Association of Regional Wall Shear Stress and Progressive Ascending Aorta Dilation in Bicuspid Aortic Valve

Abstract

Background.

Aortic 4D flow MRI can quantify regions exposed to high wall shear stress (WSS), a known stimulus for arterial wall dysfunction. However, its association with longitudinal changes in aortic dilation in patients with bicuspid aortic valve (BAV) is unknown.

Objectives.

The aim of this study was to evaluate the role of WSS as a predictor of ascending aorta (AAo) growth at five-years or greater follow-up.

Methods.

This retrospective study identified 72 patients with BAV (45±12 years) who underwent MRI for surveillance of aortic dilation at baseline and ≥5-years follow-up. 4D flow MRI analysis included the calculation of WSS heatmaps to compare regional WSS in individual patients to population averages of healthy age- and sex-matched subjects (database of 136 controls). The relative areas of the AAo and aorta (in %) exposed to elevated WSS (outside the 95% confidence interval of healthy population averages) were quantified.

Results.

At median follow-up duration of 6.0 years, mean AAo growth rate was 0.24±0.20mm/year. The fraction of the AAo exposed to elevated WSS at baseline was increased for patients with higher growth rates (>0.24 mm/year, n=32) compared to those with growth rates <0.24mm/year (19.9% [10.2–25.5] vs. 5.7% [1.5–21.3]; p=0.008). Larger areas of elevated WSS in the AAo and entire aorta were associated with higher rates of AAo dilation >0.24 mm/year (OR=1.51 CI=1.05 – 2.17, p=0.026 and OR=1.70 CI=1.01 – 3.15, p=0.046).

Conclusions.

The area of elevated AAo WSS as assessed by 4D flow MRI identified BAV patients with higher rates of aortic dilation and thus might determine which patients require closer follow up.

INTRODUCTION

Progressive aortic dilation is associated with severe complications such as ascending aorta (AAo) aneurysm, dissection and rupture. Recent guidelines recommend preventive surgery for AAo dilation depending on aortic dimensions, etiology and comorbidities or in patients with high growth rates (>3mm per year) (1, 2). However, independent predictors of AAo growth rate to identify patients at highest risk of progressive aortic dilation and secondary complications are largely unknown (2). In patients with congenitally abnormal bicuspid aortic valves (BAV) prior studies have identified the phenotype of the aortic dilation pattern as a risk factor for progression (3, 4), but the data remains heterogenous and is not used for patient management (1, 2, 5, 6).

In the past decade, 4D flow magnetic resonance imaging (MRI) has emerged as a versatile technique for in-vivo measurement of aortic 3D hemodynamics and their interactions with the vessel wall (7–9). Aortic 4D flow MRI can quantify AAo regions exposed to high wall shear stress (WSS), a known stimulus for vessel wall remodeling related with arterial medial wall degeneration (10, 11). To account for well-known changes in aortic flow and WSS with age and sex (12–14), the concept of a ‘WSS heatmap’ has recently been developed, in which areas of elevated WSS are identified by comparing the aortic WSS distribution in an individual patient to confidence intervals and population averages of WSS in healthy age and sex matched control cohorts (15, 16). Areas of elevated WSS have been associated with AAo medial wall degeneration in histopathologic examination in BAV patients (11, 17), making WSS a promising metric to predict aortic complications.

However, the relationship between areas of elevated WSS and aortic growth rates in BAV aortopathy has not been established, due to lack of long term 4D flow follow-up data and the slow growth rates of the aorta (3, 4). The aim of this study was to evaluate the diagnostic value of 4D flow metrics, including WSS, as predictors of AAo growth in BAV patients over more than five years of follow-up.

