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See detailNext-generation, personalised, model-based critical care medicine: A state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them
Chase, J. G.; Preiser, J.-C.; Dickson, J. L. et al

in BioMedical Engineering OnLine (2018), 17(1),

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to ... [more ▼]

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care. © 2018 The Author(s). [less ▲]

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See detailBeat-by-Beat Estimation of the Left Ventricular Pressure–Volume Loop Under Clinical Conditions
Davidson, S.; Pretty, C.; Kamoi, S. et al

in Annals of Biomedical Engineering (2018), 46(1), 171-185

This paper develops a method for the minimally invasive, beat-by-beat estimation of the left ventricular pressure–volume loop. This method estimates the left ventricular pressure and volume waveforms that ... [more ▼]

This paper develops a method for the minimally invasive, beat-by-beat estimation of the left ventricular pressure–volume loop. This method estimates the left ventricular pressure and volume waveforms that make up the pressure–volume loop using clinically available inputs supported by a short, baseline echocardiography reading. Validation was performed across 142,169 heartbeats of data from 11 Piétrain pigs subject to two distinct protocols encompassing sepsis, dobutamine administration and clinical interventions. The method effectively located pressure–volume loops, with low overall median errors in end-diastolic volume of 8.6%, end-systolic volume of 17.3%, systolic pressure of 19.4% and diastolic pressure of 6.5%. The method further demonstrated a low overall mean error of 23.2% predicting resulting stroke work, and high correlation coefficients along with a high percentage of trend compass ‘in band’ performance tracking changes in stroke work as patient condition varied. This set of results forms a body of evidence for the potential clinical utility of the method. While further validation in humans is required, the method has the potential to aid in clinical decision making across a range of clinical interventions and disease state disturbances by providing real-time, beat-to-beat, patient specific information at the intensive care unit bedside without requiring additional invasive instrumentation. © 2017, Biomedical Engineering Society. [less ▲]

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See detailAssessing mechanical ventilation asynchrony through iterative airway pressure reconstruction
Chiew, Y. S.; Tan, C. P.; Chase, J. G. et al

in Computer Methods & Programs in Biomedicine (2018), 157

Background and objective: Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during ... [more ▼]

Background and objective: Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation. Methods: An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn). Results and discussion: Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%. Conclusion: The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only. © 2018 Elsevier B.V. [less ▲]

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See detailA mathematical model of respiration under protective ventilation and extracorporeal CO2 removal therapy
Habran, Simon ULiege; Desaive, Thomas ULiege; MORIMONT, Philippe ULiege et al

Conference (2017, September 27)

The aim of the present study is to build a mathematical model of the respiratory system connected to an extracorporeal CO2 removal device (ECCO2RD) to optimize the gas exchanges efficiency. The model must ... [more ▼]

The aim of the present study is to build a mathematical model of the respiratory system connected to an extracorporeal CO2 removal device (ECCO2RD) to optimize the gas exchanges efficiency. The model must be simple enough to provide rapid solutions and to estimate specific parameters from available clinical data. But it also must be complex enough to be able to simulate the respiratory system when protective ventilation is used and when this system is assisted by an ECCO2RD. [less ▲]

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See detailMathematical modeling of extracorporeal CO2 removal therapy. A validation carried out on ten pigs
Habran, Simon ULiege; Desaive, Thomas ULiege; MORIMONT, Philippe ULiege et al

in Medical & Biological Engineering & Computing (2017)

The extracorporeal CO2 removal device (ECCO2RD) is used in clinics to treat patients suffering from respiratory failures like acute respiratory distress syn- drome (ARDS) or chronic obstructive pulmonary ... [more ▼]

The extracorporeal CO2 removal device (ECCO2RD) is used in clinics to treat patients suffering from respiratory failures like acute respiratory distress syn- drome (ARDS) or chronic obstructive pulmonary disease (COPD). The aim of this device is to decarboxylate blood externally with low blood flow. A mathematical model is proposed to describe protective ventilation, ARDS, and an extracorporeal CO2 removal therapy (ECCO2RT). The sim- ulations are compared with experimental data carried out on ten pigs. The results show a good agreement between the mathematical simulations and the experimental data, which provides a nice validation of the model. This model is thus able to predict the decrease of PCO2 during ECCO2RT for different blood flows across the extracorporeal lung support. [less ▲]

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See detailUntangling glycaemia and mortality in critical care
Uyttendaele, Vincent ULiege; Dickson, Jennifer L.; Shaw, Geoffrey M. et al

in Critical Care (2017), 21(1), 152

Background: Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often ... [more ▼]

