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Blood sampling typically involves puncturing a superficial vein with a needle and then creating a vacuum (e. g., with a syringe) to transfer the blood from the venous system to the collection container. In some cases, the venous walls collapse due to excessive vacuum generated. In this work, a device is presented that prevents venous collapse and can be combined with any blood collection system available on the market. The mechanism of preventing venous collapse is based on a tubular segment interconnected between the needle and the syringe, which collapses at a lower vacuum pressure than the vein. The prototype was tested and evaluated using a test rig that simulates venous blood draw. The experiments proved the functionality of the device on a vein phantom.

During laparoscopic procedures, the surgeon's view of the situs, and thus her or his performance, is dependent on the skills of the camera assistant. The surgeon lacks control over his own field of view and there is a high potential for reducing mental load and workflow issues. In this paper, a research setup of a 360° laparoscopic imaging system is presented and evaluated. The system consists of a 360° camera and a head-mounted display, through which the surgeon can inspect the situs. In a user test, the system showed advantages over a conventional laparoscope regarding orientation in the situs, intuitiveness of operation, and faster task completion.

Microscopy is an essential tool in research and science. However, it is relatively resource consuming regarding cost, time of usage, and consumable supplies. Current low-cost approaches provide good imaging quality but struggle in terms of versatility or applicability to varying setups. In this paper, a Compact Microscope Module for versatile application in custom-made setups or research projects is presented. As a first application and proof of concept, the use of the module in a High-Throughput Microscope for screening of samples in microtiter plates is shown. The Compact Microscope Module allows for simple and resource-efficient microscopy in various applications while still enabling relatively good imaging qualities.

Venepuncture is one of the most common invasive procedures performed worldwide, however, complications still occur. Currently, commercial single layer silicone phantoms used for venepuncture training do not accurately imitate the geometry and mechanical properties seen in the various patient groups. This paper presents the development of a realistic artificial venepuncture phantom. Three multilayered tissue phantoms are developed simulating venepuncture sites of paediatric, adult and geriatric patients. Silicone materials of different stiffnesses were selected to imitate the epidermis, dermis, subcutaneous fat, muscle and superficial veins. Singleaxis indentation tests were carried out on silicone samples and the multi-layered phantom inserts to characterize the material properties. The measured Young's moduli for the artificial dermis, fat and muscle show sufficient agreement with corresponding literature values. However, characterization of the complete phantom inserts showed stiffnesses four times larger than prior in-vivo studies. Future studies will work on developing a more comparable in-vivo study.

Many medical interventions in therapy and diagnostics require needle insertion into tissue. Common complications such as increased pain and formation of haematoma are caused by wrong needle positioning. It has been shown that pain experience and needle positioning can be improved by a reduction of insertion force, which can be achieved by vibrating the needle axially. An experimental setup has been designed to investigate the influences of different combinations of vibration frequencies (10, 100, and 200 Hz) and vibration amplitudes (20, 100, and 500 μm) during needle insertion into thin sheets of polyethylene terephthalate (PET). A customary 20 W loudspeaker was used to generate the vibration. The results indicate a maximum reduction of 73 % in puncture force and up to a 100 % reduction in shaft friction force. However, the additional vibration force generated by the vibration movement has to be high enough to generate positive effects in terms of force reduction.

Microscopy enables fast and effective diagnostics. However, in resource-limited regions microscopy is not accessible to everyone. Smartphone-based low-cost microscopes could be a powerful tool for diagnostic and educational purposes. In this paper, the imaging quality of a smartphone-based microscope with four different optical parameters is presented and a systematic overview of the resulting diagnostic applications is given. With the chosen configuration, aiming for a reasonable trade-off, an average resolution of 1.23 μm and a field of view of 1.12 mm2 was achieved. This enables a wide range of diagnostic applications such as the diagnosis of Malaria and other parasitic diseases.

Access to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.