METHODS

An institutional 4D flow MRI database consisting of 1061 4D flow MRI baseline exams from adult BAV patients was queried for patients who underwent an MRI between April 1, 2011 and October 31, 2020. This database contains BAV patients who underwent standard-of-care MRI for aortic dilation and/or aortic valve disease including aortic 4D flow MRI. Inclusion criteria were bicuspid aortic valve and baseline cardiothoracic MRI (including aortic 4D flow MRI) with another MRI exam at ≥5-years follow-up. Exclusion criteria were known connective tissue disease or aortic or valve surgery prior to the follow up MRI exam. A total of 117 BAV patients with baseline and follow-up cardiothoracic MRI were retrospectively identified. To evaluate the natural progression of the disease, 35 patients who underwent aortic or valve surgery prior to or between the MRI exams were excluded (including 4 with childhood repair of aortic coarctation), and 10 patients were excluded due to missing data related to incomplete data transfer. No patients meeting inclusion criteria had known connective tissue disease. Thus, a total of 72 patients (mean age 45±12 years, 50 male) were included in the final analysis.

Healthy controls (n=136, age range 19-81 years, 67 male, 69 female) with no known cardiovascular disease and a normal functioning tricuspid aortic valve were included as part of an ongoing IRB-approved study in order to compute regionally resolved 95% confidence interval values for physiologically normal aortic WSS.

Informed consent was prospectively obtained from all controls. All patients undergoing standard-of-care MRI were enrolled by retrospective chart review and waiver of consent. All subjects were included in the study according to procedures approved by the Northwestern University Institutional Review Board.

Cardiothoracic MRI

All patients and controls underwent cardiothoracic MRI on a 1.5T or 3T MRI system (MAGNETOM Aera, Avanto, Skyra, Siemens Healthcare, Erlangen, Germany). The left ventricle was covered using a stack of cine SSFP according to the SCMR guidelines (18). For patients, an aortic angiogram was acquired using either a 3D contrast enhanced (CE) MRA (spatial resolution = 0.6-1.2x0.6-1.2x1.0-2.0 mm³, flip angle = 25-40 degrees) after injection of chelated gadolinium contrast agent (Ablavar, Gadavist, Magnevist, or Multihance), or a 3D cardiac gated steady state free precession sequence (SSFP) in free breathing with diaphragm navigator (spatial resolution = 0.7-1.5x0.7-1.5x1.2-2.0 mm³, flip angle = 18-70 degrees).

A 2D cine phase contrast (PC) MRI was also acquired in patients at the sinotubular junction, perpendicular the centerline of the aorta (spatial resolution = 1.7-2.0x1.7-2.0x6.0 mm³, temporal resolution = 31-71 ms, flip angle = 20-30 degrees, velocity sensitivity (Venc) = 110-400 cm.s⁻¹).

In addition, each subject underwent free-breathing, prospectively ECG- and respiratory navigator gated 4D flow MRI covering the entire thoracic aorta in sagittal oblique orientation, 15-20 minutes after administration of gadolinium contrast agent. Scan parameters were as follows: spatial resolution = 1.8-2.7x1.8-2.7x2.4-3.5 mm³, slab coverage = 62-96 mm, temporal resolution = 33-42 ms, TR = 4.1-5.3 ms, TE = 2.2-2.8 ms, flip angle = 7-15° and Venc = 150-300 cm.s⁻¹ in all 3 directions, GRAPPA 2-3, total scan time 8-15 minutes.

MRI Data Analysis – Left Ventricular and Valve Function

Using the SSFP short axis stack, left ventricular mass, end diastolic volume, and end systolic volume were quantified by tracing endocardial and epicardial contours, including papillary muscles in the cavity, using the Simpson method. Ejection fraction and stroke volume were calculated, and end-diastolic and end-systole volume were normalized to the body surface area. Aortic regurgitant fraction was estimated from the 2D PC slice at the sinotubular junction by manually segmenting the aortic lumen with background phase offsets corrected using static tissue background correction (19). Analysis was done using cvi42 (Circle, Calgary, AB, Canada) by a single observer (GS).

