Tube-load model: A clinically applicable pulse contour analysis method for estimation of cardiac stroke volume

Personalized aortic pressure waveform estimation from brachial pressure waveform using an adaptive transfer function

BACKGROUND AND OBJECTIVE

The aortic pressure waveform (APW) provides reliable information for the diagnosis of cardiovascular disease. APW is often measured using a generalized transfer function (GTF) applied to the peripheral pressure waveform acquired noninvasively, to avoid the significant risks of invasive APW acquisition. However, the GTF ignores various physiological conditions, which affects the accuracy of the estimated APW. To solve this problem, this study utilized an adaptive transfer function (ATF) combined with a tube-load model to achieve personalized and accurate estimation of APW from the brachial pressure waveform (BPW).

METHODS

The proposed method was validated using APWs and BPWs from 34 patients. The ATF was defined using a tube-load model in which pulse transit time and reflection coefficients were determined from, respectively, the diastolic-exponential-pressure-decay of the APW and a piece-wise constant approximation. The root-mean-square-error of overall morphology, mean absolute errors of common hemodynamic indices (systolic blood pressure, diastolic blood pressure and pulse pressure) were used to evaluate the ATF.

RESULTS

The proposed ATF performed better in estimating diastolic blood pressure and pulse pressure (1.63 versus 1.94 mmHg, and 2.37 versus 3.10 mmHg, respectively, both P < 0.10), and produced similar errors in overall morphology and systolic blood pressure (3.91 versus 4.24 mmHg, and 2.83 versus 2.91 mmHg, respectively, both P > 0.10) compared to GTF.

CONCLUSION

Unlike the GTF which uses fixed parameters trained on existing clinical datasets, the proposed method can achieve personalized estimation of APW. Hence, it provides accurate pulsatile hemodynamic measures for the evaluation of cardiovascular function.

Single measurement estimation of central blood pressure using an arterial transfer function

BACKGROUND

Central blood pressure (BP) better reflects the loading conditions on the major organs and is more closely correlated with future cardiovascular events. The increased invasiveness and risk of infection prevents the routine measurement of central BP. Arterial transfer functions can provide central BP estimates from clinically available peripheral measurements. However, current methods are either generalized, potentially lacking the ability to adapt to inter and intra subject variability, or individualized based on additional, clinically unavailable, pulse transit time measurements. This work proposes a novel, self-contained method for individualizing an arterial transfer function from a single peripheral pressure measurement, capable of accurately estimating central BP in a range of hemodynamic conditions.

METHODS

Pulse wave analysis of femoral BP waves was employed to formulate initial approximations of central BP and arterial inlet flow waveforms, to serve as objective functions for the identification of all model parameters. Root mean squared error (RMSE), and systolic and pulse pressure errors were assessed with respect to invasive aortic BP measurements in a seven (7) porcine endotoxin experiments. Systolic and pulse pressure errors were analysed using Bland-Altman analysis. Method accuracy is also compared with an idealized transfer function, derived using the measured aortic-femoral pulse transit time and minimizing the RMSE of model output pressure with respect to reference aortic pressure, a generalized transfer function model, and invasive femoral pressure measurements.

RESULTS

Mean bias and limits of agreement (95% CI) for the proposed method were 1.0(-4.6, 6.7)mmHg and -1.0(-6.6, 4.6)mmHg for systolic and pulse pressure, respectively, compared to 3.6(-0.9, 8.2)mmHg and 2.7(-1.8, 7.3)mmHg for the generalized transfer function model. Mean bias and limits of agreement for femoral pressure measurements were -6.4(-15.0, 2.3)mmHg and -9.4(-18.1, -0.8)mmHg, for systolic and pulse pressure, respectively. The pooled mean and standard deviation of the RMSE produced by the single measurement method, relative to reference aortic pressure, was 4.3(1.1)mmHg, consistent with estimates produced by the idealized transfer function, 3.9(1.2)mmHg, and improving of the generalized transfer function, 4.6(1.4)mmHg.

CONCLUSIONS

The proposed single measurement method provides accurate central BP estimates from routinely available peripheral pressure measurements, and nothing else. The method allows for the individualization of transfer functions on a per patient basis to better capture changes in patient condition during the progression of disease and subsequent treatment, at no additional clinical cost.

Estimation of cardiac stroke volume from radial pulse waveform by artificial neural network

BACKGROUND AND OBJECTIVES

Stroke volume (SV) and cardiac output (CO) are the key indicators for the evaluation of cardiac function and hemodynamic status during the perioperative period, which are very important in the detection and treatment of cardiovascular diseases. Traditional CO and SV measurement methods have problems such as complex operation, low precision and poor generalization ability.

METHODS

In this paper, a method for estimating stroke volume based on cascade artificial neural network (ANN) and time domain features of radial pulse waveform (SV_ANN) was proposed. The simulation datasets of 4000 radial pulse waveforms and stroke volume (SV_meas) were generated by a 55 segment transmission line model of the human systemic vasculature and a recursive algorithm. The ANN was trained and tested by 10-fold cross-validation, and compared with 12 traditional models.

RESULTS

Experimental results showed that the Pearson correlation coefficients and mean difference between SV_ANN and SV_meas (R=0.95, mean standard deviation (SD) = 0.00 ± 6.45) were better than the best results of the 12 traditional models. Moreover, as increasing the number of training samples, the performance improvement of the ANN (R=0.94(Δ + 0.04), mean ± SD = 0.00 ± 6.38(Δ± 2.02)) was better than the other best model, namely, multiple linear regression model (MLR) (R=0.93(Δ + 0.03), mean ± SD = 0.00 ± 6.99(Δ± 1.50)).

CONCLUSIONS

A method is proposed to estimate cardiac stroke volume by the ANN with time domain features of radial pulse wave. It avoids the complicated modeling process based on hemodynamics within traditional models, improves the estimation accuracy of SV, and has a good generalization ability.

Preload & Frank-Starling curves, from textbook to bedside: Clinically applicable non-additionally invasive model-based estimation in pigs

BACKGROUND

Determining physiological mechanisms leading to circulatory failure can be challenging, contributing to the difficulties in delivering effective hemodynamic management in critical care. Continuous, non-additionally invasive monitoring of preload changes, and assessment of contractility from Frank-Starling curves could potentially make it much easier to diagnose and manage circulatory failure.

METHOD

This study combines non-additionally invasive model-based methods to estimate left ventricle end-diastolic volume (LEDV) and stroke volume (SV) during hemodynamic interventions in a pig trial (N = 6). Agreement of model-based LEDV and measured admittance catheter LEDV is assessed. Model-based LEDV and SV are used to identify response to hemodynamic interventions and create Frank-Starling curves, from which Frank-Starling contractility (FSC) is identified as the gradient.

RESULTS

Model-based LEDV had good agreement with measured admittance catheter LEDV, with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 2.2 ml [-13.8, 22.5]. Model LEDV and SV were used to identify non-responsive interventions with a good area under the receiver-operating characteristic (ROC) curve of 0.83. FSC was identified using model LEDV and SV with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 0.07 [-0.68, 0.56], with FSC from admittance catheter LEDV and aortic flow probe SV used as a reference method.

CONCLUSIONS

This study provides proof-of-concept preload changes and Frank-Starling curves could be non-additionally invasively estimated for critically ill patients, which could potentially enable much clearer insight into cardiovascular function than is currently possible at the patient bedside.