Venepuncture is one of the most common invasive procedures in medical healthcare worldwide, however failure rates are still relatively high, particularly for paediatric, geriatric, darker skinned, and obese patients. Visualisation of the veins has been shown to decrease failure rates and can be achieved through trans-illumination, near infrared reflectance, or sonography. However, these techniques are either not reliable or very expensive, resulting in them not being commonly used in the clinical workplace. This paper develops a proof of concept low-cost LED vein detection device using photocurrent generated by LEDs in reverse bias. The prototype uses two emitting LEDs and one detecting LED to identify the location of a vein via trans-illuminance and reflectance. Various light intensities and wavelengths of the LEDs are tested in regard to resolution, noise, and signal peaks. A balance between system noise and resolution is identified for each LED in relation to the emitted intensity. No significant difference was observed in relative peak height when different wavelengths were used to identify the same superficial veins. The initial proof of concept proves the LED vein detection method and provides the foundation for further low-cost LED vein detection devices to be developed.

Upper limb absence has an impact on both physical and mental health of a human being. Nowadays, the costs of commercial, externally powered prosthetic hands range between 25.000 € and 70.000 €. A first demonstrator of a lightweight low-cost prosthetic hand is produced with 3D-printing technology (Fused Deposition Modeling). Due to integration of single-axis solid-state joints the five fingers can be printed in one piece. Thermoplastic polyurethane is used for this purpose. The flexion of all fingers is achieved by moving cords which are positioned on the palmar side of each finger. Two twisted string actuators are integrated to allow the movement of the thumb and the remaining four fingers. These actuators consist of two polyethylene strands which are twisted along their main axis by a DC motor, providing a tensile force to bend the fingers. In order to achieve simultaneous but differential actuation of the four fingers (small finger, ring finger, middle finger and index finger), a differential mechanism is used. The thumb is driven by a separate unit. In order to open the hand, the elasticity of the joints’ material is taken advantage of. With the developed mechanics it is possible to perform precision and cylindrical grasps with an opposed thumb. Weights up to 260 g can be held according to the shape and size of an object. To increase the adaption to different sizes and weights of objects, the design should be modified. It can be concluded, that the presented device is a promising basis for a lightweight, low-cost prosthetic hand in the future.

Venous blood circulation can be restricted due to various conditions commonly indicating a related medical condition. However, current non-invasive methods for determining venous blood flow are limited to be either very inaccurate or expensive. Alternatively, a method to measure sap flow non-invasively in trees is through thermal mass measurement principles. This paper investigates applying the thermal mass flow measurement principle to determine venous blood flow. A simplified finite element model (FEM) and simulation are created to determine the operating behavior and expected response of a thermal mass flow meter with venous blood flow under the skin. An initial prototype of a thermal mass venous blood flow meter is designed using a Peltier-element and RTD thermistors. Initial tests were done on N = 8 subjects identifying the presence of blood flow and, testing the devices basic functionality and performance. The simplified FEM model of venous blood flow proved the thermal mass blood flow device is feasible, and determined the initial characteristics of the first prototype. The initial prototype proved to be functional detecting rises in temperature downstream of +1.4 K (0.8 - 1.8 inter- quartile range) when the blood flow was released (t = 90 s after release), compared to when blood was not flowing. The initial prototype proved to be able to detect the presence of blood flow in all subjects. However, further work is required to increase the differences in temperature values or gradient measured for a change in flow rate so the actual flow rate can be determined.

Robotic assistance in surgical interventions has been shown to improve both the surgical outcome and the surgeon’s performance. However, cost-efficient implementation of such systems in an operating room workflow is not yet resolved. Most of the current approaches use highly specialized and purpose-built robotic components, resulting in large costs. This paper presents an investigation into the usage of off-the-shelf robots as cost-efficient basis for teleoperated surgery. In addition, the modifications required of the robot and/or operating environment to make it suitable for the medical setting are assessed. The considered systems are found via a literature search and the MERODA-database, specifically focusing on systems designed for teleoperated surgery which use industrial robots. Among the 126 telemanipulated systems found, only 10 use industrial robots for surgical procedures. Overall, due to the better availability, reduced costs, and increased performance, current industrial robots should be further considered for new and versatile lowcost approaches to teleoperated surgery.