MRI Data Analysis – Aortic Dimension and Growth Rates

Aortic dimensions were measured perpendicular to the vessel centerline according to guidelines (1, 18) by the same observer (GS) with 9 years of experience in cardiovascular MRI and blinded to the 4D flow analysis. Measurements were performed on cvi42 (Circle, Calgary, Canada) using multiplanar reformatting of either the 3D CE-MRA or 3D SSFP data based on availability. Aortic dimensions at the sinus of Valsalva were quantified sinus to sinus; at other aortic locations (sino-tubular junction, mid AAo, proximal arch, mid arch, proximal descending aorta (DAo), mid DAo and diaphragmatic aorta) two orthogonal aortic lumen diameter measurements were taken and averaged.

To decrease reliance on a single location, measurements at the sino-tubular junction, mid AAo, and proximal arch were averaged to obtain the mean AAo diameter. AAo growth rate was calculated by dividing the baseline vs follow-up mean AAo diameter difference by the time interval between scans.

MRI Data Analysis – Aortic 4D flow MRI, WSS and WSS Heatmaps

4D flow MRI data analysis followed a previously described pre-processing workflow (16) with corrections for Maxwell terms, eddy currents, and velocity aliasing. Next, a 3D segmentation of the thoracic aorta distal to the aortic valve was created using a fully automated algorithm (20) and subsequently manually refined using commercial software (Mimics Innovation suite, Materialize, Leuven, Belgium) if necessary. The distal end of the AAo was defined by a 2D plane placed perpendicular to the aorta lumen immediately proximal to the brachiocephalic trunk. Systolic peak velocity (Vmax) was calculated by automatically quantifying the maximum velocity in the AAo at peak systole, as described previously (21). The degree of aortic stenosis was graded from peak velocity (mild: 2.6–2.9, moderate 3.0–4.0, severe 4.0 m/s) (22).

Patient-specific WSS heatmaps were computed relative to a WSS map of the population average for healthy age and gender matched controls as described previously (12, 15, 16, 23). Briefly, after basic assumptions (i.e. no flow occurring through the wall (16)), the 3D systolic WSS magnitude on the surface of the aorta was calculated at peak systole for each patient (figure 1, left). Individual components of WSS (axial, circumferential) were not separately analyzed. In addition to regionally resolved WSS, mean WSS and maximum WSS (98^th percentile) of the AAo and entire aorta were calculated. To identify regions of abnormal WSS, a normal population ‘atlas’ was generated for each patient based on the 4D flow derived 3D systolic WSS magnitude of 10 or more healthy controls matched for age (within 5 years of patient age) and sex. For example, for a 27 year old male patient the WSS population average comprised 10 healthy male controls ranging from 22 to 32 years old from the cohort of 136 healthy controls (see figure 1, top right and middle) (12). For each patient, a WSS heatmap was generated by spatially registering the patient data to the corresponding age/sex-matched population average WSS (figure 1, bottom). WSS regions outside the age- and sex-matched healthy control 95% confidence intervals were classified as abnormal. (11). Aortic regions of normal, depressed, and elevated WSS were mapped onto 3D visualizations of patient-specific aortas (Figure 1, bottom). The relative areas (in %) of the AAo and entire aorta exposed to elevated WSS were quantified. Typical 4D flow analysis time including preprocessing, segmentation and WSS calculation was 15-20 min.

Figure 1: WSS heatmaps and relative area of elevated WSS.

Left: For each patient, 4D flow data (top) were analyzed to calculate peak systolic 3D WSS mapped onto the 3D segmentation of the aorta (middle). Right: Peak systolic 3D aortic WSS was calculated for ≥10 healthy controls within 5 years of the target patient age to obtain a normal age and sex matched population average. Bottom: A patient specific WSS heatmap of the patient aorta was computed relative to a map of the population average. WSS regions outside the healthy 95% confidence intervals were classified as abnormal and mapped onto 3D visualizations of the patient-specific aorta (red = elevated, blue = reduced, gray = normal).

