Hemodynamic differences among hypertensive patients with and without heart failure using impedance cardiography

Impedance cardiogram based exploration of cardiac mechanisms in post-traumatic stress disorder during trauma recall

Post-traumatic stress disorder (PTSD) is an independent risk factor for developing heart failure; however, the underlying cardiac mechanisms are still elusive. This study aims to evaluate the real-time effects of experimentally induced PTSD symptom activation on various cardiac contractility and autonomic measures. We recorded synchronized electrocardiogram and impedance cardiogram from 137 male veterans (17 PTSD, 120 non-PTSD; 48 twin pairs, 41 unpaired singles) during a laboratory-based traumatic reminder stressor. To identify the parameters describing the cardiac mechanisms by which trauma reminders can create stress on the heart, we utilized a feature selection mechanism along with a random forest classifier distinguishing PTSD and non-PTSD. We extracted 99 parameters, including 76 biosignal-based and 23 sociodemographic, medical history, and psychiatric diagnosis features. A subject/twin-wise stratified nested cross-validation procedure was used for parameter tuning and model assessment to identify the important parameters. The identified parameters included biomarkers such as pre-ejection period, acceleration index, velocity index, Heather index, and several physiology-agnostic features. These identified parameters during trauma recall suggested a combination of increased sympathetic nervous system (SNS) activity and deteriorated cardiac contractility that may increase the heart failure risk for PTSD. This indicates that the PTSD symptom activation associates with real-time reductions in several cardiac contractility measures despite SNS activation. This finding may be useful in future cardiac prevention efforts.

Non-Invasive Assessment of Cardiodynamics by Impedance Cardiography during the Six-Minute Walk Test in Patients with Heart Failure

BACKGROUND

Central Illustration: Non-Invasive Assessment of Cardiodynamics by Impedance Cardiography during the Six-Minute Walk Test in Patients with Heart Failure. The six-minute walk test (6MWT) is commonly used to evaluate heart failure (HF) patients. However, several clinical factors can influence the distance walked in the test. Signal-morphology impedance cardiography (SM-ICG) is a useful tool to noninvasively assess hemodynamics.

OBJECTIVE

This study aimed to compare cardiac output (CO), heart rate (HR), and stroke volume (SV) acceleration and deceleration responses to 6MWT in individuals with HF and reduced ejection fraction (HFrEF) and healthy controls.

METHODS

This is a cross-sectional observational study. CO, HR, SV and cardiac index (CI) were evaluated before, during, and after the 6MWT assessed by SM-ICG. The level of significance adopted in the statistical analysis was 5%.

RESULTS

Twenty-seven participants were included (13 HFrEF and 14 healthy controls). CO and HR acceleration significantly differed between groups (p<0.01; p=0.039, respectively). We found significant differences in SV, CO and CI between groups (p<0.01). Linear regression showed an impaired SV contribution to CO change in HFrEF group (22.9% versus 57.4%).

CONCLUSION

The main finding of the study was that individuals with HFrEF showed lower CO and HR acceleration values during the submaximal exercise test compared to healthy controls. This may indicate an imbalance in the autonomic response to exercise in this condition.

Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review

The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches' efficacy.

Data-driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram

Pre-ejection period (PEP), an indicator of sympathetic nervous system activity, is useful in psychophysiology and cardiovascular studies. Accurate PEP measurement is challenging and relies on robust identification of the timing of aortic valve opening, marked as the B point on impedance cardiogram (ICG) signals. The ICG sensitivity to noise and its waveform's morphological variability makes automated B point detection difficult, requiring inefficient and cumbersome expert visual annotation. In this article, we propose a machine learning-based automated algorithm to detect the aortic valve opening for PEP measurement, which is robust against noise and ICG morphological variations. We analyzed over 60 hr of synchronized ECG and ICG records from 189 subjects. A total of 3657 averaged beats were formed using our recently developed ICG noise removal algorithm. Features such as the averaged ICG waveform, its first and second derivatives, as well as high-level morphological and critical hemodynamic parameters were extracted and fed into the regression algorithms to estimate the B point location. The morphological features were extracted from our proposed "variable" physiologically valid search-window related to diverse B point shapes. A subject-wise nested cross-validation procedure was performed for parameter tuning and model assessment. After examining multiple regression models, Adaboost was selected, which demonstrated superior performance and higher robustness to five state-of-the-art algorithms that were evaluated in terms of low mean absolute error of 3.5 ms, low median absolute error of 0.0 ms, high correlation with experts' estimates (Pearson coefficient = 0.9), and low standard deviation of errors of 9.2 ms. For reproducibility, an open-source toolbox is provided.

