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
- cardiovascular events
- newly diagnosed
- deep learning
- chronic kidney disease
- risk factors
- prognostic factors
- peritoneal dialysis
- type diabetes
- artificial intelligence
- big data
- patient reported outcomes
- left ventricular
- heart failure
- quality improvement
- atrial fibrillation
- patient reported