Continuous Glucose Monitoring: Lessons Learned. Bruce Buckingham, MD Professor of Pediatric Endocrinology Stanford Medical Center
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1 Continuous Glucose Monitoring: Lessons Learned Bruce Buckingham, MD Professor of Pediatric Endocrinology Stanford Medical Center
2 Medtronic Minimed Medical Advisory Board Research Support Abbott Diabetes Care Research Support NovoNordisk Medical Advisory Board Animas Medical Advisory Board Unomedical Medical Advisory Board Dexcom Research Support Bayer Medical Advisory Board Conflict of Interest
3 Websites JAEB/JDRF Medtronic Minimed Pump School
4 Online Teaching Tool Teaches how and when to calibrate each sensor Issues with interstitial lag time How to use a sensor in real time Set hyper and hypoglycemic alarms Adjust insulin doses in real time Use downloaded reports for retrospective analysis
5
6 Abbott FreeStyle Navigator Sensor Sensor Sensor Delivery Unit Transmitter Receiver
7 Medtronic Paradigm REAL- Time
8 DexCom 7+ Sensor
9 Side View Showing Needle Sensors CGMS Navigator Dexcom
10 Accuracy of Meters and Sensors YSI Beckman ISTAT Ultra Meter FreeStyle Guardian RT Dexcom 7+ Navigator n 353 1,103 1,103 5,401 1,927 20,362 Measurement Device for Reference Glucose DCCT lab DCCT lab DCCT lab YSI YSI YSI Overall Median ARD% Mean ARD% % within ISO criteria 6% 5% 5% 11% 13% 9% 6% 6% 15% 16% 13% 95% 99% 98% 76% g 76% g 82% g
11 JDRF data on Sensor Glucose Values in Non-Diabetic Subjects Fox, Diabetes Care epub 3/2010 Seventy-four healthy, non-obese children, adolescents, and adults Normal OGTT 9-65 years old Blinded Navigator or Guardian CGM for 3 to 7 days
12 Mean glucose values by time of day in Normals JDRF CGM Study Group, ADA, 2009
13 CGM values in Non-Diabetics Glucose Values Daytime (6 AM MN) Nighttime (MN-6AM) mg/dl 90.4% 90.3% 70 mg/dl 1.1% 2.2% 60 mg/dl 0.0% 0.0% > 140 mg/dl 0.5% 0.0% Standard Deviation (mg/dl) Absolute Rate of Change (mg/dlmin)
14 The Initial CGM Set-up Setting alarms Choosing a good insertion site Doing the first calibration
15 JDRF RCT Nocturnal Hypoglycemia Beck, Diabetes Care epub 3/ ,000 nights with 4 hours of glucose readings from MN to 6 AM Guardian and Navigator 86% had the full 6 hours of data without any skips 176 subjects Median of 217 nights/subject Hypoglycemia = 2 consecutive CGM readings 60 mg/dl
16 JDRF RCT Nocturnal Hypoglycemia Hypoglycemia occurred during 8.5% of nights Maximum % of nights with hypoglycemia - 28% 3% of subjects had no hypoglycemia
17 JDRF RCT Nocturnal Hypoglycemia Mean duration of hypoglycemia = 81 minutes 47% of nights had at least 1 hour of hypoglycemia 23% had at least 2 hours 11% had at least 3 hours
18 11:15 PM
19 Alarm at 1:57 AM
20
21 Duration of Hypoglycemia versus Age
22 Hypoglycemia Prior to Seizures Buckingham, Diabetes Care, 2009
23 DEAD-IN-BED SYNDROME Tanenberg, Endocr Pract 2009; 15:1-13
24 Do pumpers have a lower incidence of nocturnal hypoglycemia when compared to patients on MDI therapy? 1. Yes 2. No