Statistical Analysis

Baseline characteristics are provided as mean ± SD or median [interquartile range, IQR] as appropriate. Two patient groups were defined as slower or faster aortic growth rates based on the mean AAo growth rate of the cohort (which was normally distributed). Normality was tested using the Shapiro-Wilk test. A two tailed t-test or a Wilcoxon test were used to compare the two groups, depending on normality. Fisher’s exact test was used for categorial variables. Wilcoxon signed-rank test was used to compare 4D flow data between baseline and follow-up. Binary logistic regression was performed to identify predictors of high vs low aortic growth rates. A p value <0.05 was used to indicate statistical significance. Analysis was done using IBM SPSS 26.0 (IBM Corp, Armonk, NY).

RESULTS

The median follow-up duration was 6.0 years [5.5–6.7 years]. Baseline characteristics of the cohort are provided in table 1. Aortic dimensions at baseline and aortic growth rates are summarized in table 2. AAo growth rate was 0.24±0.20 mm/year. Using the average AAo growth rate as a threshold, 32 of 72 patients (44%) had rates of aortic growth >0.24 mm/year. Additional characteristics about the control cohort used to derive age and gender matched WSS population averages are in supplemental table S1.

Table 1:

Patient baseline characteristics.

	n=72
Age at baseline (y)	45.3 ± 12.2
Sex, male	50 (69%)
Follow up duration (y)	6.0 [5.5 – 6.7]
BSA (m2)	1.95 ± 0.21
BMI (kg.m−2)	25.1 [23.4 – 28.0]
Heart rate (bpm)	63 [59 – 72]
Hypertension	14 (19%)
Beta blockers	24 (33%)
ARB	7 (10%)
ACEI	7 (10%)
LV at baseline (n=71)
EDV/BSA (ml.m−2)	79.2 [64.5 – 94.7]
ESV/BSA (ml.m−2)	30.5 [24.1 – 37.1]
EF (%)	62.1 ± 6.5
SV (ml)	93.7 [77.2 – 111.5]
Mass/BSA (g.m−2)	54.7 [46.1 – 63.3]
Aortic regurgitant fraction (%)	6.8 [3.1 – 14.0]
Aortic regurgitant fraction grade (n=71)
None to trace	43 (61%)
Mild	20 (28%)
Moderate	8 (11%)
Aortic stenosis grade
None to trace	64 (89%)
Mild	5 (7%)
Moderate	3 (4%)

ACEI: angiotensin converting enzyme inhibitors; ARB: angiotensin receptor blockers, BMI: body mass index; BSA: body surface area; EDV: end diastolic volume; EF: ejection fraction; ESV: end systolic volume, LV: left ventricle; SV: stroke volume.

Table 2:

Aortic dimensions at baseline and growth rates of the aorta. Mean AAo is calculated from the average of sino-tubular junction, Mid AAo and proximal arch.

	Aortic dimension at baseline (mm)	Growth rate (mm/y)
Sinus of Valsalva	37.0 ± 4.2	0.14 ± 0.23
Sino-tubular junction	32.5 ± 4.2	0.17 ± 0.24
Mid AAo	38.2 ± 5.6	0.31 ± 0.25
Proximal arch	30.7 ± 4.0	0.25 ± 0.24
Mid arch	24.4 ± 2.9	0.16 ± 0.19
Isthmus	22.1 ± 2.8	0.16 ± 0.18
Mid DAo	21.5 ± 2.7	0.19 ± 0.19
Diaphragmatic aorta	20.1 ± 2.5	0.18 ± 0.19
Mean AAo	33.8 ± 4.1	0.24 ± 0.20

AAo: Ascending aorta; DAo: Descending aorta

Baseline 4D Flow Metrics for Patients with Fast vs. Slow Rates of AAo Dilation

Example WSS heatmaps from BAV patients with both fast (>0.24 mm/year) and slow rates of AAo dilation (<0.24 mm/year) are presented in figure 2, showing areas of elevated WSS in red. The patient with the higher rate of aortic dilation exhibited larger areas of elevated WSS on the heatmap compared to the patient with the lower aortic growth rate.

Figure 2: Regionally elevated WSS in patients with fast vs. slow rates of aortic dilation.