In this chapter, the basic mechanical setup is described and its potential for human-machine interaction and how it can be leveraged by design and hardware integration is discussed.

During colonoscopy there is a risk that the intestinal wall may be injured or may pain occur by the insertion of an endoscope. Surgery through endoscopes must be learned by physicians through extensive training. To simplify the insertion of endoscopes, research is being carried out on robotic - aided systems. Here, a sensor is needed to detect the contour of the intestine in order to enable an injury - free and painless insertion of the endoscope. In this paper a tube - balloon is designed for a gentle contour tracking of the intestinal anatomy. This is inserted through the working channel of the endoscope and placed in the intestinal lumen in front of the endoscope's head in the field of view of the camera. A Matlab - algorithm is used to detect the balloon in each image. The balloon appears as a two - dimensional circle, which can be detected using a Hough -Transformation. The displacement of balloon after touching the intestine wall is calculated as a vector between the circle's center and the image center. This ensures that the robot - aided endoscope can follow the intestinal contour.

Intravenous needle insertion is typically conducted manually, with needles guided into vessels by feel while looking for a brief flash of blood. This process is imprecise and leads to mispositioned needles, multiple reinsertion attempts, increased procedure time and higher costs for the hospital. We present a method for indicating that the needle has reached the vein by measuring the change in mechanical impedance of the needle as it passes through different tissue layers. Testing in a phantom indicated that this has the potential to identify transitions through tissue boundaries.

Background and Objective Hyperglycaemia is commonplace in the adult intensive care unit (ICU), and has been associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a model-based GC protocol that uniquely maintains normoglycaemia by changing both insulin and nutrition interventions, and has been proven effective in controlling blood glucose (BG) in the ICU. However, most ICU GC protocols only change insulin interventions, making the variable nutrition aspect of STAR less clinically desirable. This paper compares the performance of STAR modulating only insulin, with three simpler alternative nutrition protocols in clinically evaluated virtual trials. Methods Alternative nutrition protocols are fixed nutrition rate (100% caloric goal), CB (Cahill et al. best) stepped nutrition rate (60%, 80% and 100% caloric goal for the first 3 days of GC, and 100% thereafter) and SLQ (STAR lower quartile) stepped nutrition rate (65%, 75% and 85% caloric goal for the first 3 days of GC, and 85% thereafter). Each nutrition protocol is simulated with the STAR insulin protocol on a 221 patient virtual cohort, and GC performance, safety and total intervention workload are assessed. Results All alternative nutrition protocols considerably reduced total intervention workload (14.6–19.8%) due to reduced numbers of nutrition changes. However, only the stepped nutrition protocols achieved similar GC performance to the current variable nutrition protocol. Of the two stepped nutrition protocols, the SLQ nutrition protocol also improved GC safety, almost halving the number of severe hypoglycaemic cases (5 vs. 9, P = 0.42). Conclusions Overall, the SLQ nutrition protocol was the best alternative to the current variable nutrition protocol, but either stepped nutrition protocol could be adapted by STAR to reduce workload and make it more clinically acceptable, while maintaining its proven performance and safety.

The aging of humans induces muscle weakness. Muscle weakness results the inability to climb stairs or stand up from a chair and thus, is one of the first steps towards elderly having a less autonomous and self-sufficient life. Therefore, an accessible active knee orthosis, which especially supports these movements would be of great assistance. Recent research projects have focused on feasibility, perfection of motion control and imitation of human movement. For active knee orthoses to be accessible it must be affordable. Hence, this paper systematically evaluates the current research projects around active knee orthosis and discuss the possibilities of implementing a lowcost active knee orthosis.

