NursingCenter Blog. Continuing Education More. May 25 by Myrna B. Share this on. In a previous blog post, we discussed preload and afterload. You may recall, preload is the amount of ventricular stretch at the end of diastole.
Afterload is the pressure the myocardial muscle must overcome to push blood out of the heart during systole. The left ventricle ejects blood through the aortic valve against the high pressure of the systemic circulation, also known as systemic vascular resistance SVR. For example, if the blood vessels tighten or constrict, SVR increases, resulting in diminished ventricular compliance, reduced stroke volume and ultimately a drop in cardiac output.
If blood vessels dilate or relax, SVR decreases, reducing the amount of left ventricular force needed to open the aortic valve. Most users should sign in with their email address. If you originally registered with a username please use that to sign in. To purchase short term access, please sign in to your Oxford Academic account above. Don't already have an Oxford Academic account?
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Sign In or Create an Account. Sign In. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume 6. Chapter 3: Vascular Resistance. Bertand , M. Oxford Academic. Google Scholar. Cite Cite M. Cardiac output CO and systemic vascular resistance SVR are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses.
Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance.
The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis. These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings. The volume of blood ejected by the heart per unit time is a vital physiological parameter known as the cardiac output CO.
CO varies, depending on the oxygen and nutrient requirements of the organs and tissues; the other major determinants of CO include blood volume status, cardiac contractility and systemic vascular resistance SVR. SVR is the aggregate resistance to blood flow in the systemic circulation. Vessel sizes decrease and the SVR increases when there is an enhancement in this tone, while the suppression of this tone results in the opposite. An increase in resistance can lead to a decrease in CO, and vice versa.
Measurements of SVR and CO can provide important information about the overall hemodynamic performance of patients for diagnostic purposes. For instance, CO can be used to examine the cardiac status of critically ill patients and assist in the diagnosis of those with suspected cardiovascular diseases, like acute coronary syndromes, hypovolemia, valvular stenosis, myocarditis, cardiomyopathy and arteriosclerosis[ 1 ].
Low CO can indicate potentially adverse cardiovascular events such as cardiogenic shock[ 2 ]. Similarly, SVR can be a useful diagnostic tool, as deviation beyond the normal SVR range may be an indicator of critical illness. For example, an increase in SVR may be observed in hemorrhagic shock due to trauma, and ischemic and hemorrhagic stroke patients[ 3 ], while a depression in SVR is evident in distributive shock patients such as those with sepsis[ 4 ] or anaphylaxis.
Continuous monitoring of SVR has been suggested as a diagnostic and research tool[ 5 ]. The current gold standard for CO measurement, the thermodilution technique[ 6 ], is an invasive procedure requiring the insertion of a pulmonary artery catheter. Recent developments have enabled CO to be estimated non-invasively or with minimal invasiveness using Doppler ultrasound, thoracic bioimpedance and pulse contour analysis[ 6 — 10 ] but few of these methods have not been used extensively in clinical settings[ 11 ].
Some of the reasons for the underutilization of these non-invasive methods include the requirement of a trained operator, the cost of the required specialized equipment and their disposable components, as well as the accuracy, precision and reproducibility of the measurement methods.
As an example, the Doppler ultrasound method requires well-trained personnel to operate[ 12 ] and the uncertainty of the flow profile and diameter of the blood vessel contributes to the inaccuracy of the method[ 8 , 13 ] and the bioimpedance method has demonstrated poor results in numerous validation studies in critically ill and septic patients[ 8 ].
The minimally invasive pulse contour analysis technique requires continuous measurement of the arterial pulse pressure waveform from a peripheral artery where the waveform itself can be measured invasively or non-invasively as well as calibration against a standard method such as transpulmonary thermodilution. Pulse contour analysis is considered minimally invasive because the patients in a critical care setting are assumed to already have the central venous and arterial cannulation required for transpulmonary thermodilution and thus no additional catheter is required[ 6 ].
The Finapres or the Portapres device[ 7 , 10 ], used with the Modelflow pulse wave analysis method, can be used to continuously monitor cardiac output non-invasively, but these devices are currently not widely used in the clinical setting. None of the existing non-invasive measurement methods are without drawbacks, or are suitable for all cohorts of patients, and the search for a low-cost, easy to use technique for different situations continues.