25 Incidence of Nocturnal Hypoglycemia - Pumpers vrs MDI N % nights with a hypo Pump % MDI % p value 0.63
26 Does Fear Of Hypoglycemia Effect The Incidence Of Nocturnal Hypoglycemia? 1. Yes 2. No
27 Relationship Between Fear Of Hypoglycemia Scores and the Incidence Of Nocturnal Hypoglycemia Hypoglycemic Fear Scale Score N % nights with a hypo < % % > % p value 0.07
28 Does The Age Of The Patient Effect The Incidence Of Nocturnal Hypoglycemia? 1. Yes 2. No
29 Age and Incidence Of Nocturnal Hypoglycemia N % nights with a hypo % % % p value 0.05
30 Are A1c Levels Related To The Incidence Of Nocturnal Hypoglycemia? 1. Yes 2. No
31 A1c Levels and The Incidence Of Nocturnal Hypoglycemia N % nights with a hypo < 7% % 7% < 8% % 8% % p value 0.001
32 Hypoglycemia event rate per 100 person-years Severe Hypoglycemia Events in JDRF CGS months 7-12 months overall (N=83) baseline A1c >=7.0% (N=49) baseilne A1c <7.0% (N=34)
33 Alarm Settings Need to be individualized Unless there is concern about hypoglycemia unawareness, set the alarms to avoid frequent nuisance alarms low hypoglycemic threshold alarm of 60 mg/dl Hyperglycemic threshold alarm of 240 mg/dl May turn off predictive alarms initially May turn off rate of change alarms initially
34 The Initial CGM Set-up Setting alarms Choosing a good insertion site Doing the first calibration
35 The Initial CGM Set-up Setting alarms Choosing a good insertion site Doing the first calibration
36 MiniMed CGMS
37 Sensor Lag Freestyle Sensor Blood Glucose (mg/dl) Time (minutes) (0 = start if meal)
38 Lag Time Teaching Points CGM is a trending and NOT a treatment device. Use BG tests for treatment decisions: Insulin doses To verify symptoms of hypoglycemia To verify correction of hypoglycemia Before driving The sensor is not perfect. It will not detect every low or high event.
39 Blood Glucose (mg/dl) Projected Glucose Levels in 20 Minutes with Arrows m g / d l - m i n - 2 m g / d l - m i n - 3 m g / d l - m i n T i m e ( m i n u t e s )
40 Using Rate of Change Arrows If you are projected to be low in 20 minutes, take 10 grams of CHO to prevent the low Example: Hypoglycemic alarm goes off, actual meter glucose is 85 mg/dl, but there is a down arrow Adjust insulin dose based on arrows For 1 arrow or 45 o arrow, change by 10% For 2 arrows or 90 o arrow, change by 20%
41 DirecNet Applied Treatment Algorithms Diabetes Research in Children Network (DirecNet) Study Group. Use of the DirecNet Applied Treatment Algorithm (DATA) for diabetes management with a real-time continuous glucose monitor (the FreeStyle Navigator). Pediatric Diabetes. 2008: 1-6. Chase HP. Understanding Insulin Pumps and Continuous Glucose Monitors. Denver, CO: Children s Diabetes Foundation at Denver; 2007.
42 Topics Exercise Meal Behavior Nighttime Wearability/Usage Toddlers Children Adolescents
43 Actical Measurement of Activity and CGM tracing Eva Tsalikian, DirecNet
44 14 y.o. male A1c = 6.6%, Crews (rowing team) in PM 5/16/06 5/27/06
45 Exercise and Nocturnal Hypoglycemia (Glucose < 60 mg/dl) DirecNet, J Pediatr 2005;147: % of sedentary nights 42% of exercise nights
