Echocardiography and tomographic imaging have documented dynamic changes in aortic stenosis (AS) geometry and severity during both the cardiac cycle and stress-induced increases in cardiac output. However, corresponding pressure gradient vs. flow relationships have not been described. We recruited 16 routine transcatheter aortic valve implantations (TAVI’s) for graded dobutamine infusions both before and after implantation; 0.014″ pressure wires in the aorta and left ventricle (LV) continuously measured the transvalvular pressure gradient (ΔP) while a pulmonary artery catheter regularly assessed cardiac output by thermodilution. Before TAVI, ΔP did not display a consistent relationship with transvalvular flow (Q). Neither linear resistor (median R² 0.16) nor quadratic orifice (median R² < 0.01) models at rest predicted stress observations; the severely stenotic valve behaved like a combination. The unitless ratio of aortic to left ventricular pressures during systolic ejection under stress conditions correlated best with post-TAVI flow improvement. After TAVI, a highly linear relationship (median R² 0.96) indicated a valid valve resistance. Pressure loss vs. flow curves offer a fundamental fluid dynamic synthesis for describing aortic valve pathophysiology. Severe AS does not consistently behave like an orifice (as suggested by Gorlin) or a resistor, whereas TAVI devices behave like a pure resistor. During peak dobutamine, the ratio of aortic to left ventricular pressures during systolic ejection provides a ‘fractional flow reserve’ of the aortic valve that closely approximates the complex, changing fluid dynamics. Because resting assessment cannot reliably predict stress haemodynamics, ‘valvular fractional flow’ warrants study to explain exertional symptoms in patients with only moderate AS at rest.

Objective: The prognostic importance of a coronary stenosis depends on its functional severity and its depending myocardial mass. Functional severity can be assessed by fractional flow reserve (FFR), estimated non-invasively by a specific validated CT algorithm (FFR_CT). Calculation of myocardial mass at risk by that same set of CT data (CTmass), however, has not been prospectively validated so far. The aim of the present study was to compare relative territorial-based CTmass assessment with relative flow distribution, which is closely linked to true myocardial mass. Methods: In this exploratory study, 35 patients with (near) normal coronary arteries underwent CT scanning for computed flow-based CTmass assessment and underwent invasive myocardial perfusion measurement in all 3 major coronary arteries by continuous thermodilution. Next, the mass and flows were calculated as relative percentages of total mass and perfusion. Results: The mean difference between CTmass per territory and invasively measured myocardial perfusion, both expressed as percentage of total mass and perfusion, was 5.3±6.2% for the left anterior descending territory, −2.0±7.4% for the left circumflex territory and −3.2±3.4% for the right coronary artery territory. The intraclass correlation between the two techniques was 0.90. Conclusions: Our study shows a close relationship between the relative mass of the perfusion territory calculated by the specific CT algorithm and invasively measured myocardial perfusion. As such, these data support the use of CTmass to estimate territorial myocardium-at-risk in proximal coronary arteries.

The Official Journal of EuroPCR and the European Association of Percutaneous Cardiovascular Interventions (EAPCI)

With the increasing prevalence of aortic stenosis (AS) due to a growing elderly population, a proper understanding of its physiology is paramount to guide therapy and define severity. A better understanding of the microvasculature in AS could improve clinical care by predicting left ventricular remodeling or anticipate the interplay between epicardial stenosis and myocardial dysfunction. In this review, we combine five decades of literature regarding microvascular, coronary, and aortic valve physiology with emerging insights from newly developed invasive tools for quantifying microcirculatory function. Furthermore, we describe the coupling between microcirculation and epicardial stenosis, which is currently under investigation in several randomized trials enrolling subjects with concomitant AS and coronary disease. To clarify the physiology explained previously, we present two instructive cases with invasive pressure measurements quantifying coexisting valve and coronary stenoses. Finally, we pose open clinical and research questions whose answers would further expand our knowledge of microvascular dysfunction in AS. These trials were registered with NCT03042104, NCT03094143, and NCT02436655.

Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision-making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test has become a standardized diagnostic and prognostic marker. We aimed to explore, whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. Based on heart rate recordings and literature information, activity levels were determined and compared to results from a 6-minute walk test. The percentage of time spent in moderate activity was a predictor for the achievement of gender, age and body mass index-specific 6-minute walk distances (p < 0.001; R² = 0.48). The uncertainty of these predictions is demonstrated.

Current prognostic risk scores in cardiac surgery do not benefit yet from machine learning (ML). This research aims to create a machine learning model to predict one-year mortality of a patient after transcatheter aortic valve implantation (TAVI). We adopt a modern gradient boosting on decision trees classifier (GBDTs), specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling the identification of the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 consecutive TAVI cases, reaching a C-statistic of 0.83 with CI [0.82, 0.84]. The model has achieved a positive predictive value ranging from 57% to 64%, suggesting that the patient selection made by the heart team of professionals can be further improved by taking into consideration the clinical data we identified as important and by exploiting ML approaches in the development of clinical risk scores. Our approach has shown promising predictive potential also with respect to widespread prognostic risk scores, such as logistic European system for cardiac operative risk evaluation (EuroSCORE II) and the society of thoracic surgeons (STS) risk score, which are broadly adopted by cardiologists worldwide.