経カテーテル大動脈弁留置術後の同日退院患者候補を特定するための臨床リスク予測モデル

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.