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5/4/2015
1
Neesha Ramchandani, PNP, CDE
NYU College of Nursing & Montefiore Medical Center
May 6, 2015
Certified pump trainer for Animas, Medtronic MiniMed, OmniPod, Roche, & Tandem Diabetes
CGM trainer for Dexcom & Medtronic MiniMed Advisory Board Member, Unomedical Advisory Board Member, Halozyme Member of the Lilly Educator Network
1. Be able to describe how the closed loop/artificial pancreas system works.
2. Be able to describe the limitations of the closed loop/artificial pancreas system.
3. Be able to describe at least 2 research studies using closed loop/artificial pancreas technology and discuss the results.
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Insulin Pump Glucose Sensor Control Algorithm
Proportional‐Integral‐Derivative (PID, ePID)
Model Predictive Control (MPC) / Multiple Model Predictive Control (MMPC) / Multiple Model Probabilistic Predictive Control (MMPPC)
Fuzzy Logic (FL)
Others
Doyle et al., Diabetes Care, 2014
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4 Components:
Proportional
Integral
Derivative
Insulin on board (IFB)
One size fits all model
Has both Predictive and Adaptive modes and decides when is best to use each
Learns the needs of each individual pump user
Flexible, can include things that are a function of time of day (meals, exercise, etc.)
Easier to adapt to the user than the PID algorithm
Sometimes semi‐closed loop Pre‐bolusing seen as more
beneficial
Picture from http://abetterpancreas.wikia.com/wiki/Artificial_Pancreas_Algorithm_Project?file=Artificial_pancreas_process_control_state_machine.jpg, accessed 4/30/15
Bequette, J Diabetes SciTechnol, 2013.
Does not use differential equations to determine insulin dose
Takes other physiological parameters into account, such as illness and stress
Allows for personalized dosing for each individual patient
Mauseth et al., Diabetes Technology & Therapeutics, 2013
Picture from http://slideplayer.com/slide/1480108/, accessed 4/30/15
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Figure 2 from Bequette, J Diabetes SciTechnol, 2013.
Insulin only CL/AP systems
Able to maintain tighter glycemic control
Still had some hyperglycemia and post‐prandial hypoglycemia
Figure 2 C&D, from El‐Khatib et al, SciTransl Med, 2010.
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Insulin Quick‐acting insulins still slower than what is needed to maintain blood glucose in the 70‐180 mg/dl range
Sensor accuracy Up to 20 min lag time between blood glucose and sensor glucose readings (now better, 1‐10 min lag time)
Sensor generally less accurate on day 1, improves with time
Software Reactive systems – glucose rises first, and then more insulin is given
Newer algorithms working on overcoming this limitation
Real estate
Number of devices to carry
Start‐up/calibration time
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Attenuating hypoglycemia –Glucagon
Minimizing postprandial hyperglycemia
Pramlintide (Symlin)
Exenatide (Byetta)
Liraglutide (Victoza)
Others
Goal: Attenuate hypoglycemia
Figure 1. from El‐Khatib et al., SciTransl Med, 2010.
Glucagon stability
Multiple infusion devices required
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Goal: minimize post‐prandial hyperglycemia Renukuntla et al. 10 subjects (50%M) Average age 23.2 + 1.0 years old Average baseline HbA1c = 7.3 + 0.3% Results: Pramlintide (30 µg) delayed but did not significantly
decrease peak postprandial glucose Less prandial insulin administered (p=NS) No glucagon suppression
Conclusion: Pramlintide not the best option for a bihormonal CL/AP system
Plasma glucose
Plasma insulin
Plasma glucagon
Renukuntla et al., J Diabetes SciTechnol, 2014. Figure 1 from Renukuntla et al., J Diabetes SciTechnol, 2014.
Goal: minimize post‐prandial hyperglycemia Weinzimer et al. 8 subjects (50%M) Age 15‐28 years old Average baseline HbA1c = 7.5 + 0.7% Results:
Pramlintide (30 µg) delayed (p<0.0001) and also decreased peak postprandial glucose by an average of 25 mg/dl (p=0.006)
Significant differences in premeal insulin levels (p<0. 05)
Conclusion: Beneficial results seen with Pramlintide in the CL/AP system
Weinzimer et al., Diabetes Care, 2012.Figure 1 from Weinzimer et al. (2012).
