Background: In type 1 diabetes mellitus (T1DM), patients play an active role in their own care and need to have the knowledge to adapt decisions to their daily living conditions. Artificial intelligence applications can help people with type 1 diabetes in decision making and allow them to react at time scales shorter than the scheduled face-to-face visits. This work presents a decision support system (DSS), based on glucose prediction, to assist patients in a mobile environment. Methods: The system’s impact on therapeutic corrective actions has been evaluated in a randomized crossover pilot study focused on interprandial periods. Twelve people with type 1 diabetes treated with insulin pump participated in two phases: In the experimental phase (EP) patients used the DSS to modify initial corrective decisions in presence of hypoglycemia or hyperglycemia events. In the control phase (CP) patients were asked to follow decisions without knowing the glucose prediction. A telemedicine platform allowed participants to register monitoring data and decisions and allowed endocrinologists to supervise data at the hospital. The study period was defined as a postprediction (PP) time window. Results: After knowing the glucose prediction, participants modified the initial decision in 20% of the situations. No statistically significant differences were found in the PP Kovatchev’s risk index change (–1.23 ± 11.85 in EP vs –0.56 ± 6.06 in CP). Participants had a positive opinion about the DSS with an average score higher than 7 in a usability questionnaire. Conclusion: The DSS had a relevant impact in the participants’ decision making while dealing with T1DM and showed a high confidence of patients in the use of glucose prediction.

Objective: This study assessed the efficacy of a closed-loop (CL) system consisting of a predictive rule-based algorithm (pRBA) on achieving nocturnal and postprandial normoglycemia in patients with type 1 diabetes mellitus (T1DM). The algorithm is personalized for each patient's data using two different strategies to control nocturnal and postprandial periods. Research Design and Methods: We performed a randomized crossover clinical study in which 10 T1DM patients treated with continuous subcutaneous insulin infusion (CSII) spent two nonconsecutive nights in the research facility: one with their usual CSII pattern (open-loop [OL]) and one controlled by the pRBA (CL). The CL period lasted from 10 p.m. to 10 a.m., including overnight control, and control of breakfast. Venous samples for blood glucose (BG) measurement were collected every 20 min. Results: Time spent in normoglycemia (BG, 3.9–8.0 mmol/L) during the nocturnal period (12 a.m.–8 a.m.), expressed as median (interquartile range), increased from 66.6% (8.3–75%) with OL to 95.8% (73–100%) using the CL algorithm ( P <0.05). Median time in hypoglycemia (BG, <3.9 mmol/L) was reduced from 4.2% (0–21%) in the OL night to 0.0% (0.0–0.0%) in the CL night ( P <0.05). Nine hypoglycemic events (<3.9 mmol/L) were recorded with OL compared with one using CL. The postprandial glycemic excursion was not lower when the CL system was used in comparison with conventional preprandial bolus: time in target (3.9–10.0 mmol/L) 58.3% (29.1–87.5%) versus 50.0% (50–100%). Conclusions: A highly precise personalized pRBA obtains nocturnal normoglycemia, without significant hypoglycemia, in T1DM patients. There appears to be no clear benefit of CL over prandial bolus on the postprandial glycemia.

The use of telemedicine for diabetes care has evolved over time, proving that it contributes to patient self-monitoring, improves glycemic control, and provides analysis tools for decision support. The timely development of a safe and robust ambulatory artificial pancreas should rely on a telemedicine architecture complemented with automatic data analysis tools able to manage all the possible high-risk situations and to guarantee the patient's safety. The Intelligent Control Assistant system (INCA) telemedical artificial pancreas architecture is based on a mobile personal assistant integrated into a telemedicine system. The INCA supports four control strategies and implements an automatic data processing system for risk management (ADP-RM) providing short-term and medium-term risk analyses. The system validation comprises data from 10 type 1 pump-treated diabetic patients who participated in two randomized crossover studies, and it also includes in silico simulation and retrospective data analysis. The ADP-RM short-term risk analysis prevents hypoglycemic events by interrupting insulin infusion. The pump interruption has been implemented in silico and tested for a closed-loop simulation over 30 hours. For medium-term risk management, analysis of capillary blood glucose notified the physician with a total of 62 alarms during a clinical experiment (56% for hyperglycemic events). The ADP-RM system is able to filter anomalous continuous glucose records and to detect abnormal administration of insulin doses with the pump. Automatic data analysis procedures have been tested as an essential tool to achieve a safe ambulatory telemedical artificial pancreas, showing their ability to manage short-term and medium-term risk situations.

