Association of simultaneously measured four-limb blood pressures with cardiovascular function: a cross-sectional study

Deep-learning-based real-time individualization for reduce-order haemodynamic model

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individual LPM modeling with high accuracy to improve the practical clinical applicability of LPM. Clinical data was collected from two medical centres comprising haemodynamic indicators from 323 individuals, including brachial artery pressure waveforms, cardiac output data, and internal carotid artery flow waveforms. The data were expanded to 5000 synthesised cases that all fell within the physiological range of each indicator. LPM of the human blood circulation system was established. A double-path neural network (DPNN) was designed to input the waveforms of each haemodynamic indicator and their key features and then output the individual parameters of the LPM, which was labelled using a conventional optimization algorithm. Clinically collected data from the other 100 cases were used as the test set to verify the accuracy of the individual LPM parameters predicted by DPNN. The results show that DPNN provided good convergence in the training process. In the test set, compared with clinical measurements, the mean differences between each haemodynamic indicator and the estimate calculated by the individual LPM based on the DPNN were about 10 %. Furthermore, DPNN prediction only takes 4 s for 100 cases. The DPNN proposed in this study permits real-time and accurate individualization of LPM's. When facing medical issues involving haemodynamics, it lays the foundation for patient-specific numerical simulation, which may be beneficial for potential clinical application.

Inter-leg systolic blood pressure difference has been associated with all-cause and cardiovascular mortality: analysis of NHANES 1999-2004

BACKGROUND

Inter-leg systolic blood pressure difference (ILSBPD) has emerged as a novel cardiovascular risk factor. This study aims to investigate the predictive value of ILSBPD on all-cause and cardiovascular mortality in general population.

METHODS

We combined three cycles (1999-2004) of the National Health and Nutrition Examination Survey (NHANES) data. Levels of ILSBPD were calculated and divided into four groups based on three cut-off values of 5, 10 and 15mmHg. Time-to-event curves were estimated with the use of the Kaplan-Meier method, and two multivariable Cox proportional hazards regression models were conducted to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause and cardiovascular mortality associated with ILSBPD.

RESULTS

A total of 6 842 subjects were included, with the mean (SD) age of 59.5 (12.8) years. By December 31, 2019, 2 544 and 648 participants were identified all-cause and cardiovascular mortality respectively during a median follow-up of 16.6 years. Time-to-event analyses suggested that higher ILSBPD was associated with increased all-cause and cardiovascular mortality (logrank, p < 0.001). Every 5mmHg increment of ILSBPD brings about 5% and 7% increased risk of all-cause and cardiovascular mortality, and individuals with an ILSBPD ≥ 15mmHg were significantly associated with higher incidence of all-cause mortality (HR 1.43, 95%CI 1.18-1.52, p < 0.001) and cardiovascular mortality (HR 1.73, 95%CI 1.36-2.20, p < 0.001) when multiple confounding factors were adjusted. Subgroup and sensitivity analysis confirmed the relationship.

CONCLUSIONS

Our findings suggest that the increment of ILSBPD was significantly associated with higher risk of all-cause and cardiovascular mortality in general population.

Numerical study of hemodynamic changes in the Circle of Willis after stenosis of the internal carotid artery

BACKGROUND AND OBJECTIVES

In clinical practice a large number of patients with ischemic stroke have internal carotid artery (ICA) stenosis accompanied by Circle of Willis (CoW) stenosis. In the presence of carotid artery stenosis, CoW atherosclerosis may cause cerebral blood flow decompensation and may promote the development of ischemic stroke. The reason for the concomitant stenosis at both sites is unknown. This study investigated the hemodynamic effects of ICA stenosis on the CoW.

