Early discharge programme after transcatheter aortic valve implantation based on close follow-up supported by telemonitoring using artificial intelligence: the TeleTAVI study
Year: 2024
Type: article
Abstract: Aims Evidence regarding the safety of early discharge following transcatheter aortic valve implantation (TAVI) is limited. The aim of this study was to evaluate the safety of very early (<24) and early discharge (24–48 h) as compared to standard discharge (>48 h), supported by the implementation of a voice-based virtual assistant using artificial intelligence (AI) and natural language processing. Methods and results Single-arm prospective observational study that included consecutive patients who underwent TAVI in a tertiary hospital in 2023 and were discharged under an AI follow-up programme. Primary endpoint was a composite of death, pacemaker implantation, readmission for heart failure, stroke, acute myocardial infarction, major vascular complications, or major bleeding, at 30-day follow-up. A total of 274 patients were included. 110 (40.1%) patients were discharged very early (<24 h), 90 (32.9%) early (24–48 h), and 74 (27.0%) were discharged after 48 h. At 30-day follow-up, no significant differences were found among patients discharged very early, early, and those discharged after 48 h for the primary endpoint (very early 9.1% vs. early 11.1% vs. standard 9.5%; P = 0.88). The AI platform detected complications that could be effectively addressed. The implementation of this follow-up system was simple and satisfactory for TAVI patients. Conclusion Early and very early discharge in patients undergoing TAVI, supported by close follow-up using AI, were shown to be safe. Patients with early and very early discharge had similar 30-day event rates compared to those with longer hospital stays. The AI system contributed to the early detection and resolution of complications.
Authors: Marta Herrero-Brocal, Raquel Samper, Jorge Riquelme, Javier Pineda, Pascual Bordes, Fernando Torres-Mezcua, José Valencia, Francisco Torres-Saura, María González Manso, Raquel Ajo, Juan Arenas, Eloísa Feliu, Juan Gabriel Martínez, Juan Miguel Ruiz-Nodar
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,746
OpenAlex ID: W4404546915