Validation of a new impedance cardiography analysis algorithm for clinical classification of stress states

Pre-ejection period (PEP) is an index of sympathetic nervous system activity that can be computed from electrocardiogram (ECG) and impedance cardiogram (ICG) signals, but sensitive to speech/motion artifact. We sought to validate an ICG noise removal method, three-stage ensemble-average algorithm (TEA), in data acquired from a clinical trial comparing active versus sham non-invasive vagal nerve stimulation (tcVNS) after standardized speech stress. We first compared TEA's performance versus the standard conventional ensemble-average algorithm (CEA) approach to classify noisy ICG segments. We then analyzed ECG and ICG data to measure PEP and compared group-level differences in stress states with each approach. We evaluated 45 individuals, of whom 23 had post-traumatic stress disorder (PTSD). We found that the TEA approach identified artifact-corrupted beats with intraclass correlation coefficient > 0.99 compared to expert adjudication. TEA also resulted in higher group-level differences in PEP between stress states than CEA. PEP values were lower in the speech stress (vs. baseline rest) group using both techniques, but the differences were greater using TEA (12.1 ms) than CEA (8.0 ms). PEP differences in groups divided by PTSD status and tcVNS (active vs. sham) were also greater when using the TEA versus CEA method, although the magnitude of the differences was lower. In conclusion, TEA helps to accurately identify noisy ICG beats during speaking stress, and this increased accuracy improves sensitivity to group-level differences in stress states compared to CEA, suggesting greater clinical utility.

Cardiovascular Diseases Diagnosis by Impedance Cardiography

Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. In order to diagnose CVDs, there are a range of detection methods, among them, the impedance cardiography technique (ICG). It is a non-invasive and low-cost method. In this paper, we highlight recent advances and developments of the CDVs diagnosis mainly by the ICG method. We considered papers published during the last five years (from 2017 until 2022). Based on this study, we expressed the need for an ICG database for the different CDVs.

Perfusion indices can predict early volume depletion in a blood donor model

INTRODUCTION

Blood donation from healthy donors is used experimental model that surrogates for class 1 hemorrhage in humans. We examined changes in the perfusion index (PI) and plethysmographic variability index (PVI) in healthy blood donors after donating a unit of blood, and we evaluated the usability of these indices in detecting blood loss volumes of less than 750 mL (class 1 hemorrhagic shock trauma patients).

MATERIALS AND METHODS

This study is a prospective, cross-sectional study. 180 healthy volunteers aged 18 and over, who donated blood at the local blood bank, were included in the study consecutively. The age, gender, and body mass index of the volunteers were recorded and, before and after the blood donation, the vital signs and perfusion indices were measured.

RESULTS

Of the donors, 61.7% were men (n = 111), and the median age of all donors was 32 (IQR: 21-39). A statistically significant difference was found between the hemodynamic parameters and PIs before and after the blood donation (p < 0.01 for all parameters; median difference of PI [- 1.45, 95% CI: (- 0.9)-( - 2)], median difference of PVI [6, 95% CI: 7.77-4.23].

CONCLUSION

We evaluated the perfusion indices in the early diagnosis of blood volume loss in patients admitted to the emergency department due to trauma. After the participants donated one unit of blood, we found that their PI decreased and PVI increased compared to the measurements before the blood donation. Considering that major bleeding starts in the very early stage as minor bleeding, it is essential for emergency physicians to recognize class 1 hemorrhagic shock patients. Further, non-invasive and straightforward procedures, such as measuring PI and PVI, can be particularly useful in identifying blood loss volumes of less than 750 mL.

Clinical Use of Impedance Cardiography for Hemodynamic Assessment of Early Cardiovascular Disease and Management of Hypertension

This article is for clinicians considering impedance cardiography (ICG) for secondary prevention. ICG is an inexpensive noninvasive technology that can be used to assess hemodynamic function of the central cardiovascular system. Diverse abnormalities of ventricular function, systolic and diastolic, can be detected by ICG. Additional data pertaining to decompensation can be obtained by taking ICG readings with the patient performing postural change, from upright to supine, to quantify the compensatory response. Vascular load consists of resistive and pulsatile loads. Systemic vascular resistance can provide a measure of resistive load. Pulsatile load has two components: arterial stiffness and wave reflection. ICG can be used to calculate arterial compliance and detect aortic wave reflection. For stage 1 hypertension, a significant issue is whether a treating clinician should add pharmacotherapy to lifestyle modification. Adults who have multiple cardiovascular risk factors with stage 1 hypertension have early cardiovascular disease. ICG can be used to identify the functional abnormalities associated with the cardiovascular disease. For the management of hypertension, ICG can be used to calculate the underlying hemodynamic parameters of cardiac index and systemic vascular resistance associated with a patient's blood pressure. There can be wide ranges for cardiac index and systemic vascular resistance, with many patients having low cardiac index with high systemic vascular resistance or vice versa. These hemodynamic data can be used to customize pharmacotherapy. Drug titration can be guided by patient response to treatment using the initial hemodynamic data as a baseline for comparison to subsequent measurements from serial office visits.