Observational Study of Noninvasive Venous Waveform Analysis to Assess Intracardiac Filling Pressures During Right Heart Catheterization

Critical Information from High Fidelity Arterial and Venous Pressure Waveforms During Anesthesia and Hemorrhage

PURPOSE

Peripheral venous pressure (PVP) waveform analysis is a novel, minimally invasive, and inexpensive method of measuring intravascular volume changes. A porcine cohort was studied to determine how venous and arterial pressure waveforms change due to inhaled and infused anesthetics and acute hemorrhage.

METHODS

Venous and arterial pressure waveforms were continuously collected, while each pig was under general anesthesia, by inserting Millar catheters into a neighboring peripheral artery and vein. The anesthetic was varied from inhaled to infused, then the pig underwent a controlled hemorrhage. Pearson correlation coefficients between the power of the venous and arterial pressure waveforms at each pig's heart rate frequency were calculated for each variation in the anesthetic, as well as before and after hemorrhage. An analysis of variance (ANOVA) test was computed to determine the significance in changes of the venous pressure waveform means caused by each variation.

RESULTS

The Pearson correlation coefficients between venous and arterial waveforms decreased as anesthetic dosage increased. In an opposing fashion, the correlation coefficients increased as hemorrhage occurred.

CONCLUSION

Anesthetics and hemorrhage alter venous pressure waveforms in distinctly different ways, making it critical for researchers and clinicians to consider these confounding variables when utilizing pressure waveforms. Further work needs to be done to determine how best to integrate PVP waveforms into clinical decision-making.

An artificial intelligence-based noninvasive solution to estimate pulmonary artery pressure

Aims

Design to develop an artificial intelligence (AI) algorithm to accurately predict the pulmonary artery pressure (PAP) waveform using non-invasive signal inputs.

Methods and results

We randomly sampled training, validation, and testing datasets from a waveform database containing 180 patients with pulmonary atrial catheters (PACs) placed for PAP waves collection. The waveform database consisted of six hemodynamic parameters from bedside monitoring machines, including PAP, artery blood pressure (ABP), central venous pressure (CVP), respiration waveform (RESP), photoplethysmogram (PPG), and electrocardiogram (ECG). We trained a Residual Convolutional Network using a training dataset containing 144 (80%) patients, tuned learning parameters using a validation set including 18 (10%) patients, and tested the performance of the method using 18 (10%) patients, respectively. After comparing all multi-stage algorithms on the testing cohort, the combination of the residual neural network model and wavelet scattering transform data preprocessing method attained the highest coefficient of determination R² of 90.78% as well as the following other performance metrics and corresponding 95% confidence intervals (CIs): mean square error of 11.55 (10.22–13.5), mean absolute error of 2.42 (2.06–2.85), mean absolute percentage error of 0.91 (0.76–1.13), and explained variance score of 90.87 (85.32–93.31).

Conclusion

The proposed analytical approach that combines data preprocessing, sampling method, and AI algorithm can precisely predict PAP waveform using three input signals obtained by noninvasive approaches.

Contemporary Review of Hemodynamic Monitoring in the Critical Care Setting

Hemodynamic assessment remains the most valuable adjunct to physical examination and laboratory assessment in the diagnosis and management of shock. Through the years, multiple modalities to measure and trend hemodynamic indices have evolved with varying degrees of invasiveness. Pulmonary artery catheter (PAC) has long been considered the gold standard of hemodynamic assessment in critically ill patients and in recent years has been shown to improve clinical outcomes among patients in cardiogenic shock. The invasive nature of PAC is often cited as its major limitation and has encouraged development of less invasive technologies. In this review, the authors summarize the literature on the mechanism and validation of several minimally invasive and noninvasive modalities available in the contemporary intensive care unit. They also provide an update on the use of focused bedside echocardiography.

Comparison of clinical symptoms and bioimpedance to pulmonary capillary wedge pressure in heart failure

Introduction

Clinical symptoms of heart failure commonly include fatigue, edema, and shortness of breath. Unfortunately, clinical monitoring has proven unreliable in predicting congestion and the need for hospitalization. Biosensing wearables have been developed as a potential adjunct to clinical signs and symptoms to detect congestion before it becomes severe thus preventing a heart failure hospitalization.

Hypothesis

Clinical signs and symptoms of heart failure will correlate with thoracic bioimpedance measurements (ZOE®) and pulmonary capillary wedge pressure (PCWP).

Methods

One hundred and fifty-five subjects undergoing right heart catheterization (RHC) were prospectively enrolled. A Zo value (ohms) was obtained, jugular venous pressure (JVP) was estimated, edema graded, and shortness of breath (SOB) assessed in all subjects. RHC was performed by a scheduled cardiologist per routine. One-way ANOVA was performed to assess the relationship between variables. A Pearson correlation coefficient was used to compare the Zo value and PCWP.

Results

Neither estimated JVP (cmH2O) (p = 0.65, n = 110) nor edema scores (p = 0.12, n = 110) demonstrated a significant relationship to PCWP. The presence of subjective SOB also did not demonstrate a significant association with PCWP (p = 0.99, n = 110). There was no correlation between ZOE® and PCWP (r = -0.08, p = 0.56, n = 56).

Conclusions

These findings support the idea that traditional measures for monitoring heart failure patients are limited.

Respiratory Non-Invasive Venous Waveform Analysis for Assessment of Respiratory Distress in Coronavirus Disease 2019 Patients: An Observational Study

Supplemental Digital Content is available in the text.

Due to the rapid rate of severe acute respiratory syndrome coronavirus 2 transmission and the heterogeneity of symptoms of coronavirus disease 2019, expeditious and effective triage is critical for early treatment and effective allocation of hospital resources.