Variable results
+/‐ significant differences in peak postprandial glucose
+/‐ significant differences in amount of insulin administered
No glucagon suppression
Must be injected SQ with every meal
Tolerance to pramlintide believed to develop over time
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Goal: minimize post‐prandial hyperglycemia 10 subjects (50%M) Average age 23.2 + 1.0 years old Average baseline HbA1c = 7.3 + 0.3% Results: Exenatide (2.5 µg) significantly decreased peak postprandial
glucose (p<0.03) Less prandial insulin administered (p=NS) Exenatide suppressed endogenous glucagon response
(p<0.03)
Conclusion: Adjuvant therapy with exenatide in the CL/AP setting effectively decreases postprandial hyperglycemia, blunts glucagon response, and may decrease prandial insulin requirements without causing undue hypoglycemia.
Plasma glucose
Plasma insulin
Plasmaglucagon
Renukuntla et al., J Diabetes SciTechnol, 2014. Figure 1 from Renukuntla et al., J Diabetes SciTechnol, 2014.
Separate SQ injection, multiple times per day
Dose used for the study is not available by pen
Early exercise studies (insulin only) still resulted in hypoglycemia Insulin + glucagon minimizes exercise‐induced hypoglycemia Bionic Pancreas study
Newer studies propose adding heart rate monitoring to further minimize exercise‐induced hypoglycemia 12 subjects 9/10 exercise intensity Pilot study, p=NS for all
measures, but looks promising
Breton et al., Diabetes Technology & Therapeutics, 2014Breton et al., Diabetes Technology & Therapeutics, 2014 Figure 4 from Breton et al., Diabetes Technology & Therapeutics, 2014
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24 adults with T1DM
Overnight closed loop control using MPC algorithm
Medium meal and large meal with alcohol (wine) both tested
Overnight CL/AP control resulted in tighter glycemic control and less hypoglycemia
EATING OUT:Profiles (medians and interquartile ranges) of plasma glucose and
insulin concentrations and insulin infusion in the eating out scenario (12 participants). Outlying squares represent hypoglycemic events
(glucose level <54 mg/dl)
EATING IN:Profiles (medians and interquartile ranges) of plasma
glucose and insulin concentrations and insulin infusion in eating in scenario
(12 participants).
Figures 3 & 4. from Hovorka et al., BMJ, 2011.
% of plasma glucose values in range and out of range in both eating in and eating out scenarios, combined. Percentages represent total time plasma glucose level was below, at, and above target from midnight until the end of the closed loop delivery.
Figure 5. from Hovorka et al., BMJ, 2011.
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Overnight at diabetes camp – 56 subjects 10‐18 yrs old – BG’s much better with less hypoglycemia on CL/AP (Phillip & Battelino, NEJM, 2013)
Multiple overnights on CL/AP – 10 subjects age 46.4 + 8.5 yrs old –spent more time in target range of 80‐140 mg/dl overnight, awoke with a lower fasting glucose, and had better control then next day (Brown et al., Diabetes Technology & Therapeutics, 2015)
Multicenter European study 13 adults, average age 45 + 14 yrs old, HbA1c = 7.4
+ 0.9% Open loop x1 day then closed loop x1+ days (42
hrs); only overnight analyzed Study done in hotel to simulate “at home”
experience Results: when on CL/AP system spent more time in
target glucose range (70‐180 mg/dl; 84% vs 62%, p=0.04) and less time in hypoglycemia (1% vs 10%, p=0.01)
Conclusions: CL/AP system is safe and efficacious in an at‐home setting, and results in more time in range and less time with hypoglycemia
Del Favero et al., Diabetes, Obesity, & Metabolism, 2015 Figure 2. from Del Favero et al., Diabetes, Obesity, & Metabolism, 2015
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Multicenter, multi‐national randomized insulin only CL/AP crossover study
18 subjects completed study, age 46 + 10 years, baseline HbA1c = 7.4 +0.7%, 75% M (15/20)
Results: less time spent in hypoglycemia and less risk of hypoglycemia on CL/AP, slightly higher overall glucose average (161 vs. 152 mg/dl) Higher overall glucose average attributed to emphasis placed on avoiding hypoglycemia
Conclusion: Closed loop control via smartphone in the outpatient setting is safe and effective. Further optimization of the control algorithm is necessary to both minimize hypoglycemia and not compromise overall glycemic control.
