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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., (© 2022 The Authors. Catheterization and Cardiovascular Interventions published by Wiley Periodicals LLC.)

Background: Several mortality prediction models (MPM) are used for predicting early (30-day) mortality following transcatheter aortic valve implantation (TAVI). Little is known about their predictive performance in external TAVI populations. We aim to externally validate established MPMs on a large TAVI dataset from the Netherlands Heart Registration (NHR)., Methods: We included data from NHR-patients who underwent TAVI during 2013-2017. We calculated the predicted mortalities per MPM. We assessed the predictive performance by discrimination (Area Under Receiver Operating-characteristic Curve, AU-ROC); the Area Under Precision-Recall Curve, AU-PRC; calibration (using calibration-intercept and calibration-slope); Brier Score and Brier Skill Score. We also assessed the predictive performance among subgroups: tertiles of mortality-risk for non-survivors, gender, and access-route., Results: We included 6177 TAVI-patients with an observed early-mortality rate of 4.5% (n = 280). We applied seven MPMs (STS, EuroSCORE-I, EuroSCORE-II, ACC-TAVI, FRANCE-2, OBSERVANT, and German-AV) on our cohort. The highest AU-ROCs were 0.64 (95%CI 0.61-0.67) for ACC-TAVI and 0.63 (95%CI 0.60-0.67) for FRANCE-2. All MPMs had a very low AU-PRC of ≤0.09. ACC-TAVI had the best calibration-intercept and calibration-slope. Brier Score values ranged between 0.043 and 0.063. Brier Skill Score ranged between -0.47 and 0.004. ACC-TAVI and FRANCE-2 predicted high mortality-risk better than other MPMs. ACC-TAVI outperformed other MPMs in different subgroups., Conclusion: The ACC-TAVI model has relatively the best predictive performance. However, all models have poor predictive performance. Because of the poor discrimination, miscalibration and limited accuracy of the models there is a need to update the existing models or develop new TAVI-specific models for local populations., Competing Interests: Declaration of Competing Interest Hatem Al-Farra, Ameen Abu-Hanna, Bas AJM de Mol, WJ ter Burg, Saskia Houterman, José PS Henriques, and Anita CJ Ravelli declare no conflict of interest. Addendum. The following physicians are the members of the NHR THI Registration Committee. They represent the hospitals that have provided the data for this study. Contact with NHR THI Registration Committee can be via the e-mail info@nederlandsehartregistratie.nl M.M. Vis, Amsterdam University Medical Centers. J. Vos, Amphia Hospital. J. ten Berg, St. Antonius Hospital. W.A.L. Tonino, Catharina Hospital. C.E. Schotborgh, HagaHospital. V. Roolvink, Isala. F. Porta, Leeuwarden Medical Center. M. Stoel, Medisch Spectrum Twente. S. Kats, Maastricht University Medical Center. G. Amoroso, Onze Lieve Vrouwe Gasthuis. H.W. van der Werf, University Medical Center Groningen. P.R. Stella, University Medical Center Utrecht. P. de Jaegere, Erasmus University Medical Center., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The capacity of TAVI-programs and numbers of sites performing TAVI has rapidly increased. This necessitated the initiation of the Rotterdam TAVI Care & Cure Program, aiming to improve patient-centered care during the TAVI pathway., Methods: Consenting patients with severe aortic stenosis and an indication for TAVI will be included. The TAVI Care & Cure program will facilitate prognostic contributions to improve outcomes, patient satisfaction and quality of life in patients with valvular heart disease who are treated with a transcatheter aortic valve implantation in collaboration with the departments of cardiology, cardio-thoracic surgery, anesthesiology and geriatrics., Conclusion: With a single center observational registry, we aim to assess the TAVI patient clinical pathway, focusing on pre, peri and post interventional variables including functional status and HRQoL. We will evaluate the patient's complexity by applying an extended multidisciplinary approach, which includes a systematic application of geriatric assessments of frailty and cognitive function., (Copyright © 2019 Elsevier B.V. All rights reserved.)