Background The first step in evaluating a patient with suspected stable coronary artery disease (CAD) is the determination of the pretest probability. The European Society of Cardiology guidelines recommend the use of the CAD Consortium 1 score (CAD1), which contrary to CAD Consortium 2 (CAD2) score and Duke Clinical Score (DCS), does not include modifiable cardiovascular risk factors. Hypothesis Using scores that include modifiable risk factors (DCS and CAD2) enhances prediction of CAD. Methods We retrospectively included all patients referred to invasive coronary angiography for suspected CAD from January/2008–December/2012 (N = 2234). Pretest probability was calculated using 3 models (CAD1, DCS, and CAD2), and they were compared using the net reclassification improvement. Results Mean patient age was 63.7 years, 67.5% were male, and the majority (66.9%) had typical angina. Coronary artery disease was diagnosed in 58.5%, and the area under the curve was 0.685 for DCS, 0.664 for CAD1, and 0.683 for CAD2, with a statistically significant difference between CAD1 and the others (P < 0.001). The net reclassification improvement was 20% for DCS, related to adequate reclassification of 32% of patients with CAD to a higher risk category, and 5% for CAD2, at the cost of adequate reclassification of 34% of patients without CAD to a lower risk category. Conclusions Prediction of CAD using scores that include modifiable cardiovascular risk factors seems to improve accuracy. Our results suggest that, in high‐prevalence populations, DCS may better identify patients at higher risk and CAD2 those at lower risk for CAD.

Self-expanding prosthesis have greater eccentricity and under-expansion. Calcium burden exerts more influence in the final morphology of that type of valve. Calcification and eccentricity are associated with the development of PVR. These factors should be considered in the selection of the most appropriate type of prosthesis for each scenario.

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Introduction The use of venoarterial extracorporeal membrane oxygenation (VA-ECMO) to support patients in cardiogenic shock has been increasing in Portugal over the past few years. Nonetheless, epidemiologic, prognostic and clinical outcome data are scarce. Purpose We aim to identify clinical variables with prognostic significance in this challenging population, as well as the performance of various risk scores in mortality prediction. Methods All patients that underwent VA-ECMO support at our Cardiac ICU between 2011 and 2018 were included in the analysis. Logistic regression analysis was used to assess the relationship between clinical variables and outcomes. Results Short-term mechanical support with VA-ECMO was given to 40 patients, with a mean age of 52±11 years. At the time of the implant, the mean SOFA score was 11.2±4.0, and mean SAVE score was −4.75±4.6. Mean ECMO support duration was 116±96 hours. In 70% (N=28) of patients, VA-ECMO was successfully weaned. In-hospital mortality was observed in 52.5% of patients, which was in accordance with the predicted mortality by SOFA score (22.5% to 82% in our population risk range) and by SAVE score (60 to 70%). Those who placed the VA-ECMO as a bridge to transplant or to long-term mechanical LV assist device had greater in-hospital mortality rates (91.6 vs 41.9%, p=0.013), as well as those under ≥2 inotropic/vasopressors (69.2 vs 21.4%, p=0.012) or when adrenaline use was needed (100% vs 44.1%, p=0.01). No other between-group differences were observed in what concerns short-term mortality. After logistic regression analysis, independent predictors of in-hospital mortality included AMI setting, number of vasoactive amines used, and necessity of a LV venting device. SAVE score had the greater predictive ability in these patients (AUC = 0.638) among the most utilized clinical risk scores (SOFA score AUC = 0.37; APACHE II score AUC = 0.59; SAPS II score AUC = 0.54). Conclusion In our analysis, patients in profound cardiogenic shock on VA-ECMO support had slightly better survival rates than predicted by classical Risk Scores. The SAVE score may be the most accurate tool to predict in-hospital mortality in this specific, and yet heterogeneous, clinical subset. Other well recognized clinical markers of severity may also help refine short-term prognosis, and potentially improve organ transplant or other destination therapy prioritization.