Physiology and clinical utility of the peripheral venous waveform

The peripheral venous system serves as a volume reservoir due to its high compliance and can yield information on intravascular volume status. Peripheral venous waveforms can be captured by direct transduction through a peripheral catheter, non-invasive piezoelectric transduction, or gleaned from other waveforms such as the plethysmograph. Older analysis techniques relied upon pressure waveforms such as peripheral venous pressure and central venous pressure as a means of evaluating fluid responsiveness. Newer peripheral venous waveform analysis techniques exist in both the time and frequency domains, and have been applied to various clinical scenarios including hypovolemia (i.e. hemorrhage, dehydration) and volume overload.

A brief report on the effects of vasoactive agents on peripheral venous waveforms in a porcine model

Objectives

Non-invasive venous waveform analysis (NIVA) is a recently described, novel technique to assess intravascular volume status. Waveforms are captured with a piezoelectric sensor; analysis in the frequency domain allows for calculation of a “NIVA value” that represents volume status. The aim of this report was to determine the effects of vasoactive agents on the venous waveform and calculated NIVA values.

Design

Porcine experimental model.

Setting

Operating theatre.

Participants

A piezoelectric sensor was secured over the surgically exposed saphenous vein in eight anesthetized pigs.

Main outcome measures

NIVA value, pulmonary capillary wedge pressure (PCWP), and mean arterial pressure prior to and post intravenous administration of 150–180 µg of phenylephrine or 100 µg of sodium nitroprusside.

Results

Phenylephrine led to a decrease in NIVA value (mean 9.2 vs. 4.6, p < 0.05), while sodium nitroprusside led to an increase in NIVA value (mean 9.5 vs. 11.9, p < 0.05). Mean arterial pressure increased after phenylephrine (p < 0.05) and decreased after sodium nitroprusside (p < 0.05). PCWP did not change significantly after phenylephrine (p = 0.25) or sodium nitroprusside (p = 0.06).

Conclusions

Vasoactive agents lead to changes in non-invasively obtained venous waveforms in euvolemic pigs, highlighting a potential limitation in the ability to NIVA to estimate static volume in this setting. Further studies are indicated to understand the effects of vasoactive agents in the setting of hypovolemia and hypervolemia.

Non-Invasive Venous waveform Analysis (NIVA) for volume assessment during complex cranial vault reconstruction: A proof-of-concept study in children

Background

Non-Invasive Venous waveform Analysis (NIVA) is novel technology that captures and analyzes changes in venous waveforms from a piezoelectric sensor on the wrist for hemodynamic volume assessment. Complex cranial vault reconstruction is performed in children with craniosynostosis and is associated with extensive blood loss, potential life-threatening risks, and significant morbidity. In this preliminary study, we hypothesized that NIVA will provide a reliable, non-invasive, quantitative assessment of intravascular volume changes in children undergoing complex cranial vault reconstruction.

Objective

To present proof-of-concept results of a novel technology in the pediatric population.

Methods

The NIVA prototype was placed on each subject’s wrist, and venous waveforms were collected intraoperatively. Estimated blood loss and fluid/blood product administration were recorded in real time. Venous waveforms were analyzed into a NIVA value and then correlated, along with mean arterial pressure (MAP), to volume changes. Concordance was quantified to determine if the direction of change in volume was similar to the direction of change in MAP or change in NIVA.

Results

Of 18 patients enrolled, 14 had usable venous waveforms, and there was a significant correlation between change in NIVA value and change in volume. Change in MAP did not correlate with change in volume. The concordance between change in MAP and change in volume was less than the concordance between change in NIVA and change in volume.

Conclusion

NIVA values correlate more closely to intravascular volume changes in pediatric craniofacial patients than MAP. This initial study suggests that NIVA is a potential safe, reliable, non-invasive quantitative method of measuring intravascular volume changes for children undergoing surgery.

Non-invasive venous waveform analysis (NIVA) for volume assessment in patients undergoing hemodialysis: an observational study

BACKGROUND

Accurate assessment of volume status to direct dialysis remains a clinical challenge. Despite current attempts at volume-directed dialysis, inadequate dialysis and intradialytic hypotension (IDH) are common occurrences. Peripheral venous waveform analysis has recently been developed as a method to accurately determine intravascular volume status through algorithmic quantification of changes in the waveform that occur at different volume states. A noninvasive method to capture peripheral venous signals is described (Non-Invasive Venous waveform Analysis, NIVA). The objective of this proof-of-concept study was to characterize changes in NIVA signal with dialysis. We hypothesized that there would be a change in signal after dialysis and that the rate of intradialytic change in signal would be predictive of IDH.

METHODS

Fifty subjects undergoing inpatient hemodialysis were enrolled. A 10-mm piezoelectric sensor was secured to the middle volar aspect of the wrist on the extremity opposite to the access site. Signals were obtained fifteen minutes before, throughout, and up to fifteen minutes after hemodialysis. Waveforms were analyzed after a fast Fourier transformation and identification of the frequencies corresponding to the cardiac rate, with a NIVA value generated based on the weighted powers of these frequencies.

RESULTS

Adequate quality (signal to noise ratio > 20) signals pre- and post- dialysis were obtained in 38 patients (76%). NIVA values were significantly lower at the end of dialysis compared to pre-dialysis levels (1.203 vs 0.868, p < 0.05, n = 38). Only 16 patients had adequate signals for analysis throughout dialysis, but in this small cohort the rate of change in NIVA value was predictive of IDH with a sensitivity of 80% and specificity of 100%.

CONCLUSIONS

This observational, proof-of-concept study using a NIVA prototype device suggests that NIVA represents a novel and non-invasive technique that with further development and improvements in signal quality may provide static and continuous measures of volume status to assist with volume directed dialysis and prevent intradialytic hypotension.