Cardiac output and stroke volume variation measured by the pulse wave transit time method: a comparison with an arterial pressure-based cardiac output system

The Validation of Cardiac Index and Stroke-Volume Variation Measured by the Pulse-Wave Transit Time-Analysis Versus Conventional Pulse-Contour Analysis After Off-Pump Coronary Artery Bypass Grafting: Observational Study

OBJECTIVE

To compare the reliability of cardiac index (CI) and stroke-volume variation (SVV) measured by the pulse-wave transit-time (PWTT) method using estimated continuous cardiac output (esCCO) technique with conventional pulse-contour analysis after off-pump coronary artery bypass grafting (OPCAB).

DESIGN

A single-center, prospective, observational study.

SETTING

At a 1,000-bed university hospital.

PARTICIPANTS

A total of 21 patients were enrolled after elective OPCAB.

INTERVENTIONS

The study authors performed a method comparison study with simultaneous measurement of CI and SVV based on the esCCO technique (CI_esCCO and esSVV, correspondingly) and pulse-contour analysis (CI_PCA and SVV_PCA, correspondingly). As a secondary analysis, they also assessed the trending ability of CI_esCCO versus CI_PCA. MEASUREMENTS AND MAIN RESULTS: The authors analyzed 178 measurement pairs for CI, and 174 pairs for SVV during the 10 study stages. The mean bias between CI_esCCO and CI_PCA was 0.06 L min/m², with limits of agreement of ± 0.92 L min/m² and a percentage error (PE) of 35.3%. The analysis of the trending ability of CI measured by PWTT revealed a concordance rate of 70%. The mean bias between esSVV and SVV_PCA was -6.1%, with limits of agreement of ± 15.5% and a PE of 137%.

CONCLUSIONS

The overall performance of CI_esCCO and esSVV versus CI_PCA and SVV_PCA is not clinically acceptable. A further improvement of the PWTT algorithm may be required for an accurate and precise assessment of CI and SVV.

Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs

Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs. Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs. Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5-12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot). Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.

Comparison of parameter types for the calibration of noninvasive continuous cardiac output monitoring of patients undergoing lumbar spinal surgery in the prone position

BACKGROUND

Cardiac output (CO) decreases on reversing the patient's position to the prone position. Estimated continuous cardiac output (esCCO) systems can noninvasively and continuously monitor CO calibrated by patient information or transesophageal echocardiogram (TEE).

OBJECTIVE

To compare the accuracy, precision, and trending ability of two calibration methods of CO estimation in patients in prone position.

METHODS

The CO estimates calibrated by TEE (esT) and patient information (esP) of 26 participants were included. CO was collected at four time points. The accuracy and precision of agreement were evaluated using the Bland-Altman method. A four-quadrant plot was used for trending ability analysis.

RESULTS

The bias between esP and TEE and between esT and TEE was 0.2594 L/min (95% limits of agreement (LoA): -1.8374 L/min to 2.3562 L/min) and 0.0337 L/min (95% LoA: -0.7381 L/min to 0.8055 L/min), respectively. A strong correlation was found between ΔesP and ΔTEE (p< 0.001, CCC = 0.700) and between ΔesT and ΔTEE (p< 0.001, CCC = 0.794). The concordance rates between ΔesP and ΔTEE and between ΔesT and ΔTEE were 91.9% and 97.1%, respectively.

CONCLUSION

Despite limited accuracy and precision, esP showed acceptable trending ability. The trending ability of esCCO calibrated by the reference TEE value was comparable with that of TEE.

A machine learning strategy for fast prediction of cardiac function based on peripheral pulse wave

OBJECTIVE

Pulse wave has been considered as a message carrier in the cardiovascular system (CVS), capable of inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Clarification and prediction of cardiovascular function by means of powerful feature-abstraction capability of machine learning method based on pulse wave is of great clinical significance in health monitoring and CVDs diagnosis, which remains poorly studied.

METHODS

Here we propose a machine learning (ML)-based strategy aiming to achieve a fast and accurate prediction of three cardiovascular function parameters based on a 412-subject database of pulse waves. We proposed and optimized an ML-based model with multi-layered, fully connected network while building up two high-quality pulse wave datasets comprising a healthy-subject group and a CVD-subject group to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV), which are essential messengers in monitoring CVS conditions.

