Key result
In Japanese TAVR patients, the NHR and STS-PRoM models showed the highest discrimination for 30-day mortality (AU-ROC 0.82 and 0.78), while OCEAN-TAVI demonstrated the best 1-year performance.
Why the study?
Mortality prediction models are crucial for TAVR risk assessment, but their external validity in Japanese patients remains unclear.
How do existing short-term and long-term mortality prediction models perform in Japanese patients undergoing transcatheter aortic valve replacement?
Observational (n=1,756)
Yes
How do existing short-term and long-term mortality prediction models perform in Japanese patients undergoing transcatheter aortic valve replacement?
In Japanese TAVR patients, the STS-PRoM and NHR models provide superior short-term mortality prediction after recalibration, while the OCEAN-TAVI model offers the best overall long-term performance.
STS-PRoM and NHR models perform best short-term after recalibration in Japanese TAVR; extends validation data but leaves optimal model selection open.
OBJECTIVES: Mortality prediction models (MPMs) play a crucial role in risk assessment for transcatheter aortic valve replacement (TAVR), but their external validity in Japanese patients remains unclear. This study evaluated the performance of existing short-term (30-day) and long-term (1-year) MPMs with Japanese TAVR patients. METHODS: We analysed patients who underwent TAVR between 2016 and 2023 in the KPUM transcatheter aortic valve implantation (TAVI) registry, a prospective multicentre registry in Japan. Five short-term (30-day) MPMs (Society of Thoracic Surgeons Predicted Risk of Mortality [STS-PRoM], French Aortic National CoreValve and Edwards registry [FRANCE-2], Observational Study Of Appropriateness, Efficacy And Effectiveness of AVR-TAVR Procedures For the Treatment Of Severe Symptomatic Aortic Stenosis registry [OBSERVANT], American College of Cardiology-TAVI [ACC-TAVI], and Netherlands Heart Registration [NHR] models) and 3 long-term (1-year) MPMs (Optimized CathEter vAlvular iNtervention-TAVI [OCEAN-TAVI], Osaka University, and TAVR-Risk [TARI] models) were validated. Model performance was assessed using the area under the receiver operating characteristic curve (AU-ROC) for discrimination and calibration plots/slope/intercept for calibration. Intercept recalibration was performed for short-term MPMs. RESULTS: Among 1756 patients analysed for short-term mortality, the mortality risk was 1.4% (n = 25), while among 1235 patients analysed for long-term mortality, the mortality risk was 14.5% (n = 179), respectively. Among short-term MPMs, NHR (AU-ROC: 0.82) and STS-PRoM (0.78) showed the highest discrimination, though all models overestimated mortality risk, which improved after intercept recalibration. Among long-term MPMs, the OCEAN-TAVI and Osaka University models (AU-ROC: 0.75) showed the highest discrimination, with the OCEAN-TAVI demonstrating the best calibration. CONCLUSIONS: In Japanese TAVR patients, STS-PRoM and NHR models showed superior short-term performance after recalibration, while OCEAN-TAVI demonstrated the best overall long-term performance. Future research should explore machine learning-based models to improve risk prediction accuracy and clinical applicability.
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Seki et al. (2025) conducted an observational in Transcatheter aortic valve replacement (TAVR) (n=1,756). Mortality prediction models (MPMs) was evaluated on Short-term (30-day) and long-term (1-year) mortality prediction model performance (AU-ROC). In Japanese TAVR patients, the NHR and STS-PRoM models showed the highest discrimination for 30-day mortality (AU-ROC 0.82 and 0.78), while OCEAN-TAVI demonstrated the best 1-year performance.
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