Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Hasimbegovic, E; Papp, L; Grahovac, M; Krajnc, D; Poschner, T; Hasan, W; Andreas, M; Gross, C; Strouhal, A; Delle-Karth, G; Grabenwöger, M; Adlbrecht, C; Mach, M.
A Sneak-Peek into the Physician's Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis
J PERS MED. 2021; 11(11): 1062
Doi: 10.3390/jpm11111062
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Co-authors Med Uni Graz
- Andreas Martin
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.
- Find related publications in this database (Keywords)
- TAVR
- TAVI
- iSAVR
- aortic stenosis
- machine learning
- heart team