Stefania Guerra

Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: Preliminary Studies in Padova and Montpellier

New effort has been made to develop closed-loop glucose control, using subcutaneous (SC) glucose sensing and continuous subcutaneous insulin infusion (CSII) from a pump, and a control algorithm. An approach based on a model predictive control (MPC) algorithm has been utilized during closed-loop control in type 1 diabetes patients. Here we describe the preliminary clinical experience with this approach. In Padova, two out of three subjects showed better performance with the closed-loop system compared to open loop. Altogether, mean overnight plasma glucose (PG) levels were 134 versus 111 mg/dl during open loop versus closed loop, respectively. The percentage of time spent at PG > 140 mg/dl was 45% versus 12%, while postbreakfast mean PG was 165 versus 156 mg/dl during open loop versus closed loop, respectively. Also, in Montpellier, two patients out of three showed a better glucose control during closed-loop trials. Avoidance of nocturnal hypoglycemic excursions was a clear benefit during algorithm-guided insulin delivery in all cases. This preliminary set of studies demonstrates that closed-loop control based entirely on SC glucose sensing and insulin delivery is feasible and can be applied to improve glucose control in patients with type 1 diabetes, although the algorithm needs to be further improved to achieve better glycemic control. Six type 1 diabetes patients (three in each of two clinical investigation centers in Padova and Montpellier), using CSII, aged 36 ± 8 and 48 ± 6 years, duration of diabetes 12 ± 8 and 29 ± 4 years, hemoglobin A1c 7.4% ± 0.1% and 7.3% ± 0.3%, body mass index 23.2 ± 0.3 and 28.4 ± 2.2 kg/m2, respectively, were studied on two occasions during 22 h overnight hospital admissions 2–4 weeks apart. A Freestyle Navigator® continuous glucose monitor and an OmniPod® insulin pump were applied in each trial. Admission 1 used open-loop control, while admission 2 employed closed-loop control using our MPC algorithm.

Real-Time Improvement of Continuous Glucose Monitoring Accuracy The smart sensor concept

OBJECTIVE Reliability of continuous glucose monitoring (CGM) sensors is key in several applications. In this work we demonstrate that real-time algorithms can render CGM sensors smarter by reducing their uncertainty and inaccuracy and improving their ability to alert for hypo- and hyperglycemic events. RESEARCH DESIGN AND METHODS The smart CGM (sCGM) sensor concept consists of a commercial CGM sensor whose output enters three software modules, able to work in real time, for denoising, enhancement, and prediction. These three software modules were recently presented in the CGM literature, and here we apply them to the Dexcom SEVEN Plus continuous glucose monitor. We assessed the performance of the sCGM on data collected in two trials, each containing 12 patients with type 1 diabetes. RESULTS The denoising module improves the smoothness of the CGM time series by an average of ∼57%, the enhancement module reduces the mean absolute relative difference from 15.1 to 10.3%, increases by 12.6% the pairs of values falling in the A-zone of the Clarke error grid, and finally, the prediction module forecasts hypo- and hyperglycemic events an average of 14 min ahead of time. CONCLUSIONS We have introduced and implemented the sCGM sensor concept. Analysis of data from 24 patients demonstrates that incorporation of suitable real-time signal processing algorithms for denoising, enhancement, and prediction can significantly improve the performance of CGM applications. This can be of great clinical impact for hypo- and hyperglycemic alert generation as well in artificial pancreas devices.

Enhancing the Accuracy of Subcutaneous Glucose Sensors: A Real-Time Deconvolution-Based Approach

