A modelling framework for assessment of arterial compliance by fusion of oscillometry and pulse wave velocity information

Background and Objectives: Measurement of arterial compliance is recognized as important for clinical use and for enabling better understanding of circulatory system regulation mechanisms. Estimation of arterial compliance involves either a direct measure of the ratio between arterial volume and pressure changes or an inference from the pulse wave velocity (PWV). In this study we demonstrate an approach to assess arterial compliance by fusion of these two information sources. The approach is based on combining oscillometry as used for blood pressure inference and PWV measurements based on ECG/PPG. Enabling reliable arterial compliance measurements will contribute to the understanding of regulation mechanisms of the arterial tree, possibly establishing arterial compliance as a key measure relevant in hemodynamic monitoring. Methods: A measurement strategy, a physiological model, and a framework based on Bayesian principles are developed for measuring changes in arterial compliance based on combining oscillometry and PWV data. A simulation framework is used to study and validate the algorithm and measurement principle in detail, motivated by previous experimental findings. Results: Simulations demonstrate the possibility of inferring arterial compliance via fusion of simultaneously acquired volume/pressure relationships and PWV data. In addition, the simulation framework demonstrates how Bayesian principles can be used to handle low signal – to – noise ratio and partial information loss. Conclusions: The developed simulation framework shows the feasibility of the proposed approach for assessment of arterial compliance by combining multiple data sources. This represents a first step towards integration of arterial compliance measurements in hemodynamic monitoring using existing clinical technology. The Bayesian approach is of particular relevance for such patient monitoring settings, where measurements are repeated frequently, context is relevant, and data is affected by artefacts. In addition, the simulation framework is necessary for future clinical-study design, in order to determine device specifications and the extent to which noise affects the inference process

Method for measurement of arterial compliance by fusion of oscillometry and pulse wave velocity

Up until now estimation of arterial compliance has been performed either by analysis of arterial pressure changes with respect to volume changes or by inference based on pulse wave velocity (PWV). In this study we demonstrate the possibility of an approach to assess arterial compliance by fusing the two information sources namely the pressure/volume relationship obtained from oscillography and PWV data. The goal is to assess arterial properties easily and robustly, enhancing current hemodynamic monitoring. The approach requires as input signals: an electrocardiogram (ECG), a photo- plethysmogram (PPG) and the arterial oscillation as measured during non-invasive blood pressure measurements based on oscillometry with a cuff. These signals are fused by an algorithm using Bayesian principles underpinned by a physiological model. In our simulations, we demonstrate the feasibility to infer arterial compliance by our proposed strategy. A very first measurement on a healthy volunteer supports our findings from the simulation.Clinical Relevance - Arterial compliance/stiffness is recognized as a key hemodynamic parameter, which is not easily accessible and not a standard parameter currently. The presented method and obtained results are encouraging for future research in this area

Air cuff transducer design for occlusion-based hemodynamic measurements - An experimental and simulation study

In standard clinical practice, cuff devices are widely used for non-invasive blood pressure measurements (NIBP). However, cuff-based NIBP is prone to large errors especially in cases of hypo- and hypertension. In addition to this, the cuff measurement principle allows for estimation of a number of other hemodynamic parameters (e. g. cardiac output, arterial stiffness, augmentation index) by means of analysis of the pulse waveform and/or pulse amplitude recorded in the cuff pressure. However, in standard practice, the cuff is still only used for the measurement of BP.A key reason for the observed measurement errors and the limited set of extracted parameters lies in our poor understanding of the cuff device as a transducer; the cuff pressure response to arm volume pulsations (the cuff transfer function TFcuff) depends on a large number of factors. It is not yet clear to what extent the cuff contributes to the NIBP error, or if the cuff in its current format is reliable for measurement of arterial pulse waveform/amplitude. In this study, we investigate the isolated cuff in order to gain a better understanding of the main sources of measurement errors. By using an experimental setup which measures the cuff response to mechanically simulated arm pulsations, we find that the cuff can explain part of the NIBP errors in cases of hypotension. Also, we find that pulse waveforms can be estimated using a standard cuff; however, measurement of the pulse volume amplitude is unfeasible. We demonstrate that the observed inaccuracies can be eliminated by usage of a calibrator device which obtains real-time information on TFcuff. These findings will enable further investigation of the effects related to the pulse travel along the compressed limb and the interaction between cuff, arm tissue, distal arm hemodynamics and arterial walls for the development of improved occlusion-based measurement strategies

RegressionExplorer: Interactive exploration of logistic regression models with subgroup analysis

We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients

Modulation of pulse propagation and blood flow via cuff inflation—new distal insights

