The “systolic volume balance” method for the noninvasive estimation of cardiac output based on pressure wave analysis

Abstract

Cardiac output (CO) monitoring is essential for the optimal management of critically ill patients. Several mathematical methods have been proposed for CO estimation based on pressure waveform analysis. Most of them depend on invasive recording of blood pressure and require repeated calibrations, and they suffer from decreased accuracy under specific conditions. A new systolic volume balance (SVB) method, including a simpler empirical form (eSVB), was derived from basic physical principles that govern blood flow and, in particular, a volume balance approach for the conservation of mass ejected into and flowed out of the arterial system during systole. The formulas were validated by a one-dimensional model of the systemic arterial tree. Comparisons of CO estimates between the proposed and previous methods were performed in terms of agreement and accuracy using "real" CO values of the model as a reference. Five hundred and seven different hemodynamic cases were simulated by altering cardiac period, arterial compliance, and resistance. CO could be accurately estimated by the SVB method as follows: CO = C × PP(ao)/(T - P(sm) × T(s)/P(m)) and by the eSVB method as follows: CO = k × C × PP(ao)/T, where C is arterial compliance, PP(ao) is aortic pulse pressure, T is cardiac period, P(sm) is mean systolic pressure, T(s) is systolic duration, P(m) is mean pressure, and k is an empirical coefficient. SVB applied on aortic pressure waves did not require calibration or empirical correction for CO estimation. An empirical coefficient was necessary for brachial pressure wave analysis. The difference of SVB-derived CO from model CO (for brachial waves) was 0.042 ± 0.341 l/min, and the limits of agreement were -0.7 to 0.6 l/min, indicating high accuracy. The intraclass correlation coefficient and root mean square error between estimated and "real" CO were 0.861 and 0.041 l/min, respectively, indicating very good accuracy. eSVB also provided accurate estimation of CO. An in vivo validation study of the proposed methods remains to be conducted.

Validation of a novel and existing algorithms for the estimation of pulse transit time: advancing the accuracy in pulse wave velocity measurement

“…Agreement, accuracy, precision, variability, and the association between the model (real) and estimated PWV values were evaluated according to previously described methodology (27). In brief, we used Pearson's correlation coefficient (r), intraclass correlation coefficient (ICC), root mean square error (RMSE), coefficient of variation, bias (mean difference between test and reference measurements), SD of differences (SDD), limits of agreement, and Bland-Altman analysis.…”

Pulse-Waveform Analysis of Normal Population using Laser Speckle Flowgraphy

“…1 Pressure wave analysis was also now used to monitor or evaluate cardiac output non-invasively, which is essential for the optimal management of critically ill patients with cardiovascular diseases. 12 Finally, according to recent reports, the retina can provide an anatomical index of the effects of metabolic and hemodynamic factors, and can predict future cardiovascular outcomes. 13 There is therefore a demonstrated need for a new targeting biomarker to easily and quickly detect and evaluate vascular and capillary aging in the retina, which we believe can be provided by laser speckle flowgraphy (LSFG).…”

A Novel Geometrical Analysis of the Arterial Pulse Based on the Golden Ratio Φ (Phi): Association With Heart Rate Variability

“…The arterial pulse and particularly the pressure wave is commonly examined: (a) at the brachial artery with the main purpose to determine its maximum and minimum pressure levels using cuff-based sphygmomanometers and (b) at the radial artery by palpation in order to determine its frequency via the measurement of heart beats per minute and consequently to estimate the pulse period. Gradually, the classic, traditional sphygmomanometric techniques 28 are transforming to more advanced technologies offering the recording and computation of a plethora of hemodynamic biomarkers others than SBP, DBP, MAP and heart rate such as central (aortic) blood pressure 29 , wave reflection indices (augmentation index, reflection timing and magnitude), pulse transit time 30 and arterial stiffness 31 , cardiac output [32][33][34] and others 35,36 . More interestingly wearable devices have been also developed thus providing the possibility of ambulatory monitoring of the above mentioned parameters 37,38 .…”