Clinical Perspective: SMBG Inaccuracy
and Clinical Consequences in T1DM, an
In-Silico Study
Marc D Breton
Diabetes Technol...
• All type I diabetics as well as many type II are
encouraged to pursue strict glycemic control
to avoid chronic complicat...
• “when examining blood glucose monitor performance
in the real world, it is important to consider if an
improvement in an...
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. Accumul...
Glucose-Insulin Model in T1DM (Dalla Man & Cobelli, 2006, 2007);
Model of Sensor Errors (Breton & Kovatchev, 2008).
Simula...
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 μ...
Identification of Physiological Processes (Fluxes)
(mg/dl)
Glucose
50
100
150
200
250
0 60 120 180 240 300 360 420
(pmol/l...
Creating an In-Silico Patient
 
 
 
2 1 1 2 3
0
1 2
0
2 4 1 1 1 2 2
1 3 2
1 1
. .
. . . .
.
. .
. . . .
. .
gutabs
p...
Creating an In-Silico Population
Rate Constant of Liver
Insulin Action
0
20
40
60
80
100
min^-1
0
20
40
60
80
100
mg/kg/mi...
• Validation: For any in vivo glucose trace, is there is a
simulated “subject” or “subjects” who would have a
similar trac...
The current In-Silico Population
Adults Adolescents Children
Parameter Mean (SD) Min Max Mean (SD) Min Max Mean
(SD)
Min M...
Model of zero bias SMBG errorsMeterBG[mg/dl]
Reference BG [mg/dl]
95%
MeterBG[mg/dl]
Reference BG [mg/dl]
50 100 150 200 2...
Detection of hypoglycemia
5052545658606264666870
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
True Plasma Glucose [mg/dl]
Pro...
200
100
4h2h0h
• We use the previously described simulator and SMBG error model.
• Each in-silico patients starts the expe...
Treatment of Hyperglycemia: results
40
60
80
100
120
140
5% 10% 15% 20%
Minimumglucoseconcentrationattained[mg/dl]
SMBG induced glucose variability: method
200
100
4h2h0h
• Each in-silico patients starts the experiment fasting at 100 mg/...
SMBG induced glucose variability: method
lower 95% confidence bound [mg/dl]
higher95%confidencebound[mg/dl]
110 90 70 50
4...
• Each in-silico patient is stabilized at a nominal level using their
optimal carbohydrate ratio, correction factor and pe...
Long term effect of SMB accuracy: results
• In Silico experiments allow for fast and inexpensive study of
clinical consequences of SMBG accuracy.
• Hypoglycemic eve...
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 Diabe...
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Clinical Perspective: Clinical Needs Relative to Insulin Dosing ...

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Clinical Perspective: Clinical Needs Relative to Insulin Dosing ...

  1. 1. Clinical Perspective: SMBG Inaccuracy and Clinical Consequences in T1DM, an In-Silico Study Marc D Breton Diabetes Technology Center University of Virginia
  2. 2. • 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
  3. 3. • “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
  4. 4. Use of Simulations: example design of the Boeing B787 www.flightgear.org
  5. 5. 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.
  6. 6. Glucose-Insulin Model in T1DM (Dalla Man & Cobelli, 2006, 2007); Model of Sensor Errors (Breton & Kovatchev, 2008). Simulated Measurement • YSI/Beckman • SMBG • CGM Simulated Insulin Delivery • IV • SQ pump In Silico Subject Glucose- Insulin Model Meal GLUCOSE SYSTEM GASTRO-INTESTINAL TRACT LIVER BETA CELL MUSCLE AND ADIPOSE TISSUE INSULIN DELIVERY Plasma Glucose Plasma Insulin 60 80 100 120 140 160 180 0 60 120180240300360420 0 100 200 300 400 500 0 60 120180240300360420 Mathematical Models Based on Clinical Knowledge Treatment
  7. 7. 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 adults SI= 10.89± 4.12 10^-4 dl/kg/min per μU/ml Database Robert Rizza (Mayo Clinic, Rochester): Meal in 204 adults SI= 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/ml SIEP= 7.36± 7.12 10^-4 dl/kg/min per μU/ml SILP= 9.50± 9.60 10^-4 dl/kg/min per μU/ml SIAD= 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:
  8. 8. Identification of Physiological Processes (Fluxes) (mg/dl) Glucose 50 100 150 200 250 0 60 120 180 240 300 360 420 (pmol/l) Insulin 0 100 200 300 400 500 600 0 60 120 180 240 300 360 420 Production (mg/kg/min) 0 0.5 1 1.5 2 2.5 0 60 120 180 240 300 360 420 t (min) (mg/kg/min) Utilization 0 2 4 6 8 10 12 0 60 120 180 240 300 360 420 (pmol/kg/min) t (min) Secretion 0 2 4 6 8 10 12 14 16 0 60 120 180 240 300 360 420 (mg/kg/min) Rate of Appearance 0 2 4 6 8 10 12 14 0 60 120 180 240 300 360 420 t (min) Data Range
  9. 9. Creating an In-Silico Patient       2 1 1 2 3 0 1 2 0 2 4 1 1 1 2 2 1 3 2 1 1 . . . . . . . . . . . . . . . gutabs p p t t pii p p p d mX tm t t p tm p sc sc sc g p p l a sc a sc pl l p i i d i f k Q G k G k G U E k k G k I BW V V X G G k G k G K G G G k G V I m m I m I k I k I I m m I m I I I k I V I k I                                                       1 2 1 1 1 1 1 1 2 2 1 1 2 2 1 2 . . . . . . . . . d p u b i sc d sc a sc sc d sc a sc sto gri sto emptsto sto gri sto gut gut emptabs sto I I X p X I V J t I k I k I BW I k I k I Q 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.
  10. 10. Creating an In-Silico Population Rate Constant of Liver Insulin Action 0 20 40 60 80 100 min^-1 0 20 40 60 80 100 mg/kg/min/(pmol/l) Liver Glucose Effectiveness 0 20 40 60 80 100 min^-1 0 20 40 60 80 100 min^-1 0 20 40 60 80 100 mg/kg/min per pmol/L 0 20 40 60 80 100 mg/kg Liver Insulin Sensitivity Rate Constant of Peripheral Insulin Action Peripheral Glucose Effectiveness Peripheral Insulin Sensitivity The parameters of the in silico “population” must cover well key parameter distributions observed in vivo, thus providing comprehensive analysis of control performance.
  11. 11. • 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
  12. 12. 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.
  13. 13. Model of zero bias SMBG errorsMeterBG[mg/dl] Reference BG [mg/dl] 95% MeterBG[mg/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
  14. 14. Detection of hypoglycemia 5052545658606264666870 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% True Plasma Glucose [mg/dl] ProbabilityofMissingHypoglycemicEvent 0 2 4 6 8 10 0 5 10 15 20 5% - 4mg/dl 10% - 8mg/dl 15% - 11mg/dl 20% - 15mg/dl
  15. 15. 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
  16. 16. Treatment of Hyperglycemia: results 40 60 80 100 120 140 5% 10% 15% 20% Minimumglucoseconcentrationattained[mg/dl]
  17. 17. 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)
  18. 18. SMBG induced glucose variability: method lower 95% confidence bound [mg/dl] higher95%confidencebound[mg/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%
  19. 19. • 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
  20. 20. Long term effect of SMB accuracy: results
  21. 21. • 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
  22. 22. Essentially, all models are wrong, but some are useful George E.P. Box
  23. 23. • 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
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