Hypoglycemia Prevention via Pump Attenuation and Red-Yellow-Green “Traffic” Lights Using Continuous Glucose Monitoring and Insulin Pump Data

Hughes, Colleen; Patek, Stephen D.; Breton, Marc D.; Kovatchev, Boris

doi:10.1177/193229681000400513

Cited by 83 publications

(91 citation statements)

References 27 publications

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“…The mathematical model used to compute the net effect traces is a subcutaneous oral glucose minimal model, which has been used in previous studies. 54 By construction, the recovered net effect signal is such that when it is ''fed'' to the subcutaneous oral glucose minimal model along with the corresponding history of actual insulin delivery the model recovers the original CGM trace. The ability to ''recover'' the CGM trace with the actual record of insulin delivery becomes an ability to simulate the effect of modified insulin delivery, a technique referred to here as net effect simulation.…”

Section: Nominal Bg Tracesmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

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184

154

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Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. Materials and Methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to ''replay'' real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels £ 50 mg/dL and BG levels £ 39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD = 10%. Similarly, hyperglycemia (frequency of BG levels ‡ 250 mg/dL and BG levels ‡ 400 mg/dL) increased and displayed an abrupt slope change at MARD = 10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD = 10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

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Section: Nominal Bg Tracesmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

Self Cite

184

154

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“…A dedicated safety system triggers alarms/alerts based on two ''traffic light'' signals for hypoglycemia and hyperglycemia displayed on the DiAs GUI: green, no risk; yellow, basal rate modulation occurring to decrease risk; and red, acute risk, external intervention needed. 17 Participants were instructed to check their capillary glucose level if a red light alarm occurred.…”

Section: Clc Technologymentioning

confidence: 99%

Multinight “Bedside” Closed-Loop Control for Patients with Type 1 Diabetes

BrownSue

KovatchevBoris²,

BretonMarc³

et al. 2015

Diabetes Technology & Therapeutics

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Background: Studies of closed-loop control (CLC) systems have improved glucose levels in patients with type 1 diabetes. In this study we test a new CLC concept aiming to ''reset'' the patient overnight to nearnormoglycemia each morning, for several consecutive nights. Subjects and Methods: Ten insulin pump users with type 1 diabetes (mean age, 46.4 -8.5 years) were enrolled in a two-center (in the United States and Italy) randomized crossover trial comparing 5 consecutive nights of CLC (23:00-07:00 h) in an outpatient setting versus sensor-augmented insulin pump therapy of the same duration at home. Primary end points included time spent in 80-140 mg/dL as measured by continuous glucose monitoring overnight and fasting blood glucose distribution at 7:00 h. Results: Compared with sensor-augmented pump therapy, CLC improved significantly time spent between 80 and 140 mg/dL (54.5% vs. 32.2%; P < 0.001) and between 70 and 180 mg/dL (85.4% vs. 59.1%; P < 0.001); CLC reduced the mean glucose level at 07:00 h (119.3 vs. 152.9 mg/dL; P < 0.001) and overnight mean glucose level (139.0 vs. 170.3 mg/dL; P < 0.001) using a marginally lower amount of insulin (6.1 vs. 6.8 units; P = 0.1). Tighter overnight control led to improved daytime control on the next day: the overnight/next-day control correlation was r = 0.52, P < 0.01. Conclusions: Multinight CLC of insulin delivery (artificial pancreas) results in significant improvement in morning and overnight glucose levels and time in target range, with the potential to improve daytime control when glucose levels were ''reset'' to near-normoglycemia each morning.

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“…Hypoglycemia prediction/detection algorithms are usually coupled with specific supporting actions to improve their efficiency in preventing hypoglycemia. Different actions have been proposed, such as gradual insulin attenuation (Hughes et al (2010) (Hughes et al (2010)) alarms to alert the patient about actual or impending hypoglycemia. The statistical and linear hypoglycemia predictors with pump suspension algorithm proposed in ) were used in a clinical study, and proved to be effective in preventing hypoglycemia without provoking rebound hyperglycemia after the suspension of the pump.…”

Section: Hypoglycemia Alarm Systemsmentioning

confidence: 99%