Background: Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided. Methods: Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant. Results: SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed. Conclusions: Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition. [less ▲]

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See detailImproved Blood Glucose Forecasting Models using Changes in Insulin Sensitivity in Intensive Care Patients
Uyttendaele, Vincent ULiege; Dickson, Jennifer; Shaw, Geoff et al

Poster (2017, February 01)

Introduction: Hyperglycaemia, hypoglycaemia and glycaemic variability are associated with worsened outcomes and increased mortality in intensive care units. Glycaemic control (GC) using insulin therapy ... [more ▼]

Introduction: Hyperglycaemia, hypoglycaemia and glycaemic variability are associated with worsened outcomes and increased mortality in intensive care units. Glycaemic control (GC) using insulin therapy has shown improved outcomes, but have been proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted) is a model-based glycaemic control protocol using a stochastic model to forecast distributions of likely future changes in insulin sensitivity (SI) based on its current value. This can be used to determine likely future blood glucose (BG) levels for a given intervention, enabling the most optimal dose selection that best overlaps a clinically defined BG target band. This study presents a novel 3D model capable to predict likely future distribution of SI using both current SI and its prior variability (%ΔSI). Methods: Metabolic data from 3 clinical ICU cohorts totalling 819 episodes and 68629 hours of treatment under STAR and SPRINT protocols are used in this study. Data triplets (%ΔSIn, SIn, SIn+1) are created and binned together in a range of %ΔSI = [-100%, 200%] and SIn = [1.0e-7, 2.1e-3] in bin sizes of %ΔSI = 10% and SIn = 0.5e-4. The 5th, 50th, and 95th percentile of SIn+1 are determined for each bin where data density is high enough (>100 triplets) and compared to the previous stochastic model. The predictive power of the two models are compared by computing median [IQR] per-patient percentage prediction of SI within the 5th-95th and 25th-75th percentile ranges of model predictions. Results: Results show the previous model is over-conservative for ~77% of the data, mainly where %ΔSI is within an absolute 25% change. The percentage change in the 90% CI width in this region is reduced by ~25-40%. Conversely, non-conservative regions are also identified, with 90% CI width increased up to ~80%. Predictive power is similar for both model (60.3% [47.8%, 71.5%] vs. 51.2 [42.9%, 59.2%] within 25th-75th and 93.6% [85.7%, 97.3%] vs. 90.7% [84.4%, 94.6%] within 5th-95th range). Conclusions: The new 3D model achieved similar predictive power as the previous model by reducing the 5th-95th percentile prediction range for 77% of the data, predominantly where SI is stable. If the conservatism of the previous model reduces risk of hypoglycaemia, it also inhibits the controller’s ability to reduce BG to the normal range by safely using more aggressive dosing. The 3D new model thus better characterises patient-specific response to insulin, and allows more optimal dosing, increasing performance and safety. [less ▲]

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See detailContinuous glucose monitoring in the ICU: Clinical considerations and consensus
Krinsley, J. S.; Chase, J. G.; Gunst, J. et al

in Critical Care (2017), 21(1),

Glucose management in intensive care unit (ICU) patients has been a matter of debate for almost two decades. Compared to intermittent monitoring systems, continuous glucose monitoring (CGM) can offer ... [more ▼]

Glucose management in intensive care unit (ICU) patients has been a matter of debate for almost two decades. Compared to intermittent monitoring systems, continuous glucose monitoring (CGM) can offer benefit in the prevention of severe hyperglycemia and hypoglycemia by enabling insulin infusions to be adjusted more rapidly and potentially more accurately because trends in glucose concentrations can be more readily identified. Increasingly, it is apparent that a single glucose target/range may not be optimal for all patients at all times and, as with many other aspects of critical care patient management, a personalized approach to glucose control may be more appropriate. Here we consider some of the evidence supporting different glucose targets in various groups of patients, focusing on those with and without diabetes and neurological ICU patients. We also discuss some of the reasons why, despite evidence of benefit, CGM devices are still not widely employed in the ICU and propose areas of research needed to help move CGM from the research arena to routine clinical use. © 2017 The Author(s). [less ▲]

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See detailA proof of concept study of acoustic sensing of lung recruitment during mechanical ventilation
Rodgers, G. W.; Lau Young, J. B.; Desaive, Thomas ULiege et al

in Biomedical Signal Processing and Control (2017), 32

Advancements in health technologies are crucial to support healthcare professionals, improve patient outcomes, and best utilize increasingly scarce and under-demand healthcare resources. This research ... [more ▼]

