FRANCESCO GIORGINO

DIPARTIMENTO DELL’EMERGENZA E DEI TRAPIANTI DI ORGANI

SEZIONE DI MEDICINA INTERNA, ENDOCRINOLOGIA, ANDROLOGIA E MALATTIE METABOLICHE

Glucose Monitoring: why?

• Evaluate the efficacy of current therapy

• Make insulin and medication dose adjustments

• Monitor treatment safety

• Enable patients to better understand the impact of diet,

exercise, illness, stress, and medications on glucose

levels

• Enable patients to recognize and treat hypoglycemic

and hyperglycemic episodes

• Select the most appropriate drug therapy

modified from Bailey T.S., et al. AACE/ACE GM Consensus Statement 2016

Monitoraggio GlicemicoLa Tecnologia al Servizio della Cura

• Telemedicina

• CGM/FGM

Quale tra le seguenti ritenete la modalità più

efficace/appropriata per l’implementazione della

telemedicina in Diabetologia?

1. Real-time video conferencing

2. Comunicazione tramite websites

3. Comunicazione tramite e-mail

4. SMS e messaggi via smartphone

Key Telemedicine Components in Diabetes

1. Accurate data collection in digital

format

2. Electronic medical record

3. Protocols for distant data analysis

→ response to data, patients’

education, intervention

4. Variety of communication tools to

permit effective dialogue between

patients and HCPs (Internet,

telephone/cellphone, Bluetooth,

modem, cable)

5. System for automatically flagging

and providing feedback for outlier

data

6. Data sharing

Modified from Klonoff D, J Diabetes Sci Technol, 2009

▪ Patient-collected physiological data

(SMBG, CGM, BP)

▪ Lab data (HbA1c, lipid levels)

▪ Behavioral information (dietary

intake, exercise)

▪ Medication dosages, allergies, and

other history

▪ Subjective symptoms of

hypoglycemia or other complaints

▪ Pertinent event data (emergency

room visits, hospitalizations,

ophthalmology visits, etc.)

▪ Images of retinal photos, wounds, or

other structures

Tools Information

Internal

Il Medico «Multicanale»

Quale entità di riduzione di HbA1c può essere ottenuta

con l’uso della telemedicina nel diabete?

1. Non apprezzabile

2. 0,5%

3. 1%

4. 1,5%

5. Si è osservata solo una riduzione dei casi di marcato

scompenso glicemico

Risposta esatta: 2

Web-based Care Management in Patients with

Poorly Controlled DM (HbA1c ≥9.0%)

▪ Notebook computer, glucose meter, and blood pressure monitor

▪ Notebook computer programmed to connect to a diabetes education and management website

▪ Messaging system via the website (care manager responded to queries within 1 working day)

▪ Web-enabled diabetes educational modules

▪ Recall of participants who did not login to the website during a 2 week period

McMahon GT et al., Diabetes Care 2005

Effect of Mobile Phone Intervention on Glycaemic Control

Liang X et al. Diabetic Medicine 2010

Type 1 Diabetes

Type 2 Diabetes

Diabeo Software: Individualized Insulin Dose

Adjustments Combined With Telemedicine

Support in Poorly Controlled T1D

Charpentier et al., Diabetes Care 2011

Change

in H

bA

1c (

%)

E-log bookControl E-log book

Teleconsultation

Medicine, December 2014

-0.37%

L’uso della telemedicina nel diabete ha mostrato:

1. Un miglioramento del controllo glico-metabolico

2. Un miglioramento della qualità di vita

3. Una riduzione degli episodi di ipoglicemia

4. Un potenziale impatto su parametri lipidici e pressori

5. Tutte le precedenti

Risposta esatta: 5

Impact of the "Diabetes Interactive Diary" Telemedicine on

Metabolic Control, Risk of Hypoglycemia, and QoL in T1DRossi MC et al., Diabetes Technol Ther. 2013 Aug;15:670-9

▪ Diabetes Interactive Diary (DID): carbohydrate/bolus calculator

promoting the patient-physician communication via SMS

▪ 127 T1D patients not previously educated on carbohydrate counting

assigned to DID or standard of care

▪ No between-group difference in HbA1c levels

▪ Lower risk of grade 2 hypoglycemia with DID

▪ DID improved the "perceived frequency of hyperglycemic

episodes" scale of the DTSQ and the "social relations" and the

"fear of hypoglycemia" dimensions of the DSQLS

Web-based Intervention and Physical Outcomes

Associated with Diabetes among Adults > 60 yrsBond GE, et al., Diabetes Technol Ther. 2007;9:52–9

