Respiratory bi-directional pressure and flow data collection device with thoracic and abdominal circumferential monitoring.

Non-invasive pressure and flow data from Venturi-based sensors can be used with validated models to identify patient-specific lung mechanics. To validate applied respiratory models a secondary measurement is required. Rotary encoder-based tape measures were designed to capture change in circumference of a subject's thorax and diaphragm. Circumferential changes can be correlated to measured or modelled change in lung volume and associated muscular recruitment measures (patient work of breathing). Hence, these simple measurement devices can expedite respiratory research, by adding low-cost, accessible, and clinically useful measurements.

CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring.

Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.

Facial Emotion Recognition Focused on Descriptive Region Segmentation.

Facial emotion recognition (FER) is useful in many different applications and could offer significant benefit as part of feedback systems to train children with Autism Spectrum Disorder (ASD) who struggle to recognize facial expressions and emotions. This project explores the potential of real time FER based on the use of local regions of interest combined with a machine learning approach. Histogram of Oriented Gradients (HOG) was implemented for feature extraction, along with 3 different classifiers, 2 based on k-Nearest Neighbor and 1 using Support Vector Machine (SVM) classification. Model performance was compared using accuracy of randomly selected validation sets after training on random training sets of the Oulu-CASIA database. Image classes were distributed evenly, and accuracies of up to 98.44% were observed with small variation depending on data distributions. The region selection methodology provided a compromise between accuracy and number of extracted features, and validated the hypothesis a focus on smaller informative regions performs just as well as the entire image.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.

BACKGROUND: Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). METHODS: Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. RESULTS: The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). CONCLUSION: The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

Proof of concept non-invasive estimation of peripheral venous oxygen saturation.

BACKGROUND: Pulse oximeters continuously monitor arterial oxygen saturation. Continuous monitoring of venous oxygen saturation (SvO2) would enable real-time assessment of tissue oxygen extraction (O2E) and perfusion changes leading to improved diagnosis of clinical conditions, such as sepsis. METHODS: This study presents the proof of concept of a novel pulse oximeter method that utilises the compliance difference between arteries and veins to induce artificial respiration-like modulations to the peripheral vasculature. These modulations make the venous blood pulsatile, which are then detected by a pulse oximeter sensor. The resulting photoplethysmograph (PPG) signals from the pulse oximeter are processed and analysed to develop a calibration model to estimate regional venous oxygen saturation (SpvO2), in parallel to arterial oxygen saturation estimation (SpaO2). A clinical study with healthy adult volunteers (n = 8) was conducted to assess peripheral SvO2 using this pulse oximeter method. A range of physiologically realistic SvO2 values were induced using arm lift and vascular occlusion tests. Gold standard, arterial and venous blood gas measurements were used as reference measurements. Modulation ratios related to arterial and venous systems were determined using a frequency domain analysis of the PPG signals. RESULTS: A strong, linear correlation (r 2  = 0.95) was found between estimated venous modulation ratio (RVen) and measured SvO2, providing a calibration curve relating measured RVen to venous oxygen saturation. There is a significant difference in gradient between the SpvO2 estimation model (SpvO2 = 111 - 40.6*R) and the empirical SpaO2 estimation model (SpaO2 = 110 - 25*R), which yields the expected arterial-venous differences. Median venous and arterial oxygen saturation accuracies of paired measurements between pulse oximeter estimated and gold standard measurements were 0.29 and 0.65%, respectively, showing good accuracy of the pulse oximeter system. CONCLUSIONS: The main outcome of this study is the proof of concept validation of a novel pulse oximeter sensor and calibration model to assess peripheral SvO2, and thus O2E, using the method used in this study. Further validation, improvement, and application of this model can aid in clinical diagnosis of microcirculation failures due to alterations in oxygen extraction.

Model-based computation of total stressed blood volume from a preload reduction manoeuvre.

Total stressed blood volume is an important parameter for both doctors and engineers. From a medical point of view, it has been associated with the success or failure of fluid therapy, a primary treatment to manage acute circulatory failure. From an engineering point of view, it dictates the cardiovascular system's behavior in changing physiological situations. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a more straightforward method is developed using data from a preload reduction manoeuvre. A simple six-chamber cardiovascular system model is used and its parameters are adjusted to pig experimental data. The parameter adjustment process has three steps: (1) compute nominal values for all model parameters; (2) determine the five most sensitive parameters; and (3) adjust only these five parameters. Stressed blood volume was selected by the algorithm, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal root mean squared error of 29.2%. Computed stressed blood volume equals 486 ± 117 ml or 15.7 ± 3.6 ml/kg, which matches previous independent experiments on pigs, dogs and humans. The method proposed in this work thus provides a simple way to compute total stressed blood volume from usual hemodynamic data.

Impact of Haemodialysis on Insulin Kinetics of Acute Kidney Injury Patients in Critical Care.