Advancements in health technologies are crucial to support healthcare professionals, improve patient outcomes, and best utilize increasingly scarce and under-demand healthcare resources. This research presents an initial proof-of-concept study of simple, non-invasive monitoring techniques used in Mechanical Ventilation (MV), which is the primary therapy for Acute Respiratory Distress Syndrome (ARDS). The high levels of inter-patient variability seen in patients with ARDS have resulted in much speculation about the ideal method of determining ventilation settings, such as tidal volume (Vt) and Positive End Expiratory Pressure (PEEP). One of the oldest and simplest methods is acoustic sensing of recruitment and lung condition. This project involves using a digital recording stethoscope to monitor the acoustic output of patients in the Intensive Care Unit (ICU) during mechanical lung ventilation. During lung recruitment, ‘crackles’ can be heard within the chest cavity with a stethoscope. These crackles vary significantly, depending on the status of the patient's respiratory system and are used as an indicator of the level of alveolar recruitment. This preliminary, proof-of-concept study focused on crackle detection and involved gathering sound samples from patients in the Christchurch Hospital ICU with evidence of crackles in the chest cavity. Frequency based analysis showed that crackles can be detected as emissions with higher power levels between 100 and 300 Hz (subject to patient variability). The ability to non-invasively record, detect and quantify the intensity of crackles could provide immediate feedback to clinicians and, in the long term, aid in the optimization of ventilator therapy. © 2016 Elsevier Ltd [less ▲]

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See detailEditorial
Chase, J. G.; Benyo, B.; Cinar, A. et al

in Control Engineering Practice (2017), 58

[No abstract available]

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See detailVirtual Trials of the NICE-SUGAR Protocol: The Impact on Performance of Protocol and Protocol Compliance
Uyttendaele, Vincent ULiege; Dickson, Jennifer L.; Shaw, Geoffrey M. et al

in IFAC-PapersOnLine (2017), 50(1), 6672-6677

Hypoglycaemia, hyperglycaemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial showed no clinical benefit from intensive insulin therapy ... [more ▼]

Hypoglycaemia, hyperglycaemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial showed no clinical benefit from intensive insulin therapy. This study compares the table-based NICE-SUGAR and model-based STAR protocols to assess their relative capability to achieve safe, effective control for all patients. Validated virtual patients (n=443) were used to simulate glycaemic outcomes of the NICE-SUGAR and STAR protocols. Key outcomes evaluate tightness and safety of control for all patients: %BG in 80–144 mg/dL range (PTR); Per-Patient Mean BG (PPM_BG); and Incidence of Hypoglycaemia (BG<40 mg/dL). These metrics determine performance overall, for each patient, and safety. Results are assessed for NICE-SUGAR measuring per-protocol (~24/day) and at reported average rate (~3-hourly; ~8/day). STAR measures 1-3-hourly, averaging 12/day. Per-protocol, STAR provided tight control, with higher PTR (90.7% vs. 78.3%) and tighter median [IQR] PPM_BG (112[106-119] vs. 117[106–137] mg/dL), and greater safety from hypoglycaemia (5 (1%) vs. 10 patients (2.5%)) compared to NICE-SUGAR simulations as per protocol. The 5-95th percentile range PPM_BG for NICE-SUGAR (97–185 mg/dL) showed ~5% of NICE-SUGAR patients had mean BG above 180mg/dL matching clinically reported performance. STAR’s 5th-90th PPM_BG percentile range was (97–146 mg/dL). Measuring as recorded clinically, NICE-SUGAR had PTR of 77%, PPM_BG of 122 [110-140] mg/dL and 24(6%) of patients experienced hypoglycaemia. These results match clinically reported values well (mean BG 115 vs. 118 mg/dL clinically vs. simulation, clinically 7% of patients had a hypoglycaemic event). Glycaemic control protocols need to be both safe and effective for all patients before potential clinical benefits can be assessed. NICE-SUGAR clinical results do not match results expected from their protocol, and show reduced safety and performance in comparison to STAR. [less ▲]

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See detailGlycaemic Control in ICU: Stable Patients Tend to Remain Stable
Uyttendaele, Vincent ULiege; Dickson, Jennifer L.; Stewart, Kent et al

Poster (2017)

