different method, such as pump suspension, this parameter setting only reduced hypoglycemia to 8.7%, as can be expected by the low probability of alarming more than 30 min ahead.


CONCLUSIONS
The proposed metrics allow direct comparison of hypoglycemia prediction algorithms and selection of parameter settings for different types of hypoglycemia mitigation, as shown in the prospective in silico study in which hypoglycemia was alerted or treated with rescue carbohydrates.
Sansum Diabetes Research Institute, Santa Barbara, CA 93106, USA.
Rebecca A
RA
Harvey
Eyal
E
Dassau
Howard C
HC
Zisser
Wendy
W
Bevier
Dale E
DE
Seborg
Lois
L
Jovanovič
Francis J
FJ
Doyle
D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation; D056902:Clinical Alarms; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy*; D004040:Dietary Carbohydrates / Q000008:administration & dosage; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D007003:Hypoglycemia / Q000097:blood* / Q000139:chemically induced; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000009:adverse effects; D007328:Insulin / Q000008:administration & dosage / Q000009:adverse effects; D007332:Insulin Infusion Systems; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012189:Retrospective Studies; D012307:Risk Factors
D001786:Blood Glucose; D004040:Dietary Carbohydrates; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; D007328:Insulin; C517652:hemoglobin A1c protein, human