Non-invasive Stenotic Renal Artery Haemodynamics by in silico Medicine

An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease

Early diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.

CT-based fractional flow reserve: development and expanded application

Computations of fractional flow reserve, based on CT coronary angiography and computational fluid dynamics (CT-based FFR) to assess the severity of coronary artery stenosis, was introduced around a decade ago and is now one of the most successful applications of computational fluid dynamic modelling in clinical practice. Although the mathematical modelling framework behind this approach and the clinical operational model vary, its clinical efficacy has been demonstrated well in general. In this review, technical elements behind CT-based FFR computation are summarised with some key assumptions and challenges. Examples of these challenges include the complexity of the model (such as blood viscosity and vessel wall compliance modelling), whose impact has been debated in the research. Efforts made to address the practical challenge of processing time are also reviewed. Then, further application areas—myocardial bridge, renal stenosis and lower limb stenosis—are discussed along with specific challenges expected in these areas.

Influence of renal artery stenosis morphology on hemodynamics

OBJECTIVE

Currently, the clinical classification of the severity of renal artery stenosis (RAS) solely depends on the degree of stenosis. In addition, when the stenosis degree is between 50% and 70%, the clinical strategy is decided based on whether the RAS is hemodynamically significant. In this study, the influence of RAS morphological parameters on hemodynamics was numerically analyzed to provide a theoretical basis for clinical treatment.

METHODS

Idealized RAS models were established to investigate the hemodynamic effects of the stenosis length, asymmetric stenosis, and direction of the opening of the renal artery.

RESULTS

The longer the stenosis length, the greater is the ratio of the low time-averaged wall shear stress (WSS) and high oscillatory shear index (OSI) area distal stenosis (when the stenosis area is the same). In addition, asymmetric stenosis leads to a significant increase in the ratio of the renal artery peak systolic velocity (R-PSV) and the abdominal aorta peak systolic velocity (A-PSV) when the stenosis area is 60-70%. Furthermore, the fraction flow reserve (FFR) of the RAS model with 12 mm stenosis length, upward eccentricity and upward direction of renal artery opening was approximately equal to the cumulative value of the influence of different stenosis morphologies on FFR.

CONCLUSION

An assessment of the severity of RAS should consider the stenosis area and other morphological parameters, including the length and asymmetry of RAS as well as the direction of the opening of renal artery, particularly when the stenosis degree of RAS is between 50% and 70%.

Stenosis Indicators Applied to Patient-Specific Renal Arteries without and with Stenosis

Pulsatile flow in the abdominal aorta and the renal arteries of three patients was studied numerically. Two of the patients had renal artery stenosis. The aim of the study was to assess the use of four types of indicators for determining the risk of new stenosis after revascularization of the affected arteries. The four indicators considered include the time averaged wall shear stress (TAWSS), the oscillatory shear index (OSI), the relative reference time (RRT) and a power law model based in platelet activation modeling but applied to the endothelium, named endothelium activation indicator (EAI). The results show that the indicators can detect the existing stenosis but are less successful in the revascularized cases. The TAWSS and, more clearly, the EAI approach seem to be better in predicting the risk for stenosis relapse at the original location and close to the post-stenotic dilatation. The shortcomings of the respective indicators are discussed along with potential improvements to endothelial activation modeling and its use as an indicator for risks of restenosis.