An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

Closed-loop control of mean arterial blood pressure during surgery with alfentanil: clinical evaluation of a novel model-based predictive controller

BACKGROUND: In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. METHODS: The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). RESULTS: Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. CONCLUSION: The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

Robust meta-analytic-predictive priors in clinical trials with historical control information.

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Variability of lung function predicts loss of asthma control following withdrawal of inhaled corticosteroid treatment

BACKGROUND One aspect of a multidimensional approach to understanding asthma as a complex dynamic disease is to study how lung function varies with time. Variability measures of lung function have been shown to predict response to beta(2)-agonist treatment. An investigation was conducted to determine whether mean, coefficient of variation (CV) or autocorrelation, a measure of short-term memory, of peak expiratory flow (PEF) could predict loss of asthma control following withdrawal of regular inhaled corticosteroid (ICS) treatment, using data from a previous study. METHODS 87 adult patients with mild to moderate asthma who had been taking ICS at a constant dose for at least 6 months were monitored for 2-4 weeks. ICS was then withdrawn and monitoring continued until loss of control occurred as per predefined criteria. Twice-daily PEF was recorded during monitoring. Associations between loss of control and mean, CV and autocorrelation of morning PEF within 2 weeks pre- and post-ICS withdrawal were assessed using Cox regression analysis. Predictive utility was assessed using receiver operator characteristics. RESULTS 53 out of 87 patients had sufficient PEF data over the required analysis period. The mean (389 vs 370 l/min, p<0.0001) and CV (4.5% vs 5.6%, p=0.007) but not autocorrelation of PEF changed significantly from prewithdrawal to postwithdrawal in subjects who subsequently lost control, and were unaltered in those who did not. These changes were related to time to loss of control. CV was the most consistent predictor, with similar sensitivity and sensitivity to exhaled nitric oxide. CONCLUSION A simple, easy to obtain variability measure of daily lung function such as the CV may predict loss of asthma control within the first 2 weeks of ICS withdrawal.

Surgery for low-grade glioma infiltrating the central cerebral region: location as a predictive factor for neurological deficit, epileptological outcome, and quality of life

OBJECT A main concern with regard to surgery for low-grade glioma (LGG, WHO Grade II) is maintenance of the patient's functional integrity. This concern is particularly relevant for gliomas in the central region, where damage can have grave repercussions. The authors evaluated postsurgical outcomes with regard to neurological deficits, seizures, and quality of life. METHODS Outcomes were compared for 33 patients with central LGG (central cohort) and a control cohort of 31 patients with frontal LGG (frontal cohort), all of whom had had medically intractable seizures before undergoing surgery with mapping while awake. All surgeries were performed in the period from February 2007 through April 2010 at the same institution. RESULTS For the central cohort, the median extent of resection was 92% (range 80%-97%), and for the frontal cohort, the median extent of resection was 93% (range 83%-98%; p = 1.0). Although the rate of mild neurological deficits was similar for both groups, seizure freedom (Engel Class I) was achieved for only 4 (12.1%) of 33 patients in the central cohort compared with 26 (83.9%) of 31 patients in the frontal cohort (p < 0.0001). The rate of return to work was lower for patients in the central cohort (4 [12.1%] of 33) than for the patients in the frontal cohort (28 [90.3%] of 31; p < 0.0001). CONCLUSIONS Resection of central LGG is feasible and safe when appropriate intraoperative mapping is used. However, seizure control for these patients remains poor, a finding that contrasts markedly with seizure control for patients in the frontal cohort and with that reported in the literature. For patients with central LGG, poor seizure control ultimately determines quality of life because most will not be able to return to work.

The additive and interactive roles of aggression, prosocial behavior, and social preference in predicting resource control in young children

Distinguishing between physical and social aggression, this study examined whether the predictive effect of aggression on resource control a) is moderated by prosocial behavior and b) corresponds to a linear or a curvilinear trend. Moderating effects of children’s social preference among peers and child sex in this context were also tested. Based on a sample of 682 kindergarten children (348 girls; average age 72.7 months, 3.6 SD), multilevel regressions revealed additive linear effects of social preference and prosociality on resource control. Moderate (but not high) levels of social aggression also facilitated resource control for disliked children. There was no such threshold effect for well liked children, who increasingly controlled the resource the more socially aggressive they were. In contrast, physical aggression hampered resource control unless used very modestly. The present study has a number of positive features. First, the distinction between physical and social aggression improves our understanding of the relation between aggression and social competence and sketches a more differentiated picture of the role of different forms of aggression in resource control. Second, this study combines the concept of resource control with the concept of social preference and investigates curvilinear effects of aggression. Third, the direct observation of resource control in the Movie Viewer increases the internal validity of this study.