46 Bedtime Glucose and Nocturnal Hypoglycemia DirecNet, J Pediatr 2005;147:528-34
47 Change in Blood glucose with Exercise DirecNet, Diabetes Care 29: 20, 2006
48 Baseline vs Nadir Blood glucose with Exercise DirecNet, Diabetes Care 29: 20, 2006
49 Topics Exercise Meal Behavior Nighttime Wearability/Usage Toddlers Children Adolescents
50 Glucose Trends: CT Post-breakfast excursion Nocturnal lows
51 Insulin Action after a SQ Insulin Pump Bolus 12 Glucose Infusion Rate (mg/kg/min) Time
52 At Study Onset A1c = 8.2 And Persistent Postprandial Hyperglycemia, Especially After Breakfast
53 At 3 Month Visit A1c = 6.2 And Has Eliminated Carbohydrates At Breakfast And Gives A Prebolus Before Lunch And Dinner
54 Bolus Immediately Before Eating.
55 Boluses 5-15 min Pre-Meal Unless Low..
56 Effect of Pre and Post Meal Bolus Cobry. DTT 12: 176, 2010
57 Breakfast of Sugar Pop Cereal
58 Egg and Pancake Breakfast
59 Cereal for Breakfast
60 Sausage and Egg Breakfast
61 It is not just the amount of insulin but the timing or a premeal bolus. It is not just the amount of carbohydrates but their complexity and how much protein and fat is in the meal
62
63 Missed meal bolus
64 Missed Meal Bolus Burdick, Chase, Pediatrics 113: e221, % missed more than 1 meal bolus/week 2 missed meal bolus/week associated with A1c increase of ½%
65 Drill Bit Through Thumb
66 Drill Bit Through Thumb
67 Blunted Adrenalin Response to Hypoglycemia Diabetes Care 32: , % of young children 3-8 years old and years old failed to release adrenalin in response to hypoglycemia Parents blinded to their child s glucose level failed to recognize hypoglycemia 71% of the time This reinforces the need for real-time continuous glucose monitoring
68 JDRF CGM Study Group Primary Cohort Six Month Outcome Data NEJM and 44th EASD Annual Meeting Rome, Italy September 8,
69 Changes in A1c in >25 yr olds *Error bars stand for 95% CI.
70 Changes in A1c in yr olds P-value=0.52
71 Mean Hours of CGM Use by Age Group
72 The Teen Brain Decreasing gray matter with maturation Myelination increases through childhood Axonal pruning increases coherent white matter bundles and cognitive function Prefontal cortex does not reach adult levels until years of age Age 12 Age 16 Age 20
73 Reaction Time (msec) Reaction Time Studies Is it a good idea to set your hair on fire? Is it a good idea to drink Drano? Is it a good idea to swim with sharks? adult adolescent based on Baird & Fugelsang, 2004 and
74 Change in glycated hemoglobin Relationship Between Change in A1c and Frequency of CGM Use
75 Baseline Factors for 8-24 year olds by CGM Use CGM <4 days/week (n=18) (N=113) CGM 4-<6 days/week (n=50) CGM 6 days/week (n=45) Male 61% 52% 44% Injection Rx 39% 28% 16% Daily SMBG 4.8 ± ± ± 2.0
76 1-Year Randomized Controlled Trial Comparing Sensor-Augmented Pump (SAP) and Multiple Daily Injection (MDI) Therapies: The STAR 3 Study Richard M. Bergenstal, M.D., William V. Tamborlane, M.D., Andrew Ahmann, M.D., John B. Buse, M.D., Ph.D., George Dailey, M.D., Stephen N. Davis, M.D., Carol Joyce, M.D., Bruce A. Perkins, M.D., M.P.H., Steve M. Willi, M.D., Michael A. Wood, M.D.; for the STAR 3 Study Group STAR 3 was sponsored by Medtronic Diabetes. Novo Nordisk supplied all insulin aspart used in the study. LifeScan, Bayer Healthcare, and Becton Dickinson supplied blood glucose meters used in the study.
77 STAR 3 Recruitment and Randomization Assessed for eligibility (N=667) Randomized (N=495) SAP (N=247) MDI (N=248) n=244 Intent-to-Treat Population* n=241 n=224 n=219 Observed Case Population * Patients with 1 post-randomization A1C measurement with the last observation carried forward for the imputation of missing data.
78 Baseline Characteristics of SAP and MDI Groups SAP Group (N=244) MDI Group (N=241) Age (yr) 32 ± ± 16.5 Duration of diabetes (yr) 15.2 ± ± 12.0 Male 57.4% 55.6% White 90.6% 92.1% Employed / volunteer 57.4% 53.1% Weight (kg) 71.9 ± ± 21.8 BMI (kg/m 2 ) 25.3 ± ± 5.6 A1C (%) 8.3 ± ± 0.53
79 A1C at 3, 6, 9, 12 months: All Patients 8.0% 7.3% 8.1% 8.1% 8.0% 7.5% 7.5% 7.5% Values are means ± SE. Asterisks denote P<0.001 for comparisons between SAP group and MDI group at each time point.
80 A1C at 3, 6, 9, 12 months: Adults 19 years 7.8% 7.8% 7.9% 7.9% 7.3% 7.3% 7.3% 7.3% Values are means ± SE. Asterisks denote P<0.001 for comparisons between SAP group and MDI group at each time point.