Minimally invasive continuous glucose monitoring (CGM) sensors can greatly help diabetes management. Most of these sensors consist of a needle electrode, placed in the subcutaneous tissue, which measures an electrical current exploiting the glucose-oxidase principle. This current is then transformed to glucose levels after calibrating the sensor on the basis of one, or more, self-monitoring blood glucose (SMBG) samples. In this study, we design and test a real-time signal-enhancement module that, cascaded to the CGM device, improves the quality of its output by a proper postprocessing of the CGM signal. In fact, CGM sensors measure glucose in the interstitium rather than in the blood compartment. We show that this distortion can be compensated by means of a regularized deconvolution procedure relying on a linear regression model that can be updated whenever a pair of suitably sampled SMBG references is collected. Tests performed both on simulated and real data demonstrate a significant accuracy improvement of the CGM signal. Simulation studies also demonstrate the robustness of the method against departures from nominal conditions, such as temporal misplacement of the SMBG samples and uncertainty in the blood-to-interstitium glucose kinetic model. Thanks to its online capabilities, the proposed signal-enhancement algorithm can be used to improve the performance of CGM-based real-time systems such as the hypo/hyper glycemic alert generators or the artificial pancreas.

A Dynamic Risk Measure from Continuous Glucose Monitoring Data

BACKGROUND The quantitative analysis of glucose time-series can greatly help the management of diabetes. In particular, a static nonlinear transformation, which symmetrizes the distribution of glucose levels by bringing them in the so-called risk space, was proposed previously for both self-monitoring blood glucose and continuous glucose monitoring (CGM) and extensively used in the literature. The continuous nature of CGM data allows us to further refine the risk space concept in order to account for glucose dynamics. METHODS A new dynamic risk (DR) is proposed to explicitly consider the rate of change of glucose as a threat factor for the patient (e.g., risk levels in hypoglycemia and hyperglycemia are amplified in the presence of a decreasing and increasing glucose trend, respectively). The practical calculation of DR is made possible by the use of a regularized deconvolution algorithm that is able to deal with noise in CGM data and with the ill-conditioning of the time-derivative calculation, even in online applications. RESULTS Results on simulated and real data show that DR can be effectively computed and fruitfully used in real time (e.g., to generate early warnings of hypo-/hyperglycemic threshold crossings). Further applications of DR in the quantification of the efficiency of glucose control are also suggested. CONCLUSIONS Exploiting the information on glucose trends empowers the strength of risk measures in interpreting CGM time-series.

Real-time continuous glucose monitoring in adults with type 1 diabetes and impaired hypoglycaemia awareness or severe hypoglycaemia treated with multiple daily insulin injections (HypoDE): a multicentre, randomised controlled trial

BACKGROUND The effectiveness of real-time continuous glucose monitoring (rtCGM) in avoidance of hypoglycaemia among high-risk individuals with type 1 diabetes treated with multiple daily insulin injections (MDI) is unknown. We aimed to ascertain whether the incidence and severity of hypoglycaemia can be reduced through use of rtCGM in these individuals. METHODS The HypoDE study was a 6-month, multicentre, open-label, parallel, randomised controlled trial done at 12 diabetes practices in Germany. Eligible participants had type 1 diabetes and a history of impaired hypoglycaemia awareness or severe hypoglycaemia during the previous year. All participants wore a masked rtCGM system for 28 days and were then randomly assigned to 26 weeks of unmasked rtCGM (Dexcom G5 Mobile system) or to the control group (continuing with self-monitoring of blood glucose). Block randomisation with 1:1 allocation was done centrally, with the study site as the stratifying variable. Masking of participants and study sites was not possible. Control participants wore a masked rtCGM system during the follow-up phase (weeks 22-26). The primary outcome was the baseline-adjusted number of hypoglycaemic events (defined as glucose ≤3·0 mmol/L for ≥20 min) during the follow-up phase. The full dataset analysis comprised participants who wore the rtCGM system during the baseline and follow-up phases. The intention-to-treat analysis comprised all randomised participants. This trial is registered with ClinicalTrials.gov, number NCT02671968. FINDINGS Between March 4, 2016, and Jan 12, 2017, 149 participants were randomly assigned (n=74 to the control group; n=75 to the rtCGM group) and 141 completed the follow-up phase (n=66 in the control group, n=75 in the rtCGM group). The mean number of hypoglycaemic events per 28 days among participants in the rtCGM group was reduced from 10·8 (SD 10·0) to 3·5 (4·7); reductions among control participants were negligible (from 14·4 [12·4] to 13·7 [11·6]). Incidence of hypoglycaemic events decreased by 72% for participants in the rtCGM group (incidence rate ratio 0·28 [95% CI 0·20-0·39], p<0·0001). 18 serious adverse events were reported: seven in the control group, ten in the rtCGM group, and one before randomisation. No event was considered to be related to the investigational device. INTERPRETATION Usage of rtCGM reduced the number of hypoglycaemic events in individuals with type 1 diabetes treated by MDI and with impaired hypoglycaemia awareness or severe hypoglycaemia. FUNDING Dexcom Inc.

