Clinical Perspective: SMBG Inaccuracy and Clinical Consequences in T1DM, an

In-Silico Study

Marc D BretonDiabetes Technology Center

University of Virginia

• All type I diabetics as well as many type II are encouraged to pursue strict glycemic control to avoid chronic complications. All face the challenge to lower glucose levels while avoiding hypoglycemia.

• Accurate information about the patient’s status is needed to achieve such goals. At this time SMBG are the main source for such information, and the only one that can be repeated frequently.

Background

• “when examining blood glucose monitor performance in the real world, it is important to consider if an improvement in analytical accuracy would lead to improved clinical outcomes for patients” [Clarke 2010]

• Miscoding meters can result in significant meter bias and increase risk for hypoglycemia [Raine et al 2008].

• Clinical outcome studies are difficult to design as controlled administration of meter errors in vivo is intricate and sometime unethical.

• A viable alternative has been presented in Bruns and Boyd landmark work which made use of computer simulations to asses the influence of meter errors on insulin dosing.

Does SMBG accuracy have a clinical impact

Use of Simulations: example design of the Boeing B787

www.flightgear.org

How to build a Simulator of Glucose/Insulin Dynamics in Man

1. Mathematical models based on clinical knowledge;

2. Accumulation of data targeting specific subsystems;

3. Identification of physiological processes (fluxes);

4. Creating in silico subjects;5. Assessment of inter-subject variability (creating

in silico population);6. Software implementation (currently MATLAB);7. Validation of the simulations against in vivo

data.In silico pre-clinical experiments.

Glucose-Insulin Model in T1DM (Dalla Man & Cobelli, 2006, 2007);Model of Sensor Errors (Breton & Kovatchev, 2008).

Simulated Measurement• YSI/Beckman• SMBG• CGM

SimulatedInsulin Delivery• IV• SQ pump

In Silico Subject

Glucose- InsulinModel

Meal

GLUCOSESYSTEM

GASTRO-INTESTINAL TRACT

LIVER

BETA CELL

MUSCLE AND ADIPOSE TISSUE

INSULINDELIVERY

Plasma Glucose

Plasma Insulin

6080

100120140160180

0 60 120180240300360420

0100200300400500

0 60 120180240300360420

Mathematical Models Based on Clinical Knowledge

Treatment

Database Agneta Sunehag (Houston):OGTT in 11 adolescents (age=15±1 yr, mean ± SD)SI= 14.96 ± 10.09 10^-4 dl/kg/min per μU/ml

Database Kenneth Polonsky (St. Louis):OGTT in 10 healthy adultsSI= 10.89± 4.12 10^-4 dl/kg/min per μU/ml

Database Robert Rizza (Mayo Clinic, Rochester):Meal in 204 adultsSI= 14.5 ± 9.59 10^-4 dl/kg/min per μU/ml

Database E. Baumann & R. Rosenfield (Chicago):OGTT in 27 PrePubertal (PP, age~8 yr); 17 EarlyPubertal (EP, age~ 12 yr); 26

LatePubertal, (LP, age~ 19 yr); 52 Adult (AD, age ~43 yr)SIPP= 19.57± 11.66 10^-4 dl/kg/min per μU/mlSIEP= 7.36± 7.12 10^-4 dl/kg/min per μU/mlSILP= 9.50± 9.60 10^-4 dl/kg/min per μU/mlSIAD= 10.08± 7.92 10^-4 dl/kg/min per μU/ml

Data Accumulation

Approximately N=350 individuals pooled from several studies using triple-tracer protocols which, in addition to concentrations, gave access to fluxes:

Identification of Physiological Processes (Fluxes)(m

g/dl

)

Glucose

50

100

150

200

250

0 60 120 180 240 300 360 420

(pm

ol/l)

Insulin

0

100

200

300

400

500

600

0 60 120 180 240 300 360 420

Production

(mg/

kg/m

in)

0

0.5

1

1.5

2

2.5

0 60 120 180 240 300 360 420

t (min)

(mg/

kg/m

in)

Utilization

0

2

4

6

8

10

12

0 60 120 180 240 300 360 420

(pm

ol/k

g/m

in)

t (min)

Secretion

0

2

4

6

8

10

12

14

16

0 60 120 180 240 300 360 420

(mg/

kg/m

in)

Rate of Appearance

0

2

4

6

8

10

12

14

0 60 120 180 240 300 360 420

t (min)

Data

Range

Creating an In-Silico Patient

2 1 1 2 3

01 2

0

2 4 1 1 1 2 2

1 3 2

1 1

. .. . . .

