Influence of physiological sources on the impedance cardiogram analyzed using 4D FEM simulations

Modeling Anisotropic Electrical Conductivity of Blood: Translating Microscale Effects of Red Blood Cell Motion into a Macroscale Property of Blood

Cardiovascular diseases are a leading global cause of mortality. The current standard diagnostic methods, such as imaging and invasive procedures, are relatively expensive and partly connected with risks to the patient. Bioimpedance measurements hold the promise to offer rapid, safe, and low-cost alternative diagnostic methods. In the realm of cardiovascular diseases, bioimpedance methods rely on the changing electrical conductivity of blood, which depends on the local hemodynamics. However, the exact dependence of blood conductivity on the hemodynamic parameters is not yet fully understood, and the existing models for this dependence are limited to rather academic flow fields in straight pipes or channels. In this work, we suggest two closely connected anisotropic electrical conductivity models for blood in general three-dimensional flows, which consider the orientation and alignment of red blood cells (RBCs) in shear flows. In shear flows, RBCs adopt preferred orientations through a rotation of their membrane known as tank-treading motion. The two models are built on two different assumptions as to which hemodynamic characteristic determines the preferred orientation. The models are evaluated in two example simulations of blood flow. In a straight rigid vessel, the models coincide and are in accordance with experimental observations. In a simplified aorta geometry, the models yield different results. These differences are analyzed quantitatively, but a validation of the models with experiments is yet outstanding.

The effect of maxillary molar distalization with clear aligner: a 4D finite-element study with staging simulation

INTRODUCTION

Long-term simulation of tooth movement is crucial for clear aligner (CA) treatment. This study aimed to investigate the effect of maxillary molar distalization with CA via an automatic staging simulation.

METHOD

A finite-element method (FEM) model of maxillary dentition, periodontal ligaments, attachments, and corresponding CA was established, and a prescribed 2-mm distalization with 0.1 mm each step of the second molar was simulated. The long-term tooth movement under orthodontic force was simulated with an iterative computation method. The morphologic changes of CA during staging were simulated with the thermal expansion method.

RESULTS

Twenty steps of molar distalization were simulated. Significant distal tilting of the second molar was revealed, along with the proclination of anterior teeth, which caused the 'reversed bow effect'. For the second molar, 4.63°distal tilting at the 20th step was revealed. The intrusion of the incisors and the second molar were 0.43 mm, 0.39 mm, and 0.45 mm, respectively, at step 20. All the anterior teeth showed a proclination of approximately 1.41°-2.01° at the 20th step. The expression rate of the designed distalization of the second molar was relatively low (approximately 68%) compared to the high efficacy of interdental space opening between molars with CA (approximately 89%).

CONCLUSION

A novel method of simulating long-term molar distalization with CA with FEM was developed. The FEM results suggested distal tilting of the second molar and the proclination of anterior teeth during the molar distalization.

Electrode Positioning to Investigate the Changes of the Thoracic Bioimpedance Caused by Aortic Dissection - A Simulation Study

Impedance cardiography (ICG) is a non-invasive method to evaluate several cardiodynamic parameters by measuring the cardiac-synchronous changes in the dynamic transthoracic electrical impedance. ICG allows us to identify and quantify conductivity changes inside the thorax by measuring the impedance on the thorax during a cardiac cycle. Pathologic changes in the aorta, like aortic dissection, will alter the aortic shape as well as the blood flow and consequently, the impedance cardiogram. This fact distorts the evaluated cardiodynamic parameters, but it could lead to the possibility to identify aortic pathology. A 3D numerical simulation model is used to compute the impedance changes on the thorax surface in case of the type B aortic dissection. A sensitivity analysis is applied using this simulation model to investigate the suitability of different electrode configurations considering several patient-specific cases. Results show that the remarkable pathological changes in the aorta caused by aortic dissection alters the impedance cardiogram significantly.

