ATTD Session: Needs and solutions in Type 1 diabetes from youth to seniors: towards software prescriptions

Transcription

1 ATTD Session: Needs and solutions in Type 1 diabetes from youth to seniors: towards software prescriptions Moshe Phillip MD The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes National Center for Childhood Diabetes Schneider Children s Medical Center of Israel

2 Disclosure Information Institute Research Support: Abbott Diabetes, Andromeda Biotech, Animas, BMS, Eli Lilly, Ferring, Geffen Medical, Medtronic Diabetes, MSD Merck, Novo Nordisk, Pfizer, Prolor, Sanofi, Bristol-Myers Squibb Moshe Phillip Medical Advisory Board Member: Astra Zeneca, Bristol-Myers Squibb, Merck & Co, Roche Diagnostics, Sanofi, Eli Lilly & Company, Medtronic Board Member: CGM3 Ltd, DreaMed-Diabetes Ltd Consultant: Andromeda Biotech Chairman: NG Solutions Ltd Speaker s Bureau: Novo Nordisk, Pfizer, Sanofi, Medtronic Minimed Revital Nimri Speaker s Bureau: Novo Nordisk, Pfizer, Sanofi Shareholder: DreaMed Diabetes Active Position: DreaMed Diabetes

3 In our session- we will: Define the need for clinical decision support systems Discuss clinical cases and how do we deal with the data that the new technologies provide Discuss several ways to overcome barriers in diabetes treatment and technology implementation Introduce the idea of software prescription to tailor the treatment of patients with different devices

4 Get To Know You Do you care for patients with: A. Type 1 Diabetes B. Type 2 Diabetes C. Both D. Other

5 Get To Know You Do you take care of: A. Pediatric patients B. Adult patients C. Both D. Other

6 Get To Know You Does your clinic is based in the: A. Hospital B. Primary care A. Both B. Other

7 Get To Know You What is the percentage of your patients who use pump therapy? A. Up to 25% B % C % D. More than 75%

8 Get To Know You What is the percentage of your patients who use continuous glucose monitoring? A. Up to 10% B % C % D. More than 50%

9 Get To Know You Do you download data from your patient s devices (pump, sensor, glucometer)? A. Yes, to all of my patients B. Yes, to some of my patients C. No, I don t have the ability/manpower/time for that D. No, I don t think this is necessary

10 Get To Know You Do you enable your patients to download and send to you the data from their devices (pump, sensor, glucometer) at home to you in between visits? A. Yes, to all of my patients B. Yes, to some of my patients C. No, I don t have the ability/manpower/time for that D. No, I don t think this is necessary

11 Rate of progression of retinopathy (per 100 patient years) Rate of severe Hypoglycemia (per 100 patient years) The Goal! Relative risk Severe hypoglycemia 120 of retinopathy HbA1c (%) 0 The Diabetes Control and Complications Trial Research Group, N Eng J Med; 329: , 1993

12 Percent of patients achieving HbA1c ADA targets by age-group Kellee M. Miller et al. Dia Care 2015;38:

13 Miller KM et al, Diabetes Care ;38:971-8, 2015 Wong JC et al, Diabetes Care ;37:2702-9, 2014 How Many Hit The Target? USA Exchange registry 17,317 T1DM, 67 clinics

14 TEENs Study Glycemic Control 72% did not achieve targets Proposal to add results from the TEENS study to show in one slide «how many hit the targets» from 8 to 25 reinforcing previous slides ADA, 2014;IDF,2015 ADA 2014;IDF 2015

15 Incidence rate of severe hypoglycemia (per 100 patient-years) Diabetes Related Adverse Events are Still Frequent Incidence rate of severe hypoglycaemia in the U.S., N=225 patients, age 9-15 years Changes in diabetes-related complications in the U.S * * All NPH Basalbolus injection *pump therapy vs. NPH, P = 0.04; Severe hypoglycaemia resulting in seizure coma: pump therapy vs. NPH, P = 0.004; pump therapy vs. basal-bolus injection therapy, P = Pump therapy Katz ML, et al. Diabet Med; 29:926-32, 2012 Richard M. Bergenstal Dia Care 2015;38:

16 Current Type 1 Diabetes Comprehensive Technological Treatment Managing diabetes is complex Daily Activities Measure Glucose Doctor Visit Infuse Insulin How much insulin to deliver? How to interpret my diabetes data? How to set the patient s insulin treatment profile?

