Page 354 - Glucose Monitoring Devices

Index 361

type 1 diabetes patients, 63, 63f Automation to Simulate Pancreatic Insulin
worst-case costing, 68, 69f Response (ASPIRE) study, 330
In silico accuracy study clinical studies, 260e266
behavioral models, 65, 65f concept, 259f
clinical outcomes, 66, 67f cost-effectiveness analysis, 268
design, 64f interrupted insulin delivery, 259
long-term behavioral adaptations, 70 limitations, 268e269
meter models, 64 preset glucose threshold, 330
regression model, 66e67 rapid-acting insulin analogs, 260
simulation, 59e60, 60f real-life evidence, 267e268
UVA/Padova simulator, 63 regulatory approval, 330e331
In silico clinical trials (ISCTs) retrospective review, 331
advantages, 80e81
limitation, 81 M
physiological response, 81 Markov cohort modeling approach, 61, 65
simulation platforms, 81 Maximum-likelihood (ML) fitting, 84e87
Insulin lispro, 260 exponential PDF model, 86e87
Insulin pumps, 124e125 log-likelihood function, 86
Insulin-to-carbohydrate ratio, 309 normality test, 85e86
Integrated random walk model, 207 parameters, 87
International Diabetes Federation (IDF), 257 skew-normal PDF, 85e86
Interstitial glucose (IG) fluctuations, 159e160 Mean absolute difference (MAD), 87
Iterative learning control (ILC), 309e311 Mean absolute relative difference (MARD),
autoregressive exogenous input (ARX) model, 60e61, 192, 276, 336e337
310e311 Measurement error
feedback control, 310 Bayer Contour Next (BCN)
model predictive iterative learning control dataset, 95e96
(MPILC) algorithm, 311 histograms, 98e99, 99f
objective, 309e310 model development, 97e99, 97f
P-type, 310 model validation, 99e102, 102t
real-time information, 310 parameters and second-order statistical
simple formulation, 309e310 description, 100t
tracking error, 309e310 T1D patient decision simulator, 103e104, 103f
YSI and SMBG-YSI preprocessing, 96e97
J blood glucose (BG) concentration, 80
Juvenile Diabetes Research Foundation (JDRF), factors influencing, 79e80
328e329 FDA guidance, 80
ISO 15197:2003 standard, 80
K literature models
Kalman filter, 277 bivariate kernel density model, 82e83
Kalman filter-based approaches, 180e181 Gaussian model, 82e83
Ketoacidosis, 259 Johnson distribution PDF models, 83e84
KolmogoroveSmirnov (KS) test, 87e88, 95 probability density function (PDF) models,
82e83
L relative error, 82, 83f
Linear matrix inequalities techniques, 182 two-zone SMBG error model, 83e84
Linear minimum variance estimation problem, modeling, 80e81
208 One Touch Ultra 2 (OTU2)
Linear regression model, 61 absolute and relative error, 91e92, 92f
Low blood glucose index (LBGI), 60 dataset, 88e89, 89f
Low glucose suspend (LGS) system, histograms, 92, 93f
281e282 model development, 90e92