▪ RCT, 62 subjects (87% with T2D), 6-month follow-up

▪ Nurse-based

▪ Computer experience or computer literacy not required for eligibility

▪ Several behavioral strategies (coaching, motivational, and social

support)

▪ Mean HbA1c level decreased in the intervention group from

7.0% to 6.4% and in the control group from 7.1% to 7.0% (P =

0.01)

▪ Significant reductions in SBP, DBP, total cholesterol and weight

in the intervention group

“this study disproves the myth that seniors will not be attracted to the

Internet as a way of communication”

Interactive Telemedicine (TM): Effects on

Professional Practice and Health Care OutcomesFlodgren G et al., Cochrane Database Syst Rev 2015

• 93 eligible trials (N = 22,047 participants)

• 21 in patients with diabetes

• Education and advice for self-management, specialist consultations for

diagnosis and treatment decisions

• Telephone, e-mail, SMS

• Lower HbA1c with TM: -0.31% (95% CI -0.37 to -0.24; P < 0.00001)

at a median of nine months follow-up

• Some evidence for a decrease in LDL (4 studies, N = 1692; -12.45,

95% CI -14.23 to -10.68; P < 0.00001) and blood pressure (4

studies, N = 1770: SBP:-4.33, 95% CI -5.30 to -3.35, P < 0.00001;

DBP: -2.75 95% CI -3.28 to -2.22, P < 0.00001) with TM

Comparative Effectiveness

of Telemedicine Strategies

in T2DM

Lee SWH et al., Scientific Reports 2017

Metanalisi di 107 studi, 20.501 T2DM

HbA1c = 8,1%

Età 42-71 aa

Durata diabete 2-24 aa

Telefono, smartphone, internet, SMS,

video, computer

Operatori: specialisti, medici di base,

infermieri, dietisti, farmacisti, assistenti

sociali, pazienti/familiari di pazienti

Efficacia complessiva: HbA1c – 0.43%

Effetto più modesto su altri parametri

Diabetes Care, 36:2887-2894, 2013

-0,5

-0,4

-0,3

-0,2

-0,1

0

0 3 6 9 12 15

Ad

juste

d M

ean

Ch

an

ge i

n

Hb

A1

C (

%)

Months

ISM AC

-0.39%

-0.27%

Data are Least Square Means ± SE

Adjusted for baseline HbA1c, center

and diabetes treatment at baseline

ITT, n = 949

Mannucci E, Antenore A, Giorgino F, Scavini M. JDST 2017

SMBG nel DMT2 non Trattato con Insulina

Intensive

Structured Monitoring

(ISM)

Active Control

(AC)

Tests at fixed times (n./week) 12

(4-point daily profile,

3 times per week for 1

year)

12

(4-point daily profile, 3

times during the week

before V3 and V5)

Possible further tests 50 every 3 months 26 for one year

Standard educational program

sessions

Yes Yes

Structured SMBG data available

to the patient to guide

lifestyle changes

Yes No

Structured SMBG data available

to the investigator to

adjust diabetes medications

HbA1c and SMBG data HbA1c only

Scavini M at al., Acta Diabetologica 2012

Grant RW et al. Am J Manag Care. ; 21(2): e119–e12, 2015.

SMBG and Unused Testing Resultsn = 7320 T2DM

36.7%: results used by both patient and physician

13.7%: results used by physician only

34.4%: results used by patient only

15.2%: results unused: approx. $ 170/year

Elevated

HbA1c

HbA1c

to target

Intensification

and/or change

of drug therapy

Lifestyle

intervention

Evaluation of

clinical features

Evaluation of

clinical features

Detection of SMBG

(CGM) abnormalities

Intensification

and/or change of

drug therapy (*)

Correction of abnormal

FPG/PPG/glucose excursions

(*) considering also effects of drugs on FPG/PGG and their potential to cause hypoglycemia

Greater post-meal glucose excursions associated with older age, longer diabetes

duration, absence of obesity, hyperlipidaemia, hypertension, treatment with SUs

(multivariate analysis adjusted for pre-prandial glucose levels).

Preprandial Glucose Levels (mM)

<6.66 6.67-8-89 8.9-11.1 11.0 p value

Glucose change

with meal (mM) 1.8 ± 1.18 1.6 ± 1.24 1.5 ± 1.43 1.4 ± 1.7 <0.001

Glucose ≥ 25%

(% subjects) 59.2 40.3 27.6 19.6 <0.001

Bonora E at al, Diabetologia 2006

Monitoraggio GlicemicoLa Tecnologia al Servizio della Cura

• Telemedicina

• CGM/FGM

Non-HbA1c Glucose Metrics

Maahs DM et al. Diabetes Care 2016Danne T et al. Diabetes Care 2017

Quale dei seguenti parametri ottenibili con il CGM

ritenete più utile dal punto di vista clinico?

1. Media della glicemia

2. Media delle glicemie a digiuno, pre-prandiali e post-prandiali

3. Deviazione Standard (DS), Coefficiente di variazione (CV), range

interquartile (IQR)

4. TIR (Time in Range)

5. TATR (Time Above Target Range)

6. TBTR (Time Below Target Range)

Analisi razionale dello scarico dati

10th

25th

75th

90th

▪ HbA1c stabilmente superiore al target desiderabile

▪ Ipoglicemia problematica (no FGM in ipoglicemia inavvertita)

▪ Disponibilità all’impiego costante (>70%)

▪ Training (iniziale + refresh periodico)

▪ Adeguato periodo di «prova»

▪ Capacità di gestione del dato (paziente)

▪ Capacità di interpretazione del dato (team)

CGM/FGM: qualche spunto per l’appropriatezza

56,5

57

57,5

58

58,5

59

59,5

60

60,5

61

61,5

Non utilizzo <10 die/mese 10-19 die/mese ≥20 die/mese

Hb

A1

c (m

M/M

)p<0,0001

p<0,001

p<0,05

p<0,005

Uso del Sensore CGM e HbA1c

Lepore G et al, NMCD 2017

Pickup JC et al, BMJ 2011

rtCGM vs. SMBG and Hypoglycaemia

Beck RW et al, JAMA, 2017

DIAMOND Trial – T1DM on MDI

Effects of rtCGM on Hypoglycaemia

Specific Glucose Patterns

Hypoglycaemia between 3-6 a.m.

Reactive hyperglycaemia in the morning

Bolinder J et al, Lancet, 2016 Haak T et al, Diab Ther 2017

T1DM T2DM

Reduction of Hypoglycaemia Flash Glucose Monitoring

Le informazioni ottenibili con il sistema rtCGM

sono:

1. valore assoluto della concentrazione di glucosio

2. valore assoluto della concentrazione di glucosio, frecce di

tendenza

3. valore assoluto della concentrazione di glucosio, frecce di

tendenza, allarmi

4. valore assoluto della concentrazione di glucosio, HbA1c stimata

Risposta esatta: 3

van Beers et al., Lancet Diabetes Endocrinol 2016 Heinemann L et al., Lancet 2018

rtCGM and Reduction of HypoglycaemiaT1DM with hypoglycemia unawareness

IN

CONTROL

HypoDE

Frecce di tendenza

• Le tendenze sono indicate dalle frecce

• La tendenza si genera in base all’andamento che la glicemia

ha avuto negli ultimi 15-20 minuti e cioè se è stata stabile, se

sta aumentando o se sta diminuendo

• Essa predice ciò che accadrà nei successivi 30 min se non ci

sono nuove variazioni

• Ciascun sistema ha un sua definizione di tendenza.

Quali decisioni prendere in basealle frecce di tendenza?

Pre

esercizioIntervento convenzionale Frecce Intervento appropriato

125

mg/dLAssumere 10-15 gr di CHO ➞ stabile

Assumere 10-15 gr di

CHO

125

mg/dLAssumere 10-15 gr di CHO

⬊⬇ lenta discesa Assumere 20-30 gr di

CHO

125

mg/dLAssumere 10-15 gr di CHO ⬇⬇ rapida discesa

Assumere 30-40 gr di

CHO

125

mg/dLAssumere 10-15 gr di CHO ⬈⬆ lenta salita Non è necessario snack

125

mg/dLAssumere 10-15 gr di CHO ⬆⬆ rapida salita

Controllare BG dopo 15-

20 e se necessario

effettuare bolo

Modern Tools for Collecting Data That Can Be

Incorporated into Precision Medicine

• Gene sequencers

• Omics testing

• Lab-on-a-chip for biomarkers

• Electronic nose

• Microbiome analysis

• 3-D medical imaging

• Accelerometers

• Global positioning systems

• Wearable physiologic sensors

• Implanted physiologic sensors

• Ubiquitous video cameras

• Medication compliance

systems

• Electronic medical record

• Social media

Klonoff DC, J Diabetes Sci Technol 2015

exposome

signs and symptoms

genome

epigenetic

microbiome

other patient’s data

individual patients

“Each patient may become a big-data producer. The data we generate at home or in the wild will vastly exceed what we accumulate in clinical care”.“We’re trying to create these big collages of different data modalities — from the genomic to the environmental to the clinical — and link them back to the patient.”