Kovatchev et al., Diabetes Care, 2014.
Beacon Hill Study: 20 adults & 32 adolescents
Wore CL/AP system for 5 days in the outpatient setting Lived in a restricted area during intervention part of study
Always accompanied by study personnel
No restrictions on food or exercise; alcohol limited to low/moderate intake
Results: Average BG = 138 mg/dl
Only 4.8% (adults) & 6.1% (adolescents) of time spent with BG<70 mg/dl
Conclusion: Better glycemic control with less hypoglycemia with a bihormonal artificial pancreas in both adolescents and adults
Russell et al., NEJM, 2014.
Beacon Hill study done on children/adolescents in a summer camp setting, Summers 2013 & 2014 Controlled real‐life clinical laboratory
2013: 32 adolescents & young adults, 12‐20 years old
Wore CL/AP with glucagon x5 days
160 full days of real‐life CL/AP data collected
2014: 19 children 6‐11 years old, 4,560 hrs of data collected
Anecdotal results: “Yes. I trusted it. It works. If you check it, it’s perfect. My
blood sugars are perfect.”
“It’s like a vacation from diabetes.”
Sources: The Barton Center Fall 2013 Newsletter, available at http://www.bartoncenter.org/sites/default/files/2013%20Fall%20Newsletter.pdf, accessed 5/1/15The Barton Center Fall 2014 Newsletter, available at http://www.bartoncenter.org/sites/default/files/2014%20Fall%20Newsletter_0.pdf, accessed 5/1/14and Six Until Me blog entry from 7/30/14, available at http://sixuntilme.com/wp/2014/07/30/go‐bionic‐ed‐damiano‐clara‐barton‐camp‐bionic‐pancreas‐really‐works/
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From www.jdrf.org
Smarter insulin pumps ex. –GlucoSitter software in Medtronic pump – fully closed loop (at least 3 generations away)
Warming insulin infusion site
Faster insulins
Addition of other hormones to the system
Creation of a more stable glucagon
Implanted/internal CL/AP systems
Better Algorithms
Product photos courtesy of Insuline Medical (1&2) and Renfrew Group InSmart (3), via Google Images
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System improvements Smaller devices
Greater sensor accuracy
Faster insulin
#wearenotwaiting Open source software and devices that communicate with multiple platforms –basically mix & match your own system
Bigfoot Medical (http://www.healthline.com/diabetesmine/bigfoot‐family‐their‐diabetes‐and‐homemade‐closed‐loop‐system#1 )
DIYPS (Do‐It‐Yourself‐Pancreas‐System) (http://diyps.org/ )
The CL/AP system improves glycemic control and time that glucose is in range while minimizing hypoglycemia compared to open‐loop systems True for any time of day True for all age groups (tested in as young as 3 years old) Newer systems/algorithms are more efficacious than earlier systems At‐home systems now available for testing
All algorithms have their benefits Multi‐hormonal system is likely the best model Faster insulins, more improved sensors, and stable, easy to administer adjuvant
hormones (e.g., glucagon) are needed Timeline is always longer than anticipated People with diabetes want the solution now, and are working to create their own
platforms First at‐home systems may be available to the general public as early as 2018**
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1. Bequette, B. W. (2013). Algorithms for a closed‐loop artificial pancreas: the case for model predictive control. J Diabetes SciTechnol, 7(6), 1632‐1643.
