Showing papers by "Andrea De Gaetano published in 2016"
TL;DR: Surgery is more effective than medical treatment in achieving diabetes remission and tighter glycemic control, and shorter diabetes duration, lower fasting glycemia before surgery, and GD versus GO procedures independently predict higher rates of remission, whereas baseline HbA1c and waist circumference predict improved gly glucose control.
Abstract: OBJECTIVE Eligibility criteria for bariatric surgery in diabetes include BMI ≥35 kg/m2 and poorly controlled glycemia. However, BMI does not predict diabetes remission, and thus, predictors need to be identified.
RESEARCH DESIGN AND METHODS Seven hundred twenty-seven patients were included in a database merged from the Swedish Obese Subjects (SOS) study and two randomized controlled studies, with 415 surgical and 312 medical patients in total. Bariatric operations were divided into gastric only (GO) and gastric plus diversion (GD).
RESULTS Sixty-four percent of patients in the surgical arm and 15.0% in the medical arm experienced diabetes remission ( P < 0.001). GO yielded 60% remission, and GD yielded 76% remission. The best predictors of diabetes remission were lower baseline glycemia and shorter diabetes duration. However, when operation type was considered, GD predicted a higher likelihood of remission and greater weight loss. Patients in remission (responders) lost more weight (25% vs. 17%) and waist circumference (18% vs. 13%) and experienced better insulin sensitivity than nonresponders.
CONCLUSIONS Surgery is more effective than medical treatment in achieving diabetes remission and tighter glycemic control. Shorter diabetes duration, lower fasting glycemia before surgery, and GD versus GO procedures independently predict higher rates of remission, whereas baseline HbA1c and waist circumference predict improved glycemic control. The results show the advantage of an early operation together with better controlled glycemia on diabetes remission independently of BMI.
145 citations
TL;DR: A simple stochastic process model is formulated, able to represent the irregular decrements of gastric contents after a meal, and describes well and explains the apparently heterogeneous GE experimental results in situations where common gastric mechanics across subjects would be expected.
Abstract: Several models of Gastric Emptying (GE) have been employed in the past to represent the rate of delivery of stomach contents to the duodenum and jejunum. These models have all used a deterministic form (algebraic equations or ordinary differential equations), considering GE as a continuous, smooth process in time. However, GE is known to occur as a sequence of spurts, irregular both in size and in timing. Hence, we formulate a simple stochastic process model, able to represent the irregular decrements of gastric contents after a meal. The model is calibrated on existing literature data and provides consistent predictions of the observed variability in the emptying trajectories. This approach may be useful in metabolic modeling, since it describes well and explains the apparently heterogeneous GE experimental results in situations where common gastric mechanics across subjects would be expected.
23 citations
01 Jan 2016
TL;DR: This note focuses on a recently developed research line that makes use of a state observer to estimate insulin in real-time from glucose measurements, since it is known that insulin measurements are slower and more cumbersome to obtain, more expensive and also less accurate.
Abstract: To achieve accurate and affordable predictions of glucose and insulin plasma concentrations is of paramount importance, especially in the field of the artificial pancreas, where real-time measurements could be properly exploited in model-based glucose control algorithms. This note focuses on a recently developed research line that makes use of a state observer to estimate insulin in real-time from glucose measurements, since it is known that insulin measurements are slower and more cumbersome to obtain, more expensive and also less accurate. Based on these predictions, glucose control algorithms can be designed and can be exploited for both intravenous and subcutaneous insulin infusions. The safety, robustness, and efficacy of the observer-based control algorithms have been validated on a population of rather heterogenous virtual patients, modeled by a different, comprehensive model of the glucose–insulin system, recently accepted by the Food and Drug Administration as a substitute of animal trials.
5 citations
TL;DR: A glycemia-structured population model, based on a linear partial differential equation with variable coefficients, that summarizes the evolution of diabetes in the population in a completely different way with respect to previously published Monte Carlo aggregations of individual-based models appears to offer a good approximation of observed reality and of the features expected in the clinical setting.
Abstract: Structured models are population models in which the individuals are characterized with respect to the value of some variable of interest, called the structure variable. In the present paper, we propose a glycemia-structured population model, based on a linear partial differential equation with variable coefficients. The model is characterized by three rate functions: a new-adult population glycemic profile, a glycemia-dependent mortality rate and a glycemia-dependent average worsening rate. First, we formally analyze some properties of the solution, the transient behavior and the equilibrium distribution. Then, we identify the key parameters and functions of the model from real-life data and we hypothesize some plausible modifications of the rate functions to obtain a more beneficial steady-state behavior. The interest of the model is that, while it summarizes the evolution of diabetes in the population in a completely different way with respect to previously published Monte Carlo aggregations of individual-based models, it does appear to offer a good approximation of observed reality and of the features expected in the clinical setting. The model can offer insights in pharmaceutical research and be used to assess possible public health intervention strategies.
4 citations
16 Nov 2016
TL;DR: The IMPRESS Platform tools that have been used for tests and validation purposes in the case of Palermo Demo are presented and the outcomes and results from each tool in the platform will be described and the added value for the users who worked with them.
Abstract: In our paper we will present the IMPRESS Platform tools that have been used for tests and validation purposes in the case of Palermo Demo. The demo has been part of a national Italian field test exercise for civil protection authorities and all related services along. The system validation and tests procedures have been fulfilled in alignment with the authorities remarks. The outcomes and results from each tool in the platform will be described and the added value for the users who worked with them.
2 citations
TL;DR: An integrated platform for the in-silico simulation of trauma incidents, based on a suite of interacting mathematical models, which highlights the potential of model-based simulation in different health-related fields, such as emergency medicine and personal health systems.
Abstract: Mathematical modeling and simulation with medical applications has gained much interest in the last few years, mainly due to the widespread availability of low-cost technology and computational power. This paper presents an integrated platform for the in-silico simulation of trauma incidents, based on a suite of interacting mathematical models. The models cover the generation of a scenario for an incident, a model of physiological evolution of the affected individuals, including the possible effect of the treatment, and a model of evolution in time of the required medical resources. The problem of optimal resource allocation is also investigated. Model parameters have been identified according to the expertise of medical doctors and by reviewing some related literature. The models have been implemented and exposed as web services, while some software clients have been built for the purpose of testing. Due to its extendability, our integrated platform highlights the potential of model-based simulation in different health-related fields, such as emergency medicine and personal health systems. Modifications of the models are already being used in the context of two funded projects, aiming at evaluating the response of health systems to major incidents with and without model-based decision support.
01 Oct 2016
TL;DR: This paper addresses the problem of glucose control in the presence of sampled measurements by assuming the lack of insulin measurements, but also the availability of the glucose measurements just at sampling times and proposes a feedback algorithm to continuously deliver exogenous intra-venous insulin to the patient.
Abstract: This paper addresses the problem of glucose control in the presence of sampled measurements. This topic is important in the study and development of the Artificial Pancreas (AP), which is a general expression to describe a set of techniques for the control of the glucose behaviour by means of exogenous insulin administration in diabetic individuals. Differently from most of the approaches available in the literature, we not only assume the lack of insulin measurements, but also the availability of the glucose measurements just at sampling times. An observer is designed for the model-based reconstruction of glucose and insulin trajectories from the glucose samples. On top of that, a feedback algorithm (based on the estimated state) is designed to continuously deliver exogenous intra-venous insulin to the patient. Simulations have been performed in-silico on models of virtual patients identified from real data and in the presence of quantization errors. The preliminary results highlight the potential of the proposed approach.