Aging has a profound influence on arterial wall structure and function. We have previously reported the relationship among pulse wave velocity, age, and blood pressure in hypertensive subjects. In the present study, we aimed for a quantitative interpretation of the observed changes in wall behavior with age using a constitutive modeling approach. We implemented a model of arterial wall biomechanics and fitted this to the group-averaged pressure-area (P-A) relationship of the "young" subgroup of our study population. Using this model as our take-off point, we assessed which parameters had to be changed to let the model describe the "old" subgroup's P-A relationship. We allowed elastin stiffness and collagen recruitment parameters to vary and adjusted residual stress parameters according to published age-related changes. We required wall stress to be homogeneously distributed over the arterial wall and assumed wall stress normalization with age by keeping average "old" wall stress at the "young" level. Additionally, we required axial force to remain constant over the cardiac cycle. Our simulations showed an age-related shift in pressure-load bearing from elastin to collagen, caused by a decrease in elastin stiffness and a considerable increase in collagen recruitment. Correspondingly, simulated diameter and wall thickness increased by about 20 and 17%, respectively. The latter compared well with a measured thickness increase of 21%. We conclude that the physiologically realistic changes in constitutive properties we found under physiological constraints with respect to wall stress could well explain the influence of aging in the stiffness-pressure-age pattern observed.

BackgroundPulmonary transit time (PTT) is an indirect measure of preload and left ventricular function, which can be estimated using the indicator dilution theory by contrast-enhanced ultrasound (CEUS). In this study, we first assessed the accuracy of PTT-CEUS by comparing it with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Secondly, we tested the hypothesis that PTT-CEUS correlates with the severity of heart failure, assessed by MRI and N-terminal pro-B-type natriuretic peptide (NT-proBNP).Methods and resultsTwenty patients referred to our hospital for cardiac resynchronization therapy (CRT) were enrolled. DCE-MRI, CEUS, and NT-proBNP measurements were performed within an hour. Mean transit time (MTT) was obtained by estimating the time evolution of indicator concentration within regions of interest drawn in the right and left ventricles in video loops of DCE-MRI and CEUS. PTT was estimated as the difference of the left and right ventricular MTT. Normalized PTT (nPTT) was obtained by multiplication of PTT with the heart rate. Mean PTT-CEUS was 10.5±2.4s and PTT-DCE-MRI was 10.4±2.0s (P=0.88). The correlations of PTT and nPTT by CEUS and DCE-MRI were strong; r=0.75 (P=0.0001) and r=0.76 (P=0.0001), respectively. Bland–Altman analysis revealed a bias of 0.1s for PTT. nPTT-CEUS correlated moderately with left ventricle volumes. The correlations for PTT-CEUS and nPTT-CEUS were moderate to strong with NT-proBNP; r=0.54 (P=0.022) and r=0.68 (P=0.002), respectively.Conclusions(n)PTT-CEUS showed strong agreement with that by DCE-MRI. Given the good correlation with NT-proBNP level, (n)PTT-CEUS may provide a novel, clinically feasible measure to quantify the severity of heart failure.Clinical Trial Registry: NCT01735838

CEUS-derived PTT and nPTT correlate to a fair degree with measures of systolic and diastolic function, NT-pro-BNP, and quality of life. As CEUS-derived PTT can be obtained easily, noninvasively and at the bedside, it is a promising future measure of general cardiac performance.

Survivors of myocardial infarction are at risk of life-threatening ventricular tachycardias (VTs) later in their lives. Current guidelines for implantable cardioverter defibrillators (ICDs) implantation to prevent VT-related sudden cardiac death is solely based on symptoms and left ventricular ejection fraction. Catheter ablation of scar-related VTs is performed following ICD therapy, reducing VTs, painful shocks, anxiety, depression and worsening heart failure. We postulate that better prediction of the occurrence and circuit of VT, will improve identification of patients at risk for VT and boost preventive ablation, reducing mortality and morbidity. For this purpose, multiple time-evolving aspects of the underlying pathophysiology, including the anatomical substrate, triggers and modulators, should be part of VT prediction models. We envision digital twins as a solution combining clinical expertise with three prediction approaches: evidence-based medicine (clinical practice), data-driven models (data science) and mechanistic models (biomedical engineering). This paper aims to create a mutual understanding between experts in the different fields by providing a comprehensive description of the clinical problem and the three approaches in an understandable manner, leveraging future collaborations and technological innovations for clinical decision support. Moreover, it defines open challenges and gains for digital twin solutions and discusses the potential of hybrid modelling.

Arterial pulse wave velocity (PWV) depends on blood pressure (BP). Correction of PWV for BP is commonly performed using a statistical approach, requiring a patient cohort. We recently developed a mechanistic, model-predictive approach to assess BP-independent changes in carotid PWV (cPWV) at the level of the individual. The goal of the present study is to compare our novel technique to conventional statistical correction, in the context of anti-cancer therapy using anti-angiogenic drugs (AADs). AADs frequently lead to a PWV increase, but also to hypertension, underlining the need for BP correction of PWV measurements. We obtained carotid artery systolic and diastolic cross-sectional areas (echotracking) and corresponding BPs (tonometry) in 48 patients before starting AAD treatment (sorafenib/sunitinib), and at four follow-up visits spaced 2 weeks apart. For each patient, we derived cPWV and a baseline single-exponential BP cross-sectional area curve. Based on these baseline curves and follow-up BPs, we predicted cPWV at follow-up due to BP. By comparing predicted and measured cPWVs at follow-up, we assessed the BP-independent cPWV increase. In the same way, we assessed whether diastolic cross-sectional area (A) changed beyond the BP-induced amount. The AAD-induced BP-independent increase in cPWV was 0.43(0.09,0.77) m s (mean (95%CI), P=0.014, mechanistic approach) and 0.48(0.14,0.82) m s (P=0.006, statistical approach). A increased with 1.92(0.93,2.92) mm (P<0.001) and 2.14(1.06,3.23) mm (P<0.001), respectively. In conclusion, the present study demonstrates the feasibility and potential of our mechanistic, model-predictive approach to quantify BP-independent effects on arterial stiffness at the level of the individual, in a clinically relevant setting of AAD therapy.