Page 357 - Glucose Monitoring Devices

364 Index

Retrofitting algorithm (Continued) measurement error. See Measurement error
quasi-continuous reconstructed profile, 225 noninsulin-treated T2D, 20
selection matrix, 225e226 shortcomings, 19e21
data preprocessing, 220f, 234e237 type 1 diabetes
outpatient study data continuous subcutaneous insulin infusion
absolute and relative absolute errors, 229e230 (CSII), 6e7
accuracy outcomes metrics and statistical Diabetes Control and Complications Trial
analysis, 228 (DCCT), 5e6
Clarke’s error grid, 230, 230f glucometer, 5e6
original dataset, 227 glycated hemoglobin level, 5e7
outpatient-like dataset, 228 insulin injections, 5e6
statistical analysis, 228 type 2 diabetes (T2D)
test-set data, 229f Action to Control Cardiovascular Risk in Dia-
problem formulation betes (ACCORD) trial, 7e8
calibration errors, 220e221 Diabetes Glycemic Education and Monitoring
notation, 221e222 (DiGEM) randomized controlled trial, 8e9
semiblind deconvolution problem, 221f ESMON study, 8e9
sensor sensitivity, 220e221 HbA1c-based control group, 9e11
static, linear, time-varying deformation, meta-analyses, 11e12
220e221 Monitor Trial, 8e9
two-compartment model, 220e221 noninsulin-treated people, 8e9
real-life adjunctive data nonpharmacologically treated participants,
absolute deviation and absolute relative 7e8
deviation, 231e232, 232f pooled analysis, 11e12
accuracy outcomes metrics and statistical prospective randomized trial, 9e11
analysis, 231 randomized controlled trials, 9e11
original dataset, 231 Role of Self-Monitoring of Blood Glucose and
patient-level analysis, 232, 233f Intensive Education in Patients with Type 2
real-life-like datasets, 231 Diabetes Not Receiving Insulin (ROSES)
vs. references, 233e234, 235f trial, 9e11
retrospective Bayesian CGM recalibration ROSSO study, 7e8
autoregressive (AR) process, 225 structured SMBG, 9e11
Bayesian estimate, 224 UK Prospective Diabetes Study (UKPDS),
calibration-error model, 224 7e8
data portion, 223 Semiblind deconvolution problem, 221f
population distribution, 224 Senseonics, 150
Role of Self-Monitoring of Blood Glucose and Sensor-augmented pump (SAP) therapy, 151e152
Intensive Education in Patients with Type , 282, 294, 330
2 Diabetes Not Receiving Insulin Severe hypoglycemia events (SHE), 55
(ROSES) trial, 9e11 Single-zone Gaussian model, 95
Run-to-run (R2R) control schemes, 307e309 Smoothness relative gain (SRG) index, 213e215
Structured Testing Program study, 9e11
S Subcutaneous sensor modeling, 249e251
Self-monitoring of blood glucose (SMBG) Subspace-based state-space system identification
adherence, 20 techniques
analytical accuracy, 43 block Hankel matrix, 304
blood glucose monitoring systems (BGMSs), 21 discrete-time state-space model, 303e304
clinical accuracy, 43 Hankel matrices, 304
continuous glucose monitoring (CGM), 20e21 multioutput output-error state-space (MOESP),
guidelines, 12e14, 14te19t 305
intermittent, 112f orthogonal projection matrix, 304e305
limitations, 137 recursive system identification