Hemodynamics assessed via approximate entropy analysis of impedance cardiography time series: effect of metabolic syndrome.

The metabolic syndrome (MS), a predisposing condition for cardiovascular disease, presents disturbances in hemodynamics; impedance cardiography (ICG) can assess these alterations. In subjects with MS, the morphology of the pulses present in the ICG time series is more irregular/complex than in normal subjects. Therefore, the aim of the present study was to quantitatively assess the complexity of ICG times series in 53 patients, with or without MS, through a nonlinear analysis algorithm, the approximate entropy, a method employed in recent years for the study of several biological signals, which provides a scalar index, ApEn. We correlated ApEn computed from ICG times series data during fasting and postprandial phase with the presence of alterations in the parameters defining MS [Adult Treatment Panel (ATP) III (Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C; National Heart, Lung, and Blood Institute; American Heart Association. Circulation 109: 433-438, 2004) and the International Diabetes Federation (IDF) definition]. Results show that ApEn was significantly higher in subjects with MS compared with those without (1.81 ± 0.09 vs. 1.65 ± 0.13; means ± SD; P = 0.0013, with ATP III definition; 1.82 ± 0.09 vs. 1.67 ± 0.12; P = 0.00006, with the IDF definition). We also demonstrated that ApEn increase parallels the number of components of MS. ApEn was then correlated to each MS component: mean ApEn values of subjects belonging to the first and fourth quartiles of the distribution of MS parameters were statistically different for all parameters but HDL cholesterol. No difference was observed between ApEn values evaluated in fasting and postprandial states. In conclusion, we identified that MS is characterized by an increased complexity of ICG signals: this may have a prognostic relevance in subjects with this condition.

Impaired hemodynamic response to meal intake in insulin-resistant subjects: an impedance cardiography approach

BACKGROUND In the postprandial state, insulin regulates metabolic and cardiovascular responses. In insulin resistance, the insulin action is impaired at both levels. However, postprandial hemodynamic responses are poorly characterized in this setting. OBJECTIVE We investigated fasting and postprandial cardiac and vascular hemodynamic responses in subjects with and without insulin resistance. DESIGN Sixty-six atherosclerosis-free, healthy volunteers were studied in a fasted state and ≤180 min after ingestion of a mixed meal. The insulin sensitivity index was determined by using a minimal model analysis; hemodynamic response was monitored by using continuous impedance cardiography that allowed a reliable beat-to-beat noninvasive evaluation of stroke volume, cardiac contractility, and several derived variables. RESULTS Subjects were divided into insulin-resistant (IR; n = 33) and insulin-sensitive (IS; n = 33) groups. After fasting, IR subjects had significantly higher values of systolic and diastolic blood pressures and the systemic vascular resistance index (SVRI) than did IS subjects. In the postprandial state, acute vasodilatation was comparable and synchronous (at 30 min) in IR and IS subjects (P = 0.209), but subsequent vascular tone recovery (30-180 min) was significantly impaired in IR subjects (P = 0.018), even after adjustment for age and sex (P = 0.031). Hemodynamic dysregulation was directly correlated with metabolic disturbances in the postprandial state. In basal and postprandial states, hemodynamic variables related to cardiac function were not significantly different in IR and IS subjects. CONCLUSIONS IR subjects had a worse fasting vascular performance than did IS subjects. In the postprandial phase, insulin resistance was associated with a shorter duration of vasodilatation in the absence of an altered cardiac performance. Peripheral hemodynamic alterations in fasting and postprandial states may have a negative effect on cardiovascular performance in IR patients.