Trial reportJournal of diabetes science and technology2013
Model-based closed-loop glucose control in type 1 diabetes: the DiaCon experience.
Trial report in Journal of diabetes science and technology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Identification of Main Factors Explaining Glucose Dynamics During and Immediately After Moderate Exercise in Patients With Type 1 Diabetes.Journal of diabetes science and technology · 2015Pooled it
- Sensitivity of the Predictive Hypoglycemia Minimizer System to the Algorithm Aggressiveness Factor.Journal of diabetes science and technology · 2015Trial
- Efficacy and safety of the artificial pancreas in the paediatric population with type 1 diabetes.Journal of translational medicine · 2018Review
- A Review of the Current Challenges Associated with the Development of an Artificial Pancreas by a Double Subcutaneous Approach.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2017Review
- An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes.Journal of diabetes science and technology · 2017Article
- Predicting Plasma Glucose From Interstitial Glucose Observations Using Bayesian Methods.Journal of diabetes science and technology · 2014Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo improve type 1 diabetes mellitus (T1DM) management, we developed a model predictive control (MPC) algorithm for closed-loop (CL) glucose control based on a linear second-order deterministic-stochastic model. The deterministic part of the model is specified by three patient-specific parameters: insulin sensitivity factor, insulin action time, and basal insulin infusion rate. The stochastic part is identical for all patients but identified from data from a single patient. Results of the first clinical feasibility test of the algorithm are presented.
methodsWe conducted two randomized crossover studies. Study 1 compared CL with open-loop (OL) control. Study 2 compared glucose control after CL initiation in the euglycemic (CL-Eu) and hyperglycemic (CL-Hyper) ranges, respectively. Patients were studied from 22:00-07:00 on two separate nights.
resultsEach study included six T1DM patients (hemoglobin A1c 7.2% ± 0.4%). In study 1, hypoglycemic events (plasma glucose < 54 mg/dl) occurred on two OL and one CL nights. Average glucose from 22:00-07:00 was 90 mg/dl [74-146 mg/dl; median (interquartile range)] during OL and 108 mg/dl (101-128 mg/dl) during CL (determined by continuous glucose monitoring). However, median time spent in the range 70-144 mg/dl was 67.9% (3.0-73.3%) during OL and 80.8% (70.5-89.7%) during CL. In study 2, there was one episode of hypoglycemia with plasma glucose <54 mg/dl in a CL-Eu night. Mean glucose from 22:00-07:00 and time spent in the range 70-144 mg/dl were 121 mg/dl (117-133 mg/dl) and 69.0% (30.7-77.9%) in CL-Eu and 149 mg/dl (140-193 mg/dl) and 48.2% (34.9-72.5%) in CL-Hyper, respectively.
conclusionsThis study suggests that our novel MPC algorithm can safely and effectively control glucose overnight, also when CL control is initiated during hyperglycemia.
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