how diabetes-technology is changing diabetes treatment and ... · nightscout remote monitoring...

How Diabetes-Technology is Changing Diabetes

Treatment and the Doctor-Patient Relationship

Diabetes & Technology Management Webinar 03/2020

Thomas Züger, MDUDEM, [email protected]

Glucose Regulation in Healthy Individuals

Diabetes Technology 03/2020 - Thomas Züger, UDEM Inselspital

Insulin

Pancreas

Glucose Sources

exogenousendogenous

Blood GlucoseGlucose

Disposal

Feedback-Loop

«Glucose-Sensing»

2

Glucose Regulation in individuals with diabetes


Insulin

Pancreas

Glucose Sources

exogenousendogenous


Disposal

Feedback-Loop

«Glucose-Sensing»

3


https://scopeblog.stanford.edu/2014/05/08/new-research-keeps-diabetics-safer-during-sleep/

4

https://scopeblog.stanford.edu/2014/05/08/new-research-keeps-diabetics-safer-during-sleep/

Glucose Regulation in Individuals with Diabetes


Insulin

Pancreas

Glucose Sources

exogenousendogenous


Disposal

Feedback-Loop

«Glucose-Sensing»

Algorithm controlled

Closed-Loop

5

Closing the Loop – Artificial Pancreas


Züger T et al. Ther Umsch. 2017;74(8):529-536. doi: 10.1024/0040-5930/a0009536

Low glucose suspend & smart guard

Diabetes Technology 03/2020 - Thomas Züger, UDEM Inselspital 7

Minimed 670G – first Hybrid Closed-Loop system


Algorithm

Mealtime Insulin-Bolus by Patient

Algorithm Target 6.7 mM

8

Open Source

• Open Source «do it yourself» project

• Realtime access to CGM- (andinsulinpump-) data

• Allows for intergration ofdifferent devices and algorithmsto control glucose

http://www.nightscout.info/Diabetes Technology 03/2020 - Thomas Züger, UDEM Inselspital 9

#OpenAPS - components


Nightscout

Remote Monitoring software

Last CGM-measurement

Current glucose (CGM)

Insulin on board (IOB)

CHO stop

Insulin stopBasal rate

Last activity OpenAPSGlucose change

expected glucose course

Components

Insulin-pump

CGM sensor/transmitter

-APP

10

#OpenAPS – data repository

• 80 OpenAPS data donors, 53 years of CGM data


3.9 – 10 mmol/l 77.5%

>10 mmol/l18.2%

< 3.9mmol/l; 4.3%

Time in/out of range (%, mmol/L)

Melmer & Züger et al. Diabetes Obes Metab.2019Züger T et al., ATTD Madrid 2020

2014 – 2018UDEM

Time in range(3.9-10.0 mmol/l)

Time aboverange (>10.0

mmol/l)

Time belowrange (≤ 3.9

mmol/l)

Type 1 DM (n=188)

60.9 ±17.5 % 34.6 ± 18.6 % 4.6 ± 4.9 %

No (Hybrid) Closed Loop

11

Technology in other areas of diabetes control

1

2

3 4

5

https://apps.garmin.com/en-US/apps/2be02a1d-1d52-4db6-8bd5-6f66fe5ca992


Glucose Sensor

Night Scout

12

Sport & Diab-Tech


map

elevation

CGM-Data13

Use of Technology in Type 1 Diabetes – CH/Germany/Austria

Diabetes Technology 03/2020 - Thomas Züger, UDEM InselspitalVan den Boom et al.; Diabetes Care. 2019 Nov;42(11):2050-2056. doi: 10.2337/dc19-0345

14

https://www.ncbi.nlm.nih.gov/pubmed/31488568

The Tech-Paradoxon - Technology alone might not be the solution….


CGM use DM1

CGM use DM1

2010 - 2012

2016 - 2018

2010 - 2012

2016 - 2018

Foster NC et al., Diabetes Technol Ther. 2019 Feb;21(2):66-72.

US-Registry

15

https://www.ncbi.nlm.nih.gov/pubmed/30657336

so much data/information, so little time!


