Continuous left ventricular ejection fraction monitoring by aortic pressure waveform analysis

Intrinsic Frequency Analysis and Fast Algorithms

Intrinsic Frequency (IF) has recently been introduced as an ample signal processing method for analyzing carotid and aortic pulse pressure tracings. The IF method has also been introduced as an effective approach for the analysis of cardiovascular system dynamics. The physiological significance, convergence and accuracy of the IF algorithm has been established in prior works. In this paper, we show that the IF method could be derived by appropriate mathematical approximations from the Navier-Stokes and elasticity equations. We further introduce a fast algorithm for the IF method based on the mathematical analysis of this method. In particular, we demonstrate that the IF algorithm can be made faster, by a factor or more than 100 times, using a proper set of initial guesses based on the topology of the problem, fast analytical solution at each point iteration, and substituting the brute force algorithm with a pattern search method. Statistically, we observe that the algorithm presented in this article complies well with its brute-force counterpart. Furthermore, we will show that on a real dataset, the fast IF method can draw correlations between the extracted intrinsic frequency features and the infusion of certain drugs.

Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance

Background

The aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which’s inter-beat evolution could add significant new data and insight to improve diagnosis and treatment. The method developed uses the clinically available inputs of aortic pressure, heart rate and baseline end-systolic volume (via echocardiography) to determine the outputs of left ventricular pressure, volume and dead space volume, and thus the TVE curve. This approach avoids directly assuming the shape of the TVE curve, allowing more effective capture of intra- and inter-patient variability.

Results

The resulting TVE curve was experimentally validated against the TVE curve as derived from experimentally measured left ventricular pressure and volume in animal models, a data set encompassing 46,318 heartbeats across 5 Piétrain pigs. This simulated TVE curve was able to effectively approximate the measured TVE curve, with an overall median absolute error of 11.4% and overall median signed error of −2.5%.

Conclusions

The use of clinically available inputs means there is potential for real-time implementation of the method at the patient bedside. Thus the method could be used to provide additional, patient specific information on intra- and inter-beat variation in heart function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12938-017-0338-7) contains supplementary material, which is available to authorized users.

Effects of various cardiovascular drugs on indices obtained with two-dimensional speckle tracking echocardiography of the left atrium and time-left atrial area curve analysis in healthy dogs

OBJECTIVE

To evaluate the effects of dobutamine, esmolol, milrinone, and phenylephrine on left atrial phasic function of healthy dogs.

ANIMALS

9 healthy Beagles.

PROCEDURES

Following sedation with propofol on each of 4 experimental days, dogs were administered a constant rate infusion of dobutamine (5 μg/kg/min), esmolol (500 μg/kg/min), milrinone (25 μg/kg, IV bolus, followed by 0.5 μg/kg/min), or phenylephrine (2 μg/kg/min). There was at least a 14-day interval between experimental days. Each drug was administered to 6 dogs. Conventional and 2-D speckle tracking echocardiography were performed before (baseline) and after administration of the cardiovascular drug, and time-left atrial area curves were derived to calculate indices for left atrial reservoir, conduit, and booster pump functions (left atrial phasic function) and left ventricular contractility and lusitropy.

RESULTS

Compared with baseline values, indices for left atrial reservoir and booster pump functions and left ventricular contractility and lusitropy were significantly increased following dobutamine administration; indices for left atrial phasic function and left ventricular lusitropy were changed insignificantly, and indices for left ventricular contractility were significantly impaired following esmolol administration; indices for left atrial phasic function and left ventricular relaxation were changed insignificantly, and indices for left ventricular systolic function were significantly augmented following milrinone administration; and indices for left atrial phasic function and left ventricular lusitropy were changed insignificantly, and indices of ventricular contractility were significantly impaired following phenylephrine administration.

CONCLUSIONS AND CLINICAL RELEVANCE

Results indicated that, following administration of dobutamine, esmolol, milrinone, or phenylephrine to healthy dogs, left atrial phasic function indices were fairly stable and did not parallel changes in left ventricular function indices.

Emax monitoring by aortic pressure waveform analysis

Emax- the maximal left ventricular elastance- is perhaps the best available scalar index of contractility. However, the conventional method for its measurement involves obtaining multiple ventricular pressure-volume loops at different loading conditions and is thus impractical. We previously proposed a more practical technique for tracking Emax from just a single beat of an aortic pressure waveform based on a lumped parameter model of the left ventricle and arteries. Here, we tested the technique against the conventional Emax measurement method in animals during inotropic interventions. Our results show that the estimated Emax changes corresponded fairly well to the reference changes, with a correlation coefficient of 0.793. With further development and testing, the technique could ultimately permit continuous and less invasive monitoring of Emax.

