Update and, internal and temporal-validation of the FRANCE-2 and ACC-TAVI early-mortality prediction models for Transcatheter aortic Valve Implantation (TAVI) using data from the Netherlands heart registration (NHR)

International Journal of Cardiology: Heart & Vasculature

Anita C.J. Ravelli¹  Bas A.J.M. de Mol²  Hatem Al-Farra³  Saskia Houterman³  José P.S. Henriques³  W.J.P.P. ter Burg⁴  Ameen Abu-Hanna⁵ 

[1] Corresponding author at: Heart center and the department of medical informatics, Amsterdam UMC - Location AMC, University of Amsterdam, P.O. Box 22660, 1105 AZ Amsterdam, the Netherlands.;Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands;Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands;Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands;Netherlands Heart Registration, Utrecht, the Netherlands;

关键词: Transcatheter Aortic Valve Implantation (TAVI);    Prediction model;    Model updating;    External Validation;    Model recalibration;    Closed-testing procedure;   

DOI  :  

来源: DOAJ

【 摘 要 】

Background: The predictive performance of the models FRANCE-2 and ACC-TAVI for early-mortality after Transcatheter Aortic Valve Implantation (TAVI) can decline over time and can be enhanced by updating them on new populations. We aim to update and internally and temporally validate these models using a recent TAVI-cohort from the Netherlands Heart Registration (NHR). Methods: We used data of TAVI-patients treated in 2013–2017. For each original-model, the best update-method (model-intercept, model-recalibration, or model-revision) was selected by a closed-testing procedure. We internally validated both updated models with 1000 bootstrap samples. We also updated the models on the 2013–2016 dataset and temporally validated them on the 2017-dataset. Performance measures were the Area-Under ROC-curve (AU-ROC), Brier-score, and calibration graphs. Results: We included 6177 TAVI-patients, with 4.5% observed early-mortality. The selected update-method for FRANCE-2 was model-intercept-update. Internal validation showed an AU-ROC of 0.63 (95%CI 0.62–0.66) and Brier-score of 0.04 (0.04–0.05). Calibration graphs show that it overestimates early-mortality. In temporal-validation, the AU-ROC was 0.61 (0.53–0.67).The selected update-method for ACC-TAVI was model-revision. In internal-validation, the AU-ROC was 0.63 (0.63–0.66) and Brier-score was 0.04 (0.04–0.05). The updated ACC-TAVI calibrates well up to a probability of 20%, and subsequently underestimates early-mortality. In temporal-validation the AU-ROC was 0.65 (0.58–0.72). Conclusion: Internal-validation of the updated models FRANCE-2 and ACC-TAVI with data from the NHR demonstrated improved performance, which was better than in external-validation studies and comparable to the original studies. In temporal-validation, ACC-TAVI outperformed FRANCE-2 because it suffered less from changes over time.

【 授权许可】

Unknown