Objective: STAR is a glycaemic control (GC) protocol with proven safety and performance. It uses a cohort-based 2D stochastic model of model-based, patient-specific insulin sensitivity (SI). Given current ... [more ▼]

Objective: STAR is a glycaemic control (GC) protocol with proven safety and performance. It uses a cohort-based 2D stochastic model of model-based, patient-specific insulin sensitivity (SI). Given current SI, it predicts a range of future SI values to dose insulin based on specified risk of hypoglycaemia. This study examines whether considering the prior change in SI (%SI) as an input to a 3D stochastic model can reduce the conservatism and provide more accurate estimates. Method: Metabolic data from 3 clinical ICU cohorts (819 episodes and 68629 hours) in Christchurch (SPRINT and STAR) and Hungary (STAR) are used. Triplets (%ΔSIn, SIn, SIn+1) are created for every hour to create a 3D stochastic model with inputs (%ΔSIn, SIn) and output SIn+1. The 5-95th percentile prediction width of the 3D model is compared at every %ΔSIn value to the 2D model 5-95th width. A narrower band for the 3D model indicates the 2D model used is over-conservative and GC could be more aggressive, while a wider bound indicates increased risks. Results: The 2D model is over-conservative for 77% of hours, mainly where %ΔSI is within an absolute 25% change, with 25-40% narrower prediction ranges. Predictive power is similar for both models, but much closer to the ideal value of 90% for the 3D model, indicating greater patient-specificity. Cross-validations show these results generalise well to different ICU populations. Conclusions: By reducing prediction range for 77% of hours across 3 ICU cohorts, predominantly where SI is stable, the new 3D model shows stable patients tend to remain stable in terms of %ΔSI. The new model better characterises patient-specific response to insulin, allowing more optimal dosing while increasing performance and safety. [less ▲]

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See detailPractical identifiability analysis of a minimal cardiovascular system model.
Pironet, Antoine; Docherty, Paul D.; Dauby, Pierre ULiege et al

in Computer Methods & Programs in Biomedicine (2017)

BACKGROUND AND OBJECTIVE: Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and ... [more ▼]

BACKGROUND AND OBJECTIVE: Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and resistance. To do so, the model parameters have to be estimated from data collected at the patient's bedside. This work considers a seven-parameter model of the cardiovascular system and investigates whether these parameters can be uniquely determined using indices derived from measurements of arterial and venous pressures, and stroke volume. METHODS: An error vector defined the residuals between the simulated and reference values of the seven clinically available haemodynamic indices. The sensitivity of this error vector to each model parameter was analysed, as well as the collinearity between parameters. To assess practical identifiability of the model parameters, profile-likelihood curves were constructed for each parameter. RESULTS: Four of the seven model parameters were found to be practically identifiable from the selected data. The remaining three parameters were practically non-identifiable. Among these non-identifiable parameters, one could be decreased as much as possible. The other two non-identifiable parameters were inversely correlated, which prevented their precise estimation. CONCLUSIONS: This work presented the practical identifiability analysis of a seven-parameter cardiovascular system model, from limited clinical data. The analysis showed that three of the seven parameters were practically non-identifiable, thus limiting the use of the model as a monitoring tool. Slight changes in the time-varying function modeling cardiac contraction and use of larger values for the reference range of venous pressure made the model fully practically identifiable. [less ▲]

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See detailGeneralisability of a Virtual Trials Method for Glycaemic Control in Intensive Care.
Dickson, Jennifer L.; Stewart, Kent W.; Pretty, Christopher G. et al

in IEEE transactions on bio-medical engineering (2017)

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 ... [more ▼]

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 hours 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. [less ▲]

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See detailMinimally invasive estimation of ventricular dead space volume through use of Frank-Starling curves.
Davidson, Shaun; Pretty, Chris; Pironet, Antoine et al

in PLoS ONE (2017), 12(4), 0176302

This paper develops a means of more easily and less invasively estimating ventricular dead space volume (Vd), an important, but difficult to measure physiological parameter. Vd represents a subject and ... [more ▼]

