Feasibility of non-invasive measurement of central blood pressure and arterial stiffness in shock

Photoplethysmography for the Assessment of Arterial Stiffness

This review outlines the latest methods and innovations for assessing arterial stiffness, along with their respective advantages and disadvantages. Furthermore, we present compelling evidence indicating a recent growth in research focused on assessing arterial stiffness using photoplethysmography (PPG) and propose PPG as a potential tool for assessing vascular ageing in the future. Blood vessels deteriorate with age, losing elasticity and forming deposits. This raises the likelihood of developing cardiovascular disease (CVD), widely reported as the global leading cause of death. The ageing process induces structural modifications in the vascular system, such as increased arterial stiffness, which can cause various volumetric, mechanical, and haemodynamic alterations. Numerous techniques have been investigated to assess arterial stiffness, some of which are currently used in commercial medical devices and some, such as PPG, of which still remain in the research space.

Correlation analysis of central arterial pressure parameters and the severity of atherosclerotic lesions in coronary arteries: A retrospective study

To investigate the correlation between central arterial pressure (CAP) parameters and the severity of atherosclerotic lesions in the coronary arteries, understand the value of CAP in assessing the risk of coronary heart disease (CHD), and provide a theoretical basis for the prevention and treatment of CHD. Between January 2021 and January 2022, 224 patients admitted to our hospital for complete coronary angiography (CAG) were included in this retrospective study. CAP parameters, including central systolic pressure (CSP), diastolic pressure (CDP), and pulse pressure (CPP), and Gensini scores were collected; the association between CAP parameters and the severity of coronary lesions was analyzed using the Pearson correlation coefficient (r) and multivariate regression analysis. CPP was significantly higher in the coronary multi-branch lesion group than in the single-branch lesion group in patients with CHD (P < .05). CSP, CDP, and CPP were significantly higher in the high Gensini score group than in the low Gensini score group for coronary vascular lesions; furthermore, CSP and CPP were significantly higher in the high Gensini score group than in the medium Gensini score group (P < .05). Pearson correlation analysis showed that CSP and CPP were positively and CDP was negatively correlated with the severity of coronary artery lesions in patients with CHD (P < .05). Logistic regression analysis showed that a history of diabetes, CSP, CDP, and CPP were independent risk factors for severe atherosclerotic lesions in the coronary arteries (P < .05). noninvasive CAP-related indices, such as CSP, CDP, and CPP, are independently correlated with and can be used to predict the severity of coronary lesions in patients with CHD, which may be beneficial for guiding clinical diagnosis and treatment.

Central Aortic Blood Pressure Waveform Estimation with a Temporal Convolutional Network

A novel temporal convolutional network (TCN) model is utilized to reconstruct the central aortic blood pressure (aBP) waveform from the radial blood pressure waveform. The method does not need manual feature extraction as traditional transfer function approaches. The data acquired by the SphygmoCor CVMS device in 1,032 participants as a measured database and a public database of 4,374 virtual healthy subjects were used to compare the accuracy and computational cost of the TCN model with the published convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model. The TCN model was compared with CNN-BiLSTM in the root mean square error (RMSE). The TCN model generally outperformed the existing CNN-BiLSTM model in terms of accuracy and computational cost. For the measured and public databases, the RMSE of the waveform using the TCN model was 0.55 ± 0.40 mmHg and 0.84 ± 0.29 mmHg, respectively. The training time of the TCN model was 9.63 min and 25.51 min for the entire training set; the average test time was around 1.79 ms and 8.58 ms per test pulse signal from the measured and public databases, respectively. The TCN model is accurate and fast for processing long input signals, and provides a novel method for measuring the aBP waveform. This method may contribute to the early monitoring and prevention of cardiovascular disease.