METHODS

We developed a three-dimensional/zero-dimensional (3D/0D) closed-loop geometric multiscale model of the cerebral artery to quantify the hemodynamic indicators, including time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI). Mild (<50 %), moderate (50-69 %) and severe (>69 %) ICA stenoses were established based on 3D models of cerebral arteries in two volunteers. Geometric multiscale computing models were numerically evaluated to obtain local hemodynamic changes in the CoW in order to assess the risk of stenosis in the CoW.

RESULTS

Model calculations showed that for all 3D models the A1 segment of the anterior cerebral artery (ACA) or the posterior communicating artery (PCA) within the CoW exhibited a hemodynamic environment with high OSI (>0.2) and low TAWSS (<1 Pa) when the ICA had a moderate stenosis. While in the case of mild and severe stenosis in ICA, there is no such phenomenon. The proportion of the surface area possessing high OSI and low TAWSS in the A1 segment of the ACA or in the PCA was mostly greater than 60 %, which might potentially cause the formation and development of atherosclerosis in CoW and finally lead to CoW stenosis.

CONCLUSIONS

Therefore, although moderate carotid artery stenosis may not cause ischemic stroke, it may cause hemodynamic changes in the CoW, which in turn may promote CoW stenosis and cause CoW decompensation. In clinical treatment attention should be paid not only to stenosis of the carotid arteries but also to changes in the hemodynamic environment within the CoW, in order to prevent the adverse effects of CoW stenosis.

Association between interleg systolic blood pressure difference and apparent peripheral neuropathy in US adults with diabetes: a cross-sectional study

BACKGROUND

Interleg systolic blood pressure difference (ILSBPD) is associated with peripheral artery disease, but the relationship between ILSBPD and apparent peripheral neuropathy in diabetic patients remains unclear. We explored the relationship between ILSBPD and apparent peripheral neuropathy and examined the possible effect modifiers in US adults with diabetes.

METHODS

One thousand and fifty-one diabetic participants were included in the study with complete data on systolic blood pressure of the lower extremities and Semmes-Weinstein 10-g monofilament testing from the 1999-2004 National Health and Nutritional Examination Surveys. Systolic blood pressure in the lower extremities was measured using an oscillometric blood pressure device with the patient in the supine position. Apparent peripheral neuropathy was defined as the presence of monofilament insensitivity.

RESULTS

Every 5-mmHg increment in ILSBPD is associated with an about 14% increased risk of apparent peripheral neuropathy in crude model, but after adjustment for covariates, the correlation became nonsignificant (P = 0.160). When participants were divided into groups based on ILSBPD cutoffs of 5, 10 and 15 mmHg in different analyses, there was a significantly increased risk of apparent peripheral neuropathy in the ILSBPD ≥ 15 mmHg group (OR 1.79, 95% CI 1.11-2.91, P = 0.018), even after adjusting for confounders. In subgroup analysis, no interaction effect was found (all P for interaction > 0.05).

CONCLUSIONS

In US adults with diabetes, an increase in the ILSBPD (≥ 15 mmHg) was associated with a higher risk of apparent peripheral neuropathy.

Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs

Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs. Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs. Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5-12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot). Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.

Arterial stiffness and contralateral differences in blood pressure: The Atherosclerosis Risk in Communities (ARIC) study