YEAR 20002-3 min writing summary

11 min checking of blood glucose values(SMBG) 2

No continuous glucose monitoring (CGM)No wearables

Rare in between visits contact

YEAR 202016 min electronic medical record (EMR) per visit 1

20 min insulin pump data + 15 min pump settings (bolus calculator etc.) 2

30 – 40 % pat. with CGM (10 – 20 pages of CGM data) 3, 4

Integration of fitness data, meal data etc.Mail, phone-calls, data-sharing (cloud-solutions)

1 Overhage JM et al., Ann Intern Med, 20202 Comellas MJ et al., Diabetes Technol Ther, 20173 Foster NC et al., Diabetes Technol Ther, 20194 Hood KK et al., Diabetes Care, 2020 16

growing numbers – decreasing resources


350 Mio

420 Mio

500 Mio

https://www.diabetesatlas.org/en/sections/demographic-and-geographic-outline.html17

https://www.diabetesatlas.org/en/sections/demographic-and-geographic-outline.html

The burden of diabetes for patients with diabetes (PWD)


Burden of time • 2005: Average 58 min a day on self-care 1

• 2018 2: • adult with T2DM 66 min a day• child with T1DM 78 min a day

1 Safford MM et al., American Board Fa Pract, 20052 Shubrook JH et al., Diabetes Spectr, 2018

Daily activitiesGlucose Measurement & Recording

Insulin / Drug Management

Visit with health care providers

18

Who takes care of the diabetes management?


“It is clear that diabetes remains unique among chronic conditions in the extent to which therapy is controlled by the patient and factors that shape behaviour”

David G. Marrero; American Diabetes Association President

19

Self-adjustement of therapy between clinical visits


• Survey in 47 patients with DM1 on insulin-pump (average 6 years Diabetes duration = experienced)

never sometimes frequent all the time Nimiri R, Phillip M, ATTD Madrid, 2020

20

Obstacles for technology utilization and self-adjustment

• Need to download data & contact HCP• 50-70% of patients never download data of various

devices between visits 1

• Lack of knowledge / ability / confidence• Patients report low confidence and competence to

undertake retrospective review of CGM data 2

• Lack of ability to interpret data (amount of data / information overload) 3

• Decision making fatigue & alarm fatigue 2


1 Foster NC et al., Diabetes Technol Ther, 20192 Lawton J et al., BMC Endcrine Disorders, 20183 Welsh JB et al, Diabetes Thera, 2019

21

New solutions are required


“digital” support in treatment optimization (for patients and health care providers)

22

Acceptance of automated decision support by PWD


Willing to try

Would you be willing to use an algorithm that adjusts your insulin pump settings

after downloading your devices at home?71.7%

Would you be willing to use an algorithm that suggests insulin dosing on real-

time?68.1%

Would you be willing to get text messages each time you need insulin adjustment? 50%

Would you trust dosing recommendations given by automatic algorithm? 82.6%

Do you think that automated algorithm for insulin dosing will release some of the

burden of managing diabetes?76%

Nimri R., Carpel S, Gavan M, Phillip M, ATTD 2020, Madrid23

Optimization of Diabetes-/Insulintreatment


Physical activity

glucose

insulin wearables

food

Local hub: Reporting/Transfer/

Display

Real-time advice

Basal insulin

Correction factor

CHO Ratio

Reminder/info

HCPAppointment, Telemedicine

office advice

security

Diabetes data science:•Metabolic models•Pattern recognition•Algorithms for decision support & automatic control

Data integration/ storage

(virtual patient image)

EMR / Personalized treatment

Adapted from Boris Kovatchev & Bruce Bode

24

Decision support – how it may look like for PWD


The last 3 times after exercise you had night-time hypoglycemia!

Ok – what do you recommend Reduce your

basal insulin by 20% for 8h and eat 10g of Carbs after exercise

25

2020 to 2030 – the decade of the Digital Diabetes Clinic?!


Social Platform

Reminder Messages

EMR Integration

Visit planning

(care priority-zation)

Outcome follow-up

Data Transfer

Diabetesapps

Tele-medicine

Research

Decision supportEducation

Consensus on Digital/Virtual Diabetes Clinic, ATTD Madrid 2020

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Adapted from Phillip M, ATTD 2020, Madrid

Consensus on Digital/Virtual diabetes clinicMadrid February 18th 2020 – Highlights

• Digital – Virtual diabetes clinic should be developed for all patients with diabetes (PWD)

• Digital clinic aim to empower PWD to self treat their diabetes

• Data ownership should be regulated worldwide – it’s for the patients to decide whom to share it with

• Digital clinic may replace some of the F2F appointments and increase interactions between visits

• Easy, passive transmission of data from all different devices to integrate, interpret and create a meaningful advice for both HCPs and PWD

how diabetes-technology is changing diabetes treatment and ... · nightscout remote monitoring...

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