Aorta WSS heatmap examples for 2 patients, arranged by rate of aortic dilation (fast vs. slow, defined as greater or less than 0.24 mm/year, respectively). Each panel represents right anterior and left posterior views of the patient specific WSS heatmap illustrating abnormal WSS relative to individually age and sex matched WSS population averages. On the left: BAV patient with a slow rate of aortic dilation exhibiting mostly normal aortic WSS. On the right: BAV patient a high rate of aortic dilation demonstrating clearly visible areas of elevated WSS.

These findings were corroborated by WSS heatmap analysis across the entire cohort, as illustrated in figure 3 and table 3. At baseline, the fraction of the AAo exposed to elevated WSS was increased for patients with higher rates of aortic dilation (>0.24 mm/year) compared to those with growth rates less than 0.24 mm/year (19.9% [10.2–25.5] vs. 5.7% [1.5–21.3]; p=0.008). Similar differences were observed for baseline area of elevated WSS over the entire thoracic aorta (9.1% [4.8- 14.4] vs. 3.4% [1.2 -9.7]; p=0.009). Peak systolic velocity was increased in patients with higher growth rates (1.74 m.s⁻¹ [1.48- 2.07] vs 1.48 m.s⁻¹ [1.37- 1.79]; 0.030) (table 3). In the 58 patients with a 4D flow MRI available at follow-up, peak systolic maximal velocity increased from 1.64 m.s⁻¹ [1.43- 2.01] to 1.71 m.s⁻¹ [1.43- 2.14] (p=0.001) while mean and maximum WSS remained stable over time (0.69 Pa [0.64 – 0.84] vs. 0.75 Pa [0.63 – 0.86], p=0.607 and 1.51 [1.26 – 1.87] vs. 1.51 [1.31 – 1.95], p=0.078 respectively).

Figure 3: Incidence of elevated WSS in patients with high vs. low rates of aortic dilation.

Histograms of the relative area of elevated WSS in the ascending aorta (AAo, left) and the entire thoracic aorta (right) for n=40 patients with lower rates of AAo growth <0.24mm/year (blue bars) compared to n=32 patients with higher rates of progressive AAo dilation >0.24mm/year (red bars).

Table 3:

Hemodynamic differences at baseline between groups with higher vs. lower rates of AAo dilation (n=72).

	GR mean AAo > 0.24 mm/year (n=32)	GR mean AAo < 0.24mm/year (n=40)	p
AAo Vmax (m.s−1)	1.74 [1.48- 2.07]	1.48 [1.37- 1.79]	0.030
AAo Mean WSS (Pa)	0.76 [0.67- 0.93]	0.66 [0.62- 0.82]	0.058
AAo Max WSS (Pa)	1.59[1.37- 2.01]	1.38 [1.21- 1.74]	0.055
AAo relative area of elevated WSS (%)	19.9 [10.2- 25.5]	5.7 [1.5- 21.3]	0.008
Entire aorta relative area of elevated WSS (%)	9.1 [4.8- 14.4]	3.4 [1.2 −9.7]	0.009

AAo: Ascending Aorta; GR: growth rate, Vmax: maximum velocity, WSS: wall shear stress

Predictors of High Rates of Progressive AAo Dilation

Age, body surface area, body mass index, treatment with angiotensin receptor blockers and angiotensin converting enzyme inhibitors, baseline AAo diameter and aortic valve regurgitant fraction were not significantly associated with higher rates of AAo dilation >0.24mm/year (table 4). Both treatment by beta blockers and decreased heart rate were predictors of progressive AAo dilation >0.24mm/year (OR 3.03 CI 1.10 – 8.39, p=0.032; for 10 bpm, OR 0.55 CI 0.33 –0.90, p=0.018)

Table 4.

Univariate binary logistic regression for higher rates of AAo dilation (>0.24mm/year) in BAV patients (n=72).