2. Breton, M. D., Brown, S. A., Karvetski, C. H., Kollar, L., Topchyan, K. A., Anderson, S. M., & Kovatchev, B. P. (2014). Adding heart rate signal to a control‐to‐range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes. Diabetes TechnolTher, 16(8), 506‐511. doi: 10.1089dia.2013.0333
3. Brown, S. A., Kovatchev, B. P., Breton, M. D., Anderson, S. M., Keith‐Hynes, P., Patek, S. D., . . . Cobelli, C. (2015). Multinight "bedside" closed‐loop control for patients with type 1 diabetes. Diabetes TechnolTher, 17(3), 203‐209. doi: 10.1089/dia.2014.0259
4. Del Favero, S., Place, J., Kropff, J., Messori, M., Keith‐Hynes, P., Visentin, R., . . . Renard, E. (2015). Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes. Diabetes Obes Metab, 17(5), 468‐476. doi: 10.1111/dom.12440
5. Doyle, F. J., 3rd, Huyett, L. M., Lee, J. B., Zisser, H. C., & Dassau, E. (2014). Closed‐loop artificial pancreas systems: engineering the algorithms. Diabetes Care, 37(5), 1191‐1197. doi: 10.2337/dc13‐2108
6. El‐Khatib, F. H., Russell, S. J., Nathan, D. M., Sutherlin, R. G., & Damiano, E. R. (2010). A bihormonal closed‐loop artificial pancreas for type 1 diabetes. SciTransl Med, 2(27), 27ra27. doi: 10.1126/scitranslmed.3000619
7. Hovorka, R., Kumareswaran, K., Harris, J., Allen, J. M., Elleri, D., Xing, D., . . . Evans, M. L. (2011). Overnight closed loop insulin delivery (artificial pancreas) in adults with type 1 diabetes: crossover randomised controlled studies. Bmj, 342, d1855. doi: 0.1136/bmj.d1855
8. Kovatchev, B. P., Renard, E., Cobelli, C., Zisser, H. C., Keith‐Hynes, P., Anderson, S. M., . . . Doyle, F. J., 3rd. (2014). Safety of outpatient closed‐loop control: first randomized crossover trials of a wearable artificial pancreas. Diabetes Care, 37(7), 1789‐1796. doi: 10.2337/dc13‐2076
9. Mauseth, R., Hirsch, I. B., Bollyky, J., Kircher, R., Matheson, D., Sanda, S., & Greenbaum, C. (2013). Use of a "fuzzy logic“ controller in a closed‐loop artificial pancreas. Diabetes TechnolTher, 15(8), 628‐633. doi: 10.1089dia.2013.0036
10. O'Grady, M. J., Retterath, A. J., Keenan, D. B., Kurtz, N., Cantwell, M., Spital, G., . . . Ly, T. T. (2012). The use of an automated, portable glucose control system for overnight glucose control in adolescents and young adults with type 1 diabetes. Diabetes Care, 35(11), 2182‐2187. doi: 10.2337/dc12‐0761
11. Phillip, M., Battelino, T., Atlas, E., Kordonouri, O., Bratina, N., Miller, S., . . . Danne, T. (2013). Nocturnal glucose control with an artificial pancreas at a diabetes camp. N Engl J Med, 368(9), 824‐833. doi: 0.1056/NEJMoa1206881
12. Renukuntla, V. S., Ramchandani, N., Trast, J., Cantwell, M., & Heptulla, R. A. (2014). Role of glucagon‐like peptide‐1 analogue versus amylin as an adjuvant therapy in type 1 diabetes in a closed loop setting with ePID algorithm. J Diabetes SciTechnol, 8(5), 1011‐1017. doi: 10.1177/1932296814542153
13. Russell, S. J., El‐Khatib, F. H., Sinha, M., Magyar, K. L., McKeon, K., Goergen, L. G., . . . Damiano, E. R. (2014). Outpatient glycemic control with a bionic pancreas in type 1 diabetes. N Engl J Med, 371(4), 313‐325. doi: 10.1056NEJMoa1314474
14. Weinzimer, S. A., Sherr, J. L., Cengiz, E., Kim, G., Ruiz, J. L., Carria, L., . . . Tamborlane, W. V. (2012). Effect of pramlintide on prandial glycemic excursions during closed‐loop control in adolescents and young adults with type 1 diabetes. Diabetes Care, 35(10), 1994‐1999. doi: 10.2337/dc12‐0330
15. Various websites referenced throughout the slides
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