The photoplethysmogram PPG sensor is a non-invasive, low-cost and easy to use device that is routinely used in clinical settings to measure blood oxygen saturation levels. The device usually consists of light emitter single or dual wavelength and a photodetector, packaged in a small and highly portable form factor. The sensor is usually applied to the earlobe or the finger of the patient and it is comfortable enough to enable continuous measurement over a long period of time; in this study, the PPG signal was used as one of the inputs to estimate CO or SVR.
Recently, Wang et al. In the studies by Awad et al. Although the bias of their CO and SVR estimations was small, the precision was considered not sufficiently high for providing absolute values suitable for clinical use.
A multivariate approach to classify SVR into discrete categories based on PPG features was previously developed by our research group and showed good results, especially in identifying patients with low SVR[ 16 ]; but the method did not provide an estimate of the actual SVR value.
In many small hospital or prehospital care settings, the ability to follow the longitudinal trend of CO and SVR is very important as it enables monitoring of the effects of treatment on patient outcomes.
The development of an approach that can provide an accurate estimate of SVR based on PPG measurement was therefore considered desirable, as it is the first step to enable trend monitoring. In this study, it is proposed that a more accurate estimation of SVR and CO may be obtained using a multivariate regression model based on the use of PPG and routine cardiovascular measurements.
Specifically, unique features extracted from the PPG variability PPGV , which were anticipated to improve the estimation accuracy, were used in the regression model. Furthermore, stringent model selection and assessment of generalized performance of this estimation model were implemented with a nested leave-one-out cross validation procedure LOOCV. The study was approved by the Human Research Ethics Committee of the Prince of Wales Hospital, and conducted according to the Australian national guidelines on ethical research involving human subjects, as well as the World Medical Association Declaration of Helsinki.
Arterial blood pressure was measured invasively using a radial artery catheter, and MAP was subsequently recorded. CVP was measured invasively using a catheter inserted into the superior vena cava while the CO was measured via a pulmonary artery catheter using the thermodilution method. No other interventions were performed during the measurement period. SVR, in units of dyn. The PPG signal was sampled at either Hz or 1 kHz and recorded for a duration of approximately 10 min.
The signal was not subjected to any high-pass filtering prior to sampling to preserve the baseline variation. As described in a previous publication[ 17 ], the 48 sets of data used in the analysis were obtained after the exclusion of 16 sets of poor quality PPG signals from a larger pool of 64 subjects which contained severe motion artifact or baseline drift, frequent ectopic abnormal beats, and barely recognizable cardiac pulses which prohibited reliable pulse detection.
The derivation of various PPG features and their respective meanings have been explained in detail in a previous publication by the authors[ 16 ]. The two main categories are the spectral features derived from the low frequency LF, 0. The morphological feature used is the pulse width PW , which is the normalized width in time of the PPG pulse at half of its amplitude peak to trough.
Several notch-related features described in the previous work of the authors[ 16 ] were not included in the feature pool because not all patients had a clear dicrotic notch or inflection point. This complicates the accurate extraction of the feature value.
It was found that these features caused unstable LOOCV feature selection described in a later subsection when left in the feature pool. The CO or SVR was estimated as a weighted sum of a chosen subset of M features from the pool of all extracted features, described in the previous section, by means of linear least square modelling.
In the following exposition, the unknown variable to be estimated is assumed to be CO, but the methods are equally applicable to estimate SVR. The set of weights, w , which defines the CO least square model for the M selected features, can be solved by using the relationship. An unknown CO value can be estimated using the set of features extracted from the PPG waveform from the relationship in 2 , once the appropriate weights have been discovered.
The set of features were selected using a feature selection algorithm and the model is then validated using the LOOCV method, as described in the following section.
The degree of agreement between the estimated and the measured CO is evaluated using Bland-Altman plots[ 20 ]. The squared and cubed value, as well as the logarithms of each feature is added into the feature pool to expand the seven existing features to a total of 28 features in the feature pool.
These features, referred to as the transformed features in this paper, are included to ensure any non-linear relationship between the features and the CO is captured by the multivariate model. If all of the subjects are used in the training phase to obtain the model weight vector w which is subsequently used to estimate y i , the MSE in 5 will typically be underestimated.
This is due to the reason that the model is optimized to reduce the MSE of the training data, but not on unseen data. The data used for testing must not be used in either the training or feature selection phases in order to obtain an unbiased measure of the generalized performance expected error rate for new, unseen data when testing is done.
In this study, a nested leave-one-out cross-validation scheme was used. This was achieved by repeatedly dividing the data into three sets training, feature selection and test sets such that the data in the test set did not appear in the feature selection or training sets for a given iteration.
This procedure was repeated 48 times, leaving out one subject on each run.
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