Transcatheter aortic valve implantation in severe aortic stenosis does not necessarily reverse left ventricular myocardial damage: data of long-term follow-up.

AIMS: Aortic stenosis (AS) is causing myocardial damage and replacement is mainly indicated based on symptoms. Non-invasive estimation of myocardial work (MW) provides a less afterload-dependent too for assessing myocardial function. 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 AND RESULTS: We conducted an observational, cross-sectional, single-centre study. Patients were selected based on the validated indication for a TAVI. Standardized echocardiographies were repeated. A total of 102 patients were included. The mean age was 85 years, 45% were female, 68% had high blood pressure, and 52% had a coronary disease. One-fifth was suffering from low-flow-low-gradient AS. A follow-up was performed at 22 ± 9.5 months after the TAVI. No TAVI dysfunction was observed. Left ventricular (LV) ejection fraction was stable (62 ± 8%), and global longitudinal strain had improved (-14.0 ± 3.7 vs. -16.0 ± 3.6%, P < 0.0001). No improvement of the MW parameters was noticed (LV global work index 2099 ± 692 vs. 2066 ± 706 mmHg%, P = 0.8, LV global constructive 2463 ± 736 vs. 2463 ± 676 mmHg%, P = 0.8). Global wasted work increased [214 (149; 357) 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 more attention in the pharmacological management of these AS patients.

Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography.

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.