Development and validation of a prediction model for early mortality after transcatheter aortic valve implantation (TAVI) based on the Netherlands Heart Registration (NHR): The TAVI‐NHR risk model

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.