Clinical evaluation of a decision support system for glucose infusion in hypoglycaemic clamp experiments.

AIM To provide a preliminary evaluation of the accuracy and safety of Gluclas decision support system suggestions in a hypoglycaemic clamp study. METHODS This analysis was performed using data from 32 participants (four groups with different glucose-insulin regulation: post Roux-en-Y gastric bypass with and without postprandial hypoglycaemia syndrome, postsleeve gastrectomy and non-operated controls) undergoing Gluclas-assisted hypoglycaemic clamps (target: 2.5 mmol/L for 20 minutes at 150 minutes after oral glucose ingestion). Gluclas provided glucose infusion rate suggestions upon manual entry of blood glucose values (every 5 minutes), which were either followed or overruled by investigators after critical review. Accuracy and safety were evaluated by mean absolute error (MAE), mean absolute percentage error (MAPE), average glucose level, coefficient of variation (CV) and minimal glucose level during the 20-minute hypoglycaemic period. RESULTS Investigators accepted 84% of suggestions, with a mean deviation of 30.33 mg/min. During the hypoglycaemic period, the MAE was 0.16 (0.12-0.24) (median [interquartile range]) mmol/L and the MAPE was 6.12% (4.80%-9.29%). CV was 4.90% (3.58%-7.27%), with 5% considered the threshold for sufficient quality. The minimal glucose level was 2.40 (2.30-2.50) mmol/L. CONCLUSIONS Gluclas achieved sufficiently high accuracy with minimal safety risks in a population with differences in glucose-insulin dynamics, underscoring its applicability to various patient groups

Counter-regulatory responses to postprandial hypoglycaemia in patients with post-bariatric hypoglycaemia vs surgical and non-surgical control individuals

Aims/hypothesis Post-bariatric hypoglycaemia is an increasingly recognised complication of bariatric surgery, manifesting particularly after Roux-en-Y gastric bypass. While hyperinsulinaemia is an established pathophysiological feature, the role of counter-regulation remains unclear. We aimed to assess counter-regulatory hormones and glucose fluxes during insulin-induced postprandial hypoglycaemia in patients with post-bariatric hypoglycaemia after Roux-en-Y gastric bypass vs surgical and non-surgical control individuals. Methods In this case–control study, 32 adults belonging to four groups with comparable age, sex and BMI (patients with post-bariatric hypoglycaemia, Roux-en-Y gastric bypass, sleeve gastrectomy and non-surgical control individuals) underwent a postprandial hypoglycaemic clamp in our clinical research unit to reach the glycaemic target of 2.5 mmol/l 150–170 min after ingesting 15 g of glucose. Glucose fluxes were assessed during the postprandial and hypoglycaemic period using a dual-tracer approach. The primary outcome was the incremental AUC of glucagon during hypoglycaemia. Catecholamines, cortisol, growth hormone, pancreatic polypeptide and endogenous glucose production were also analysed during hypoglycaemia. Results The rate of glucose appearance after oral administration, as well as the rates of total glucose appearance and glucose disappearance, were higher in both Roux-en-Y gastric bypass groups vs the non-surgical control group in the early postprandial period (all p<0.05). During hypoglycaemia, glucagon exposure was significantly lower in all surgical groups vs the non-surgical control group (all p<0.01). Pancreatic polypeptide levels were significantly lower in patients with post-bariatric hypoglycaemia vs the non-surgical control group (median [IQR]: 24.7 [10.9, 38.7] pmol/l vs 238.7 [186.3, 288.9] pmol/l) (p=0.005). Other hormonal responses to hypoglycaemia and endogenous glucose production did not significantly differ between the groups. Conclusions/interpretation The glucagon response to insulin-induced postprandial hypoglycaemia is lower in post-bariatric surgery individuals compared with non-surgical control individuals, irrespective of the surgical modality. No significant differences were found between patients with post-bariatric hypoglycaemia and surgical control individuals, suggesting that impaired counter-regulation is not a root cause of post-bariatric hypoglycaemia