Critically ill patients are occasionally associated with an abrupt decline in renal function secondary to their primary diagnosis. The effect and impact of haemodialysis (HD) on insulin kinetics and endogenous insulin secretion in critically ill patients remains unclear. This study investigates the insulin kinetics of patients with severe acute kidney injury (AKI) who required HD treatment and glycaemic control (GC). Evidence shows that tight GC benefits the onset and progression of renal involvement in precocious phases of diabetic nephropathy for type 2 diabetes. The main objective of GC is to reduce hyperglycaemia while determining insulin sensitivity. Insulin sensitivity (SI ) is defined as the body response to the effects of insulin by lowering blood glucose levels. Particularly, this study used SI to track changes in insulin levels during HD therapy. Model-based insulin sensitivity profiles were identified for 51 critically ill patients with severe AKI on specialized relative insulin nutrition titration GC during intervals on HD (OFF/ON) and after HD (ON/OFF). The metabolic effects of HD were observed through changes in SI over the ON/OFF and OFF/ON transitions. Changes in model-based SI at the OFF/ON and ON/OFF transitions indicate changes in endogenous insulin secretion and/or changes in effective insulin clearance. Patients exhibited a median reduction of -29 % (interquartile range (IQR): [-58, 6 %], p = 0.02) in measured SI after the OFF/ON dialysis transition, and a median increase of +9 % (IQR -15 to 28 %, p = 0.7) after the ON/OFF transition. Almost 90 % of patients exhibited decreased SI at the OFF/ON transition, and 55 % exhibited increased SI at the ON/OFF transition. Results indicate that HD commencement has a significant effect on insulin pharmacokinetics at a cohort and per-patient level. These changes in metabolic behaviour are most likely caused by changes in insulin clearance or/and endogenous insulin secretion.

Non-identifiability of the Rayleigh damping material model in magnetic resonance elastography.

Magnetic Resonance Elastography (MRE) is an emerging imaging modality for quantifying soft tissue elasticity deduced from displacement measurements within the tissue obtained by phase sensitive Magnetic Resonance Imaging (MRI) techniques. MRE has potential to detect a range of pathologies, diseases and cancer formations, especially tumors. The mechanical model commonly used in MRE is linear viscoelasticity (VE). An alternative Rayleigh damping (RD) model for soft tissue attenuation is used with a subspace-based nonlinear inversion (SNLI) algorithm to reconstruct viscoelastic properties, energy attenuation mechanisms and concomitant damping behavior of the tissue-simulating phantoms. This research performs a thorough evaluation of the RD model in MRE focusing on unique identification of RD parameters, µI and ρI. Results show the non-identifiability of the RD model at a single input frequency based on a structural analysis with a series of supporting experimental phantom results. The estimated real shear modulus values (µR) were substantially correct in characterising various material types and correlated well with the expected stiffness contrast of the physical phantoms. However, estimated RD parameters displayed consistent poor reconstruction accuracy leading to unpredictable trends in parameter behaviour. To overcome this issue, two alternative approaches were developed: (1) simultaneous multi-frequency inversion; and (2) parametric-based reconstruction. Overall, the RD model estimates the real shear shear modulus (µR) well, but identifying damping parameters (µI and ρI) is not possible without an alternative approach.

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients.

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ≥ 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

Dynamic functional residual capacity can be estimated using a stress-strain approach.

BACKGROUND: Acute Respiratory Distress Syndrome (ARDS) results in collapse of alveolar units and loss of lung volume at the end of expiration. Mechanical ventilation is used to treat patients with ARDS or Acute Lung Injury (ALI), with the end objective being to increase the dynamic functional residual capacity (dFRC), and thus increasing overall functional residual capacity (FRC). Simple methods to estimate dFRC at a given positive end expiratory pressure (PEEP) level in patients with ARDS/ALI currently does not exist. Current viable methods are time-consuming and relatively invasive. METHODS: Previous studies have found a constant linear relationship between the global stress and strain in the lung independent of lung condition. This study utilizes the constant stress-strain ratio and an individual patient's volume responsiveness to PEEP to estimate dFRC at any level of PEEP. The estimation model identifies two global parameters to estimate a patient specific dFRC, ß and mß. The parameter ß captures physiological parameters of FRC, lung and respiratory elastance and varies depending on the PEEP level used, and mß is the gradient of ß vs. PEEP. RESULTS: dFRC was estimated at different PEEP values and compared to the measured dFRC using retrospective data from 12 different patients with different levels of lung injury. The median percentage error is 18% (IQR: 6.49) for PEEP=5 cmH2O, 10% (IQR: 9.18) for PEEP=7 cmH2O, 28% (IQR: 12.33) for PEEP=10 cmH2O, 3% (IQR: 2.10) for PEEP=12 cmH2O and 10% (IQR: 9.11) for PEEP=15 cmH2O. The results were further validated using a cross-correlation (N=100,000). Linear regression between the estimated and measured dFRC with a median R² of 0.948 (IQR: 0.915, 0.968; 90% CI: 0.814, 0.984) over the N=100,000 cross-validation tests. CONCLUSIONS: The results suggest that a model based approach to estimating dFRC may be viable in a clinical scenario without any interruption to ventilation and can thus provide an alternative to measuring dFRC by disconnecting the patient from the ventilator or by using advanced ventilators. The overall results provide a means of estimating dFRC at any PEEP levels. Although reasonable clinical accuracy is limited to the linear region of the static PV curve, the model can evaluate the impact of changes in PEEP or other mechanical ventilation settings.

Overview of glycemic control in critical care: relating performance and clinical results.

BACKGROUND: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. METHODS: The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoctitration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95(th) percentile (+/-2sigma) standard error (SE(95%)) to enable better comparison across cohorts. RESULTS: Model-based control trials lower blood glucose into a 72-110 mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE(95%) shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. SUMMARY: Model-based methods provide tighter, more adaptable one method fits all solutions, using methods that enable patient-specific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to the current one size fits all sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or one method fits all control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient.