EchoNavigator® technology facilitates transapical mitral paravalvular leak closure: a case report.

BACKGROUND: Catheter-based closure has emerged as a less invasive alternative to surgery in high-risk patients with paravalvular leak (PVL) and clinically significant regurgitation with feasibility and efficacy demonstrated in multiple studies. CASE SUMMARY: A 72-year-old female with a past history of long-standing rheumatic heart disease underwent mechanical mitral valve replacement in 2008. Ten years later, redo surgery was performed due to a worsening mitral PVL and the leakage was closed by direct pledget-supported sutures, preserving the mechanical valve. She was recently admitted again for haemolytic anaemia and congestive heart failure (New York Heart Association Classes III-IV) due to a recurrent mitral PVL. We report our initial clinical experience using a novel software solution (EchoNavigator®-system) for intuitive guidance during a catheter-based transapical mitral PVL closure. DISCUSSION: Transapical mitral PVL closure with a specifically designed device demonstrated in our case to be a better option than redo surgery. Recently introduced fusion imaging modalities enhanced visualization of soft tissue anatomy and device location improving enormously the results of this challenging intervention.

Artificial intelligence: Neural network model as the multidisciplinary team member in clinical decision support to avoid medical mistakes.

OBJECTIVE: The continuous uninterrupted feedback system is the essential part of any well-organized system. We propose aLYNX concept that is a possibility to use an artificial intelligence algorithm or a neural network model in decision-making system so as to avoid possible mistakes and to remind the doctors to review tactics once more in selected cases. METHOD: aLYNX system includes: registry with significant factors, decisions and results; machine learning process based on this registry data; the use of the machine learning results as the adviser. We show a possibility to build a computer adviser with a neural network model for making a choice between coronary aortic bypass surgery (CABG) and percutaneous coronary intervention (PCI) in order to achieve a higher 5-year survival rate in patients with angina based on the experience of 5107 patients. RESULTS: The neural network was trained by 4679 patients who achieved 5-year survival. Among them, 2390 patients underwent PCI and 2289 CABG. After training, the correlation coefficient (r) of the network was 0.74 for training, 0.67 for validation, 0.71 for test and 0.73 for total. Simulation of the neural network function has been performed after training in the two groups of patients with known 5-year outcome. The disagreement rate was significantly higher in the dead patient group than that in the survivor group between neural network model and heart team [16.8% (787/4679) vs. 20.3% (87/428), P = 0.065)]. CONCLUSION: The study shows the possibility to build a computer adviser with a neural network model for making a choice between CABG and PCI in order to achieve a higher 5-year survival rate in patients with angina.