Late VAs are common after LVAD implantation. The VT-LVAD score may help to identify patients at risk of late VAs and guide ICD indications in previously nonimplanted patients. (Determination of Risk Factors of Ventricular Arrhythmias [VAs] after implantation of continuous flow left ventricular assist device with continuous flow left ventricular assist device [CF-LVAD] [ASSIST-ICD]; NCT02873169).

Background: Cardiac allograft vasculopathy (CAV) is a major contributor of heart transplant recipient mortality. Little is known about the prototypes of CAV trajectories at the population level. We aimed to identify the different evolutionary profiles of CAV and to determine the respective contribution of immune and nonimmune factors in CAV development. Methods: Heart transplant recipients were from 4 academic centers (Pitié-Salpêtrière and Georges Pompidou Hospital, Paris, Katholieke Universiteit Leuven, and Cedars-Sinai, Los Angeles; 2004–2016). Patients underwent prospective, protocol-based monitoring consisting of repeated coronary angiographies together with systematic assessments of clinical, histological, and immunologic parameters. The main outcome was a prediction for CAV trajectory. We identified CAV trajectories by using unsupervised latent class mixed models. We then identified the independent predictive variables of the CAV trajectories and their association with mortality. Results: A total of 1301 patients were included (815 and 486 in the European and US cohorts, respectively). The median follow-up after transplantation was 6.6 (interquartile range, 4–9.1) years with 4710 coronary angiographies analyzed. We identified 4 distinct profiles of CAV trajectories over 10 years. The 4 trajectories were characterized by (1) patients without CAV at 1 year and nonprogression over time (56.3%), (2) patients without CAV at 1 year and late-onset slow CAV progression (7.6%), (3) patients with mild CAV at 1 year and mild progression over time (23.1%), and (4) patients with mild CAV at 1 year and accelerated progression (13.0%). This model showed good discrimination (0.92). Among candidate predictors assessed, 6 early independent predictors of these trajectories were identified: donor age ( P <0.001), donor male sex ( P <0.001), donor tobacco consumption ( P =0.001), recipient dyslipidemia ( P =0.009), class II anti–human leukocyte antigen donor-specific antibodies ( P =0.004), and acute cellular rejection ≥2R ( P =0.028). The 4 CAV trajectories manifested consistently in the US independent cohort with similar discrimination (0.97) and in different clinical scenarios, and showed gradients for overall-cause mortality ( P <0.001). Conclusions: In a large multicenter and highly phenotyped prospective cohort of heart transplant recipients, we identified 4 CAV trajectories and their respective independent predictive variables. Our results provide the basis for a trajectory-based assessment of patients undergoing heart transplantation for early risk stratification, patient monitoring, and clinical trials. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04117152.

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Aims Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome with various causes that may influence prognosis. Methods and results We extracted the electronic medical records for 2180 consecutive patients hospitalized between 2016 and 2019 for decompensated heart failure. Using a text mining algorithm looking for a left ventricular ejection fraction ≥50% and plasma brain natriuretic peptide level >100 pg/mL, we identified 928 HFpEF patients. We screened for a prevailing cause of HFpEF according to European guidelines and found that 418 (45.0%) patients had secondary HFpEF due to either myocardial (n = 125, 13.5%) or loading condition abnormalities (n = 293, 31.5%), while the remaining 510 (55.0%) patients had idiopathic HFpEF. We assessed the association between the causes of HFpEF and survival collected up to 31 December 2020 using Cox proportional hazards analysis. Even though patients with idiopathic HFpEF were older, frequently female, and had frequent co‐morbidities and a higher crude mortality rate compared with secondary HFpEF patients, their prognosis was similar after adjustment for age and sex. Unsupervised clustering analysis revealed three main phenogroups with different distribution of idiopathic vs. secondary HFpEF. The phenogroup with the highest proportion of idiopathic HFpEF (69%) had (i) an excess rate of non‐cardiac co‐morbidities including chronic obstructive pulmonary disease (31%) or obesity (41%) and (ii) a better prognosis compared with the two other phenogroups enriched with secondary HFpEF. Conclusions Aetiological classification provides clinical and prognostic information and may be useful to better decipher the clinical heterogeneity of HFpEF.