17 Current Type 1 Diabetes Comprehensive Technological Treatment Diabetes management platforms Glucose Measurement Devices Patients Physician Educator Nurse Dietitian Social Worker Psychologist Insulin Analogues Insulin Delivery Devices

18 Data Interpretation is Complex

19 Key Challenges Patients Physicians Lack of knowledge / ability to perform the insulin decision cycle Swamped with information, data from CGM, SMBG, pump Poor accessibility to expert physician for optimize the insulin treatment profile (both in developed and developing countries) NO treatment tools for diabetes self management and optimizing insulin treatment profile between clinic visits Need expertise to deduct from the data (the patient s glucose levels, insulin injections, meals, physical activity and more) how to optimize the insulin treatment profile Swamped with information, data from CGM, SMBG, pump that makes it difficult to retrieve a more efficient treatment strategy Even for experts, it takes a lot of time to perform this optimization Worldwide shortage of Diabetologists/ Endocrinologists

20 Case presentations- The challenges app connects personal health data to electronic medical records Diabetes Management Software Dexcom CLARITY TM Ambulatory Glucose Profile Glooko Tidepool's platform Diasend

21 Case 1 (Adolescent who use pump and sensor) BE, 12.6 years old boy, 42 Kg, 153 cm, Tanner 3 T1D 1 since the age of 4.5 years CSII since T1D onset, CGM use since last year Active in sport, treated with Ritalin (methylphenidate) 10 mg HbA1c 6 % (42 mmol/mol Hb)

22 Case 1 (Adolescent who use pump and sensor) Glucose Level mmol/l Mon Tue Wed Thu Fri Sat Sun Time mg/dl mmol/l Average Median SD % Time with in range mg/dl ( mmol/l) 80 % Time > 180 mg/dl (10 mmol/l) 16 % Time < 70 mg/dl (3.9 mmol/l) 4

23 Case 1 (Adolescent who use pump and sensor) Total Daily Insulin Dose: 0.8 u/kg/day 80% of insulin dose given as basal C:I 1:15, CF 1:50 Target glucose levels 120 mg/dl alert limit for high BG alert limit for low BG Median Average Insulin units Basal/Bolus Ratio per Day Basal Insulin Rate

24 Case 1: Question What action would be most appropriate? A. Increase insulin basal rate at first part of the night B. Change basal/bolus ratio C. Increase insulin basal rate at second part of the night D. Reduce evening basal rate

25 Case 1: Answer What action would be most appropriate? A. Increase insulin basal rate at first part of the night B. Change basal/bolus ratio C. Increase insulin basal rate at second part of the night D. Reduce evening basal rate

26 % of Time Basal-Bolus Makes the Difference Over the Weekend MD-Logic Study ITT Results [60h] P=N.S 80 P= P= P= % < 60 mg/dl % Within mg/dl % > 180 mg/dl Glucose Levels [mg/dl] N=22 (Average ± STD) Total Insulin Dose [Units] Total Night Insulin Dose [Units] MD-Logic ± ± 4.0 Control ± ± 3.3 P* N.S N.S MD-Logic ITT analysis, (N=22). Median (IQR) SAP Total Basal Insulin [Units] 55.5 ± ± Total Bolus Dose [Units] 67.0 ± ±

27 Case 1: Answer Basal (u/h ) Bolus (units ) Basal (u/h ) Bolus (units ) Bolus Basal Basal (u/h ) Bolus (units ) Basal (u/h ) Bolus (units ) Temporary Basal Basal (u/h ) Bolus (units ) Basal (u/h ) Bolus (units ) Overcorrection of Hypoglycemia Basal (u/h ) Bolus (units )

28 Approach to Retrospective Analysis of CGM-Profile Check for common limitations for optimal CGM use by patients and diabetes teams alike: Avoid calibration issues Determining meaningful alarm settings Place of insertion Sensor adhesive issues Contineous use vs. intermitent : before clinic visits, changing insulin treatment, changing lifestyle etc. Adjust expectations

29 Approach to Retrospective Analysis of CGM-Profile 1.. Prerequisite for pattern analysis of CGM-profiles: Representative data Data duration capture (how many days of data have been provided?) Varification Review patient history (typical daily routin): insulin regiment, time & amount of meals, exersise... Pump occlusion, sick days, menestural period, holidays, exeptional events...