Background Insulin therapy for glycaemic control (GC) in critically ill patients may improve outcomes by reducing hyperglycaemia and glycaemic variability, which are both associated with increased morbidity and mortality. However, initial positive results have proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted) is a model-based glycaemic control protocol using a risk-based dosing approach. STAR uses a 2D stochastic model to predict distributions of likely future changes in model-based insulin sensitivity (SI) based on its current value, and determines the optimal intervention. Objectives This study investigates the impact of a new 3D stochastic model on the ability to predict more accurate future SI distributions, which would allow more aggressive insulin dosing and improved glycaemic control. Methods The new 3D stochastic model is built using both current SI and its prior variation to predict future SI distribution from 68,629 h of clinical data (819 GC episodes). The 5th–95th percentile range of predicted SI distribution are calculated and compared with the 2D model. Results Results show the 2D model is over-conservative compared to the 3D case for more than 77% of the data, predominantly where SI is stable (|%ΔSI| ≤ 25%). These formerly conservative prediction ranges are now ∼30% narrower with the 3D model, which safely enables more aggressive insulin dosing for these patient hours. In addition, distributions of predicted SI within the 5th–95th percentile range are much closer to the ideal value of 90% for more patients with the 3D model. Conclusions The new 3D model better characterises patient specific metabolic variability and patient specific response to insulin, allowing more optimal insulin dosing to increase performance and safety.

Microscopy enables fast and effective diagnostics. However, its functionality is not accessible to everyone. Smartphone-based low-cost microscopes could be a powerful tool for diagnostics and educational purposes. Current smartphone-based microscopy approaches struggle with high cost, poor image quality and/or insufficient smartphone compatibility. In this paper, a very feasible and effective lowcost microscope is presented which addresses these issues. To minimize cost, a monolithic foldable structure is designed for production by injection molding. The design has a high order of functional integration, minimizing the number of components, while still enabling a micrometer focusing accuracy.

In the intensive care unit (ICU), stress-induced insulin resistance leading to hyperglycemia is commonplace. If safe and effective glycemic control (GC) can be provided, a significant reduction in the negative effects of dysglycemia can be achieved. The Intensive Control Insulin-Nutrition-Glucose (ICING) model has worked particularly well in guiding patient-specific GC. The current method to identify patient- and time- specific insulin sensitivity (SI) with this model employs a discrete stepwise hourly function, which is effective, but not physiologically representative at hourly transitions. 2nd Order B-spline basis functions (BF) are investigated as a more continuous and physiologically relevant alternative for two different cohorts of patient data (Benchmark, 20 patients; Stochastic TARgeted (STAR) sub-cohort, 72 patients). Various knot-widths (KW) of 2nd Order B-spline BFs were investigated and compared to the currently used step function BF in terms of physiological relevance, identifiability, robustness to false measurements, and susceptibility to noise to ensure the most physiologically realistic representation of SI. The 180 min KW 2nd Order B-spline BFs provided the most physiologically realistic fit to the blood glucose (BG) measurements in both cohorts (Standard Deviation of relative fitting error: Benchmark 8.7% vs. 9.35% BG meter, STAR sub-cohort 6.0% vs. 6.0% BG meter), while also having significantly less susceptibility to false BG measurements compared to the current BF. The 180 min 2nd Order B-spline BF provides a smooth second order continuous, more physiologically representative, and identifiable model of the dynamics of SI, and a more realistic resulting BG solution. The outcome provides a promising new addition to the ICING model and STAR GC protocol, while also opening up several new potential continuous time analyses of SI dynamics, not previously possible.

Background : Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia). Objective: This paper presents a model-based virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations. Methods: Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross simulation) were compared. Results: Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and cross-simulation results were found to have overall similarity and consistency, though results may differ in the first 24–48 h due to different cohort starting BG and underlying SI. Conclusions and Significance: Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.