RESULTS

Our ML model is validated through consistency analysis of the ML-predicted three cardiovascular function parameters with clinical measurements and is proven through error analysis to have capability of achieving a high-accurate prediction on TPR and SV for both healthy-subject group (accuracy: 85.3%, 86.9%) and CVD-subject group (accuracy: 88.3%, 89.2%).

DISCUSSION

The independent sample t-test proved that our subject groups could represent the typical physiological characteristics of the corresponding population. While we have more subjects in our datasets rather than previous studies after strict data screening, the proposed ML-based strategy needs to be further improved to achieve a disease-specific prediction of heart failure and other CVDs through training with larger datasets and clinical measurements.

CONCLUSION

Our study points to the feasibility and potential of the pulse wave-based prediction of physiological and pathological CVS conditions in clinical application.

Noninvasive cardiac output measurement is inaccurate in patients with severe aortic valve stenosis undergoing transcatheter aortic valve implantation

Background

Noninvasive cardiac output (CO) measured using ClearSight™ eliminates the need for intra-arterial catheter insertion. The purpose of this study was to examine the accuracy of non-invasive CO measurement in patients with severe aortic stenosis (AS).

Methods

Twenty-eight patients undergoing elective transcatheter aortic valve implantation were prospectively enrolled in this study. The CO was simultaneously measured twice before and twice after valve deployment (total of four times) per patient, and the CO was compared between the ClearSight (CO_ClearSight) system and the pulmonary artery catheter (PAC) thermodilution (CO_TD) method as a reference. The Bland-Altman analysis was used to compare the percentage errors between the methods.

Results

A total of 112 paired data points were obtained. The percentage error between the CO_ClearSight and CO_TD was 43.1%. The paired datasets were divided into the following groups according to the systemic vascular resistance index (SVRI): low (< 1,200 dyne s/cm⁵/m²) and normal (1,200–2,500 dyne s/cm⁵/m²). The percentage errors were 44.9% and 49.4%, respectively. The discrepancy of CO between CO_ClearSight and CO_TD was not significantly correlated with SVRI (r = −0.06, P < 0.001). The polar plot analysis showed the trending ability of the CO_ClearSight after artificial valve deployment was 51.1% which below the acceptable cut-off (92%).

Conclusions

The accuracy and the trending ability of the ClearSight CO measurements were not acceptable in patients with severe AS. Therefore, the ClearSight system is not interchangeable with the PAC thermodilution for determining CO in this population.

Pulse wave transit time during exercise testing reflects the severity of heart disease in cardiac patients

The pulse wave transit time (PWTT) is easily measured as the time from the R wave of an electrocardiogram to the arrival of the pulse wave measured by an oxygen saturation monitor at the earlobe. We investigated whether the change of PWTT during exercise testing reflects cardiopulmonary function. Eighty-nine cardiac patients who underwent cardiopulmonary exercise testing (CPX) were enrolled. We analyzed the change of PWTT during exercise and the relationship between the shortening of the PWTT and CPX parameters. PWTT was significantly shortened from rest to peak exercise (204.6 ± 33.6 vs. 145.6 ± 26.4 msec, p < 0.001) in all of the subjects. The patients with heart failure had significantly higher PWTT at peak exercise than the patients without heart failure (152.7 ± 27.1 vs. 140.4 ± 24.8 msec, p = 0.031). The shortening of PWTT from rest to peak exercise showed significant positive correlations with the peak O₂ uptake (VO₂) (r = 0.56, p < 0.001), anaerobic threshold (r = 0.40, p = 0.016), and % increase of systolic blood pressure during exercise (r = 0.75, p < 0.001), and a negative correlation with the slope of the increase in ventilation versus the increase in CO₂ output (VE-VCO₂ slope) (r = - 0.42, p = 0.010) in the patients with heart failure. PWTT was shortened during exercise as the exercise intensity increased. In the patients with heart failure, the shortening of PWTT from rest to peak exercise was smaller in those with lower exercise capacity and those with higher VE-VCO₂ slope, an established index known to reflect the severity of heart failure.