30 Is the Data Representative? E.I, 12 years old boy, 42 Kg T1D since 4.5 years Treatment with CSII Total Daily Insulin Dose u/kg/day History of poor glycemic control % Insulin Delivery Data A1C 12%, 108 mmol/mol Hb Glucosmeter Data

31 How Much Retrospective Data to Analyse? A minimum duration of 12 days of CGM data is needed to infer glycemic variation Neylon OM et al, Journal of Diabetes Science and Technology 2014, Vol. 8(2) A minimum of 14 days of CGM data provides identification of individual glucose patterns Dunn TC, Crouther N. Assessment of the variance of the ambulatory glucose profile over 3 to 20 days of continuous glucose monitoring. Abstract 1054, presented at EASD 2010.

32 Approach to Retrospective Analysis of CGM-Profile 1.. Prerequisite for pattern analysis of CGM-profiles: Evaluating diabetes treatment adherence need for pateint education Time of pre-meal bolus Use of Bolus Calculator Meals with no bolus, no correction boluses, blinded boluses... Amount, type and frequency of food Over-treatment of hypoglycemia or hyperglycemia Insulin stucking Rate of infusion set changing

33 Approach to Retrospective Analysis of CGM-Profile Need for Patient Education When is the ideal time for meal bolus? A. In all young patients, meal bolus should be injected after meal B min before the meal, if pre-meal blood glucose was within target or above C. 60 min after meal, if meal preceded treatment of hypoglycemia D. With the meal, if pre-meal blood glucose was within target

34 Randomized three-period open-label crossover intervention study 10 Patients with type 1 diabetes on CSII Insulin administration 0, -15 or -30 minutes Dosing min before meal is optimal Cobry et al.; Scaramuzza et al; DT&T 2010

35 Need for Life Style Modification When is the ideal time for meal bolus?

36 Approach to Retrospective Analysis of CGM-Profile Then meaningful in-depth analysis of glucose regulation of CGMprofiles may be conducted. Review patterns: Glucose stability Glucose levels Daily glucose patterns

37 Patterns Presented at Different Software

38 Patterns Presented at Different Software

39 Patterns Presented at Different Software (

40 Patterns Presented at Different Software

41 Case 2: Adolescent with recurrent episodes of DKA P.D, 16 years old boy T1D at the age of 9 years Poor glycemic control, A1c 9-14% Previously treated at two different hospitals Treated with pump therapy and sensor, due to recurrent events of DKA, therapy was switched to Levemir twice a day and Lispro (1:15 and 1:40) but.. In the recent year was hospitalized 7 times due to DKA P.D expressed his will to switch again to pump therapy

42 Case 2: Adolescent with recurrent episodes of DKA

43 Case 2: Question What would you do? A. Keep therapy with MDI & Increase lunch C:I B. Keep therapy with MDI & Increase basal insulin (Levamir) at evening C. In order to have a more compliant patient change to pump therapy D. Hospitalized for re-education and evaluation

44 Case 2: Adolescent with recurrent episodes of DKA Coefficient of Variance 100 SD/MEAN= CV% /206 = 50% CV < 33% stable glucose level CV > 50% instable glucose level CV 33-50% intermediate stability

45 Case 2: Answer What would you do? A. Keep therapy with MDI & Increase lunch C:I B. Keep therapy with MDI & Increase basal insulin (Levamir) at evening C. In order to have a more compliant patient change to pump therapy D. Hospitalized for re-education and evaluation

46 Case 2: (Adolescent who use MDI and Intermittent Sensor) Median Average mg/dl mmol/l Average Median SD Median & IQR (25-75%) Variability, / 201 = 40% Lantus injection given by the parents in the morning No DKA at the last 2 years

47 Case 3: Adolescent who use pump therapy & SMBG AS, 12.5 years old boy T1D since the age of 8 years 55Kg, 155cm Pump therapy in the last 3 years A1c = 9% CV = 29%

48 Case 3: Adolescent who use pump therapy & SMBG Total Daily Dose: 1u/kg/day Basal Rate range unit/h C:I 1:15 day, 1:25 night CF 1:50 day, 1:100 night calculated: Basal Rate range unit/h C:I 1:8 day, 1:10 night CF 1:30 day, 1:60 night Average glucose measurements 5/day Average 8-10 boluses/day