Systematic Variability in ICG Recordings Results in ICG Complex Subtypes - Steps Towards the Enhancement of ICG Characterization

The quality of an impedance cardiography (ICG) signal critically impacts the calculation of hemodynamic parameters. These calculations depend solely on the identification of ICG characteristic points on the ABEXYOZ complex. Unfortunately, contrary to the relatively constant morphology of the PQRST complex in electrocardiography, the waveform morphology of ICG data is far from stationary, which causes difficulties in the accuracy of the automated detection of characteristic ICG points. This study evaluated ICG recordings obtained from 10 volunteers. The results indicate that there are several different waveforms for the ABEXYOZ complex; there are up to five clearly distinct waveforms for the ABEXYOZ complex in addition to those that are typically reported. The differences between waveform types increased the difficulty of detecting ICG points. To accurately detect all ICG points, the ABEXYOZ complex should be analyzed according to the corresponding waveform type.

Do mathematical model studies settle the controversy on the origin of cardiac synchronous trans-thoracic electrical impedance variations? A systematic review

Impedance cardiography (ICG) is a method to evaluate cardiac-stroke volume and cardiac-output by measuring the cardiac-synchronous changes in the dynamic trans-thoracic electrical impedance (ΔZ). Clinical evaluations on the accuracy of ICG showed varying results. Consequently, the classic assumption in ICG-the aorta as a main source of ΔZ-is questioned and subsequently investigated in simulation studies using mathematical models of the electrical resistivity of the human body. The aim is to review the consensus in mathematical modelling studies that investigate the origin of the ΔZ as measured in ICG. In a systematic literature search, studies were identified and surveyed with reference to characteristics, such as included organs and their resistivity and geometries, electrode positions and calculation of ΔZ, to review the consensus between mathematical modelling studies that investigate the origin of the ΔZ as measured in ICG. Thirteen papers showed considerable variation in the model's characteristics with varying or contradicting outcomes for the ΔZ 's origin. For instance, 11 studies excluded perfused muscle tissue, implying implicitly their insignificance, while 3 other studies included muscle tissue and indicated it as the most important origin of ΔZ. In conclusion, the reviewed papers show a lack of consensus with respect to both the modelled characteristics as well as the model outcomes and, as a result, these studies failed to settle the controversy on ΔZ 's origin. Recommendations have been added to improve future mathematical model studies.

Effect of patent ductus arteriosus and patent foramen ovale on left ventricular stroke volume measurement by electrical velocimetry in comparison to transthoracic echocardiography in neonates

This prospective single-center observational study compared impedance cardiography [electrical velocimetry (EV)] with transthoracic echocardiography (TTE, based on trans-aortic flow) and analyzed the influence of physiological shunts, such as patent ductus arteriosus (PDA) or patent foramen ovale (PFO), on measurement accuracy. Two hundred and ninety-one triplicate simultaneous paired left ventricular stroke volume (LVSV) measurements by EV (LVSV_EV) and TTE (LVSV_TTE) in 99 spontaneously breathing neonates (mean weight 3270 g; range 1227-4600 g) were included. For the whole cohort, the mean absolute LVSV_EV was 5.5 mL, mean LVSV_TTE was 4.9 mL, resulting in an absolute Bland-Altman bias of -0.7 mL (limits of agreement LOA -3.0 to 1.7 mL), relative bias -12.8 %; mean percentage error MPE 44.9 %; true precision TP_EV 33.4 % (n = 99 aggregated data points). In neonates without shunts (n = 32): mean LVSV_EV 5.0 mL, mean LVSV_TTE 4.6 mL, Bland-Altman bias -0.4 mL (LOA -2.8 to 2.0 mL), relative bias -8.2 %; MPE 50.7 %; TP_EV 40.9 %. In neonates with shunts (PDA and/or PFO; n = 67): mean LVSV_EV 5.8 mL, mean LVSV_TTE 5.0 mL, bias -0.8 mL (LOA -3.1 to 1.5 mL), relative bias -14.8 %, MPE 41.9 %, TP_EV 29.3 %. Accuracy was affected by PDA and/or PFO, with a significant increase in the relative difference in LVSV_EV versus LVSV_TTE: Subjects without shunts -2.9 % (n = 91), PFO alone -9.6 % (n = 125), PDA alone -14.0 % (n = 12), and PDA and PFO -18.5 % (n = 63). Physiological shunts (PDA and/or PFO) in neonates affect measurement accuracy and cause overestimation of LVSV_EV compared with LVSV_TTE.