Impact of development of job control and task-related stressors on resigned job attitude.

With more experience in the labor market, some job characteristics increase, some decrease. For example, among young employees who just entered the labor market, job control may initially be low but increase with more routine and experience. Job control is a job resource that is valued in itself and is positively associated with job satisfaction; but job control also helps dealing with stressors at work. There is little research on correlated changes, but the existing evidence suggests a joint development over time. However, even less is known about the relevance of such changes for employees. Usually, research tends to use mean levels to predict mean levels in outcomes but development in job control and stressors may be as relevant for job satisfaction as having a certain level in those job characteristics. Job satisfaction typically is regarded as a positive attitude towards one’s work. What has received less attention is that some employees may lower their expectations if their job situation does not reflect their needs, resulting in a resigned attitude towards one’s job. The present study investigates the development of job control and task-related stressors over ten years and tests the predictive value of changes in job control and task-related stressors for resigned attitude towards one’s job. We used data from a Swiss panel study (N=356) ranging over ten years. Job control, task-related stressors (an index consisting of time pressure, concentration demands, performance constraints, interruptions, and uncertainty about tasks), and resigned attitude towards one’s job were assessed in 1998, 1999, 2001, and 2008. Latent growth modeling revealed that growth rates of job control and task-related stressors were not correlated with one another. We predicted resigned attitude towards one’s job in 2008 a) by initial levels, and b) by changes in job control and stressors, controlling for resigned attitude in 1998. There was some prediction by initial levels (job control: β = -.15, p < .05; task-related stressors: β = .12, p = .06). However, as expected, changes in control and stressors predicted resigned attitude much better, with β = -.37, p < .001, for changes in job control, and β = .31, p < .001, for changes in task-related stressors. Our data confirm the importance of having low levels of task-related stressors and higher levels of job control for job attitudes. However, development in these job characteristics seems even more important than initial levels.

Lake-effect snow as the dominant control of mesic-forest distribution in Michigan, USA

1Recent studies demonstrated the sensitivity of northern forest ecosystems to changes in the amount and duration of snow cover at annual to decadal time scales. However, the consequences of snowfall variability remain uncertain for ecological variables operating at longer time scales, especially the distributions of forest communities. 2The Great Lakes region of North America offers a unique setting to examine the long-term effects of variable snowfall on forest communities. Lake-effect snow produces a three-fold gradient in annual snowfall over tens of kilometres, and dramatic edaphic variations occur among landform types resulting from Quaternary glaciations. We tested the hypothesis that these factors interact to control the distributions of mesic (dominated by Acer saccharum, Tsuga canadensis and Fagus grandifolia) and xeric forests (dominated by Pinus and Quercus spp.) in northern Lower Michigan. 3We compiled pre-European-settlement vegetation data and overlaid these data with records of climate, water balance and soil, onto Landtype Association polygons in a geographical information system. We then used multivariate adaptive regression splines to model the abundance of mesic vegetation in relation to environmental controls. 4Snowfall is the most predictive among five variables retained by our model, and it affects model performance 29% more than soil texture, the second most important variable. The abundance of mesic trees is high on fine-textured soils regardless of snowfall, but it increases with snowfall on coarse-textured substrates. Lake-effect snowfall also determines the species composition within mesic forests. The weighted importance of A. saccharum is significantly greater than of T. canadensis or F. grandifolia within the lake-effect snowbelt, whereas T. canadensis is more plentiful outside the snowbelt. These patterns are probably driven by the influence of snowfall on soil moisture, nutrient availability and fire return intervals. 5Our results imply that a key factor dictating the spatio-temporal patterns of forest communities in the vast region around the Great Lakes is how the lake-effect snowfall regime responds to global change. Snowfall reductions will probably cause a major decrease in the abundance of ecologically and economically important species, such as A. saccharum.