In developing countries airborne micro-organisms are one of the most common causes of surgical wound infection. In the operating room low-turbulence displacementflow has been shown to reduce the influence of airborne bacteria. However, due to complexity and cost, current systems are not accessible to all potential users. In this paper a low-cost ventilation system is presented that provides lowturbulence displacement-flow over a surgical operating environment. A stand-alone, simple to assemble, mobile airflow system was developed using the principle of speed compensation for laminar airflow. From visual inspection and measurements, the system was seen to generate a continuous downwards displacement of air over the protected area around the situs. The entire system can be assembled quickly with minimal standard tools, making it very suitable for mobile use, such as in disaster areas. Future work is required to achieve the vertical dimension of a laminar flow field required by the DIN 1946-4. Overall, the low-cost and simple system could provide many potential users access to the technology and thus improving hygiene conditions in their operating environment.

Background: Glycaemic control (GC) of critical care patients with abnormal blood glucose (BG) level can reduce mortality and improve clinical outcomes. Model based GC protocol allows personalised and effective control of BG level of the patients. As a part of the protocol the patient’s state is predicted by a stochastic model. Improving accuracy of patient state prediction would enable to develop more effective model-based GC algorithms. Methods: The temporal behaviour of the metabolic system of intensive care patients under glycaemic control was analysed and three patient cohorts from three geographically distant hospitals were compared with each other. The three hospitals used the same glycaemic control protocol, but provided different treatment environment. The patients, based on the time function of their state changes - described by the insulin sensitivity parameter (SI(t)) - were classified and the distribution of the patients from different cohorts were examined. Results: In the study no major differences were found in the distribution of the geographically distinct patient cohorts. As the SI value describes the metabolic state of the patient this result suggests that the temporal pattern of the metabolic state changes is similar in each patient cohorts. The patient state descriptor parameter (SI) is identified by using a physiological model. The accuracy of the model and the temporal changes in the accuracy are also analysed by a similar classification methodology than the one used for patient state change classification. The classified time function was the modelling noise identified by a stochastic model. The patients from different hospitals were distributed evenly between the resulted classes, thus modelling accuracy is found to be similar in the three patient cohorts. These results confirms previous studies, however in the previous studies mainly statistical comparison were made rather than the comparison of the temporal pattern of the state descriptor parameters. The correlation between the patient state and the modelling accuracy based classification is also analysed by comparing the classes resulted in by the above described two studies. As high portion of the patients are classified into the same classes by the two classification study we can state that the temporal pattern of the state change correlates with the temporal pattern of the modelling error.

Introduction of the stochastic noise in the modelling of blood-glucose dynamics becoming more and more acceptable because of the high complexity of the physiological processes. The representation of the stochastic noise term in the phenomenological as well as in data-driven models until now limited to stationary Gaussian process. In this paper the statistical nature of the stochastic blood-glucose system model noise is investigated to prove or disprove this general assumption. To ensure the generalization of the noise term a Wiener process ( W ( t )) with time depending diffusion coefficient (σ( t ) W ( t )) was considered. This stochastic term was embedded into the phenomenological ICING ( Intensive Control Insulin-Nutrition-Glucose ) model and then σ( t ) was identified by using clinical measurements from nine patients. The mean value, the standard deviation, as well as the covariance of the slide distributions of the stochastic glucose trajectories generated by the Ito type process were investigated. We have found that this stochastic term is Gaussian process but not stationary. Our final goal is to find general representation of the stochastic noise term by a parametric time series processes.

