Performance of the heart team approach in daily clinical practice in high-risk patients with aortic stenosis.

OBJECTIVE: The heart team (HT) approach plays a key role in selecting the optimal treatment strategy for patients with aortic stenosis (AS). However, little is known about the HT decision process and its impact on outcomes. The aim of this study was to identify the factors associated with the HT decision and evaluate clinical outcomes according to the treatment choice. METHODS: The study included a total of 286 consecutive patients with AS referred for discussion in the weekly HT meeting in a cardiovascular institute over 2 years. Patients were stratified according to the selected therapeutic approach: medical treatment (MT), surgical (SAVR), or transcatheter (TAVR) aortic valve replacement. Baseline characteristics involved in making a therapeutic choice were identified and a decision-making tree was built using classification and regression tree methodology. RESULTS: Based on HT discussion, 53 patients were assigned to SAVR, 210 to TAVR, and 23 to MT. Older patients (≥88 years old) were mainly assigned to TAVR or MT according to the logistic EuroSCORE (

Comprehensive physiological evaluation of epicardial and microvascular coronary domains using vascular conductance and zero flow pressure.

AIMS: Assessment of the coronary circulation has been based largely on pressure ratios (epicardial) and resistance (micro-vessels). Simultaneous assessment of epicardial (CEPI) and microvascular conductance (CMICRO) provides an intuitive approach using the same units for both coronary domains and expressing the actual deliverability of blood. The aim of this study was to develop a novel integral method for assessing the functional severity of epicardial and microvascular disease. METHODS AND RESULTS: We performed intracoronary pressure and Doppler flow velocity measurements in 403 vessels in 261 patients with stable coronary artery disease. Hyperaemic mid-to-late diastolic pressure and flow velocity (PV) relationships were calculated. The slope of the aortic PV indicates the overall conductance and the slope of the distal PV relationship represents CMICRO. The intercept with the x-axis represents zero-flow pressure (Pzf). CEPI was derived from microvascular and overall conductance. Median CEPI was higher compared to CMICRO (4.2 [2.1-8.0] versus 1.3 [1.0-1.7] cm/s/mmHg, p<0.001). CMICRO was independent of stenosis severity (1.3 [1.0-1.7] in FFR ≤0.80 versus 1.4 [1.0-1.8] in FFR >0.8, p=0.797). ROC curves (using FFR and HSR concordant vessels as standard) demonstrated an excellent ability of CEPI to characterise significant stenoses (AUC 0.93). When CEPI

Clustering strategies for optimal trial selection in multisensor environments. An eigenvector based approach.

BACKGROUND: Quite often, magnetoencephalography (MEG) measurements are contaminated by a series of artifacts that degrade the quality of the various source localization methods applied to them. In particular, eye blinking, minor head movement and related activities are a constant source of measurement contamination. In order to solve this problem, trial selection and rejection is applied, a task that is usually performed manually. NEW METHOD: The present work shows an automatic trial selection and rejection algorithm based on clustering techniques. These techniques employ a measurement of the dissimilarity of the items belonging to a set. This measure, based on the projection of the eigenvector corresponding to the largest eigenvalue of the covariance matrix, is provided and its rationale is explained. Subsequently, covariance matrices belonging to the selected cluster are averaged and used in the well-known Linearly Constrained Minimum Variance (LCMV) Beamformer. RESULTS: The results show a marked improvement of the specificity of the localization algorithm compared to the application of the LCMV without clustering. COMPARISON WITH EXISTING METHOD(S): The method shows a marked reduction in computational cost compared with other data cleaning procedure widely used: Independent Component Analysis (ICA). CONCLUSIONS: Thus, we propose clustering techniques to be used in brain localization activity algorithms.