81 A1C at 3, 6, 9, 12 months: Pediatrics 7 18 years 8.4% 8.6% 8.5% 8.3% 7.7% 7.8% 7.9% 7.5% Values are means ± SE. Asterisks denote P<0.001 for comparisons between SAP group and MDI group at each time point.
82 Relationship of Sensor Use and A1C at 1 year Values are means ± SE.
83 Relationship of Sensor Use and A1C at 1 year Values are means ± SE.
84 Relationship of Sensor Use and A1C at 1 year Values are means ± SE.
85 Relationship of Sensor Use and A1C at 1 year Values are means ± SE.
86 Relationship of Sensor Use and A1C at 1 year Values are means ± SE. P=0.003 for association between sensor wear and A1C reduction at 1 year.
87 Decreased Hypoglycemia with CGM Use Battelino, Diabetes Care, April, Children (45%) and Adults (55%) A1c < 7.5% (mean 6.9%) Doing 5 SMBG tests each day Mean days of sensor wear/week: Pediatrics=5.6, Adults=4.9
88 Decreased Hypoglycemia with CGM Use Battelino, Diabetes Care, April, 2011
89 Decreased Hypoglycemia with CGM Use Battelino, Diabetes Care, April, 2011
90 Real-Trend Study Raccah, Diabetes Care, 32:2245, 2009 MDI patients with A1c > 8% randomized to sensor augmented pump or pump (CSII) Intention to treat: 55 assigned to real-time (22 children) 60 assigned to CSII (24 children) Per Protocol (sensor use > 70% of time) 32 patients (ll children) 39 patients (24 children)
91 Real-Trend Study Intention to Treat Raccah, Diabetes Care, 32:2245, 2009
92 Real-Trend Study Per Protocol Raccah, Diabetes Care, 32:2245, 2009
93 Use of RT-CGM in adults on 60 Adults MDI or CSII Garg, Diabetes Care 34: , using MDI 30 using CSII 4 weeks blinded Dexcom 7+ wear 20 weeks unblinded Dexcom 7+ wear
94 Garg, Diabetes Care 34: , 2011
95 Garg, Diabetes Care 34: , 2011
96 Possible Candidates Are not at goal despite adequate BG testing. Have a fear of hypoglycemia Have a history of hypoglycemia unawareness or severe hypoglycemia Pregnancy/ Preconception Gastroparesis Athletes Patients on medications like pramlintide and exenatide. May wear the sensor intermittently to better understand their own diabetes
97 Real-Time Data Constant feedback on how multiple variables impact glucose control. The immediacy of the feedback helps identify causality. This ability offers both benefits and challenges. Benefits: How different foods impact BG is more readily apparent. Data can be used to prevent or detect earlier extremes in glucose. Challenges: Concerns about insulin stacking Patients need to feel empowered to utilize the data
98 PATIENCE is Important
99 Major Positive Changes in Diabetes Management Changes in meal behavior Less carbohydrates with breakfast Giving insulin bolus10-15 minutes before a meal Adjustment of overnight basal rates was easier Use of alarms, especially to give quick correction doses and treat pending hypoglycemia
100 Expectations Need To Be Realistic The sensor will not be as accurate as their meter, but will provide trend analysis There will be false positive and false negative alarms Patients will see fluctuations in their glucose levels they were not previously aware of
101 Principal Concepts Use sensor values in making real time decisions If using a pump always use the bolus calculator Download the sensor results every 3-6 days, and review the data Goal is to increase the number or percent of glucose levels within target ranges
102 Who Should Use CGM? There is no formula that will predict who may benefit from CGM use. People likely to benefit are those who: Are prepared to see the data. Are willing to make changes. Are willing to wear the CGM system.
103 Education Perfection is not the goal. ISF vs BG Calibration should be done when the glucose is not changing rapidly. How to utilize trend data (real-time data) Insulin and other medication action profiles. Review retrospective data regularly to identify patterns that may require more permanent changes.
104 CGM Research Findings Overall patients who use real-time CGM at least 5 days a week: Lower their A1c levels Lower the time spent in hypoglycemia Reduce glycemic variability
105 In Conclusion Real-time continuous monitoring is here to stay We need to learn how to obtain maximum benefit from this powerful tool There are advantages and disadvantages with each device Real-time monitoring is a behavior modification tool, it is only successful if behavior is modified (until we have a closed-loop)
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