Closed-loop control strategies for improved human-device interaction in medical devices

A seconda del livello di automazione di un dispositivo medico, l'utilizzatore può essere significativamente coinvolto nella catena di controllo. L'operatore può fornire al controllore le informazioni necessarie ad aggiornare le dinamiche del suo modello o ad innescare azioni correttive, può attuare queste azioni correttive e confermare o sovrascrivere le decisioni del controllore. I sistemi che richiedono di interagire con un operatore sono solitamente definiti "human-in-the-loop''. Tuttavia, gli umani non sono delle macchine. Il loro coinvolgimento nella catena di controllo può gravare sulla qualità della loro vita e deteriorare l'accuratezza del controllore, considerato che errori umani sono pressoché inevitabili. Poiché le interazioni tra uomo e dispositivi medici sono sempre più frequenti nel campo biomedico, cresce la necessità di sviluppare delle soluzioni metodologiche per ridurre errori e costi umani. L'obiettivo di questa tesi di dottorato è quello di sviluppare delle strategie di controllo closed-loop avanzate per una migliore interazione tra uomo e macchina, in diversi dispositivi medici. Questo lavoro investiga quattro applicazioni biomedicali, spaziando tra diversi sistemi biologici, diversi ruoli dell'umano nella catena di controllo e diverse sfide legate all'interazione tra uomo e dispositivo.Depending on the level of automation of a biomedical device, users may be significantly involved in the control loop. Humans may provide the information necessary to the controller to adapt model dynamics or to trigger corrective actions, they may actuate these corrective actions manually, and confirm or override the control strategy. Systems requiring user interaction are usually referred to as "human-in-the-loop''. Nevertheless, humans are not machines. Involving humans in the loop can affect the quality of the control -- as human errors are unavoidable -- and represent a major burden in their lifestyle. As human-device interactions become more and more common in the biomedical field, the necessity for methodological solutions to reduce human errors and costs also arises. The aim of this Ph.D. thesis is to develop advanced closed-loop control strategies for an improved human-device interaction in various medical devices. Four biomedical case studies will be investigated to encompass different biological systems, different roles of humans in the loop and different challenges related to human-device interaction

Empirical Bayes Regression Models: an application to British river peak flows

non

Multi-Input Multi-Output Model Predictive Control for Drugs Delivery in Clinical Anesthesia

Automatic drugs delivery during anesthesia can lead to faster sedation, increase patient’s safety, and reduce the burden on anesthesiologists. This preliminary study proposes a Model Predictive Control (MPC) algorithm to control the induction and maintenance of the states of hypnosis, analgesia and neuromuscular blockade by means of automatic infusion of three drugs: Propofol, Remifentanil, and Atracurium. The study is conducted in a simulated environment based on a non- linear model describing the patient physiology that includes synergic effects among drugs and interpatient variability, as well as measurement noise and a variable surgical stimulation disturbance. Simulations on 24 virtual subjects show fast and safe induction of anesthesia. In terms of median [5th , 95th ] percentiles, the MPC algorithm maintained hypnosis in a suitable range for 98.9 [93.7, 99.3]% of the time, analgesia for 100 [65.4, 100]% of the time, and neuromuscular blockade for 100 [100, 100]% of the time

Incorporating Sparse and Quantized Carbohydrates Suggestions in Model Predictive Control for Artificial Pancreas in Type 1 Diabete

