A personalised and adaptive insulin dosing decision support system for type 1 diabetes

People with type 1 diabetes (T1D) rely on exogenous insulin to maintain stable glucose levels. Despite the advent of diabetes technologies such as continuous glucose monitors and insulin infusion pumps, the majority of people with T1D do not manage to bring back glucose levels into a healthy target after meals. In addition to patient compliance, this is due to the complexity of the decision-making on how much insulin is required. Commercial insulin bolus calculators exist that help with the calculation of insulin for meals but these lack fine-tuning and adaptability. This thesis presents a novel insulin dosing decision support system for people with T1D that is able to provide individualised insulin dosing advice. The proposed research utilises Case-Based Reasoning (CBR), an artificial intelligence methodology, that is able to learn over time based on the behaviour of the patient and optimises the insulin therapy for various diabetes scenarios. The decision support system has been implemented into a user-friendly smartphone-based patient platform and communicates with a clinical platform for remote supervision. In-silico studies are presented demonstrating the overall performance of CBR as well as metrics used to adapt the insulin therapy. Safety and feasibility of the developed system have been assessed incrementally in clinical trials; initially during an eight-hour study in hospital settings followed by a six-week study in the home environment of the user. Human factors play an important role in the clinical adoption of technologies such as the one proposed. System usability and acceptability were evaluated during the second study phase based on feedback obtained from study participants. Results from in-silico tests show the potential of the proposed research to safely automate the process of optimising the insulin therapy for people with T1D. In the six-week study, the system demonstrated safety in maintaining glycemic control with a trend suggesting improvement in postprandial glucose outcomes. Feedback from participants showed favourable outcomes when assessing device satisfaction and usability. A six-month large-scale randomised controlled study to evaluate the efficacy of the system is currently ongoing.Open Acces

Personalised Clinical Decision Support For Diabetes Management Using Real-time Data

PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project to develop a personalised clinical decision support system for Type 1 diabetes self-management. The tool provides insulin bolus dose advice and carbohydrate recommendations, tailored to the needs of individuals. The former is determined by Case-Based Reasoning (CBR), an artificial intelligence technique that adapts to new situations according to past experience. The latter uses a predictive computer model that also promotes safety by providing glucose alarms, low-glucose insulin suspension and fault detection

Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability

Background and objective: Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters. Although their clinical benefit has been demonstrated, their utilisation has not been widespread and their performance remains suboptimal, mainly because of their lack of flexibility and adaptability. One of the difficulties that people with diabetes, clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements. In this work, we propose a novel method that aims to automatically adjust the parameters of a bolus calculator. Periodic usage of a continuous glucose monitoring device is required for this purpose. Methods: To test the proposed method, an in silico evaluation under real-life conditions was carried out using the FDA-accepted Type 1 diabetes mellitus (T1DM) UVa/Padova simulator. Since the T1DM simulator does not incorporate intra-subject variability and uncertainty, a set of modifications were introduced to emulate them. Ten adult and ten adolescent virtual subjects were assessed over a 3-month scenario with realistic meal variability. The glycaemic metrics: mean blood glucose; percentage time in target; percentage time in hypoglycaemia; risk index, low blood glucose index; and blood glucose standard deviation, were employed for evaluation purposes. A t-test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment. Results: The proposed method statistically improved (p < 0.05) all glycemic metrics evaluating hypoglycaemia on both virtual cohorts: percentage time in hypoglycaemia (i.e. BG <70 mg/dl) (adults: 2.7 +/- 4.0 vs. 0.4 +/- 0.7, p=0.03; adolescents: 7.1 +/- 7.4 vs. 1.3 +/- 2.4, p=0.02) and low blood glucose index (LBGI) (adults: 1.1 +/- 1.3 vs. 0.3 +/- 0.2, p=0.002; adolescents: 2.0 +/- 2.19 vs. 0.7 +/- 1.4, p = 0.05). A statistically significant improvement was also observed on the blood glucose standard deviation (BG SD mg/dL) (adults: 33.5 +/- 13.7 vs. 29.2 +/- 8.3, p = 0.01; adolescents: 63.7 +/- 22.7 vs. 44.9 +/- 23.9, p = 0.01). Apart from a small increase in mean blood glucose on the adult cohort (129.9 +/- 11.9 vs. 133.9 +/- 11.6, p = 0.03), the rest of the evaluated metrics, despite showing an improvement trend, did not experience a statistically significant change. Conclusions: A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management.This work has been financially supported by the Wellcome Trust and by the National Institute for Health Research (NIHR) comprehensive biomedical research centres (BRCs) in England. J. Bondia acknowledges Genera Etat Valenciana for the mobility grant BEST/2014/037 at Centre for Bio-inspired Technology, Imperial College London.Herrero, P.; Pesl, P.; Bondía Company, J.; Reddy, M.; Oliver, N.; Georgiou, P.; Toumazou, C. (2015). Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability. Computer Methods and Programs in Biomedicine. 119(1):1-8. doi:10.1016/j.cmpb.2015.02.003S18119