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Making the Most of Continuous Glucose Monitoring

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. Making the Most of Continuous Glucose Monitoring.

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Making the Most of Continuous Glucose Monitoring

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  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. Making the Most of Continuous Glucose Monitoring • CGM Basics • Teaching Real-Time Use • Analyzing Downloaded Data

  3. CGM Options “Personal” CGM Medtronic 530G, 630G integrated pump w/Enlite sensor DexComG5 Medtronic 670G Freestyle Libre (pending distribution) “Professional” CGM Medtronic iPro Dexcom G4 Professional Freestyle Libre Pro

  4. How They Work Glucose sensor is inserted in subcutaneous tissue and connected to a transmitter SCSensor • Glucose sensor sends values to the transmitter 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 Pump or Handheld Monitor

  5. Interstitial Fluid and “Lag Time” • 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 Plasma(V1) (V2) Illustration adapted from Rebrin K, et al. Am J Physiol. 1992;277:E561–E571.

  6. Understanding Lag Time Glucose Rising: SG likely lower than actual BG

  7. Understanding Lag Time Glucose Falling: SG likely higher than actual BG

  8. Understanding Lag Time Glucose Stable: SG likely in equilibrium with BG

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

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

  11. The Numbers: How Accurate Is It Really? MARD Compared to YSi (lab) State-of-the-art BG Meters: 4-6% Dexcom G4 (w/505 software), G5: 9-10% Other Leading BG Meters: 10-11% Medtronic Guardian: 10-11% Freestyle Libre: 12-13% Medtronic Enlite: 12-14%

  12. Can The Numbers Be Trusted? Two years ago, 81% of CGM Users openly admitted to using CGM glucose values for determining insulin doses.* *T1D Exchange Research, 2015

  13. Can The Numbers Be Trusted? YES. but… • Not if a CGM novice • Not during sensor day 1 • Not when recovering from hypoglycemia • Not in state of rapid rise or fall • Not if recent calibration off >20% • Not if acetaminopthen taken in past 4 hrs • Not if symptoms don’t match SG value

  14.  Alerts 

  15. Types of Alerts Hi/Low Alert: Cross specified high or low thresholds Predictive Alert: Anticipated crossing of high or low thresholds (Medtronic only) Rate of Change:Rapid rise or fall Threshold Suspend:Cross low threshold (Medtronic only)

  16. Hi/Low Alert:  must balance benefit vs nuisance  low: ≥ 80 mg/dl  high: start very high (300?), titrate down to allowable postprandial peak Predictive Alert:  potential for false positives  set for short time interval (≤ 10 min) Rate of Change:  >3 mg/dl/min fall rate may be useful Low Suspend(Medtronic only)  can reduce incidence of hypoglycemia  false positives are common

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

  18. CGM Turns MountainsintoMolehills

  19. CGM Alerts Are LikeBLOOD SUGAR BUMPERS!

  20. Timely, consistent response is Key! Act on the highs - hydrate - exercise - bolus (less IOB) 2. Act on the lows - rapid carbs

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

  22. Adjust Boluses Based on Arrows Add enough to offset 60 mg/dl rise • Add enough to offset 30 mg/dl rise •  No change to usual dose • Subtract enough to offset 30 mg/dl drop • Subtract enough to offset 60 mg/dl drop

  23. Simplify It For Patients! Example: Glucose = 180, Target = 120, Correction Factor = 30 (dose = 2u) Carbs = 30g, I:C ratio = 10 (dose = 3u) Usual Dose = 5u •  Add enough to offset 30 mg/dl rise (1u) • 5u + 1u = 6u •  +2u • +1u •  no change • -1 u •  -2u

  24. Answer This! Betty Lou’s blood sugar is rising going into lunch. She should: • Take her usual insulin dose (based on BG and carbs) • Take her usual dose, but delay her meal • Take more than her usual dose

  25. Other Applications for Trend/Curve:Hyperglycemia Treatment: (When the levee trend graph breaks) • Break within 2 hours of bolus: do not correct! • No break within 2 hours of bolus: Correct!

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

  27. Completely Overwhelmed!

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

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

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

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

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

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

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

  35. 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

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

  37. 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.

  38. 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.

  39. 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.

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

  41. Case Study 3c: Basal Insulin Regulation • Pump user, fasted (and no bolus) from 10am to 5pm. BG stable 1pm-5pm. Basal setting verified 12-4.

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

  43. 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.

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

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

  46. Case Study 8: Stress Response • 55 y.o. T1, pump user • No food or bolus 6am-3pm • Dentist appointment (root canal) at noon STRESS CAN RAISE BLOOD GLUCOSE!!!

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

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

  49. YOU DON’T HAVE TO COVER ALL THIS IN ONE SESSION!!! • Statistical comparisons • Meal/Correction doses • Bolus action curve • Postprandial patterns • Basal fine-tuning • Patterns of hypoglycemia • Lifestyle effects • Medication effectiveness

  50. Ingredients For Success Set up the right expectations Encourage use 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 properly Minimize “nuisance” alarms

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