Transcatheter aortic valve replacement acutely improves left ventricular mechanical efficiency in severe aortic stenosis: effects of different phenotypes

Quantification of cardiac pumping mechanics in TAVI patients: A pilot study utilizing minimally invasive method for pressure-volume analysis

The ventriculo-arterial coupling (VAC) and left ventricle (LV) mechanics are crucial and play an important role in the pathophysiology of aortic stenosis (AS). The pressure-volume (PV) analysis is a powerful tool to study VAC and LV mechanics. We proposed a novel minimally-invasive method for PV analysis in patients with severe AS receiving transcatheter aortic valve implantation (TAVI). Patients with severe AS were prospectively enrolled in a single center. LV pressure and cardiac output were recorded before and after TAVI. We constructed the PV loop for analysis by analyzing LV pressure and the assumed flow. 26 patients were included for final analysis. The effective arterial elastance (Ea) decreased after TAVI (3.7 ± 1.3 vs. 2.9 ± 1.1 mmHg/mL, p < 0.0001). The LV end-systolic elastance (Ees) did not change immediately after TAVI (2.4 ± 1.3 vs. 2.6 ± 1.1 mmHg/mL, p = 0.3670). The Ea/Ees improved after TAVI (1.8 ± 0.8 vs. 1.2 ± 0.4, p < 0.0001), demonstrating an immediate improvement of VAC. The stroke work (SW) did not change (7669.6 ± 1913.8 vs. 7626.2 ± 2546.9, p = 0.9330), but the pressure-volume area (PVA) decreased (14469.0 ± 4974.1 vs. 12177.4 ± 4499.9, p = 0.0374) after TAVI. The SW/PVA increased after TAVI (0.55 ± 0.12 vs. 0.63 ± 0.08, p < 0.0001) representing an improvement of LV efficiency. We proposed a novel minimally invasive method for PV analysis in patients with severe AS receiving TAVI. The VAC and LV efficiency improved immediately after TAVI.

Invasive Real Time Biventricular Pressure-Volume Loops to Monitor Dynamic Changes in Cardiac Mechanoenergetics During Structural Heart Interventions: Design and Rationale of a Prospective Single-Center Study

Background

Transcatheter valvular interventions affect cardiac and hemodynamic physiology by changing ventricular (un-)loading and metabolic demand as reflected by cardiac mechanoenergetics. Real-time quantifications of these changes are scarce. Pressure-volume loop (PVL) monitoring appraises both load-dependent and load-independent compounds of cardiac physiology including myocardial work, ventricular unloading, and ventricular-vascular interactions. The primary objective is to describe changes in physiology induced by transcatheter valvular interventions using periprocedural invasive biventricular PVL monitoring. The study hypothesizes transcatheter valve interventions modify cardiac mechanoenergetics that translate into improved functional status at 1-month and 1-year follow-up.

Methods

In this single-center prospective study, invasive PVL analysis is performed in patients undergoing transcatheter aortic valve replacement or tricuspid or mitral transcatheter edge-to-edge repair. Clinical follow-up is per standard of care at 1 and 12 months. This study aims to include 75 transcatheter aortic valve replacement patients and 41 patients in both transcatheter edge-to-edge repair cohorts.

Results

The primary outcome is the periprocedural change in stroke work, potential energy, and pressure-volume area (mmHg mL^-1). The secondary outcomes comprise changes in a myriad of parameters obtained by PVL measurements, including ventricular volumes and pressures and the end-systolic elastance-effective arterial elastance ratio as a reflection of ventricular-vascular coupling. A secondary endpoint associates these periprocedural changes in cardiac mechanoenergetics with functional status at 1 month and 1 year.

Conclusions

This prospective study aims to elucidate the fundamental changes in cardiac and hemodynamic physiology during contemporary transcatheter valvular interventions.

Kidney injury as post-interventional complication of TAVI

Transcatheter aortic valve implantation (TAVI) is an accepted treatment approach of aortic stenosis. In the beginning, this technique was executed in high-risk patients only. Today, intermediate-risk patients are also amenable for TAVI, as long as the transfemoral approach is chosen. Numerous predictors have been identified that could lead to periprocedural complications and are defined by patient co-morbidities as well as being inherent to the technical approach. Although vascular complications and postinterventional paravalvular regurgitation have been minimized over the past years by revised technologies and techniques, there is a prevailing individual risk brought about by the specific pathophysiology of the cardiorenal syndrome.