This study investigates blood glucose (BG) measurement interpolation techniques to represent intermediate BG dynamics, and the effect resampling of retrospective BG data has on key glycemic control (GC) performance results. Many GC protocols in the ICU have varying BG measurement intervals with gaps ranging from 0.5 to 4 hrs. Sparse data poses problems in model fitting techniques and GC performance comparisons, and thus interpolation is required to assume a continuous solution. Retrospective data from SPRINT in the Christchurch Hospital Intensive Care Unit (ICU) (2005-2007) was used to analyze various interpolation techniques. Piece-wise linear, spline and cubic interpolation functions, which force lines through data, as well as 1st and 2nd Order B-spline basis functions, used to identify the data, are investigated. Dense data was thinned to increase sparsity and obtain measurements (Hidden measurements) for comparison after interpolation. All of the piece-wise functions performed considerably better than the fitted interpolation functions. Linear piece-wise interpolation performed the best having a mean RMSE 0.39 mmol/L, within 2 standard deviations of the BG sensor error. The effect of minutely vs hourly sampling of the interpolated trace on key GC performance statistics was investigated using the retrospective data received from STAR GC in the Christchurch Hospital Intensive Care Unit (ICU), New Zealand (2011-2015). Minutely sampled BG resulted in significantly different key GC performance when compared to raw sparse BG measurements. Linear piece-wise interpolation provides the best estimate of intermediate BG dynamics and all analyses comparing GC protocol performance should use minutely linearly interpolated BG data.

Elevated blood glucose (BG) concentrations (Hyperglycaemia) is a common complication in the adult intensive care unit (ICU), and is associated with increased morbidity and mortality. However, it has been shown that effective glycaemic control (GC) can reduce morbidity and mortality. STAR is a model-based GC protocol that uniquely maintains normal BG by changing both insulin and nutrition interventions, and has been proven to be effective in controlling BG levels in the ICU. However, most GC protocols in the ICU only change insulin interventions, making the variable feed aspect of STAR less clinically desirable. This paper investigates two simpler feeding protocols as an alternative. Fixed feed (100% calorific goal) and stepped feed (60%, 80% and 100% calorific goal for the first 3 days of GC, and then 100% thereafter) protocols, in conjunction with the STAR protocol, are simulated with clinically validated virtual trials on a 221 virtual patient cohort. The GC safety, performance and workload is compared for each of the different feeding protocols. The variable and stepped feeding protocol achieved very similar GC performance and safety, with a per-patient median time in the targeted 4.4-8.0 mmol/L BG range of 89.0% vs. 88.3% respectively and the number of patients BG

Critically ill patients exhibit a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia difficult. Clinical practices vary significantly, thus making glycemic control (GC) protocols difficult to generalize across units. This study examines the generalizability of a nonlinear, model-based GC protocol, STAR (Stochastic TARgeted), across two different intensive care units (ICUs) with very different practices and use of GC. Patient data from the ICUs at Christchurch Hospital, New Zealand (267 Patients) and Kalman Pandy Hospital, Hungary (47 Patients) are examined. Safety and performance are examined, where complete generalizability would be indicated by similar glycemic performance distributions using non-parametric statistics as appropriate. STAR spent over 86% of time in the target BG band of 4.4-8.0 mmol/L per-episode in both Christchurch and Gyula, with the BG (blood glucose) distributions being almost identical. STAR provided safe GC with very few patients experiencing mild hypoglycemia (

This paper develops a novel gray-box form of the ICING (Intensive Control Insulin-Nutrition-Glucose) model (Lin et al. (2011)) used both for glycemic control of Intensive Care patients and implementation of virtual trials. The computations of the system trajectories and their statistical features like mean value, standard deviation, and slice distribution were carried out using a stochastic Runge-Kutta method in the presence of Wiener-type diffusion process term. Parameter estimation of the resulting stochastic model is achieved via maximum likelihood technique. The global optimization problem was solved using genetic algorithms, simulated annealing and Nelder-Mead procedures. The parameter estimation has been carried out at different system noise levels, and the optimal parameters corresponding to the maximum of the likelihood function were selected. While the gray-box model yielded improvement, it was not significant according to the likelihood ratio test in the case of the examined model parameters. Further investigations including estimation of more parameters simultaneously, adding diffusion terms to more equations, would be necessary to yield a definitive improvement on this deterministic baseline model. The Mathematica code used is transparent and can be easily applied to develop other similar stochastic system models.

