Key result
TAVI-NHR risk model outperforms ACC-TAVI and IRRMA in predicting 30-day mortality with a 0.71 c-statistic.
Why the study?
Currently available mortality prediction models have suboptimal performance when predicting early (30-day) mortality following TAVI across external populations.
Does the TAVI-NHR risk model improve prediction of 30-day mortality after TAVI compared to existing models in a Dutch population?
Observational (n=9,144)
Yes
Does the TAVI-NHR risk model improve prediction of 30-day mortality after TAVI compared to existing models in a Dutch population?
Effect estimate: c-statistic 0.71 (95% CI 0.64-0.78)
p-value: p=< 0.05
A newly developed 10-variable risk model based on a Dutch national registry showed fair discrimination and good calibration for predicting 30-day mortality after TAVI, outperforming existing models.
TAVI-NHR may aid 30-day mortality stratification in Dutch TAVI patients; leaves open external validation before wider use.
BACKGROUND: The currently available mortality prediction models (MPM) have suboptimal performance when predicting early mortality (30-days) following transcatheter aortic valve implantation (TAVI) on various external populations. We developed and validated a new TAVI-MPM based on a large number of predictors with recent data from a national heart registry. METHODS: We included all TAVI-patients treated in the Netherlands between 2013 and 2018, from the Netherlands Heart Registration. We used logistic-regression analysis based on the Akaike Information Criterion for variable selection. We multiply imputed missing values, but excluded variables with >30% missing values. For internal validation, we used ten-fold cross-validation. For temporal (prospective) validation, we used the 2018-data set for testing. We assessed discrimination by the c-statistic, predicted probability accuracy by the Brier score, and calibration by calibration graphs, and calibration-intercept and calibration slope. We compared our new model to the updated ACC-TAVI and IRRMA MPMs on our population. RESULTS: We included 9144 TAVI-patients. The observed early mortality was 4.0%. The final MPM had 10 variables, including: critical-preoperative state, procedure-acuteness, body surface area, serum creatinine, and diabetes-mellitus status. The median c-statistic was 0.69 (interquartile range [IQR] 0.646-0.75). The median Brier score was 0.038 (IQR 0.038-0.040). No signs of miscalibration were observed. The c-statistic's temporal-validation was 0.71 (95% confidence intervals 0.64-0.78). Our model outperformed the updated currently available MPMs ACC-TAVI and IRRMA (p value < 0.05). CONCLUSION: The new TAVI-model used additional variables and showed fair discrimination and good calibration. It outperformed the updated currently available TAVI-models on our population. The model's good calibration benefits preprocedural risk-assessment and patient counseling.
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Al‐Farra et al. (2022) conducted an observational in Transcatheter aortic valve implantation (TAVI) (n=9,144). TAVI-NHR risk model vs. ACC-TAVI and IRRMA mortality prediction models was evaluated on Early mortality (30-days) prediction discrimination (c-statistic) (c-statistic 0.71, 95% CI 0.64-0.78, p=< 0.05). The new TAVI-NHR risk model for predicting 30-day mortality after TAVI showed fair discrimination (c-statistic 0.71; 95% CI 0.64-0.78) and outperformed the ACC-TAVI and IRRMA models (p<0.05).
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