49 Case 3: Question What would you do? A. Decrease C: I ratio for all 3 meals B. Decrease C:I ratio and CF C. Increase nighttime basal and decrease C:I ratio D. Re-calculate pump settings

50 Case 3: Answer What would you do? A. Decrease C: I ratio for all 3 meals B. Decrease C:I ratio and CF C. Increase nighttime basal and decrease C:I ratio D. Re-calculate pump settings

51 Case 3: Answer Basal Rate range unit/h C:I 1:7 day, 1:10 night CF 1:40 day, 1:60 night A1C=7.6 %

52 Case 5: (Child who use MDI and sensor) A.M 8 years old girl, 21 Kg, 118 cm T1D since 2 years of age Well Controlled A1c: % 8 units Lantus at the evening and 2-4 units Humalog for 3 main meals Sensor use for the last 2 years

53 Case 5: Question What would you do? A. Increase dose of evening Lantus B. Increase breakfast dose C. Change time of Lantus injection to morning D. Change breakfast insulin analog to regular insulin

54 Case 5: (Child who use MDI and sensor) meal time

55 Case 5: Question What would you do now? A. Increase dose of evening Lantus B. Increase breakfast dose C. Change time of Lantus injection to morning D. Change breakfast insulin analog to regular insulin

56 Case 5: Answer

57 Example Fear of Nocturnal Hypoglycemia F.S, 24 years old man T1D since age 6 years Treated with Tregludec insulin and Humalog Glycemic control 8-8.4% Hypoglycemia unawareness

58 Example Fear of Nocturnal Hypoglycemia F.S, 24 years old man T1D since age 6 years Treated with 640G pump with predicted low glucose suspend Glycemic control 7-7.4%

59 Preliminary Results SmartGuard Study Number of Hypoglycemic Events Presented at ATTD Viena, 2015 Accepted DC

60 Day Time Control After Overnight Closed-Loop Control Examples from the 6 weeks study Night/MD-Logic Day/SAP Therapy Night/MD-Logic Day/SAP Therapy Patient I :Male, 15 y, A1c 9.4% (79 mmol/l) Patient II: Female, 21 y, A1c 7% (53mmol/l) MD-AID SAP Median [IQR]

61 MD-Logic Pooled Analysis Of 4 nights 1 and 6 weeks 2 Overnight Studies Histogram of Mean Overnight Glucose Levels, PP MD-Logic SAP N=848 N= (33,75) % 45 (25,64) % Mean Overnight Glucose Levels [mg/dl] 1 Nimri R et al, Pediatric Diabetes2014 ;15: Nimri R et al, Diabetes Care 2014;37:

62 NEW ERA OF PERSONALIZED TREATMENT OF DIABETES USING DECISION SUPPORT TOOLS

63 A Shortage of Diabetes Doctors in the US There are too few of the diabetes doctor specialists called endocrinologists There are 29.1 million people with diabetes of which 21 million are diagnosed (An estimated 8.1 million people live with diabetes but do not know it.) 1 As of 2011, there are 4,841 practicing endocrinologists in adult medicine and 1,016 in pediatric medicine 2 An increase in the prevalence of diabetes mellitus further expands the demand for endocrinologists and HCPs 2,3 1 CDC, National Diabetes Statistics Report, Vigersky RA et al, JCEM, 99: , HIS, The Complexity of Physicians Supply and Demand: Projections from , 2016 Update, April 5 th 2016

64 A Shortage of Diabetes HCPs in the EU

65 Density of Physicians (total number per 1000 Population) WHO, Global Health Observatory data, accessed Feb 2017,

66 Managing Diabetes is Complex

67 The Majority of Patients with Diabetes Are Not in Control Treated by expert HCPs Multidisciplinary team approach Diabetes data is downloaded and is the basis for decisions and guidelines Good accessibility Treated by trained physicians Long periods between visits Multidisciplinary team? Poorly controlled Diabetes data download not available and expertise doesn t exists

68 Clinical Decision Support definition Clinical Decision Support (CDS) System provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. CDS encompasses a variety of tools to enhance decisionmaking in the clinical workflow

69 The Patient s Visit to the Clinic Download data Review the data from different devices (sometimes using different diabetes management platforms) Detective Work and recommendations

70 More Than 1,100 APPS Available for Patients With Diabetes and Their Caregiver The Best Diabetes iphone & Android Apps of the Year

