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INTRODUCTION: Aortic stenosis (AS) is causing myocardial damages and replacement is mainly indicated based on symptoms. Non-invasive estimation of myocardial work (MW) provide a less afterload dependent tool that, we sought to look at the impact of transcatheter aortic valve implantation (TAVI) on the myocardium at long-term follow-up and according to current indications. METHODS: We conducted an observational, cross-sectional, single-center study. Patients were selected based on the validated indication for a TAVI. Standardized echocardiographies were repeated. RESULTS: 102 patients were included. Mean age was 85-year-old, 45% were female, 68% get high-blood pressure and 52% had a coronary disease. One fifth was suffering from low-flow low-gradient aortic stenosis. Follow-up was performed at 22 ± 9.5 months after the TAVI. No TAVI-dysfunction was observed. LVEF was stable (62 ± 8%), and global longitudinal strain get improved (-14.0% ± 3.7 vs -16.0% ± 3.6, p-value <0.0001). No improvement of the MW-parameters was noticed (Global Work Index (LV GWI) 2099 ± 692mmHg% vs 2066 ± 706mmHg%, p=0.8, Global Constructive (LV GCW) 2463 ± 736mmHg% vs 2463 ± 676mmHg%, p=0.8). Global Wasted Work increased (214 [149; 357] mmHg% vs 247 [177; 394] mmHg%, p= 0.0008). CONCLUSION: In a population of severe symptomatic AS-patients who had undergone a TAVI, the non-invasive myocardial indices that assess the LV performance at long term follow-up did not improve. These results are questioning the timing of the intervention and the need for a more attention in the pharmacological management of these AS-patients.
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Aims: Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning. Methods and results: We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization). Conclusion: This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.
