Inadequacy of Existing Clinical Prediction Models for Predicting Mortality after Transcatheter Aortic Valve Implantation

Glen Martin, Matthew Sperrin, Peter Ludman, Mark de Belder, Chris Gale, William Toff, Iain Buchan, Mamas Mamas

Research output: Contribution to journalArticlepeer-review

102 Downloads (Pure)


The performance of emerging Transcatheter Aortic Valve Implantation (TAVI) clinical prediction models (CPMs) in national TAVI cohorts distinct from those where they have been derived is unknown. This study aimed to investigate the performance of the German Aortic Valve, FRANCE-2, OBSERVANT and American College of Cardiology (ACC) TAVI CPMs compared with the performance of historic cardiac CPMs such as the EuroSCORE and STS-PROM, in a large national TAVI registry.


The calibration and discrimination of each CPM were analysed in 6676 patients from the UK TAVI registry, as a whole cohort and across several subgroups. Strata included gender, diabetes status, access route and valve type. Furthermore, the amount of agreement in risk classification between each of the considered CPMs was analysed at an individual patient level.


The observed 30-day mortality rate was 5.4%. In the whole cohort, the majority of CPMs over-estimated the risk of 30-day mortality, although the mean ACC score (5.2%) approximately matched the observed mortality rate. The areas under ROC curve were between 0.57 for OBSERVANT and 0.64 for ACC. Risk classification agreement was low across all models, with Fleiss's kappa values between 0.17 and 0.50.


Although the FRANCE-2 and ACC models outperformed all other CPMs, the performance of current TAVI-CPMs was low when applied to an independent cohort of TAVI patients. Hence, TAVI specific CPMs need to be derived outside populations previously used for model derivation, either by adapting existing CPMs or developing new risk scores in large national registries.

Despite surgical aortic valve replacement (SAVR) being the definitive treatment strategy for severe symptomatic Aortic Stenosis (AS), a significant proportion of patients are not offered surgery due to co-morbidities or frailty that contribute to high surgical risks and adverse outcomes in such patient groups (1). Transcatheter aortic valve implantation (TAVI) has emerged as an efficacious but less invasive treatment option in high and intermediate operative risk patients 2., 3., 4. and 5.. As such, treatment allocation between medical management, SAVR and TAVI depends on multiple factors, but key is the assessment of the patient's procedural risk. Clinical prediction models (CPMs), which quantify the risks associated with the proposed treatment strategy at an individual patient level, can aid heart-teams in this clinical decision-making process and are vital for audit purposes between TAVI centres.
Original languageEnglish
Pages (from-to)A34
Number of pages1
Publication statusPublished - 3 Nov 2016
EventBCS Annual Conference: 'Prediction and Prevention' - Manchester Central, Manchester, United Kingdom
Duration: 6 Jun 20168 Jun 2016


Dive into the research topics of 'Inadequacy of Existing Clinical Prediction Models for Predicting Mortality after Transcatheter Aortic Valve Implantation'. Together they form a unique fingerprint.

Cite this