• J J Liebenberg, A Doubell, J Van Wyk, M Kidd, T Mabin, and H Weich.
• Division of Cardiology, Department of Internal Medicine, Tygerberg Hospital and Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa. liebjurg@gmail.com.
• S. Afr. Med. J. 2020 May 29; 110 (6): 491-496.

BackgroundTranscatheter aortic valve implantation (TAVI) has undergone rapid expansion internationally over the past 15 years. In view of resource constraints in developing countries, a major challenge in applying this technology lies in identifying patients most likely to benefit. The development of a risk prediction model for TAVI has proved elusive, with a reported area under the curve (AUC) of 0.6 - 0.65. The available models were developed in a First-World setting and may not be applicable to South Africa (SA).ObjectivesTo evaluate novel indicators and to develop a TAVI risk prediction model unique to the SA context. The current work represents the important initial steps of derivation cohort risk model development and internal validation.MethodsSeven-year experience with 244 successive TAVI implants in three centres in Western Cape Province, SA, was used to derive risk parameters. All outcomes are reported in accordance with the Valve Academic Research Consortium definitions. Multiple preprocedural variables were assessed for their impact on 1-year survival using univariate and multivariate models.ResultsFactors found not to correlate with 1-year survival included age, renal function and aortic valve gradients. The commonly used surgical risk prediction models (Society of Thoracic Surgeons score and EuroSCORE) showed no correlation with outcomes. Factors found to correlate best with 1-year survival on multivariate analysis were preprocedural body mass index (BMI) (favouring higher BMI), preprocedural left ventricular end-diastolic dimension (LVED) and ejection fraction (EF) (favouring smaller LVED and higher EF), absence of atrial fibrillation, and three novel parameters: independent living, ability to drive a car, and independent food acquisition/cooking. Discriminant analysis of these factors yielded an AUC of 0.8 (95% confidence interval 0.7 - 0.9) to predict 1-year survival, with resubstitution sensitivities and specificities of 72% and 71%, respectively.ConclusionsApart from existing predictors, we identified three novel risk predictors (independent living, ability to drive a car, and independent food acquisition/cooking) for 1-year survival in TAVI candidates. These novel parameters performed well in this early evaluation, with an AUC for predicting 1-year survival higher than the AUCs for many of the internationally derived parameters. The parameters are inexpensive and easy to obtain at the initial patient visit. If validated prospectively in external cohorts, they may be applicable to other resource-constrained environments.

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