A Clinical Risk Prediction Model for Identifying Patient Candidates for Same-day Discharge After Transcatheter Aortic Valve Replacement

TL;DR

This paper introduces a machine learning model to identify patients suitable for same-day discharge after a heart valve procedure, potentially improving hospital efficiency.

Contribution

A novel clinical risk prediction model using random forest to identify TAVR patients suitable for same-day discharge.

Findings

The model successfully identified 23.6% of patients as low-risk for adverse events within 6 hours post-TAVR.

The low-risk group had no in-hospital events and no 30-day mortality.

External testing showed 94% sensitivity in predicting adverse events.

Abstract

Same-day discharge after transcatheter aortic valve replacement (TAVR) may be feasible for selected patients if a low risk for adverse clinical events can be defined. We aimed to develop a clinical risk prediction model to facilitate same-day discharge planning.

A random forest machine learning algorithm was used to build a prediction model of adverse events occurring in-hospital after TAVR. Patients were categorized into low, moderate, or high-risk groups based on their estimated scores.

Overall, 730 patients (median age, 81 years; 58.9% men) who had transfemoral TAVR performed with conscious sedation were examined. The risk score was built utilizing 9 clinical parameters. The prediction model had a median area under the receiver operating characteristic curve of 0.76. For determining the probability of events that would disallow same-day discharge, the model successfully identified 172 patients (23.6% of the population) as low-risk for same-day discharge, or for having an event rate of <3%, with all events occurring within 6 hours after TAVR. The low-risk group had no in-hospital events after a 6-hour observation, and no mortality at the 30-day follow-up. External testing in 158 patients showed 94% sensitivity in predicting overall adverse events and identified a low-risk group using the clinical risk score.

In this analysis, ∼1 in 4 patients may be candidates for same-day discharge after TAVR. This prediction model can identify such patients, with findings that may have implications for hospital resource allocation in those undergoing TAVR.

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