Auto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization.

Several closed or hybrid loop controllers for Blood Glucose (BG) regulation, which are also known as Artificial Pancreas (AP) Systems or Automated Insulin Delivery systems (AIDs), are in development worldwide. Most AIDs are designed and evaluated for short-term performance, with a particular emphasis on the post-meal period. However, if controllers are not adapted properly to account for variations in physiology that affect Insulin Sensitivity (IS), the AIDs may perform inadequately. In this work, the performance of two Reinforcement Learning (RL) agents trained under both piecewise and continuous reward functions is evaluated in-silico for long-term adaptation of a Fully Automated Insulin Delivery (fAID) system. An automatic adaptive discretization scheme that expands the state space as needed is also implemented to avoid disproportionate state space exploration. The proposed agents are evaluated for long-term adaptation of the Automatic Regulation of Glucose (ARG) algorithm, considering variations in IS. Results show that both RL agents have improved performance compared to a rule-based decision-making approach and the baseline controller for the majority of the adult population. Moreover, the use of a continuous shaped reward function proves to enhance the performance of the agents further than a piecewise one.

First Outpatient Clinical Trial of a Full Closed-Loop Artificial Pancreas System in South America.

BACKGROUND: The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented. METHOD: Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165. RESULTS: The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL. CONCLUSION: The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.

Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas.

In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity ( SI) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM control-oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation.

Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps.

BACKGROUND AND OBJECTIVE: Although there has been significant progress towards closed-loop type 1 diabetes mellitus (T1DM) treatments, most diabetic patients still treat this metabolic disorder in an open-loop manner, based on insulin pump therapy (basal and bolus insulin infusion). This paper presents a method for automatic insulin bolus shaping based on insulin-on-board (IOB) as an alternative to conventional bolus dosing. METHODS: The methodology presented allows the pump to generate the so-called super-bolus (SB) employing a two-compartment IOB dynamic model. The extra amount of insulin to boost the bolus and the basal cutoff time are computed using the duration of insulin action (DIA). In this way, the pump automatically re-establishes basal insulin when IOB reaches its basal level. Thus, detrimental transients caused by manual or a-priori computations are avoided. RESULTS: The potential of this method is illustrated via in-silico trials over a 30 patients cohort in single meal and single day scenarios. In the first ones, improvements were found (standard treatment vs. automatic SB) both in percentage time in euglycemia (75g meal: 81.9 ±â€¯15.59 vs. 89.51 ±â€¯11.95, ρ ≃ 0; 100g meal: 75.12 ±â€¯18.23 vs. 85.46 ±â€¯14.96, ρ ≃ 0) and time in hypoglecymia (75g meal: 5.92 ±â€¯14.48 vs. 0.97 ±â€¯4.15, ρ=0.008; 100g meal: 9.5 ±â€¯17.02 vs. 1.85 ±â€¯7.05, ρ=0.014). In a single day scenario, considering intra-patient variability, the time in hypoglycemia was reduced (9.57 ±â€¯14.48 vs. 4.21 ±â€¯6.18, ρ=0.028) and improved the time in euglycemia (79.46 ±â€¯17.46 vs. 86.29 ±â€¯11.73, ρ=0.007). CONCLUSIONS: The automatic IOB-based SB has the potential of a better performance in comparison with the standard treatment, particularly for high glycemic index meals with high carbohydrate content. Both glucose excursion and time spent in hypoglycemia were reduced.

Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses.

BACKGROUND: Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial. METHODS: A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller. RESULTS: For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported. CONCLUSIONS: The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.

Reporte de altos requerimientos de insulina en pacientes críticos pediátricos con COVID-19. Experiencia con monitoreo remoto continuo de glucosa / Report of high insulin requirements in pediatric critically ill patients with COVID-19. Experience with continuous remote glucose monitoring

Los pacientes en estado crítico con COVID-19 sufren hiperglucemias sostenidas de difícil manejo. A esto se suma el desafío de minimizar la exposición al contagio. En el presente artículo analizamos la evolución metabólica de dos pacientes pediátricos con COVID-19 admitidos en unidad de cuidados intensivos (UCI) para pacientes COVID-19 del Hospital "Prof. Dr. Juan P. Garrahan" de la Ciudad Autónoma de Buenos Aires, Argentina, que requirieron tratamiento con insulina endovenosa y cuya glucemia fue monitoreada de manera remota con la plataforma InsuMate® desarrollada en la Universidad Nacional de La Plata. Los pacientes requirieron tasas de infusión de insulina en dosis marcadamente mayores que las de otros pacientes críticos que impresionaron relacionadas con los valores de marcadores de inflamación. La infusión pudo ajustarse con cuatro monitoreos diarios de glucosa y las métricas obtenidas con el monitor de glucosa. El uso del sistema de monitoreo remoto continuo de glucosa permitió disminuir la frecuencia de monitoreo glucémico durante el tratamiento.

Critically ill patients with COVID-19 suffer from sustained hyperglycemia that is difficult to manage. Added to this is the challenge of minimizing exposure to contagion. In this article we analyze the metabolic evolution of two pediatric patients with COVID-19 admitted to the intensive care unit (ICU) for COVID-19 patients at the Hospital "Prof. Dr. Juan P. Garrahan "from the Autonomous City of Buenos Aires, Argentina, who required treatment with intravenous insulin and whose blood glucose was remotely monitored with the InsuMate® platform developed at the National University of La Plata. The patients required insulin infusion rates in doses markedly higher than those of other critically ill patients, who were impressively related to the values of inflammation markers. The infusion could be adjusted with four daily glucose monitors and the metrics obtained with the glucose monitor. The use of the continuous remote glucose monitoring system made it possible to decrease the frequency of glycemic monitoring during treatment.