.. .

. . . .

. .

gutabsp p t t pii p p p d

mX tmt t p

tm

psc sc sc

g

p p l a sc a sc

pl l

pi

i

d i

f k QG k G k G U E k k G k I

BWV V X G

G k G k GK G

GG k G

V

I m m I m I k I k I

I m m I m I

II k I

V

I k I

1

2

1 1 1 1

1 1 2 2

1 1

2 2 1

2

. .

. ..

. .

. .

d

pu b

i

sc d sc a sc

sc d sc a sc

sto gri sto

emptsto sto gri sto

gut gut emptabs sto

I

IX p X I

V

J tI k I k I

BWI k I k IQ k Q M t

Q k Q k Q

Q k Q k Q

Qgut Qst2 Qst1

Gp

Gsc

Gt

Id

I1 X

Il Ip

Isc2Isc1

meal

insulin

EGP

Uid

UiiEt

An in silico subject is a complex entity of 26 individual parameters. When we run control, we don’t know in advance how such a “subject” would react.

Creating an In-Silico Population

Rate Constant of Liver Insulin Action

0

20

40

60

80

100

0.0010.011

0.0220.032

0.0430.053

0.064

min^-1

0

20

40

60

80

100

0.0110.022

0.0320.043

0.0530.064

mg/kg/min/(pmol/l)

LiverGlucose Effectiveness

0

20

40

60

80

100

0.0000.003

0.0060.009

0.0120.015

0.018

min^-1

0

20

40

60

80

100

0.000.03

0.050.07

0.090.11

0.13

min^-1

0

20

40

60

80

100

0.000.03

0.060.09

0.120.15

0.18

mg/kg/min per pmol/L

0

20

40

60

80

100

1.251.45

1.651.86

2.062.26

mg/kg

LiverInsulin Sensitivity

Rate Constant of Peripheral Insulin Action

PeripheralGlucose Effectiveness

PeripheralInsulin Sensitivity

The parameters of the in silico “population” must cover well key parameter distributions observed in vivo, thus providing comprehensive analysis of control performance.

• Validation: For any in vivo glucose trace, is there is a simulated “subject” or “subjects” who would have a similar trace under the same conditions?– Traces from hyper-insulemic clamp in adults with T1DM,

NIH/NIDDK study RO1 DK 51562.– Traces from children with T1DM, DirectNet

• Accepted by FDA in January 2008 as a replacement for pre-clinical trials in closed loop studies.

• Has been used as the foundation of several Investigational Devices Exemption applications (3 at UVa)

Validation and Regulatory Acceptance

The current In-Silico Population

Adults Adolescents Children

Parameter Mean (SD) Min Max Mean (SD) Min Max Mean (SD)

Min Max

Weight (kg) 79.7 (12.8) 52.3 118.7 54.7 (9.0) 37.0 88.7 39.8 (6.8) 27.6 60.7

Insulin (U/day) 47.2 (15.2) 21.3 98.4 53.1 (18.2) 22.6 141.5 34.6 (9.1) 17.6 56.1

Carb ratio (g/U) 10.5 (3.3) 4.6 21.1 9.3 (2.9) 3.2 19.9 14.0 (3.8) 8.0 25.5

Biometric Characteristics of the Population of In Silico “Subjects”

N=300+30 Simulated Subjects that Can Be:• Screened & measured;• “Admitted” to the CRC and subjected to tests, such as

oral glucose tolerance test;• Individual parameters can be derived and used to

initialize the control algorithm.