A large interarm difference in brachial systolic blood pressure (SBP) (≥10 or ≥15 mmHg) is strongly associated with elevated cardiovascular events and mortality. Evidence demonstrating whether such contralateral differences in SBP occur in ankle blood pressure and its association with arterial stiffness is scarce. The aims of this study were to characterize arm and ankle contralateral SBP differences in a sample of community‐dwelling older adults (5077), and to determine whether this difference is associated with arterial stiffness assessed by pulse wave velocity (PWV) between the heart and ankle (haPWV), femoral artery and ankle (faPWV), and brachial artery and ankle (baPWV) in the right and left sides. Prevalence of interarm SBP differences ≥10 and ≥15 mmHg was 5.1% and .7%, respectively; the corresponding prevalence for interankle SBP was 24.9% and 12.0%. Higher BMI and lower ankle‐brachial index (ABI) were significantly correlated with greater interarm SBP differences. Increased age, higher BMI, lower ABI, and greater contralateral differences in haPWV, faPWV, and baPWV were significantly correlated to greater interankle SBP differences. Interankle SBP difference ≥15 mmHg was significantly associated with contralateral differences of >80 cm/s in haPWV (OR = 1.94 [95% CI = 1.52–2.49]), >165 cm/s in faPWV (OR = 1.64 [95% CI = 1.27–2.12]), and >240 cm/s in baPWV (OR = 2.43 [95% CI = 1.94–3.05]). The associations remained significant after adjustment for age, sex, race, BMI, smoking status, and ABI. Compared with interarm differences, interankle differences in SBP are common in older adults. The magnitude of interankle, but not interarm, differences in SBP is associated with various measures of arterial stiffness.

A Numerical Model for Simulating the Hemodynamic Effects of Enhanced External Counterpulsation on Coronary Arteries

Traditional enhanced external counterpulsation (EECP) used for the clinical treatment of patients with coronary heart disease only assesses diastolic/systolic blood pressure (Q = D/S > 1.2). However, improvement of the hemodynamic environment surrounding vascular endothelial cells of coronary arteries after long-term application of EECP is the basis of the treatment. Currently, the quantitative hemodynamic mechanism is not well understood. In this study, a standard 0D/3D geometric multi-scale model of the coronary artery was established to simulate the hemodynamic effects of different counterpulsation modes on the vascular endothelium. In this model, the neural regulation caused by counterpulsation was thoroughly considered. Two clinical trials were carried out to verify the numerical calculation model. The results demonstrated that the increase in counterpulsation pressure amplitude and pressurization duration increased coronary blood perfusion and wall shear stress (WSS) and reduced the oscillatory shear index (OSI) of the vascular wall. However, the impact of pressurization duration was the predominant factor. The results of the standard model and the two real individual models indicated that a long pressurization duration would cause more hemodynamic risk areas by resulting in excessive WSS, which could not be reflected by the change in the Q value. Therefore, long-term pressurization during each cardiac cycle therapy is not recommended for patients with coronary heart disease and clinical treatment should not just pay attention to the change in the Q value. Additional physiological indicators can be used to evaluate the effects of counterpulsation treatment.

A patient-specific modelling method of blood circulatory system for the numerical simulation of enhanced external counterpulsation

Lumped parameter model (LPM) is a common numerical model for hemodynamic simulation of human's blood circulatory system. The numerical simulation of enhanced external counterpulsation (EECP) is a typical biomechanical simulation process based on the LPM of blood circulatory system. In order to simulate patient-specific hemodynamic effects of EECP and develop best treatment strategy for each individual, this study developed an optimization algorithm to individualize LPM elements. Physiological data from 30 volunteers including approximate aortic pressure, cardiac output, ankle pressure and carotid artery flow were clinically collected as optimization objectives. A closed-loop LPM was established for the simulation of blood circulatory system. Aiming at clinical data, a sensitivity analysis for each element was conducted to identify the significant ones. We improved the traditional simulated annealing algorithm to iteratively optimize the sensitive elements. To verify the accuracy of the patient-specific model, 30 samples of simulated data were compared with clinical measurements. In addition, an EECP experiment was conducted on a volunteer to verify the applicability of the optimized model for the simulation of EECP. For these 30 samples, the optimization results show a slight difference between clinical data and simulated data. The average relative root mean square error is lower than 5%. For the subject of EECP experiment, the relative error of hemodynamic responses during EECP is lower than 10%. This slight error demonstrated a good state of optimization. The optimized modeling algorithm can effectively individualize the LPM for blood circulatory system, which is significant to the numerical simulation of patient-specific hemodynamics.

Pulse-Wave-Pattern Classification with a Convolutional Neural Network

Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.