ARTERIAL PULSE WAVEFORM ANALYSIS

“When using a sphygmomanometer, we pauperize our senses and weaken clinical acuity.”

          ­- British Medical Journal, some 100 years ago, on forgetting art of arterial pulse analysis.

Qualitative arterial pulse wave analysis has been exploited, for millennia to diagnose various diseases, by Indians, Chinese, Greeks and Roman physicians. Gallen described 27 type of pulses, in ‘on Prognosis from the Pulses’ written before 210 AD, and Wang Shu-he gave description of 24 types of pulses, in Mai Jung, in 220 AD. Traditional Indian teaching had largely been oral, from master to student, hence little documentation is available.

Quantitative arterial pulse wave analysis was first attempted by, Leonhard Euler in eighteen century, when he applied the principle of conservation of mass and energy, to a tubular model of cardiovascular system.

Frederick Akbar Mohamed established the foundation of pulse wave analysis, in a short medical lifetime, from 1872 to 1884. He described the normal radial pressure waveform and showed the difference, between this and the carotid wave. He showed the effect of high blood pressure, on the radial waveform, and used the waveform to describe the natural history of essential hypertension.

In 1899 Otto Frank proposed Windkessel model of blood flow, which incorporated law of conservation of mass/energy and vascular stiffness.

Windkessel model describes, heart and systemic arterial system, like a closed hydraulic circuit, comprised of a water pump, connected to a chamber filled with water, except for a pocket of air. As it is pumped, the water compresses the air, which in turn pushes the water out of the chamber. Air is analogous to aortic compliance, recoil of which during diastole maintains the diastolic flow of blood.

Aorta and larger arteries are more compliant and can accommodate systolic ejected blood volume, whereas peripheral arteries are more resistant to forward blood flow. These two components, compliance and resistance, comprise the two element Windkessel model.

Wesseling and colleague added a third component, impedance to forward flow, from aortic valve, and developed three element Windkessel model. It integrated aortic impedance assessment, as well as factors affecting aortic compliance and peripheral resistance.

In 1904 Erlanger and Hooker suggested that stroke volume is proportional to the pulse pressure. He further noted that this relationship is fluctuating, as it is also dependent on systolic time and elasticity/compliance of arteries. The aortic and larger vessel compliance is affected by factors like age, gender, height and weight. Peripheral vascular resistance is affected by disease pathology and vasoactive drugs.

In hemodynamically unstable, critically ill patients, all these hemodynamic variables (LV ejection rate, aortic compliance, peripheral vascular resistance) are rapidly changing. Therefore cardiac output monitoring by pulse wave analysis may be subject to error. Hence this method of cardiac output monitoring need frequent calibration by, gold standard method,that is thermodilution cardiac output monitoring.

Kochoukos, in 1970, described a more accurate method of stroke volume estimation, by measurement of, Area Under Systolic portion of the arterial pulse waveform.

Wesseling and colleagues further developed this concept, by measuring the Area Under Systolic portion of arterial pulse waveform (Asys), from the end of diastole to the end of the ejection phase. Dividing Asys by aortic impedance provides stroke volume. This equation is further modified to include correction factor for the nonlinearity of aortic compliance.

There are three basic technologies, being use by different systems, arterial Pulse Contour, arterial Pulse Power and arterial Pressure to estimate stroke volume. 

Each utilize unique proprietary algorithm to do this. All these technologies are based on the fact that pulse pressure is directly proportional to stroke volume and inversely related to aortic compliance. However, to compensate for the fluctuating relationship between pulse pressure and stroke volume, each of these technologies use different approaches and algorithms.

Pulse Contour Analysis: this technique is utilized by PiCCO pulse contour system, Pulsion.

Area under the systolic portion of the curve, from initial rise to the closure of aortic valve, reflects the amount of blood ejected during systole (stroke volume). However it requires identification of aortic valve closure (dicrotic notch), which may be difficult. Therefore a correction factor is used utilizing wesseling method.

Pulse contour analysis is subject to error by difficulty in determining dicrotic notch which can arise because of many reasons. It is also affected by changing compliance of aorta and aortic flow during diastole.

However this technique requires external calibration by thermodilution method.

Arterial Pulse Power Analysis: This technique is use by LiDCO plus system.

Arterial pulse power analysis avoid the problems associated with pulse contour analysis as it is non morphology based (does not use pulse contour).

It is based on the principle of mass conservation. It assumes that the net power change in a single cardiac cycle, is the balance between, input of mass (stroke volume) minus removal of mass (blood distributed to peripheral circulation).

Ejection of blood into aorta (stroke volume) during each cardiac cycle, causes fluctuation in blood pressure around a mean value. By using mathematical algorithm, analysis of fluctuations in blood pressure around a mean value, allow determination of changes in stroke volume, with each cardiac cycle.

As the whole arterial pulse wave (cardiac cycle) is taken into consideration, it is independent of the position of reflected wave. Also effect of arterial damping are minimized with pulse power analysis, therefore any arterial site (peripheral as well as central) can be used.

Need external calibration (lithium dilution method is used in LiDCO system).

Arterial Pressure Based Cardiac output monitoring (APCO): This technique is utilized by Flo Trac/ Vigileo.

This technologies estimated stroke volume by measuring standard deviation, of full pulse wave, over a given period and a robust waveform characteristic assessment.

Pulse Pressure (PP) is measured by standard deviation of arterial pressure (σ AP) around mean arterial pressure (MAP) value, making it independent of the effect of vascular tone.

Arterial pulse wave is analyzed over 20 seconds at the rate of 100 times per second by creating 2000 data points and σ AP is calculated.

Stroke volume is estimated by multiplying σ AP by a conversion factor khi (×). 

Khi is calculated by analyzing MAP, vascular compliance and waveform characteristics. Vascular compliance is estimated by patient’s demographics (age, gender, height) and waveform characteristics by skewness (degree of symmetry) and kurtosis (degree of peak or flatness) of individual arterial pulse waveform.

As this technique does not utilize pulse contour and continuously adjusts for the ever-changing vascular tone, by using continuously adjusted conversion factor (Khi), it does not require calibration by thermodilution method.

Any arterial site can be used.     

Limitations of Pulse waveform analysis based cardiac output monitoring:

False leveling of pressure transducer or damping effect (underdamping or overdamping) may lead to erroneous calculation and estimation of stroke and parameters. Therefore these mechanical factors should always be kept in mind and corrected.

References:

Arterial waveform analysis for the anestheiosologist: past, present and future concepts. Robert H. Anesth Analg 2011;113

A brief history of arterial wave mechanics. Kim H. Parker. Med Biol Eng Comput, 2009:47

Functional hemodynamic monitoring, Pinsky, 2007.

Monitoring cardiac function in intensive care S M Tibby, Murdoch. Arch Dis Child 2003;88

Cardiac output monitoring: an integrative perspective. Jamal A. Alhashemi. Critical Care2011, 15.

Cardiac output monitoring: basic science and clinical application. S. Jhanji. Anesthesia, 2008.

Hemodynamic monitoring: Evolving technologies and clinical practice. Mary E. Lough, 2015