Depressed Cardiac Mechanical Energetic Efficiency: A Contributor to Cardiovascular Risk in Common Metabolic Diseases-From Mechanisms to Clinical Applications

Cardiac mechanical energetic efficiency is the ratio of external work (EW) to the total energy consumption. EW performed by the left ventricle (LV) during a single beat is represented by LV stroke work and may be calculated from the pressure-volume loop area (PVLA), while energy consumption corresponds to myocardial oxygen consumption (MVO₂) expressed on a per-beat basis. Classical early human studies estimated total mechanical LV efficiency at 20-30%, whereas the remaining energy is dissipated as heat. Total mechanical efficiency is a joint effect of the efficiency of energy transfer at three sequential stages. The first step, from MVO₂ to adenosine triphosphate (ATP), reflects the yield of oxidative phosphorylation (i.e., phosphate-to-oxygen ratio). The second step, from ATP split to pressure-volume area, represents the proportion of the energy liberated during ATP hydrolysis which is converted to total mechanical energy. Total mechanical energy generated per beat-represented by pressure-volume area-consists of EW (corresponding to PVLA) and potential energy, which is needed to develop tension during isovolumic contraction. The efficiency of the third step of energy transfer, i.e., from pressure-volume area to EW, decreases with depressed LV contractility, increased afterload, more concentric LV geometry with diastolic dysfunction and lower LV preload reserve. As practical assessment of LV efficiency poses methodological problems, De Simone et al. proposed a simple surrogate measure of myocardial efficiency, i.e., mechano-energetic efficiency index (MEEi) calculated from LV stroke volume, heart rate and LV mass. In two independent cohorts, including a large group of hypertensive subjects and a population-based cohort (both free of prevalent cardiovascular disease and with preserved ejection fraction), low MEEi independently predicted composite adverse cardiovascular events and incident heart failure. It was hypothesized that the prognostic ability of low MEEi can result from its association with both metabolic and hemodynamic alterations, i.e., metabolic syndrome components, the degree of insulin resistance, concentric LV geometry, LV diastolic and discrete systolic dysfunction. On the one part, an increased reliance of cardiomyocytes on the oxidation of free fatty acids, typical for insulin-resistant states, is associated with both a lower yield of ATP per oxygen molecule and lesser availability of ATP for contraction, which might decrease energetic efficiency of the first and second step of energy transfer from MVO₂ to EW. On the other part, concentric LV remodeling and LV dysfunction despite preserved ejection fraction can impair the efficiency of the third energy transfer step. In conclusion, the association of low MEEi with adverse cardiovascular outcome might be related to a multi-step impairment of energy transfer from MVO₂ to EW in various clinical settings, including metabolic syndrome, diabetes, hypertension and heart failure. Irrespective of theoretical considerations, MEEi appears an attractive simple tool which couldt improve risk stratification in hypertensive and diabetic patients for primary prevention purposes. Further clinical studies are warranted to estimate the predictive ability of MEEi and its post-treatment changes, especially in patients on novel antidiabetic drugs and subjects with common metabolic diseases and concomitant chronic coronary syndromes, in whom the potential relevance of MEE can be potentiated by myocardial ischemia.

Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI

BACKGROUND

Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate predictions of clinical outcomes. This study aims to predict different intrahospital clinical outcomes in patients undergoing TAVI using a machine learning-based approach. The main clinical outcomes include all-cause mortality, stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations.

METHODS AND RESULTS

The dataset consists of 451 consecutive patients undergoing elective TAVI between February 2014 and June 2016. The applied machine learning methods were neural networks, support vector machines, and random forests. Their performance was evaluated using five-fold nested cross-validation. Considering all 83 features, the performance of all machine learning models in predicting all-cause intrahospital mortality (AUC 0.94-0.97) was significantly higher than both the STS risk score (AUC 0.64), the STS/ACC TAVR score (AUC 0.65), and all machine learning models using baseline characteristics only (AUC 0.72-0.82). Using an extreme boosting gradient, baseline troponin T was found to be the most important feature among all input variables. Overall, after feature selection, there was a slightly inferior performance. Stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations could not be accurately predicted.

CONCLUSIONS

Machine learning has the potential to improve patient selection and risk management of interventional cardiovascular procedures, as it is capable of making superior predictions compared to current logistic risk scores.