Rob Eerdekens

The primary focus of this thesis is to acquire a more advanced understanding of AS and its pathophysiologic behavior (at rest and during stress conditions) and the metrics used for evaluation of (related) CAD. Traditional diagnosis and classification of AS relies on static metrics, like the aortic valve area, derived from Gorlin and Gorlin’s model. Because these derived metrics are mainly achieved during rest conditions, they fail to capture the dynamics of the AV and related patients’ symptoms. The stress aortic valve index (SAVI), the ratio of the aortic valve pressure to the left ventricular pressure during systolic ejection at stress conditions, was designed to capture these dynamics. It can quantify flow limitation over the aortic valve during the systolic ejection period and simultaneously give a more nuanced understanding of the pathophysiology of aortic valve disease. Based on the results from different Chapters (3, 4 and 5), we succeeded to give a better understanding of aortic valve physiology and pathophysiology compared with traditional evaluation of the aortic valve. In addition to aortic valve physiology, this thesis explores the interaction between AS and CAD. Hereby highlighting the changes of various metrics, like CFR and MRR, as a result of aortic valve intervention. Furthermore, we looked at how metrics used for coronary evaluation, like MRR, are affected by different pathologies and we evaluated the effect of hydrostatic pressure gradient on FFR.

In Chapter 1 we explain the foundation for this thesis. We start with a general overview of the aortic valve anatomy and physiology and the effect aortic valve stenosis can have on patients’ prognosis and symptoms. Thereafter, an overview of traditional evaluation of AS and its flaws is presented, subsequently addressing the basis and implementation of SAVI and its current supportive evidence. Next, we discuss the coronary (micro)circulation and the tools to evaluate it. We conclude with the effect hydrostatic pressure can have on coronary evaluation and how coronary physiology is affected by AS.

Chapter 2, Chapter 3, Chapter 4, and Chapter 5 can be basically glued together. In these chapters we give a 360⁰ overview of the aortic valve and AS, with the use of numerous modalities and metrics, in normal aortic valves, aortic valves in which a stenosis is gradually created (swine model), and moderate AS, at rest and during stress conditions. In Chapter 2, the evaluation of moderate AS is discussed and the basis and rationale for a more comprehensive evaluation are provided, including the use of SAVI. The design of the SAVI-AoS trial (Stress aortic valve index in moderate symptomatic AS patients) is described. In Chapter 3, we analyze symptomatic patients with moderate AS (n= 52), and a preserved left ventricular ejection fraction. Moderate AS patients have similar impaired prognosis as severe AS patients. However, both European and American guidelines only recommend aortic valve intervention in severe AS. Based on results from invasive and noninvasive assessment, including SAVI, dobutamine and bicycle stress echocardiography, and computed tomography including valvular calcium scoring, we give new insight in AS behavior and pitfalls of its current evaluation. We describe profound hemodynamic variability among moderate AS patients, with nearly half of the patients showing a similar stress response as in severe AS. While clinicians were blinded for SAVI results, we found that SAVI is an independent predictor for early aortic valve intervention, while other modalities, like sex-based calcium scoring and AVA, are not. Consequently, high-risk ‘moderate AS’ patients who might benefit at an earlier stage from aortic valve intervention can be identified by SAVI, giving foundation for future randomized trials. To understand how a stenosis behaves during stress conditions, it is also important to understand how normal aortic valves behave during stress. Therefore, in Chapter 4, we conducted the SAVI-NORM trial. In this study, patients with normal aortic valves were evaluated invasively and non-invasively, at rest and during stress conditions. Based on the results, we concluded that the current understanding of aortic valve physiology is incorrect. The quadratic relationship between pressure and flow, used for the calculation of AVA, is not applicable for normal aortic valves. In the majority of normal AVs we found a decrease in mean pressure as the flow increased, indicative for a negative resistance. Compared to patients from another cohort, the physiology of a normal valve is better than after aortic valve replacement. Additional caveats on current AS evaluation are outlined based on the results displayed in Chapter 5. In this chapter we evaluate the pressure loss versus flow curves in normal aortic valves and during the creation of a gradual stenosis in a swine model. We show that the mean pressure loss is highly linear to the reduction in peak flow (“load line” behavior). This finding contrasts with the current quadratic model that is used for describing and interpreting AS severity. Our findings suggest that measuring a peak pressure loss over a valvular stenosis can give valuable insights in how to manage stenosis, for example with aortic valve intervention.

