Clinical Perspective: SMBG Inaccuracy and Clinical Consequences in T1DM, an In-Silico Study
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Transcript of Clinical Perspective: SMBG Inaccuracy and Clinical Consequences in T1DM, an In-Silico Study
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