1. Making the Most of Continuous Glucose Monitoring Gary Scheiner MS, CDE Owner & Clinical Director Integrated Diabetes Services LLC Wynnewood, PA AADE 2014 Diabetes Educator of the Year gary@integrateddiabetes.com (877) 735-3648

2. CGM Options FDA Approved Medtronic 530G integrated pump w/Enlite sensor Medtronic Guardian ® REAL-Time System DexCom G4 Platinum/Share Medtronic iPro, Dexcom G4 Professional Pending FDA Approval Freestyle Libre Medtronic 640G Dexcom G5

3. How They Work Glucose sensor is inserted in subcutaneous tissue and connected to a transmitter SC Sensor Transmitter Pump or Handheld Monitor Glucose sensor sends values to the transmitter Transmitter then sends data wirelessly to a pump or handheld monitor every 5 minutes, where data can be viewed and acted upon in real-time

4. Interstitial Fluid and “Lag Time” (V2) Plasma (V1) Capillary glucose must diffuse into the interstitial fluid (ISF) ISF glucose levels may lag capillary levels by 5–15 minutes When glucose levels are stable, ISF glucose levels and capillary blood glucose levels are similar Overall, the sensor glucose trends are more important than the absolute measurements Illustration adapted from Rebrin K, et al. Am J Physiol. 1992;277:E561–E571.

5. What Do We Get in Real Time? Numbers Alerts Trends

6. The Numbers: Ballpark Estimates +/- 20% if >80 (4.4) +/- 20 mg/dl if <80 (+/- 1 mmol/l if < 4.4)

7. The Numbers: The Ballpark Is Getting Closer! Compared to YSi (lab) Medtronic Sof-Sensor: +/- 17-18% Dexcom 7+: +/- 15-16% Medtronic Enlite: +/- 12-14% Freestyle Navigator (n/a) : +/- 12-13% Dexcom G4: +/- 9-10%

8. Can The Numbers Be Trusted? Not during first 1-2 cycles of using the system Not during the first 12-24 hrs after sensor insertion If BG Reasonably Stable If Recent calibrations in-line

9. Types of Alerts Hi/Low Alert: Cross specified high or low thresholds Predictive Alert: Anticipated crossing of high or low thresholds Rate of Change: Rapid rise or fall

10. The Value of Alerts: Minimizing the DURATION and MAGNITUDE of BG Excursions

11. CGM Turns Mountains into Molehills

12. CGM Alerts Are Like BLOOD SUGAR BUMPERS!

13. Response is Key 1.Fingerstick (if nec.) 2.Act on the glucose value

14. Setting Alerts Hi/Low alert thresholds are not BG target ranges Balance need for alerts against “nuisance factor” Predictive alerts lose value the further the advance warning (keep below 10 min) Rate of FALL alerts helpful for long-term hypo prevention (>3 mg/dl/min) (.17)

15. Decision-Making Based on Trend Information Self-Care Choices o To snack? o To check again soon? o To exercise? o To adjust insulin? Key Situations o Driving o Sports o Tests o Bedtime

16. Bolus Adjustment Based on Trend Information BG Stable: Usual Bolus Dose BG Rising Gradually:  bolus slightly* BG Rising Sharply:   bolus modestly** BG Dropping Gradually:  bolus slightly* BG Dropping Sharply:   bolus modestly** * Enough to offset 25 mg/dl (1.5 mmol/l) ** Enough to offset 50 mg/dl (3 mmol/l)

17. Hypo Treatment Based on Trend Information Predictive Hypo Alert or Hypo Alert & recovering: Subtle Treatment 50% of usual carbs Med-High G.I. food Hypo Alert & Dropping: Aggressive Treatment Full or increased carbs High G.I. food vs

18. What Can We Get From Analyzing CGM Data? (a retrospective journey)

19. Completely Overwhelmed!

20. 1.Are bolus amounts appropriate? –Meal doses –Correction doses 2.How long do boluses work? 3.What is the magnitude of postprandial spikes? 4.Is basal insulin holding BG steady? Objectives-Based Analysis

21. 5.Are asymptomatic lows occurring? –Are there rebounds from lows? –Are lows being over/under treated? 6.How does exercise affect BG? –Immediate –Delayed effects 7.Is amylin/GLP-1 doing the job? Objectives-Based Analysis

22. 8.How do various lifestyle events affect BG? –Hi-Fat meals –Unusual foods –Stress –Illness –Work/School –Sex –Alcohol Objectives-Based Analysis

23. These Are a Few of My Favorite Stats… qMean (avg) glucose q% Of Time Above, Below, Within Target Range qStandard Deviation q# Of High & Low Excursions Per Week 23

24. Case Study 1a: Fine-Tuning Meal/Correction Boluses Breakfast and lunch doses may be too low Dinner dose appears OK Glucose (mg/dL) 400 300 200 100 0 3 AM 6 AM 9 AM 12 PM 3 PM 6 PM 9 PM Night-snack dose clearly insufficient 34-y.o. pump user

25. Case Study 1b: Fine-Tuning Meal/Correction Boluses Dropping low 2-3 hours after dinner. Consider decreasing dinner bolus. Dropping low 2-3 hours after dinner. Consider decreasing dinner bolus. 5-year-old on MDI; levemir BID.

