A clinical risk score of myocardial fibrosis predicts adverse outcomes in aortic stenosis

Aims Midwall myocardial fibrosis on cardiovascular magnetic resonance (CMR) is a marker of early ventricular decompensation and adverse outcomes in aortic stenosis (AS). We aimed to develop and validate a novel clinical score using variables associated with midwall fibrosis. Methods and results One hundred forty-seven patients (peak aortic velocity (Vmax) 3.9 [3.2,4.4] m/s) underwent CMR to determine midwall fibrosis (CMR cohort). Routine clinical variables that demonstrated significant association with midwall fibrosis were included in a multivariate logistic score. We validated the prognostic value of the score in two separate outcome cohorts of asymptomatic patients (internal: n = 127, follow-up 10.3 [5.7,11.2] years; external: n = 289, follow-up 2.6 [1.6,4.5] years). Primary outcome was a composite of AS-related events (cardiovascular death, heart failure, and new angina, dyspnoea, or syncope). The final score consisted of age, sex, Vmax, high-sensitivity troponin I concentration, and electrocardiographic strain pattern [c-statistic 0.85 (95% confidence interval 0.78–0.91), P 57%). In the internal outcome cohort, AS-related event rates were >10-fold higher in high-risk patients compared with those at low risk (23.9 vs. 2.1 events/100 patient-years, respectively; log rank P < 0.001). Similar findings were observed in the external outcome cohort (31.6 vs. 4.6 events/100 patient-years, respectively; log rank P < 0.001). Conclusion We propose a clinical score that predicts adverse outcomes in asymptomatic AS patients and potentially identifies high-risk patients who may benefit from early valve replacement

Left ventricular hypertrophy, fibrosis and decompensation in patients with aortic stenosis

Aortic valve stenosis is the most common valvular heart disease in the Western world. It currently affects more than 7% of the population over the age of 60, with severe stenosis affecting in excess of 3% of people over the age of 75. In parallel with an aging population, the prevalence of aortic stenosis and need for surgery are expected to double over the next 20 years increasing further the burden on healthcare resources. Left untreated aortic stenosis leads to an abnormally high pressure load on the left ventricle, a pathological process that induces myocyte hypertrophy and fibrosis. Initially, the adaptive process of increased wall thickness maintains normal wall stress, contraction and cardiac output. However, ultimately this becomes maladaptive leading to ventricular stiffness, an increase in myocyte hypertrophy and myocardial fibrosis eventually causing diastolic and systolic dysfunction and increased morbidity and mortality. At present there is no effective medical therapy capable of altering this course and aortic valve intervention, usually in the form of surgical aortic valve replacement, is recommended by international guidelines in patients with severe stenosis and evidence of LV decompensation (either on the basis of symptoms or a reduced ejection fraction). Following aortic valve intervention patients demonstrate a variable degree of regression of the ventricular hypertrophy with favorable prognosis demonstrated in the cohort of patients with the highest level of regression. In this chapter we will discuss the prevalence and mechanism of left ventricular hypertrophy, fibrosis and decompensation in patients with aortic stenosis. Through case examples we will illustrate common cases of patients with hypertrophy relating to AS and analyze the most recent guidelines from the American Heart Association/American College of Cardiology (2014) and European Society of Cardiology (2012) on managing patients with aortic stenosis

A machine-learning framework to identify distinct phenotypes of aortic stenosis severity

OBJECTIVES : The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity. BACKGROUND : In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty. METHODS : Using echocardiography (ECHO) measurements (ECHO cohort, n ¼ 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT cohort, n ¼ 752) and cardiovascular magnetic resonance (CMR) imaging (CMR cohort, n ¼ 160). The classifier’s prognostic value was further validated using clinical outcomes (aortic valve replacement [AVR] and death) observed in the ECHO and CMR cohorts. RESULTS : In 1,964 patients from the 3 multi-institutional cohorts, 1,346 (68%) subjects had either nonsevere or discordant AS severity. Machine learning identified 1,117 (57%) patients as having high-severity and 847 (43%) as having low-severity AS. High-severity patients in CT and CMR cohorts had higher valve calcium scores and left ventricular mass and fibrosis, respectively than the low-severity group. In the ECHO cohort, progression to AVR and progression to death in patients who did not receive AVR was faster in the high-severity group. Compared with the conventional classification of disease severity, machine-learning–based severity classification improved discrimination (integrated discrimination improvement: 0.07; 95% confidence interval: 0.02 to 0.12) and reclassification (net reclassification improvement: 0.17; 95% confidence interval: 0.11 to 0.23) for the outcome of AVR at 5 years. For both ECHO and CMR cohorts, we observed prognostic value of the machine-learning classifications for subgroups with asymptomatic, nonsevere or discordant AS. CONCLUSIONS : Machine learning can integrate ECHO measurements to augment the classification of disease severity in most patients with AS, with major potential to optimize the timing of AVR

Markers of myocardial damage predict mortality in patients with aortic stenosis

Background: Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined. Objectives: Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality. Methods: Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years' follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome. Results: There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m2) and small (LVEDVi ≤55 mL/m2) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort. Conclusions: Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR

Large-Scale Whole-Genome Sequencing of Three Diverse Asian Populations in Singapore

Because of Singapore's unique history of immigration, whole-genome sequence analysis of 4,810 Singaporeans provides a snapshot of the genetic diversity across East, Southeast, and South Asia.</p