Background: The automation of glucose control has been an important goal of diabetes treatment for many decades. The first artificial pancreas experiences were in-hospital, closely supervised, small-scale, and short-term studies that demonstrated their superiority over continuous subcutaneous insulin infusion therapy. At present, long-term outpatient studies are being conducted in free-living scenarios. Areas of Uncertainty: The integration of multiple devices increases patients' burden and the probability of technical risks. Control algorithms must be robust to manage disturbance variables, such as physical exercise, meal composition, stress, illness, and circadian variations in insulin sensitivity. Extra layers of safety could be achieved through remote supervision. Dual-hormone systems reduce the incidence and duration of hypoglycemia, but the availability of stable pumpable glucagon needs to be solved. Faster insulin analogues are expected to improve all types of artificial pancreas. Therapeutic Advances: Artificial pancreas safety and feasibility are being demonstrated in outpatient studies. Artificial pancreas use increases the time of sensor-measured glucose in near-normoglycemia and reduces the risk of hyperglycemia and hypoglycemia. The benefits are observed both in single- and dual-hormone algorithms and in full- or semi-closed loop control. A recent meta-analysis including 41 randomized controlled trials showed that artificial pancreas use achieves a reduction of time in hyperglycemia (2 hours less than control treatment) and in hypoglycemia (20 minutes less); mean levels of continuous glucose sensor fell by 8.6 mg/dL over 24 hours and by 14.6 mg/dL overnight. The OpenAPS community uses Do It Yourself artificial pancreas in the real world since 2013, and a recent retrospective cross-over study (n = 20) compared continuous glucose sensor readings before and after initiation: mean levels of blood glucose fell by 7.4 mg/dL over 24 hours and time in range increased from 75.8% to 82.2% (92 minutes more). Conclusions: The outpatient use of artificial pancreas is safe and improves glucose control in outpatients with type 1 diabetes compared with the use of any type of insulin-based treatment. The availability of open-source solutions and data sharing is needed to foster the development of new artificial pancreas approaches and to promote the wide use of Big Data tools for knowledge discovery, decision support, and personalization.

Background: Closed-loop control algorithms in diabetes aim to calculate the optimum insulin delivery to maintain the patient in a normoglycemic state, taking the blood glucose level as the algorithm's main input. The major difficulties facing these algorithms when applied subcutaneously are insulin absorption time and delays in measurement of subcutaneous glucose with respect to the blood concentration. Methods: This article presents an inverse controller (IC) obtained by inversion of an existing mathematical model and validated with synthetic patients simulated with a different model and is compared with a proportional-integral-derivative controller. Results: Simulated results are presented for a mean patient and for a population of six simulated patients. The IC performance is analyzed for both full closed-loop and semiclosed-loop control. The IC is tested when initialized with the heuristic optimal gain, and it is compared with the performance when the initial gain is deviated from the optimal one (±10%). Conclusions: The simulation results show the viability of using an IC for closed-loop diabetes control. The IC is able to achieve normoglycemia over long periods of time when the optimal gain is used (63% for the full closed-loop control, and it is increased to 96% for the semiclosed-loop control).

We present a linear time-varying Luenberger observer (LTVLO) using compartmental models to estimate the unmeasurable states in patients with type 1 diabetes. The proposed LTVLO is based on the linearization in an operation point of the virtual patient (VP), where a linear time-varying system is obtained. LTVLO gains are obtained by the selection of the asymptotic eigenvalues where the observability matrix is assured. The estimation of the unmeasurable variables is done using Ackermann's methodology. The Lyapunov approach is used to prove the stability of the time-varying proposal. In order to evaluate the proposed methodology, we designed three experiments: 1) VP obtained with Bergman's minimal model; 2) VP obtained with Hovorka's model; and 3) real patient data set. For both experiments 1) and 2), it is applied a meal plan to the VP, where the dynamic response of each state model is compared with the response of each variable of the time-varying observer. Once the observer is obtained in experiment 2), the proposal is applied to experiment 3) with data extracted from real patients, and the unmeasurable state space variables are obtained with the LTVLO. LTVLO methodology has the feature of being updated each time instant to estimate the states under a known structure. The results are obtained using simulation with MATLAB and Simulink. The LTVLO estimates the unmeasurable states from in silico patients with high accuracy by means of the update of Luenberger gains at each iteration. The accuracy of the estimated state space variables is validated through thefit parameter.