The accurate glycaemic control in Intensive Care Units (ICUs) is a critical issue for physicians as a result of stress-induced blood glucose variability and high risk of hypoglycaemia. The STAR (Stochastic TARgeted) glycaemic control protocol is a model-based protocol, providing reduced risk of hypoglycaemia and stabilized blood glucose level by modulating insulin and nutrition inputs. The STAR framework is flexible, it allows clinicians to specify the target blood glucose range and the targeted nutrition intake of the patients (i.e. goal feed) according to the hospital’s feeding regime and the actual need of the patient. Thus, the differences in the clinical application of STAR in different hospitals can effect the clinical outcomes and the average delivered amount of nutritions during the patient’s treatment. This paper aims to analyse these effects by comparing the treatment outcomes of patients treated in two different hospitals, one in Christchurch, New Zealand and one in Gyula, Hungary.

Critically ill patients often experience stress-induced hyperglycaemia, which results in increased morbidity and mortality. Glycaemic control (GC) can be implemented in the intensive care unit (ICU) to safely manage hyperglycaemia. Two protocols SPRINT and STAR, have been implemented in the Christchurch ICU, and have been successful in treating hyperglycaemia while decreasing the risk of hypoglycaemia. This paper presents a new GC protocol that implements the probabilistic, stochastic forecasting methods of STAR, while formalizing the control methodology using model predictive control (MPC) theory to improve the ability to tune the dynamic response of the controller. This Stochastic Model Predictive (STOMP) controller predicts the response to a given insulin/nutrition intervention, and attributes weighted penalty values to several key performance metrics. The controller thus chooses an intervention at each hour that minimizes the sum of these penalties over a prediction window of 6 h, which is twice as long as the 3-h window used in STAR. Clinically validated virtual trials were used to evaluate the relative performance of STOMP. Results showed STOMP was able to obtain results very similar to STAR with both protocols maintaining approximately 85% of time within 4.4–8.0 mmol/L glycaemic band, and only 4–5 patients of the 149 patient STAR cohort having blood glucose (BG)

Background Hyperglycaemia is commonplace in the adult intensive care unit (ICU), associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a proven, effective model-based ICU GC protocol that uniquely maintains normo-glycaemia by changing both insulin and nutrition interventions to maximise nutrition in the context of GC in the 4.4–8.0 mmol/L range. Hence, the level of nutrition it provides is a time-varying estimate of the patient-specific ability to take up glucose. Methods First, the clinical provision of nutrition by STAR in Christchurch Hospital, New Zealand (N = 221 Patients) is evaluated versus other ICUs, based on the Cahill et al. survey of 158 ICUs. Second, the inter- and intra- patient variation of nutrition delivery with STAR is analysed. Nutrition rates are in terms of percentage of caloric goal achieved. Results Mean nutrition rates clinically achieved by STAR were significantly higher than the mean and best ICU surveyed, for the first 3 days of ICU stay. There was large inter-patient variation in nutrition rates achieved per day, which reduced overtime as patient-specific metabolic state stabilised. Median intra-patient variation was 12.9%; however, the interquartile range of the mean per-patient nutrition rates achieved was 74.3–98.2%, suggesting patients do not deviate much from their mean patient-specific nutrition rate. Thus, the ability to tolerate glucose intake varies significantly between, rather than within, patients. Conclusions Overall, STAR’s protocol-driven changes in nutrition rate provide higher nutrition rates to hyperglycaemic patients than those of 158 ICUs from 20 countries. There is significant inter-patient variability between patients to tolerate and uptake glucose, where intra-patient variability over stay is much lower. Thus, a best nutrition rate is likely patient specific for patients requiring GC. More importantly, these overall outcomes show high nutrition delivery and safe, effective GC are not exclusive and that restricting nutrition for GC does not limit overall nutritional intake compared to other ICUs.

