Preclinical stiff heart is a marker of cardiovascular morbimortality in apparently healthy population

Background: The prognostic significance of impaired left ventricular (LV) relaxation and increased LV stiffness as precursor of heart failure with preserved ejection fraction and death is still largely unknown in apparently healthy subjects. Methods: We constituted a cohort of 353 patients with normal ejection fraction (>45%) and no significant heart disease, based on a total of 3,575 consecutive left-sided heart catheterizations performed. We measured peak negative first derivative of LV pressure (-dP/dt) and operating chamber stiffness (Κ) using a validated equation. Patients were categorized as having: 1) normal diastolic function, 2) isolated relaxation abnormalities (-dP/dt > 1860mm Hg/sec and K <0.025mm Hg/ml), or 3) predominant stiff heart (K ≥0.025mm Hg/ml). Results: During a follow-up of at least 5 years, the incidence of the primary composite endpoint (death, major arterial event, heart failure, and arrhythmia) was 23.2% (82 patients). Compared to isolated relaxation abnormalities, predominant stiff heart showed stronger prognostic significance for all events (p=0.002), namely heart failure (HR, 2.9; p=0.0499), cardiac death (HR, 5.8; p=0.03), and heart failure and cardiac death combined (HR, 3.7; p=0.003). Conclusion: In this apparently healthy population referred to our center for cardiac catheterization, the prevalence of diastolic dysfunction was very high. Moreover, predominant stiff heart was a better predictor of cardiovascular outcomes than isolated relaxation abnormalities

Unsupervised Clustering of Patients with Severe Aortic Stenosis: A Myocardial Continuum.

International audienceBACKGROUND: Traditional statistics, based on prediction models with a limited number of prespecified variables, are probably not adequate to provide an appropriate classification of a condition that is as heterogeneous as aortic stenosis (AS). AIMS: To investigate a new classification system for severe AS using phenomapping. METHODS: Consecutive patients from a referral centre (training cohort) who met the echocardiographic definition of an aortic valve area (AVA) ≤q~1~cm(2) were included. Clinical, laboratory and imaging continuous variables were entered into an agglomerative hierarchical clustering model to separate patients into phenogroups. Individuals from an external validation cohort were then assigned to these original clusters using the K nearest neighbour (KNN) function and their 5-year survival was compared after adjustment for aortic valve replacement (AVR) as a time-dependent covariable. RESULTS: In total, 613 patients were initially recruited, with a mean±standard deviation AVA of 0.72±0.17~cm(2). Twenty-six variables were entered into the model to generate a specific heatmap. Penalized model-based clustering identified four phenogroups (A, B, C and D), of which phenogroups B and D tended to include smaller, older women and larger, older men, respectively. The application of supervised algorithms to the validation cohort (n=1303) yielded the same clusters, showing incremental cardiac remodelling from phenogroup A to phenogroup D. According to this myocardial continuum, there was a stepwise increase in overall mortality (adjusted hazard ratio for phenogroup D vs A 2.18, 95% confidence interval 1.46-3.26; P<0.001). CONCLUSIONS: Artificial intelligence re-emphasizes the significance of cardiac remodelling in the prognosis of patients with severe AS and highlights AS not only as an isolated valvular condition, but also a global disease