People with type 1 diabetes (T1D) face the challenge of administering exogenous insulin to maintain blood glucose (BG) levels in a safe physiological range, so as to avoid (possibly severe) complications. By automatizing insulin infusion, the artificial pancreas (AP) assists patients in this challenge. While insulin can decrease BG, having another input inducing glucose increase could further improve BG control. Here, we develop a model predictive control (MPC) algorithm that, in addition to insulin infusion, also provides suggestions of carbohydrates (CHOs) as a second, glucose-increasing, control input. Since CHO consumption has to be manually actuated, great care is paid in limiting the extra burden that may be caused to patients. By resorting to a mixed logical-dynamical MPC formulation, CHO intake is designed to be sparse in time and quantized. The algorithm is validated on the UVa/Padua T1D simulator, a well-established large-scale model of T1D metabolism, accepted by Food and Drug Administration (FDA). Compared with an insulin-only MPC, the new algorithm ensures increased time spent in the safe physiological range in 75% of patients. The improvement is limited for those already well controlled by the state-of-art strategy but relevant for the others: the 25th percentile of this metric is increased from 74.75% to 79.06% in the population. This is achieved while simultaneously decreasing time spent in hypoglycemia (from 0.5% to 0.12% in median) and with limited manual interventions (2.86 per day in median)

Gluclas: A software for computer-aided modulation of glucose infusion in glucose clamp experiments

Background and Objective: The glucose clamp (GC) is an experimental technique for assessing several aspects of glucose metabolism. In these experiments, investigators face the non-trivial challenge of accurately adjusting the rate of intravenous glucose infusion to drive subjects’ blood glucose (BG) concentration towards a desired plateau level. In this work we present Gluclas, an open-source software to support researchers in the modulation of glucose infusion rate (GIR) during GC experiments. Methods: Gluclas uses a proportional-integrative-derivative controller to provide GIR suggestions based on BG measurements. The controller embeds an anti-wind-up scheme to account for actuator physical limits and suitable corrections of control action to accommodate for possible sampling jitter due to manual measurement and actuation. The software also provides a graphic user interface to increase its usability. A preliminary validation of the controller is performed for different clamp scenarios (hyperglycemic, euglycemic, hypoglycemic) on a simulator of glucose metabolism in healthy subjects, which also includes models of measurement error and sampling delay for increased realism. In silico trials are performed on 50 virtual subjects. We also report the results of the first in-vivo application of the software in three subjects undergoing a hypoglycemic clamp. Results: In silico, during the plateau period, the coefficient of variation (CV) is in median below 5% for every protocol, with 5% being considered the threshold for sufficient quality. In terms of median [5th percentile, 95th percentile], average BG level during the plateau period is 12.18 [11.58 - 12.53] mmol/l in the hyperglycemic clamp (target: 12.4 mmol/), 4.92 [4.51 – 5.14] mmol/l in the euglycemic clamp (target: 5.5 mmol/) and 2.38 [2.33 – 2.64] in the hypoglycemic clamp (target: 2.5 mmol/). Results in vivo are consistent with those obtained in silico during the plateau period: average BG levels are between 2.56 and 2.68 mmol/l (target: 2.5 mmol/l); CV is below 5% for all three experiments. Conclusions: Gluclas offered satisfactory control for tested GC protocols. Although its safety and efficacy need to be further validated in vivo, this preliminary validation suggest that Gluclas offers a reliable and non-expensive solution for reducing investigator bias and improving the quality of GC experiments

Psychological Impact of a Pandemic Widespread in Healthcare Workers: The Italian and Swiss Perspective Early After of {CoVid}-19 Outbreak

Background. We investigated the COVID19-related psychological impact in healthcare workers three weeks after its onset in Italy and in Italian-speaking regions of Switzerland. All professional groups of public hospitals in Italy and Switzerland were asked to complete a 38 questions online survey investigating demographic, marital and working status, presence of stress symptoms and need for psychological support. Results. Within 38 hours a total of 3,038 responses were collected. The subgroup analysis identified specific categories at risk according to age, type of work and region of origin. Critical care workers, in particular females, reported an increased number of working hours, decline in confidence in the future, presence of stress symptoms and need for psychological support. People reporting stress symptoms and those with children declared a higher need for psychological support. Conclusions. The large number of participants in such a short time advocates for a high interest on topic among hospital workers. The COVID19 outbreak could have been and still be a repeated trauma for many health professionals, with risk of future psychiatric sequelae. It is of outstanding importance to implement short and long-term measures to mitigate impact of the emotional burden of this pandemic while at the same time dealing with its clinical challenges