71 Apps & Diabetes Help counting calories and weight management Allow to upload glucose readings directly from the meter. Create a record of numbers so the information is available to patients at all times. View statistics breakdown in graph form. Share the information with the doctor. Large library of low-fat, low-sugar recipes. Help to interact with fellow patients so patients can talk, share tips, and learn together

72 Apps & Diabetes Scan a barcode to get the nutrition data on the food patients buy? Access to a library of videos that can help patients learn more about diabetes - friendly diets and how to make healthy lifestyle choices

73 Existing Decision Support Systems IN SOME OF THEM: The Insulin dosing is based on SMBG data and known calculators No real personalized approach There is personalized pattern recognition based on CGM, nutrition and big data analysis There are general suggestions for lifestyle, and medical interventions

74 The Voice of HCPs Treating Diabetes Focus groups in three countries 26 healthcare professionals including: Endocrinologists Diabetologists Diabetes specialist nurses Diabetes educators Responded to self developed questionnaire Associated psychosocial exploration data of the expectations of HCPs by Professor Katharine Barnard, un-published data

75 Expectations of HCPs from Decision Support System Easy to use and save time Personalized to individual patients As good as a HCP that does it on a regular basis Provide specific advices Reliable and trustworthy Associated psychosocial exploration data of the expectations of HCPs by Professor Katharine Barnard, un-published data

76 What should Decision Support System for HCPs include? Systemic Analysis of the data Reduce errors Decision-making more consistent within/across a clinical team Pump settings Carbohydrate Ratio, Correction Factor and Basal Plan Specific pattern recognition Facilitating a discussion with the patient about behaviors impacting blood glucose Associated psychosocial exploration data of the expectations of HCPs by Professor Katharine Barnard, un-published data

77 Introducing The Digital Clinic Download data in the clinic or at home Unified Platform for Diabetes Management A digital detective that provides with recommendations

78 DreaMed ADVISORPro Decision support tool that enhances the decision-making skills that HCPs use every day Cloud-based technology, so its support and guidance are accessible from anywhere Evolves by continuous learning and reporting Flexible to meet the needs of a diverse group of patients

79 DreaMed ADVISORPro Simplifies the Complexity of Diabetes Data Specific Behavioral analytics Rapid acting delivery compliance Reasons for hypoglycemia / hyperglycemia patterns Other tips for proper care Insulin dosing optimization Basal Plan Bolus sensitivity & bolus carbohydrate ratio (as CF/CR or in other sliding scale) Glucose target recommendation

80 DreaMed s Concept in Technology Development MD-Logic Concept ALL

81 How Similar Is the Algorithm Output to What I Would Recommend?

82 The EXPERTS Survey Study Design: Data analysis of existing patient's sensor and insulin pump data without any intervention 3 Physicians per site anonymous patient data 1 ADVISORPro

83 The EXPERTS Survey Study Analysis Plan Basal, CR and CF plan were compared at every hour: )Basal Plan (pump Format Time of Day 00:00 07:00 19:00 Value [u/h] Basal Plan (Decision Points Format) Hour Value [u/h] Decision Point

84 The EXPERTS Survey Study Interim Results Patient Characteristics Mean(SD) Gender [F/M] Age [yr] Weight [kg] Height [cm} HbA1c [%] Years with Diabetes [yr] (N=8) 4/ (3.7) 56.2(16.2) 158.5(12.9) 8.0(0.5) 6.5(2.6) Physician Characteristics 3 Physicians from Hannover 2 Physicians from Slovenia In this interim analysis we have 192 decision points (equal to 24 times 8 patients)

85 In what percentage of cases will 2 physicians from the same center agree on the changes in basal rate? a. 80% b. 60% c. 40% d. 20%

86 The EXPERTS Survey Study Interim Results There are differences between physicians advice on the same data set Agreed on changes to basal No change of basal vs more basal No change of basal vs less basal Do not agree on how to change basal

87 The EXPERTS Survey Study Interim Results The difference between ADVISORPro and physicians is similar to the differences among the physicians themselves Agreed on changes to basal No change of basal vs more basal No change of basal vs less basal Do not agree on how to change basal

88 The EXPERTS Survey Study Interim Results Agreed on changes to basal No change of CR vs less insulin through CR No change of CR vs more insulin through CR Do not agree on how to change basal

