Personalised preinterventional risk stratification of mortality, length of stay and hospitalisation costs in transcatheter aortic valve implantation using a machine learning algorithm: a pilot trial.
Maria ZisiopoulouAlexander BerkowitschLeonard RedlichThomas WaltherStephan FichtlschererDavid M LeistnerPublished in: Open heart (2024)
A novel TAVI-specific model predicts 1-year mortality, LoS and costs after TAVI using simple, established, transparent and inexpensive metrics before implantation. Based on this preliminary evidence, TAVI team members and patients can make informed decisions based on a few key metrics. Validation of this score in larger patient cohorts is needed.
Keyphrases
- transcatheter aortic valve implantation
- aortic stenosis
- aortic valve
- ejection fraction
- aortic valve replacement
- machine learning
- transcatheter aortic valve replacement
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- risk factors
- deep learning
- palliative care
- peritoneal dialysis
- type diabetes
- prognostic factors
- left ventricular
- cardiovascular disease
- patient reported outcomes
- coronary artery disease
- big data