Applications of Technologies to Your Patient. Irl B. Hirsch, MD Professor of Medicine University of Washington School of Medicine Seattle, Washington

Transcription

1 Applications of Technologies to Your Patient Irl B. Hirsch, MD Professor of Medicine University of Washington School of Medicine Seattle, Washington

2 Disclosures Consultant: Abbott Diabetes Care, Roche Diagnostics, Intarcia, Valeritas

3 Downloading Is an Important Component of Modern-Day Diabetes Therapy Yet many of us don t download in our offices and clinics due to the time/money perceived this would cost. My question to you: is that attitude appropriate in 2017?

4 What You Need (Minimal) A program which allows downloading of various meters/pumps/sensors Clinipro (Numedics.com), Glooko/Diasend, Carelink, Tidepool Ideally, many of the native softwares are also available A better solution: immediate upload to the cloud Livongo, Accucheck Aviva Connect, Dexcom Clarity

5 ONE Reasons Why Downloading Is So Important: Understanding the Mean and Estimated A1C 24 year-old woman, MDI, using Dexcom, 14 years T1D This patient s HbA1c is 8.2%. She has a glycation gap due to iron deficiency anemia

6 What Alters A1C Hematologic conditions Anemia Accelerated erythrocyte turnover Thalassemia Sickle cell disease Reticulocytosis Hemolysis Physiologic States Aging Pregnancy Drugs/Medications Alcohol Opioids Vitamin C Vitamin E Aspirin Erythropoetin Dapsone Ribavirin Disease States HIV infection Uremia Hyperbilirubinemia Dyslipidemia Cirrhosis Hypothyroidism* Medical Therapies Blood transfusion Hemodialysis In a typical diabetes practice, 14%-25% of A1C measurements are Miscellaneous misleading Glycation rate Protein turnover Race and ethnicity* Laboratory assay Glycemic Variability Smoking Mechanical heart valves? Exogenous testosterone?

7 Calculation To Determine SD Target SMBG SD X 3 < MEAN SD X 2 < mean, may be difficult for some type 1 patients. Formulas only relevant for mean BG between CGM SD X 3 < MEAN Need better metrics: CV, TIR, TBR, TAR all to be correlated with outcomes

8 Example: George Read aggregate mean/sd

9 Example: George Read aggregate mean/sd Read frequency of testing

10 Example: George Read aggregate mean/sd Read frequency of testing Review time-specific means/sds?

11 CASE 1: 20 y/o T1D, Down Syndrome T1D X 11 years, CSII and CGM. Last SH 5 years ago; HbA1c 7.6%

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13 High basals in the evening do help to cushion dinner but usually result in hypoglycemia if not snacking. Timing of nocturnal basal change is important! Hypoglycemic seizure at 1:30am. Why?

14 Case 2: Carol, Who Shows Why Threshold Suspend Is Such an Important Advance 54-year-old woman with hypoglycemia unawareness and frequent severe hypoglycemia, yet who continues to be too aggressive with insulin Husband often travels

15 Case2: Download Too much insulin! Went to sleep; did not note trend No rebound, good basal Patient wakes, notes alarm and suspend, and over-treats! HYPO; threshold suspend without patient s knowledge

16 Case 2: Conclusions About Carol Needs to be more conservative with insulin at bedtime and pay more attention to trend Threshold suspend works well and does not result in rebound Over-treatment of hypoglycemia remains a longterm problem

17 CASE 3: Christie 32 y/o woman on 8 units glargine BID with pre-meal lispro 1:15, ISF 50 day, 60 at HS; jogs at 7am 5X s/week; b fast at 8a, lunch at 1p, dinner at 7p Mean/SD 126/47; A1C 6.0% Poor prandial replacement 1. Too much basal Too much prandial with downward trend

18 CASE 4 45 y/o African man moved to Seattle to work for a coffee company (we don t have Dunkin Donuts in Seattle) T2DM X 10 years, on insulin X8 years BMI 36; A1C 8.4% Has been on SAP for 4 years

19 MEAN 187; SD 75

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21 Good basal BC: 17U BC: 11.5 U 60 g CHO 44 g CHO Stayed high: last bolus not enough No bolus: too much basal? Insulin given with food Insulin given with food

22 With All of This Technology, His Major Problem is Easy to Fix Insulin needs to be given before one eats! Why is this so difficult for so many patients?

23 CASE 5 44 y/o woman, T1D X 22 years, using Omnipod and Dexcom Frustrated with downloading choices Decided to try Tidepool (Tidepool.org) Last HbA1c 6.8%

24 Blip CGM SMBG Toggle cursor: 29 g carb, suggested bolus 2.4 u, delivered 1.7 u Ouch!

25 Case 5a: Twin Sister! Dexcom, No Pump Most variability after breakfast and bedtime Highest average after lunch

26 BAD DAY Move the cursor

27 GOOD DAY

28 CASE 6 60 y/o woman, 41 years T1D using Animas Vibe Past history significant for PDR s/p PRP, DAN with gastroparesis After many years of no diagnosis, found in 2010 to have stiffperson s syndrome HbA1c prior to SPS Dx usually in the 7-8% range, after Dx most in 9-11% range % of total daily insulin is basal insulin What s going on?

29 Case 6: AGP from 60 y/o Woman with SPS Feb 2016, HbA1c 10.1% h

30 CASE 6: The Answer to the Problem No bolus

31 CASE 7 69 y/o woman with 35 years T1D Stage 3 CKD, + CAD s/p stent, +PDR, recent toe amputation No hx severe hypoglycemia HbA1c stable X years around 8% Refuses to test more than 2-3 times/day (and sometimes less than that!) What to do?

32 CASE 7 Freestyle Libre Pro: Where to start?

33 CASE 7

34 CASE 7 What she needs: Less basal Give prandial insulin (and check BG) before eating!!

35 What I Want To See in A Download Basic statistics Overall and time specific means/sd SMBG: minimum: SD X 2 < mean (better if SD X 3 < mean) if mean CGM: SD X 3 < mean CSII: Basic insulin stats TDD, % basal, over-ride % for bolus calculator Daily summary to better understand ICR, ISF, basal rates, and if appropriate over-under-rides for trends or anticipated exercise CGM: basic BG stats Overall patterns and daily decision making to best understand how patient thinks through each challenge

36 Most Common Patient Mistakes Over-calibrate/under-calibrate Don t look at sensor often-only react (if then) to alarms Over-react to alarms not taking into account lag times -results in insulin stacking Not using enough SMBG to make decisions!

37 Our Greatest Gap in Diabetes Technology? Many Smart Insulin Pens in development

38 Conclusions Although this may not be the case for all clinicians, we have found developing a specific infrastructure for meter, pump, and CGM use in our clinic to be worthwhile While there are many similarities with CGM use and CSII (new technology, early adapters, etc.), one fundamental difference is the ability to download the data allowing the clinicians to see how the patients act, react, and even think about their diabetes management. One needs to see both cumulative and detailed data to understand how to assist their patients

39 My Thoughts There are two reasons why endocrinologists don t download the technology (which is critical to understanding how to best assist patients) No infrastructure in the office leading to poor inefficiency and perception of not time or cost effective They were never taught how to do this! Resources for how to do this are scarce, is still relatively new, and is changing rapidly

40 What I Now Realize: Be Nice My thought: Be nice to your patients: download their data Steven Wright s thought: Be nice to your children, as they will choose your nursing home.