Predictors of Length-of-Stay Among Transcatheter Aortic Valve Replacement Patients Using a Supervised Machine Learning Algorithm
ML algorithms may have an important role in identifying novel predictors of short and prolonged length of stay following TAVR. These efforts may facilitate targeted quality improvement programs to decrease length of stay post-TAVR.
Authors
- Vijayadithyan Jaganathan
- Kirsten Tolstrup
- Krishan Soni (ORCID: https://orcid.org/0000-0003-0152-3906)
- Gregory Judson
- Skye Lawrence
- Jeff Luck (ORCID: https://orcid.org/0000-0001-7044-6567)
- Vaikom S. Mahadevan (ORCID: https://orcid.org/0000-0001-6354-1873)
- Rakan Khaki
- Harsh Agrawal
Institutions
- University of Massachusetts Chan Medical School (US)
- University of California, San Francisco (US)
- EP Analytics (United States) (US)
- University of California San Francisco Medical Center (US)
- Maine Medical Center (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1016/j.jacadv.2025.101902
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00