89 The EXPERTS Survey Study Interim Results Agreed on changes to basal No change of CF vs less insulin through CF No change of CF vs more insulin through CF Do not agree on how to change basal

90 A Paint By Salvador Dali What do you see? A. Old Couple B. Holy Grail C. Two Musicians D. Beautiful Lady Salvador Dali 1930

91 How do we know that the algorithm is as good as expert HCPs (to compliment their efforts)?

92 The MD-Logic Advise4U Pilot Study Design: An open label, parallel (two-arm), randomized controlled prospective study N=34 Run in Period 3 weeks Every Three weeks new recommendations on pump settings and / or verbal recommendations were issued to the patients

93 The MD-Logic Advise4U Pilot Study We hypothesize that the use of DreaMed s ADVISORPro will achieve similar glycemic control to that achieved by medical guided recommendations Endpoint: Primary: Non-inferiority in time within mg/dl Safety: Non inferiority in time below 70 mg/dl

94 The MD-Logic Advise4U Pilot Study Inclusion Criteria: Documented T1D for at least 1 year prior to study enrolment Age 10 years and up to 25 years HBA1c 11% Insulin pump therapy for at least 4 months BMI SDS below the 97th percentile

95 The MD-Logic Advise4U Pilot Study Interim Results 15 patients, with similar metabolic control at basis, completed two cycles of changes of pump settings Patient Characteristics Mean(SD) Gender [F/M] Age Group [Child / Adol / Adult] Age [yr] Weight [kg] Height [cm} HbA1c [%] Years with Diabetes [yr] Control (N=8) 4/4 3/2/ (4.4) 57.9(18.6) 160.8(12.7) 7.9(0.95) 7.7(6) Advisor (N=7) 1/6 2/3/ (4.7) 59.0 (15.9) (13.5) 8.2 (0.9) 8.1 (4.6)

96 The MD-Logic Advise4U Pilot Study Interim Results Glucose Control After 6 Weeks of Intervention Control (N=8) Advisor (N=7) 52% 42% 50% 49% 7% 2% Time Below 70mg/dl Time Within mg/dl Time Above 180 mg/dl

97 The MD-Logic Advise4U Pilot Study Interim Results Example 1: Advisor is sensitive to basal/bolus ratio Basal Insulin is meant to offset glucose and ketones production Physicians often use the basal to treat high glucose levels due to bolus insulin omission High basal ratio (of TDD) is associated with increased risk of hypoglycemia

98 The MD-Logic Advise4U Pilot Study Interim Results Basal Insulin Delivery - Analysis Mean values Control (N=8) Advisor (N=7) Basal Ratio (of TDD) at baseline Change in Total Daily Basal between Baseline and 6 weeks [Units] Change in Number of Basal Periods between Baseline and 6 weeks [Units] Basal Ratio (of TDD) at 6 weeks 50% % 53% % More info at Dr. Nimri s Presentation, Friday(Feb 17 th ), 17:50-18:00, Maillot Hall

99 Personalized and Specific

100 Time Basal value [U/h]- current Basal value [U/h]- suggested 00: : : : : : Time CR value [gram/u]- current CR value [gram/u]- suggested Your patient tends to override the amount of insulin recommended by the bolus wizard. 00: : : : : : Time 00:00 06:00 CF value [mg/dl/u]-current CF value [mg/dl/u]- suggested IQ R Before Visit Median IQ R After Visit Median

101 Take Home Messages Decision support systems represent a new evolution in the treatment of patients with diabetes and are HERE TO STAY. Next generation of DSS turns pure information into specific and personalized actionable recommendations Interim results show that DSS can be in agreement with expert physicians in similar way to their agreement with each other Interim results show that DSS can be as good as expert Diabetologists in controlling glucose levels

102 Where Are We Heading?

103 Acknowledgment Dr. Revital Nimri Dr. Alon Farfel Prof Shlomit Shalitin Dr Ariel Tenenboim Dr Tal Oron Dr Judith Nir Dr Rachel Belo Alona Hamu Orna Hermon Prof Thomas Danne Prof Olga Kordonouri Dr Torben Biester Prof Tadej Battelino Dr Natasa Bratina Eran Atlas Ido Muller Aviel Fogel Noam Greenboim Yaron Matiash Nitzan Goldfeder Tomer Segall Dan Erez

104