Influence of heart motion on cardiac output estimation by means of electrical impedance tomography: a case study

Electrical impedance tomography (EIT) is a non-invasive imaging technique that can measure cardiac-related intra-thoracic impedance changes. EIT-based cardiac output estimation relies on the assumption that the amplitude of the impedance change in the ventricular region is representative of stroke volume (SV). However, other factors such as heart motion can significantly affect this ventricular impedance change. In the present case study, a magnetic resonance imaging-based dynamic bio-impedance model fitting the morphology of a single male subject was built. Simulations were performed to evaluate the contribution of heart motion and its influence on EIT-based SV estimation. Myocardial deformation was found to be the main contributor to the ventricular impedance change (56%). However, motion-induced impedance changes showed a strong correlation (r = 0.978) with left ventricular volume. We explained this by the quasi-incompressibility of blood and myocardium. As a result, EIT achieved excellent accuracy in estimating a wide range of simulated SV values (error distribution of 0.57 ± 2.19 ml (1.02 ± 2.62%) and correlation of r = 0.996 after a two-point calibration was applied to convert impedance values to millilitres). As the model was based on one single subject, the strong correlation found between motion-induced changes and ventricular volume remains to be verified in larger datasets.

Validation of stroke volume and cardiac output by electrical interrogation of the brachial artery in normals: assessment of strengths, limitations, and sources of error

The goal of this study is to validate a new, continuous, noninvasive stroke volume (SV) method, known as transbrachial electrical bioimpedance velocimetry (TBEV). TBEV SV was compared to SV obtained by cardiac magnetic resonance imaging (cMRI) in normal humans devoid of clinically apparent heart disease. Thirty-two (32) volunteers were enrolled in the study. Each subject was evaluated by echocardiography to assure that no aortic or mitral valve disease was present. Subsequently, each subject underwent electrical interrogation of the brachial artery by means of a high frequency, low amplitude alternating current. A first TBEV SV estimate was obtained. Immediately after the initial TBEV study, subjects underwent cMRI, using steady-state precession imaging to obtain a volumetric estimate of SV. Following cMRI, the TBEV SV study was repeated. Comparing the cMRI-derived SV to that of TBEV, the two TBEV estimates were averaged and compared to the cMRI standard. CO was computed as the product of SV and heart rate. Statistical methods consisted of Bland-Altman and linear regression analysis. TBEV SV and CO estimates were obtained in 30 of the 32 subjects enrolled. Bland-Altman analysis of pre- and post-cMRI TBEV SV showed a mean bias of 2.87 % (2.05 mL), precision of 13.59% (11.99 mL) and 95% limits of agreement (LOA) of +29.51% (25.55 mL) and -23.77% (-21.45 mL). Regression analysis for pre- and post-cMRI TBEV SV values yielded y = 0.76x + 25.1 and r(2) = 0.71 (r = 0.84). Bland-Altman analysis comparing cMRI SV with averaged TBEV SV showed a mean bias of -1.56% (-1.53 mL), precision of 13.47% (12.84 mL), 95% LOA of +24.85% (+23.64 mL) and -27.97% (-26.7 mL) and percent error = 26.2 %. For correlation analysis, the regression equation was y = 0.82x + 19.1 and correlation coefficient r(2) = 0.61 (r = 0.78). Bland-Altman analysis of averaged pre- and post-cMRI TBEV CO versus cMRI CO yielded a mean bias of 5.01% (0.32 L min(-1)), precision of 12.85% (0.77 L min(-1)), 95% LOA of +30.20 % (+0.1.83 L min(-1)) and -20.7% (-1.19 L min(-1)) and percent error = 24.8%. Regression analysis yielded y = 0.92x + 0.78, correlation coefficient r(2) = 0.74 (r = 0.86). TBEV is a novel, noninvasive method, which provides satisfactory estimates of SV and CO in normal humans.