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Predictors of Length-of-Stay Among Transcatheter Aortic Valve Replacement Patients Using a Supervised Machine

Gregory L Judson¹, Jeff Luck², Skye Lawrence²

¹Department of Medicine Division of Cardiology, University of California-San Francisco, San Francisco, California, USA; Department of Medicine Division of Cardiology, Maine Medical Center, Portland, Maine, USA.

|June 25, 2025

Summary

This summary is machine-generated.

Machine learning algorithms can predict short and long hospital stays after transcatheter aortic valve replacement (TAVR). This approach identifies new predictors, potentially improving patient care and reducing length of stay.

Keywords:

TAVR length of stay machine learning

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Area of Science:

Cardiovascular Medicine
Medical Informatics
Health Services Research

Background:

Hospital length of stay (LOS) after transcatheter aortic valve replacement (TAVR) is improving, yet accurate prediction remains challenging.
Identifying factors influencing early and late discharge is crucial for optimizing patient management and hospital resources.

Purpose of the Study:

To develop a novel machine learning (ML) algorithm for predicting short length of stay (SLOS) and long length of stay (LLOS) in TAVR patients.
To identify previously unrecognized predictors of LOS following TAVR.

Main Methods:

Analysis of 9,172 outpatient TAVR procedures from 21 US centers (2017-2021) using the Biome dataset.
Development and testing of supervised random forest ML algorithms to predict SLOS (<36 hours) and LLOS (≥72 hours).
Comparison of ML model performance against standard multivariable models.

Main Results:

ML models identified 20 (SLOS) and 22 (LLOS) important predictors.
ML models demonstrated superior predictive power (SLOS AUC 0.82, LLOS AUC 0.85) compared to multivariable models (SLOS AUC 0.65, LLOS AUC 0.65).
Novel predictors identified include procedural duration, postprocedure physical therapy, and day of the week of the procedure.

Conclusions:

Machine learning algorithms show promise in identifying novel predictors for both short and prolonged hospital stays post-TAVR.
These findings can inform targeted quality improvement initiatives to reduce TAVR length of stay.