In this paper the performance of an insulin sensitivity profile estimation method is analysed from the the aspects of clinical applicability. In our previous studies it has been demonstrated that the grey box variant of the ICING (Intensive Control Insulin-Nutrition-Glucose) model may be successfully applied to improve the estimation accuracy of the insulin sensitivity profile of intensive care patients. This may enable the application of the model in the clinical treatment, especially in tight glycaemic control. The sensitivity estimation accuracy itself and the accuracy improvement compared to previous methods are highly variable, it strongly depends on the range of blood glucose concentration. In the present study the insulin sensitivity estimation is analysed from the clinically relevant aspects. Modelling error represented by the difference of measured and computed the blood glucose concentration was considered on the total glucose concentration range (measured in mmol/l) - 0 G G ≤ 4), normoglycaemic (4 G ≤ 6.5), mild-hyperglycaemic (6.5 G ≤ 10), and severe-hyperglycaemic (10 G ≤ 20). The results of the computations indicate that the SDE model was significantly better in the normoglycaemic and mild-hyperglycaemic ranges, somewhat better in the hypoglycaemic range and nearly the same in the severe-hyperglycaemic range. The 97 % of all of the concentration values were in the normoglycaemic and the mild-hyperglycaemic range (5841 values), which amplifies our statement in these ranges, but further study is necessary the ensure the verdict for the hypoglycaemic and severe-hyperglycaemic ranges.

In this paper the insulin sensitivity profile and the diffusion term as stepwise functions were determined for the grey box variant of the ICING (Intensive Control Insulin-Nutrition-Glucose) model used for virtual trial methodology. The suggested technique can separate system noise, which was earlier lumped into the insulin sensitivity profile itself. In this way one can get smaller residual for the model fitting as well as more precise value for the insulin sensitivity. This fact can improve the prediction process estimating insulin sensitivity value for the future time span and therefore it can ensure better clinical treatment. Maximum likelihood method has been employed to compute the values of the insulin sensitivity profile and the diffusion term for every half hour intervals. The global optimization problem was solved using genetic algorithm, simulated annealing method and Nelder-Mead procedure employing parallel computation. The application of the grey box model was able to reduce the errors sometimes with even more than 50%. According to the likelihood ratio test, the stochastic variant of the ICING model yielded significant improvement comparing to the white box one.

Background The changes in metabolic pathways and metabolites due to critical illness result in a highly complex and dynamic metabolic state, making safe, effective management of hyperglycemia and hypoglycemia difficult. In addition, clinical practices can vary significantly, thus making GC protocols difficult to generalize across units.The aim of this study was to provide a retrospective analysis of the safety, performance and workload of the stochastic targeted (STAR) glycemic control (GC) protocol to demonstrate that patient-specific, safe, effective GC is possible with the STAR protocol and that it is also generalizable across/over different units and clinical practices. Methods Retrospective analysis of STAR GC in the Christchurch Hospital Intensive Care Unit (ICU), New Zealand (267 patients), and the Gyula Hospital, Hungary (47 patients), is analyzed (2011–2015). STAR Christchurch (BG target 4.4–8.0 mmol/L) is also compared to the Specialized Relative Insulin and Nutrition Tables (SPRINT) protocol (BG target 4.4–6.1 mmol/L) implemented in the Christchurch Hospital ICU, New Zealand (292 patients, 2005–2007). Cohort mortality, effectiveness and safety of glycemic control and nutrition delivered are compared using nonparametric statistics. Results Both STAR implementations and SPRINT resulted in over 86 % of time per episode in the blood glucose (BG) band of 4.4–8.0 mmol/L. Patients treated using STAR in Christchurch ICU spent 36.7 % less time on protocol and were fed significantly more than those treated with SPRINT (73 vs. 86 % of caloric target). The results from STAR in both Christchurch and Gyula were very similar, with the BG distributions being almost identical. STAR provided safe GC with very few patients experiencing severe hypoglycemia (BG