Beat-to-beat estimation of the continuous left and right cardiac elastance from metrics commonly available in clinical settings

INTRODUCTION

Functional time-varying cardiac elastances (FTVE) contain a rich amount of information about the specific cardiac state of a patient. However, a FTVE waveform is very invasive to directly measure, and is thus currently not used in clinical practice. This paper presents a method for the estimation of a patient specific FTVE, using only metrics that are currently available in a clinical setting.

METHOD

Correlations are defined between invasively measured FTVE waveforms and the aortic and pulmonary artery pressures from 2 cohorts of porcine subjects, 1 induced with pulmonary embolism, the other with septic shock. These correlations are then used to estimate the FTVE waveform based on the individual aortic and pulmonary artery pressure waveforms, using the "other" dysfunction's correlations as a cross validation.

RESULTS

The cross validation resulted in 1.26% and 2.51% median errors for the left and right FTVE respectively on pulmonary embolism, while the septic shock cohort had 2.54% and 2.90% median errors.

CONCLUSIONS

The presented method accurately and reliably estimated a patient specific FTVE, with no added risk to the patient. The cross validation shows that the method is not dependent on dysfunction and thus has the potential for generalisation beyond pulmonary embolism and septic shock.

Algorithmic processing of pressure waveforms to facilitate estimation of cardiac elastance

Background

Cardiac elastances are highly invasive to measure directly, but are clinically useful due to the amount of information embedded in them. Information about the cardiac elastance, which can be used to estimate it, can be found in the downstream pressure waveforms of the aortic pressure (P_ao) and the pulmonary artery (P_pa). However these pressure waveforms are typically noisy and biased, and require processing in order to locate the specific information required for cardiac elastance estimations. This paper presents the method to algorithmically process the pressure waveforms.

Methods

A shear transform is developed in order to help locate information in the pressure waveforms. This transform turns difficult to locate corners into easy to locate maximum or minimum points as well as providing error correction.

Results

The method located all points on 87 out of 88 waveforms for P_pa, to within the sampling frequency. For P_ao, out of 616 total points, 605 were found within 1%, 5 within 5%, 4 within 10% and 2 within 20%.

Conclusions

The presented method provides a robust, accurate and dysfunction-independent way to locate points on the aortic and pulmonary artery pressure waveforms, allowing the non-invasive estimation of the left and right cardiac elastance.

Continuous and less invasive central hemodynamic monitoring by blood pressure waveform analysis

Blood pressure waveform analysis may permit continuous (i.e., automated) and less invasive (i.e., safer and simpler) central hemodynamic monitoring in the intensive care unit and other clinical settings without requiring any instrumentation beyond what is already in use or available. This practical approach has been a topic of intense investigation for decades and may garner even more interest henceforth due to the evolving demographics as well as recent trends in clinical hemodynamic monitoring. Here, we review techniques that have appeared in the literature for mathematically estimating clinically significant central hemodynamic variables, such as cardiac output, from different blood pressure waveforms. We begin by providing the rationale for pursuing such techniques. We then summarize earlier techniques and thereafter overview recent techniques by our collaborators and us in greater depth while pinpointing both their strengths and weaknesses. We conclude with suggestions for future research directions in the field and a description of some potential clinical applications of the techniques.

An adaptive transfer function for deriving the aortic pressure waveform from a peripheral artery pressure waveform

We developed a new technique to mathematically transform a peripheral artery pressure (PAP) waveform distorted by wave reflections into the physiologically more relevant aortic pressure (AP) waveform. First, a transfer function relating PAP to AP is defined in terms of the unknown parameters of a parallel tube model of pressure and flow in the arterial tree. The parameters are then estimated from the measured PAP waveform along with a one-time measurement of the wave propagation delay time between the aorta and peripheral artery measurement site (which may be accomplished noninvasively) by exploiting preknowledge of aortic flow. Finally, the transfer function with its estimated parameters is applied to the measured waveform so as to derive the AP waveform. Thus, in contrast to the conventional generalized transfer function, the transfer function is able to adapt to the intersubject and temporal variability of the arterial tree. To demonstrate the feasibility of this adaptive transfer function technique, we performed experiments in 6 healthy dogs in which PAP and reference AP waveforms were simultaneously recorded during 12 different hemodynamic interventions. The AP waveforms derived by the technique showed agreement with the measured AP waveforms (overall total waveform, systolic pressure, and pulse pressure root mean square errors of 3.7, 4.3, and 3.4 mmHg, respectively) statistically superior to the unprocessed PAP waveforms (corresponding errors of 8.6, 17.1, and 20.3 mmHg) and the AP waveforms derived by two previously proposed transfer functions developed with a subset of the same canine data (corresponding errors of, on average, 5.0, 6.3, and 6.7 mmHg).