Parameter	OR	95% CI	p
Age, 10 y	0.89	0.60 – 1.31	0.541
Heart rate, 10 bpm	0.55	0.33 – 0.90	0.018
BSA, 0.1m2	1.23	0.97 – 1.57	0.085
BMI	1.03	0.92 – 1.16	0.608
Hypertension, yes	0.64	0.19 – 2.14	0.466
Raphe, no	1.89	0.58– 6.15	0.291
RN fusion, yes	1.57	0.50 – 4.92	0.438
Betablockers, yes	3.03	1.10 – 8.39	0.032
ARB, yes	0.38	0.07 – 2.02	0.254
ACEI, yes	0.18	0.02 – 1.60	0.125
AAo diameter, 5 mm	1.09	0.61 – 1.94	0.764
Aortic regurgitant fraction, 10%	1.28	0.83 – 1.97	0.258
4D flow metrics at baseline
AAo Vmax, 0.1 m.s−1	1.08	0.98 – 1.19	0.132
AAo Mean WSS, 0.1 Pa	1.37	0.92 – 2.04	0.124
AAo Max WSS, 0.1 Pa	1.06	0.96 – 1.17	0.231
AAo relative area of elevated WSS, 10%	1.51	1.05 – 2.17	0.026
Entire aorta relative area of elevated WSS, 10%	1.79	1.01 – 3.15	0.046

AAo: ascending aorta; ACEI: angiotensin converting enzyme inhibitors; ARB: angiotensin receptor blockers, BMI: body mass index; BSA: body surface area; RN: right to non-coronary; Vmax: maximal velocity; WSS: wall shear stress

Univariate binary logistic regression for higher rates of AAo dilation (>0.25mm/year) in BAV patients (n=72).

Among 4D flow metrics, the percent areas of elevated WSS in the heatmaps of the AAo and entire thoracic aorta were the only predictors of higher rates of progressive AAo dilation >0.24 mm/year (for 10% increase, OR 1.51 CI 1.05 – 2.17, p= 0.026 and OR 1.79 CI 1.01–3.15, p=0.046 respectively) (table 4).

In a multivariate binary logistic regression, the percent area of elevated WSS in the AAo heatmap at baseline remained significantly associated with a dilation rate >0.24mm/y after adjustment for age, sex, heart rate and baseline AAo diameter (model 1, table 5) and after adjustment for valve fusion type and presence of a raphe (model 2, table 5).

Table 5.

Multivariate binary logistic regression for higher rates of AAo dilation (>0.24mm/year) in BAV patients (n=72).

Parameter	OR	95% CI	p
Model 1
Age, 10 y	0.79	0.49 – 1.25	0.315
Sex, M	1.28	0.41 – 4.00	0.672
Heart rate, 10 bpm	0.53	0.31 −0.91	0.024
AAo diameter, 5 mm	1.45	0.70 – 3.04	0.321
AAo relative area of elevated WSS, 10%	1.63	1.08 – 2.47	0.019
Model 2
Age, 10 y	0.82	0.53 – 1.26	0.370
Sex, M	1.40	0.46 – 4.22	0.552
Raphe, no	1.29	0.34 – 4.93	0.709
RN fusion, yes	1.83	0.50 – 6.75	0.365
AAo relative area of elevated WSS, 10%	1.60	1.09 – 2.35	0.017

AAo: ascending aorta; WSS: wall shear stress

DISCUSSION

This study demonstrates the potential of 4D flow MRI derived WSS heatmaps as an imaging biomarker that may help identify BAV patients with higher risk of progressive aortic dilation. Notably, the fraction of the area exposed to elevated WSS on heatmaps was greater in patients with faster rates of AAo dilation. On logistic regression, maximum and mean systolic WSS were not predictive of faster aortic growth rate at ≥5-year follow-up, while the fraction of the area exposed to elevated WSS on heatmaps was a predictor of faster aortic growth rate. These observations indicate the importance of relating changes in aortic WSS in the individual patient to WSS distributions of age and sex matched control populations - an inherent property of the WSS heatmap concept.