In standard critical care practice, cuff sphygmomanometry is widely used for intermittent blood pressure (BP) measurements. However, cuff devices offer ample possibility of modulating blood flow and pulse propagation along the artery. We explore underutilized arrangements of sensors involving cuff devices which could be of use in critical care to reveal additional information on compensatory mechanisms. In our previous work, we analyzed the response of the vasculature to occlusion perturbations by means of observations obtained non-invasively. In this study, our aim is to (1) acquire additional insights by means of invasive measurements and (2) based on these insights, further develop cuff-based measurement strategies. Invasive BP experimental data is collected downstream from the cuff in two patients monitored in the OR. It is found that highly dynamic processes occur in the distal arm during cuff inflation. Mean arterial pressure increases in the distal artery by 20 mmHg, leading to a decrease in pulse transit time by 20 ms. Previous characterizations neglected such distal vasculature effects. A model is developed to reproduce the observed behaviors and to provide a possible explanation of the factors that influence the distal arm mechanisms. We apply the new findings to further develop measurement strategies aimed at acquiring information on pulse arrival time vs. BP calibration, artery compliance, peripheral resistance, artery-vein interaction

Modulation of pulse travel and blood flow during cuff inflation: An experimental case study

The blood pressure (BP) cuff can be used to modulate blood flow and propagation of pressure pulse along the artery. In our previous work, we researched methods to adapt cuff modulation techniques for pulse transit time vs. BP calibration and for measurement of other hemodynamic indices of potential interest to critical care, such as arterial compliance. A model characterized the response of the vasculature located directly under the cuff, but assumed that no significant changes occur in the distal vasculature.This study has been tailored to gain insights into the response of distal BP and pulse transit time to cuff inflation. Invasive BP data collected downstream from the cuff demonstrates that highly dynamic processes occur in the distal arm during cuff inflation. Mean arterial pressure increases in the distal artery by up to 20 mmHg, leading to a decrease in pulse transit time of up to 20 ms. Clinical Relevance: Such significant changes need to be taken into account in order to improve non-invasive BP estimations and to enable inference of other hemodynamic parameters from vasculature response to cuff inflation. A simple model is developed in order to reproduce the observed behaviors. The lumped-parameter model demonstrates opportunities for cuff modulation measurements which can reveal information on parameters such as systemic resistance, distal arterial, venous compliances and artery-vein interaction

New Hemodynamic Parameters in Peri-Operative and Critical Care: Challenges in Translation

Hemodynamic monitoring technologies are evolving continuously—a large number of bedside monitoring options are becoming available in the clinic. Methods such as echocardiography, electrical bioimpedance, and calibrated/uncalibrated analysis of pulse contours are becoming increasingly common. This is leading to a decline in the use of highly invasive monitoring and allowing for safer, more accurate, and continuous measurements. The new devices mainly aim to monitor the well-known hemodynamic variables (e.g., novel pulse contour, bioreactance methods are aimed at measuring widely-used variables such as blood pressure, cardiac output). Even though hemodynamic monitoring is now safer and more accurate, a number of issues remain due to the limited amount of information available for diagnosis and treatment. Extensive work is being carried out in order to allow for more hemodynamic parameters to be measured in the clinic. In this review, we identify and discuss the main sensing strategies aimed at obtaining a more complete picture of the hemodynamic status of a patient, namely: (i) measurement of the circulatory system response to a defined stimulus; (ii) measurement of the microcirculation; (iii) technologies for assessing dynamic vascular mechanisms; and (iv) machine learning methods. By analyzing these four main research strategies, we aim to convey the key aspects, challenges, and clinical value of measuring novel hemodynamic parameters in critical care

Intelligent checklists improve checklist compliance in the intensive care unit: a prospective before-and-after mixed-method study

Background: We examined whether a context and process-sensitive ‘intelligent’ checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds. Methods: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. Daily ICU ward rounds were observed during two periods of 8 weeks. We compared paper checklists (control) with a dynamic (digital) clinical checklist (DCC, intervention). The primary outcome was compliance with best clinical practice, measured as the percentages of checked items and unchecked critical items. Secondary outcomes included ICU stay and the usability of digital checklists. Data are presented as median (interquartile range). Results: Clinical characteristics and severity of critical illness were similar during both control and intervention periods of study. A total of 36 clinicians visited 197 patients during 352 ward rounds using the paper checklist, compared with 211 patients during 366 ward rounds using the DCC. Per ICU round, a median of 100% of items (94.4–100.0) were completed by DCC, compared with 75.1% (66.7–86.4) by paper checklist (P=0.03). No critical items remained unchecked by the DCC, compared with 15.4% (8.3–27.3) by the paper checklist (P=0.01). The DCC was associated with reduced ICU stay (1 day [1–3]), compared with the paper checklist (2 days [1–4]; P=0.05). Usability of the DCC was judged by clinicians to require further improvement. Conclusions: A digital checklist improved compliance with best clinical practice, compared with a paper checklist, during ward rounds on a mixed ICU. Clinical trial registration: NCT 03599856