This paper develops a means of more easily and less invasively estimating ventricular dead space volume (Vd), an important, but difficult to measure physiological parameter. Vd represents a subject and condition dependent portion of measured ventricular volume that is not actively participating in ventricular function. It is employed in models based on the time varying elastance concept, which see widespread use in haemodynamic studies, and may have direct diagnostic use. The proposed method involves linear extrapolation of a Frank-Starling curve (stroke volume vs end-diastolic volume) and its end-systolic equivalent (stroke volume vs end-systolic volume), developed across normal clinical procedures such as recruitment manoeuvres, to their point of intersection with the y-axis (where stroke volume is 0) to determine Vd. To demonstrate the broad applicability of the method, it was validated across a cohort of six sedated and anaesthetised male Pietrain pigs, encompassing a variety of cardiac states from healthy baseline behaviour to circulatory failure due to septic shock induced by endotoxin infusion. Linear extrapolation of the curves was supported by strong linear correlation coefficients of R = 0.78 and R = 0.80 average for pre- and post- endotoxin infusion respectively, as well as good agreement between the two linearly extrapolated y-intercepts (Vd) for each subject (no more than 7.8% variation). Method validity was further supported by the physiologically reasonable Vd values produced, equivalent to 44.3-53.1% and 49.3-82.6% of baseline end-systolic volume before and after endotoxin infusion respectively. This method has the potential to allow Vd to be estimated without a particularly demanding, specialised protocol in an experimental environment. Further, due to the common use of both mechanical ventilation and recruitment manoeuvres in intensive care, this method, subject to the availability of multi-beat echocardiography, has the potential to allow for estimation of Vd in a clinical environment. [less ▲]

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See detailMinimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance.
Davidson, Shaun; Pretty, Chris; Pironet, Antoine et al

in BioMedical Engineering OnLine (2017), 16(1), 42

BACKGROUND: The aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which's inter-beat ... [more ▼]

BACKGROUND: The aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which's inter-beat evolution could add significant new data and insight to improve diagnosis and treatment. The method developed uses the clinically available inputs of aortic pressure, heart rate and baseline end-systolic volume (via echocardiography) to determine the outputs of left ventricular pressure, volume and dead space volume, and thus the TVE curve. This approach avoids directly assuming the shape of the TVE curve, allowing more effective capture of intra- and inter-patient variability. RESULTS: The resulting TVE curve was experimentally validated against the TVE curve as derived from experimentally measured left ventricular pressure and volume in animal models, a data set encompassing 46,318 heartbeats across 5 Pietrain pigs. This simulated TVE curve was able to effectively approximate the measured TVE curve, with an overall median absolute error of 11.4% and overall median signed error of -2.5%. CONCLUSIONS: The use of clinically available inputs means there is potential for real-time implementation of the method at the patient bedside. Thus the method could be used to provide additional, patient specific information on intra- and inter-beat variation in heart function. [less ▲]

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See detailImproved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement.
Kamoi, Shun; Pretty, Christopher; Balmer, Joel et al

in BioMedical Engineering OnLine (2017), 16(1), 51

BACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques ... [more ▼]

BACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques for continuous estimation of stroke volume (SV) from pressure measurement can be unreliable during hemodynamic instability, which is inevitable for patients requiring significant treatment. For this reason, pressure contour methods must be improved to capture changes in vascular properties and thus provide accurate conversion from pressure to flow. METHODS: This paper presents a novel pressure contour method utilizing pulse wave velocity (PWV) measurement to capture vascular properties. A three-element Windkessel model combined with the reservoir-wave concept are used to decompose the pressure contour into components related to storage and flow. The model parameters are identified beat-to-beat from the water-hammer equation using measured PWV, wave component of the pressure, and an estimate of subject-specific aortic dimension. SV is then calculated by converting pressure to flow using identified model parameters. The accuracy of this novel method is investigated using data from porcine experiments (N = 4 Pietrain pigs, 20-24.5 kg), where hemodynamic properties were significantly altered using dobutamine, fluid administration, and mechanical ventilation. In the experiment, left ventricular volume was measured using admittance catheter, and aortic pressure waveforms were measured at two locations, the aortic arch and abdominal aorta. RESULTS: Bland-Altman analysis comparing gold-standard SV measured by the admittance catheter and estimated SV from the novel method showed average limits of agreement of +/-26% across significant hemodynamic alterations. This result shows the method is capable of estimating clinically acceptable absolute SV values according to Critchely and Critchely. CONCLUSION: The novel pressure contour method presented can accurately estimate and track SV even when hemodynamic properties are significantly altered. Integrating PWV measurements into pressure contour analysis improves identification of beat-to-beat changes in Windkessel model parameters, and thus, provides accurate estimate of blood flow from measured pressure contour. The method has great potential for overcoming weaknesses associated with current pressure contour methods for estimating SV. [less ▲]

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