Model of zero bias SMBG errorsM

eter

BG

[mg/

dl]

Reference BG [mg/dl]

95%

Met

er B

G [m

g/dl

]

Reference BG [mg/dl]

50 100 150 200 250 300 350 400

50

100

150

200

250

300

350

400

• We use the ISO format: i.e. fixed relative error over 75 mg/dl and fixed error below.

• We experiment with four levels of accuracy: 5% - 4mg/dl, 10% - 8mg/dl, 15% - 11mg/dl, and 20% - 15mg/dl which is the current ISO standard

Detection of hypoglycemia

50525456586062646668700%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

True Plasma Glucose [mg/dl]

Prob

abili

ty o

f Miss

ing

Hypo

glyc

emic

Eve

nt

0 5 10 15 200

2

4

6

8

10

5% - 4mg/dl

10% - 8mg/dl

15% - 11mg/dl

20% - 15mg/dl

200

100

4h2h0h

• We use the previously described simulator and SMBG error model.• Each in-silico patients starts the experiment stable at 200 mg/dl• For each patient, a perfect bolus is computed that brings the

patient at exactly 100 mg/dl within 4 hours.• At time 0 glucose is measured using a simulated SMBG and a bolus

is computed using the optimal patient correction factor.• 100 adults in-silico patents are tested 10 times per level of error

(i.e. 40 times total)

Treatment of hyperglycemia: method

Treatment of Hyperglycemia: results

40

60

80

100

120

140

5% 10% 15% 20%

Min

imum

glu

cose

con

cent

ratio

n att

aine

d [m

g/dl

]

SMBG induced glucose variability: method

200

100

4h2h0h

• Each in-silico patients starts the experiment fasting at 100 mg/dl.• At time 0 glucose is measured using a simulated SMBG and a bolus is computed

using the optimal patient correction factor and carbohydrate ratio (built in the simulator) so as to cover 60% of the meal, so as to necessitate a correction later on.

• 2 hours later a second measure is taken and a correction bolus is computed based on the patient optimal correction factor.

• 100 adults in-silico patents are tested 10 times per level of error (i.e. 40 times total)

SMBG induced glucose variability: method

lower 95% confidence bound [mg/dl]

high

er 9

5% c

onfid

ence

bou

nd [m

g/dl

]

110 90 70 50

400

300

180

110

• Decrease in accuracy augments patient’s risks:• At 5% error: 3% unsafe• At 20% error: 6% unsafe

• Decrease in accuracy augments glucose variability (spread of the cloud of points)

305

82

95%

White: 5% -- Black: 20%

• Each in-silico patient is stabilized at a nominal level using their optimal carbohydrate ratio, correction factor and perfect knowledge of glucose level.

• The patient’s nominal risk for hypoglycemia is recorded.• Each patient is then studied for 10 simulated days during which their

control is based on the SMBG model previously described.• In some subject SMBG errors caused an increased risk of hypoglycemia, and

we dialed the risk back to its nominal value.• Limiting the risk of hypoglycemia can cause an increased average glucose,

reflecting the detrimental effect of hypoglycemia on glucose control observed in vivo.

• This rise in average glucose is transformed into an increase in HBA1c using the ADA formula: 28.7*A1c-46.7=G

Long term effect of SMB accuracy: method

Long term effect of SMB accuracy: results

• In Silico experiments allow for fast and inexpensive study of clinical consequences of SMBG accuracy.

• Hypoglycemic events of 60mg/dl are missed 10 times more often when using SMBG with 20% accuracy vs. 10%

• The risk of hypoglycemia after the treatment of mild hyperglycemia is practically inexistent up to an error level of 10% and rises with the magnitude of SMBG errors.

• Glucose variability post meal increase with SMBG errors

• Long term glucose control is affected by SMBG accuracy (+0.4% HbA1c at 20% vs nominal), under the hypothesis of a fixed risk for hypoglycemia.

Conclusion

Essentially, all models are wrong, but some are useful

George E.P. Box

• Diabetes Technology Society, Dr David Klonoff

• Dr Boris Kovatchev, UVa

• Dr David Bruns, Dr James Boyd, UVa

• The Diabetes Technology Center at UVa

• Juvenile Diabetes Research Foundation

Acknowledgement