The Impact of Non–Model-Related Variability on Blood Glucose Prediction

Randløv, Jette; Poulsen, Jens Ulrik; Hejlesen, Ole

doi:10.1089/dia.2006.0039

Cited by 21 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…22,23 The meal carbohydrate content is assumed known to the patient through carbohydrate counting. [24][25][26] While the technique is only approximate and can be prone to inaccuracy, 27 it remains the key clinical strategy recommended to estimate the glycemic effect of meals for the purpose of adjusting insulin dosage. …”

Section: Discussionmentioning

confidence: 99%

In Silico Simulation of Long-Term Type 1 Diabetes Glycemic Control Treatment Outcomes

Wong

Chase

Hann

et al. 2008

J Diabetes Sci Technol

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

In Silico Simulation of Long-Term Type 1 Diabetes Glycemic Control Treatment Outcomes

Wong

Chase

Hann

et al. 2008

J Diabetes Sci Technol

View full text Add to dashboard Cite

“…Meal carbohydrate content is assumed to be known with some error through carbohydrate counting [39][40][41]. While this technique is only approximate [42], it remains the key clinical method recommended to estimate the glycaemic effect of meals for the purpose of adjusting insulin dosage [5].…”

Section: Glucose Measurement Insulin Type and Mealsmentioning

confidence: 99%

An adaptive clinical Type 1 diabetes control protocol to optimize conventional self‐monitoring blood glucose and multiple daily‐injection therapy

Wong

Chase

Compte

et al. 2008

Adaptive Control & Signal

View full text Add to dashboard Cite

The objective of this study was to develop a safe, robust and effective protocol for the clinical control of Type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements, and multiple daily injection (MDI) with insulin analogues.A virtual patient method is used to develop an in silico simulation tool for Type 1 diabetes using data from a Type 1 diabetes patient cohort (n = 40). The tool is used to test two prandial insulin protocols, an adaptive protocol (AC) and a conventional intensive insulin therapy (IIT) protocol (CC) against results from a representative control cohort as a function of SMBG frequency. With the prandial protocols, optimal and suboptimal basal insulin replacement using a clinically validated, forced-titration regimen is also evaluated. A Monte Carlo (MC) analysis using variability and error distributions derived from the clinical and physiological literature is used to test efficacy and robustness. MC analysis is performed for over 1 400 000 simulated patient hours. All results are compared with control data from which the virtual patients were derived.In conditions of suboptimal basal insulin replacement, the AC protocol significantly decreases HbA 1c for SMBG frequencies 6/day compared with controls and the CC protocol. With optimal basal insulin, mild and severe hypoglycaemia is reduced by 86-100% over controls for all SMBG frequencies. Control with the CC protocol and suboptimal basal insulin replacement saturates at an SMBG frequency of 6/day. The forced-titration regimen requires a minimum SMBG frequency of 6/day to prevent increased hypoglycaemia. Overaggressive basal dose titration with the CC protocol at lower SMBG frequencies is likely caused by uncorrected postprandial hyperglycaemia from the previous night. AN ADAPTIVE CLINICAL TYPE 1 DIABETES CONTROL PROTOCOL 409From the MC analysis, a defined peak in control is achieved at an SMBG frequency of 8/day. However, 90% of the cohort meets American Diabetes Association recommended HbA 1c with just 2 measurements a day. A further 7.5% requires 4 measurements a day and only 2.5% (1 patient) required 6 measurements a day. In safety, the AC protocol is the most robust to applied MC error. Over all SMBG frequencies, the median for severe hypoglycaemia increases from 0 to 0.12% and for mild hypoglycaemia by 0-5.19% compared with the unrealistic no error simulation. While statistically significant, these figures are still very low and the distributions are well below those of the controls group.An adaptive control protocol for Type 1 diabetes is tested in silico under conditions of realistic variability and error. The adaptive (AC) protocol is effective and safe compared with conventional IIT (CC) and controls. As the fear of hypoglycaemia is a large psychological barrier to appropriate glycaemic control, adaptive model-based protocols may represent the next evolution of IIT to deliver increased glycaemic control with increased safety over conventional methods, while still utilizing the most commonly used forms of inte...

show abstract

“…5 Graff and colleagues 6 found that patients overestimated carbohydrate content at breakfast, on average, by +8.5% (range, -93% to +100%) and underestimated carbohydrate content at lunch, on average, by -28% (range, -97% to +43%). Kildegaard and associates 4 observed an average intraindividual variation of 30% for estimates of carbohydrate meal content. 4 Methods for better estimating carbohydrate and improving insulin dosages are under investigation.…”

Section: Introductionmentioning

confidence: 97%

“…Kildegaard and associates 4 observed an average intraindividual variation of 30% for estimates of carbohydrate meal content. 4 Methods for better estimating carbohydrate and improving insulin dosages are under investigation. 7 Establishing analytical performance of blood glucose meters (BGMs) is the subject of regulatory and clinical debate.…”

Section: Introductionmentioning

confidence: 97%