26. Case Study 1c: Fine-Tuning Meal/Correction Boluses BG Rising 9pm-1am. Consider structured night snacks with increased bolus amount. BG Rising 9pm-1am. Consider structured night snacks with increased bolus amount. Teenager on a pump; stays up late.

27. Case Study 1d: Fine-Tuning Meal/Correction Boluses Pumper, dropping low after correcting for highs during the night Corr. Bolus  Consider increasing nighttime correction factor / insulin sensitivity

28. Case Study 2a: Postprandial Analysis Young adult on MDI. HbA1c are higher than expected based on SMBG Tired and lethargic after meals Significant postprandial spikes (300s). Consider pramlintide before meals. Significant postprandial spikes (300s). Consider pramlintide before meals. Glucose (mg/dL) 400 300 200 100 Meal

29. Case Study 2b: Postprandial Analysis Pump user, usually bolusing right before eating. Potatoes w/dinner most nights. Spiking primarily after dinner. Consider lower g.i. food or pre-bolusing. Spiking primarily after dinner. Consider lower g.i. food or pre-bolusing.

30. Case Study 2c: Postprandial Analysis Pump user, 6 months pregnant Pre-bolusing (15-20 min) at most meals. Spiking primarily after breakfast. Consider “splitting” breakfast or walking post-bkfst. Spiking primarily after breakfast. Consider “splitting” breakfast or walking post-bkfst.

31. Case Study 3a: Basal Insulin Regulation Pump user, 6 months pregnant Generally not eating (or bolusing) after 8pm. BG rising 1am-6am. Consider raising basal insulin 12am-5am. BG rising 1am-6am. Consider raising basal insulin 12am-5am.

32. Case Study 3b: Basal Insulin Regulation Glucose (mg/dL) 400 300 200 100 0 3 AM 6 AM 9 AM 12 PM 3 PM 6 PM 9 PM Basal dose is likely too high. Consider reducing. Type 1 diabetes; using insulin glargine & MDI History of morning lows Snacking at night and not “covering” w/bolus

33. Case Study 4: Determination of Insulin Action Curve 3-Hour Duration 5-Hour Duration 4-Hour Duration 12am 3am 6am

34. Case Study 5: Detection of Silent Hypoglycemia Type1 college student; on pump Frequent fasting highs (9-10 AM). Wanted to raise overnight basal rates. Dropping & rebounding during the night. Consider decreasing basal in early part of night. Dropping & rebounding during the night. Consider decreasing basal in early part of night.

35. Case Study 6: Effectiveness of Amylin/GLP-1 15 mcg pramlintide 60 mcg pramlintide

36. Case Study 7: Response Curve to Different Food Types Postprandial peak: cereal > oatmeal > yogurt Postprandial peak: cereal > oatmeal > yogurt Cereal Oatmeal Yogurt

37. Case Study 8a: Responses to Lifestyle Events (stress) Type 1 diabetes; pump user 40 years old; athletic Handsome, excellent speaker Gets flat tire; eats 15g carbs to prepare for tire change Spare is flat too!! STRESS CAN RAISE BLOOD GLUCOSE… A LOT!!! Late for meeting Glucose (mg/dL) 400 300 200 100 0 9 AM 12 PM 3 PM 6 PM 9 PM

38. Case Study 8b: Responses to Lifestyle Events (exercise) Experiencing delayed-onset hypoglycemia from heavy workouts. Consider temp basal reduction. Pump user Basal rates confirmed overnight “yellow” night: light cardio workout prior evening “Red” night: Lifting & cardio workout prior evening

39. Case Study 8c: Responses to Lifestyle Events (dining out) Delayed rise from high-fat meals. Consider using temp basal increase. Delayed rise from high-fat meals. Consider using temp basal increase. Saturday Nights, Dinner Out Pump user Normal fasting readings during the week, but high on weekends

40. Ingredients For Success Have the right expectations Wear the CGM at least 90% of the time Look at the monitor 10-20 times per day Do not over-react to the data; take IOB into account Adjust your therapy based on trends/patterns Calibrate appropriately Minimize “nuisance” alarms

41. Questions?