An Actor-Critic based controller for glucose regulation in type 1 diabetes

A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of ±25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.

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.

A Physiological Controller for Turbodynamic Ventricular Assist Devices Based on a Measurement of the Left Ventricular Volume

The current article presents a novel physiological control algorithm for ventricular assist devices (VADs), which is inspired by the preload recruitable stroke work. This controller adapts the hydraulic power output of the VAD to the end-diastolic volume of the left ventricle. We tested this controller on a hybrid mock circulation where the left ventricular volume (LVV) is known, i.e., the problem of measuring the LVV is not addressed in the current article. Experiments were conducted to compare the response of the controller with the physiological and with the pathological circulation, with and without VAD support. A sensitivity analysis was performed to analyze the influence of the controller parameters and the influence of the quality of the LVV signal on the performance of the control algorithm. The results show that the controller induces a response similar to the physiological circulation and effectively prevents over- and underpumping, i.e., ventricular suction and backflow from the aorta to the left ventricle, respectively. The same results are obtained in the case of a disturbed LVV signal. The results presented in the current article motivate the development of a robust, long-term stable sensor to measure the LVV.

All cause and disease specific mortality in patients with knee or hip osteoarthritis: population based cohort study

Objective To examine all cause and disease specific mortality in patients with osteoarthritis of the knee or hip. Design Population based cohort study. Setting General practices in the southwest of England. Participants 1163 patients aged 35 years or over with symptoms and radiological confirmation of osteoarthritis of the knee or hip. Main outcome measures Age and sex standardised mortality ratios and multivariable hazard ratios of death after a median of 14 years’ follow-up. Results Patients with osteoarthritis had excess all cause mortality compared with the general population (standardised mortality ratio 1.55, 95% confidence interval 1.41 to 1.70). Excess mortality was observed for all disease specific causes of death but was particularly pronounced for cardiovascular (standardised mortality ratio 1.71, 1.49 to 1.98) and dementia associated mortality (1.99, 1.22 to 3.25). Mortality increased with increasing age (P for trend <0.001), male sex (adjusted hazard ratio 1.59, 1.30 to 1.96), self reported history of diabetes (1.95, 1.31 to 2.90), cancer (2.28, 1.50 to 3.47), cardiovascular disease (1.38, 1.12 to 1.71), and walking disability (1.48, 1.17 to 1.86). However, little evidence existed for increased mortality associated with previous joint replacement, obesity, depression, chronic inflammatory disease, eye disease, or presence of pain at baseline. The more severe the walking disability, the higher was the risk of death (P for trend <0.001). Conclusion Patients with osteoarthritis are at higher risk of death compared with the general population. History of diabetes, cancer, or cardiovascular disease and the presence of walking disability are major risk factors. Management of patients with osteoarthritis and walking disability should focus on effective treatment of cardiovascular risk factors and comorbidities, as well as on increasing physical activity.

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.

Model-based control of neuromuscular block using mivacurium: design and clinical verification

BACKGROUND: Short-acting agents for neuromuscular block (NMB) require frequent dosing adjustments for individual patient's needs. In this study, we verified a new closed-loop controller for mivacurium dosing in clinical trials. METHODS: Fifteen patients were studied. T1% measured with electromyography was used as input signal for the model-based controller. After induction of propofol/opiate anaesthesia, stabilization of baseline electromyography signal was awaited and a bolus of 0.3 mg kg-1 mivacurium was then administered to facilitate endotracheal intubation. Closed-loop infusion was started thereafter, targeting a neuromuscular block of 90%. Setpoint deviation, the number of manual interventions and surgeon's complaints were recorded. Drug use and its variability between and within patients were evaluated. RESULTS: Median time of closed-loop control for the 11 patients included in the data processing was 135 [89-336] min (median [range]). Four patients had to be excluded because of sensor problems. Mean absolute deviation from setpoint was 1.8 +/- 0.9 T1%. Neither manual interventions nor complaints from the surgeons were recorded. Mean necessary mivacurium infusion rate was 7.0 +/- 2.2 microg kg-1 min-1. Intrapatient variability of mean infusion rates over 30-min interval showed high differences up to a factor of 1.8 between highest and lowest requirement in the same patient. CONCLUSIONS: Neuromuscular block can precisely be controlled with mivacurium using our model-based controller. The amount of mivacurium needed to maintain T1% at defined constant levels differed largely between and within patients. Closed-loop control seems therefore advantageous to automatically maintain neuromuscular block at constant levels.

Determinants of reduced walking speed in people with musculoskeletal pain

OBJECTIVE: Maintenance of good walking speed is essential to independent living. People with musculoskeletal disease often have reduced walking speed. We investigated determinants of slower walking, other than musculoskeletal disease, that might provide valuable additional targets for therapy. METHODS: We analyzed data from the Somerset and Avon Survey of Health, a community based survey of people aged over 35 years. A total of 2703 participants who reported hip or knee pain at baseline (1994/1995) were studied, and reassessed in 2002-2003; 1696 were available for followup, and walking speed was tested in 1074. Walking speed (m/s) was used as outcome measure. Baseline characteristics, including comorbidities and socioeconomic factors, were tested for their ability to predict reduced walking speed using multiple linear regression analysis. RESULTS: Age, female sex, and immobility at baseline were predictive of slower walking speed. Other independent risk factors included the presence of cataract, low socioeconomic status, intermittent claudication, and other cardiovascular conditions. Having a cataract was associated with a decrease of 0.10 m/s (95% CI 0.03, 0.16). Those in social class V had a walking speed 0.22 m/s (95% CI 0.126, 0.31) slower than those in social class I. CONCLUSION: Comorbidities, age, female sex, and lower socioeconomic position determine walking speed in people with joint pain. Issues such as poor vision and social-economic disadvantage may add to the effect of musculoskeletal disease, suggesting the need for a holistic approach to management of these patients.

The effects of impingement and dysplasia on stress distributions in the hip joint during sitting and walking: a finite element analysis

Soft tissue damage has been observed in hip joints with pathological geometries. Our primary goal was to study the relationship between morphological variations of the bony components of the hip and resultant stresses within the soft tissues of the joint during routine daily activities. The secondary goal was to find the range of morphological parameters in which stresses are minimized. Computational models of normal and pathological joints were developed based on variations of morphological parameters of the femoral head (Alpha angle) and acetabulum (CE angle). The Alpha angle was varied between 40 degrees (normal joint) and 80 degrees (cam joint). The CE angle was varied between 0 degrees (dysplastic joint) and 40 degrees (pincer joint). Dynamic loads and motions for walking and standing to sitting were applied to all joint configurations. Contact pressures and stresses were calculated and crosscompared to evaluate the influence of morphology. The stresses in the soft tissues depended strongly on the head and acetabular geometry. For the dysplastic joint, walking produced high acetabular rim stresses. Conversely, for impinging joints, standing-to-sitting activities that involved extensive motion were critical, inducing excessive distortion and shearing of the tissue-bone interface. Zones with high von Mises stresses corresponded with clinically observed damage zones in the acetabular cartilage and labrum. Hip joint morphological parameters that minimized were 20 degrees

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.