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

Abstract

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...

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

“…Note that Equations (2)- (3) define an intravenous insulin pathway, per ICU standard insulin delivery, but all pharmaco-dynamics and kinetics are otherwise general and have been used in type 1 diabetes analysis, as well [102][103][104]. The insulin kinetics in Equations (2)-(3) are similar looking to those of the well known Minimal Model, but have very different meaning as defined.…”

Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient

“…To mimic this error, 10% nor-138 mally distributed noise was applied to the BG data. Misrepresentation 139 and error are also common in a patient's carbohydrate estimates [47]. Thus, the acquired data neglected the snacks (P C ) and 10% uniformly 141 distributed noise was applied to P X .…”

“…However, this type of treatment is still exper- 46 imental and has high cost and complexity [17,4]. Hence, it may be 47 more practical to improve upon conventional approaches such as 48 self-monitored glucose with multiple daily insulin injections [46,47]. 49 Knowledge of relevant patient-specific parameters would benefit 50 model-based therapy support for insulin dosing information.…”