Two theories attempt to explain progressive aortic dilation in BAV. The ‘primary aortopathy’ hypothesis postulates a genetic origin of BAV disease based on the assumption that the matrix of the aortic wall is fragile and susceptible to dilation due to pathological histological composition. In contrast, the ‘hemodynamic hypothesis’ postulates that mechanotransduction forces caused by BAV mediated abnormal blood flow can cause structural alterations to the aorta wall. This second hypothesis has been an ongoing subject of many prior studies in BAV disease. Previous studies by Hope et al (7, 9) suggested that visually graded eccentric blood flow in the AAo was associated with accelerated aortic growth in patients with BAV, but findings were limited by semi-quantitative image analysis and small cohort size. Other studies reported that hemodynamic markers such as flow displacement, higher rotational (helical) flow, and systolic outflow angle are correlated with dilation of the thoracic aorta (7, 24–26). An echocardiographic study by Michelena et al (27) provided evidence that changes in aortic hemodynamics were related to aortopathy, aorta growth, and patient outcomes. A number of studies have shown that aortic WSS is a quantifiable mechanism directly associated with aortic dilation and alteration of the vessel wall architecture (8, 11, 17, 28, 29). While various WSS derived metrics have been proposed, such as low or oscillatory WSS (30), our work is focused on elevated WSS at peak systole. The present study provides additional evidence supporting the ‘hemodynamic hypothesis’ by documenting significant associations between elevated WSS at baseline with progressive aortic dilation in BAV at long-term follow-up. Nonetheless, both the primary aortopathy and hemodynamics hypotheses are likely to contribute: there is evidence of a genetic basis to BAV, such as a 3:1 male predominance and familial clustering (31, 32). Aortic dilation, triggered by genetic wall abnormalities, could be further exacerbated by unfavorable hemodynamic conditions such as BAV mediated out flow jets and elevated ascending aortic WSS. The stability of WSS over time in our study cohort indicates that elevated WSS may drive aortic dilation. However long-term follow-up data in patients enrolled prior to any aortic dilatation are warranted to fully understand the mechanisms of aortic dilatation

To refine our approach, WSS heatmaps were designed to provide patient specific metrics by adjusting WSS measurements for age and sex. This is relevant as aortic hemodynamics, including WSS, change substantially with aging (12, 14). Of note, similar to previous studies (33, 34), peak systolic WSS remained stable over the follow-up period in our study cohort, and previous work has shown that WSS is reproducible on test-retest data (35).

Compared to a previous study with similar methodology in 13 BAV patients who received 4D flow MRI prior to aortic surgery, the percentage area of elevated WSS in the ascending aorta on heatmaps was lower in our cohort (7% compared to 33% at the outer and 7% at the inner AAo) (15). We speculate this is related to reduced disease severity in our patients, as they did not meet surgical criteria even five years after baseline MRI evaluation.

Several recent studies have shown that AAo dilation is a slow gradual process. In an echocardiographic study, Della Corte et al (3) reported a median AAo growth rate ranging from 0.20-0.35mm/year for BAV patients during a 4-year follow up. In an echocardiographic study with 353 BAV patients, Detain et al (4) observed mean AAo growth rates of 0.42 ± 0.6mm/year in BAV patients based on a 3.6-year follow up period, but a large proportion (43%) with no or very minor progression of aortic dilation. Although growth rates in our study cohort were low, our observed growth rate of 0.24mm/year is comparable to these earlier studies with similar follow-up duration (3, 4). We speculate that this is related to the inclusion of patients with existing >5-year follow up MRI data without surgery. BAV patients with aortic growth on the order of several mm/year would have most likely been referred for aortic surgery prior to 5-year follow-up.

Previous studies found that WSS was influenced by aortic valve stenosis (25, 28), and we observed a significant difference in AAo peak velocity between the groups with faster and slower rates of aortic growth. While out of the scope of this study, the influence of valve dysfunction on WSS heatmaps and patient outcomes (e.g. referral for surgery) merits further investigation.

It is important to note that our study investigated associations of regional metrics (WSS) with a measure of changes in aortic dimensions over time. Nonetheless, the WSS heatmap concept employed in this study was used to quantify the size of the area exposed to elevated WSS in the entire AAo. This measure of altered AAo hemodynamics was correlated with average AAo dimensions and growth rates derived from the same aortic regions.