When talking about behavior, it is good to understand the stenotic behavior, but it is also important to understand patients’ behavior before and after aortic valve intervention. In Chapter 6 we studied patients with a health watch before and after TAVI and compared findings with current tools (like six-minute walking test and a quality of health questionnaire) to evaluate changes in quality of life or condition. We observed that when patients are stimulated to do tests, in a clinical setting, walking distance and quality of life improved after TAVI. In contrast, patients at home continued to conduct the same activities like before TAVI, as reflected by no increase in walking distance and heart rate. This could be the basis for a more tailored approach for monitoring and could aid in the rehabilitation process after TAVI by implementing personalized care and ensuring sustained improvements in health and mobility.

In Chapter 7 we examine the changes in CFR following AVR. AS causes increased LV pressure and wall stress, resulting in LVH and changes in resting coronary flow, and reduced hyperemic myocardial perfusion. After SAVR or TAVI, regression of LVH leads to reduction in resting flow, but stress flow remains unchanged, which translates to an improved CFR.

Chapter 8 addresses the acute changes of myocardial and coronary physiology after TAVI, focusing on coronary resistance components before and after valve replacement. The rising filling pressures and LVH shift the myocardial resistance line to the right and flatten its slope, indicative for a higher resistance and higher zero-flow pressure. Notably there are no significant changes in FFR, CFR and MRR after the procedure, which suggests that the changes in resistance are due to changes in intercept rather than slope. With this understanding of changes in intercept, we can better identify which patients may benefit from TAVI and which may not.

In Chapter 9 we looked at the topic of the presence of significant CAD in relation to TAVI. Three key time points can be identified for PCI in TAVI patients: before, during and after TAVI. We conclude that the incidence of PCI at the time of TAVI is infrequent, PCI rates remain low (≈ 10%) when severe AS is diagnosed. After TAVI, we observed that while PCI is challenging, it can be done safely and with high success rates. Additionally, TAVI plus PCI occurs at half the rate of SAVR plus CABG, but this has no impact on all-cause death, myocardial infarction, or symptom improvement. As in previous chapters, changes in coronary physiology should also be kept in mind. Future RCTs will provide further insight into this topic.

In Chapter 10 for the first time we explored the acute changes of the microvasculature in patients presenting with STEMI undergoing primary PCI and its prognostic implications. In 446 patients, MRR ≤ 1.25 was found to be a strong and independent predictor of long-term clinical outcome. Such patients exhibit a higher rate of all-cause mortality or hospitalization for heart failure, compared to which with MRR > 1.25 (27.3% vs. 5.9%, HR 4.16, p<0.001). The synergistic effect MRR (a metric for the vasodilatory capacity of the microcirculation) has with IMR (a metric for the minimal microvascular resistance), was also highlighted in these patients. In Chapter 11 we evaluated the impact of the HPG on indices for functional lesion severity, including FFR, Pd/Pa and dPR by simulating HPG effects using data from the CONTRAST study. In 602 pressure tracings, HPG resulted in a 3.18 ± 1.30 mmHg change in Pd and led to slight increases in FFR, Pd/Pa, and dPR (0.02 ± 0.04 for all indices, p= 0.69). When correcting for HPG, lesion severity could be reclassified in 13-22% of patients with the greatest effect on NHPR and in the LAD. However, limited effect of HPG on the gray zone (FFR: 0.75–0.80 NHPRs: 0.86–0.93) was observed. In < 1% the HPG resulted in crossover from significant (FFR < 0.75) to nonsignificant (FFR > 0.80). To further evaluate the effect of HPG, we conducted the PW-COMPARE study which is discussed in Chapter 12. Here, we compared a fluid-filled (no effect of HPG) and sensor-tipped pressure wire and the effect of on FFR (mean difference of -0.01 ± 0.03) and Pd/Pa (mean difference of -0.01 ± 0.04). The discrepancies due to HPG were largest in the LAD. The difference in measurements were vessel specific, but attributable to the HPG.

We conclude with Chapter 13, with a general discussion and future perspectives, were all findings throughout the chapters are summarized and were directions for future research are given.