A methodological limitation related to use of two different MR angiography techniques at baseline and follow-up in some patients may have increased the variability of the measurement of aortic dimensions. However, we used different MR angiography techniques in only 8 of the total n=72 BAV patients (11%) enrolled in our study. As a result, differences in MRA techniques may have increased the variability of aortic measurements but we are confident that had only a minimal impact (bias) on the relationship with WSS.

A counterintuitive finding in our study was the higher risk of faster aortic growth in patients undergoing beta blocker therapy. This should be interpreted with caution, as patients were not randomized for treatment, and patients with faster aortic growth or who were otherwise considered higher risk by their physicians may have been more likely to be prescribed beta blockers.

Our study has further limitations. The total number of patients enrolled in our study was small due to the recent application of the 4D flow approach. While this limits the ability to translate these findings to practice, it provides promising insight for future studies in larger cohorts. Similarly, a larger reference sample for WSS are needed to improve the definition of normal ranges. To our knowledge, this is the largest longitudinal study to date to investigate the predictive value of 4D flow MRI to assess risk for AAo dilation in BAV patients. However, our study may be limited by a selection bias related to the retrospective design. This may have led to exclusion of patients with higher baseline aortic dimensions or severe aortic valve stenosis as they are more likely to have undergone surgery during the 5 year follow-up interval. In addition, studying younger patients could add valuable information on the early development of BAV aortopathy. While the average age of our cohort was 45 (similar to previous works (3, 4)) younger patients with mild BAV disease may have been excluded due to lack of standard-of-care surveillance MRI in this group. Finally, the retrospective design also makes it difficult to account for the effects of other parameters such as the blood pressure or treatments. To address the potential selection bias, a future prospective study including observations at regular intervals is warranted to study aortic WSS, clinical factors, and rate of aortic growth more systematically.

CONCLUSION

In BAV patients, the relative area of abnormally elevated WSS on 4D flow derived aorta WSS heatmaps was associated with faster rates of progressive AAo dilation over a greater than 5-year observation period. The detection of elevated WSS as a possible mechanism for progressive AAo dilation may help to identify BAV patients at greater risk for aortic complications. Future studies in larger cohorts are warranted to confirm these findings and to assess the diagnostic value of 4D flow derived WSS for the management of patients with AAo dilation.

CENTRAL ILLUSTRATION: Elevated WSS secondary to altered aortic flow is associated with higher rates of progressive dilation of the ascending aorta.

Top left: example of abnormal aortic flow patterns in the ascending aorta visualized using 4D flow MRI derived systolic 3D streamlines (higher velocities appear in red). Top right: WSS heatmap from the same patient, obtained using an age/gender matched population average of healthy volunteers showing areas of abnormally elevated WSS in red (outside the 95% confidence interval of the age/gender matched control population). Bottom: Histogram of the relative areas of elevated WSS in the ascending aorta for n=40 patients with low rates of aortic growth <0.24mm/year (blue bars) compared to n=32 patients with higher rates of progressive aortic dilation >0.24mm/year (red bars).

CLINICAL PERSPECTIVE (CLINICAL COMPETENCIES and TRANSLATIONAL OUTLOOK).

Competency in Medical Knowledge

The area of elevated WSS in the AAo using 4D flow can predict higher rates of aortic dilation and might help to determine which BAV patients require closer follow up.

Translational Outlook

The findings of this study indicate a potential role of altered WSS as a mechanism of arterial wall remodeling leading to higher rates of progressive aortic dilation, thus exposing BAV patients to a greater risk for aortic complications. Future studies in larger cohorts are warranted to confirm these findings and to refine in which subtypes of BAV patients WSS heatmaps might be most predictive of patient outcome.

ABBREVIATIONS:

AAo

ascending aorta

DAo

descending aorta

BAV

bicuspid aortic valve

BSA

body surface area

BMI

body mass index

MRI

magnetic resonance imaging

WSS

wall shear stress