Prediction of in-hospital death following aortic valve replacement: A new accurate model

Matthew Richardson, Neil Howell, Nick Freemantle, Ben Bridgewater, Domenico Pagano

    Research output: Contribution to journalArticlepeer-review

    Abstract

    OBJECTIVES: Aortic valve replacement (AVR) is accepted as the standard treatment for severe symptomatic aortic valve stenosis and regurgitation. As novel treatments are introduced for patients at high risk for conventional surgery, it is important to have models that accurately predict procedural risk. The aim of this study was to develop and validate a risk-stratification model to predict in-hospital risk of death for patients undergoing AVR and to compare the model with existing algorithms. METHODS: We reviewed data from the Central Cardiac Adult Database, which holds prospectively collected clinical information on all adult patients undergoing cardiac surgery in National Health Service (NHS) hospitals and some private providers in the UK and Ireland. We included all the patients undergoing AVR with or without coronary artery bypass grafting. The study population consists of 55 157 patients undergoing surgery between 1 April 2001 and 31 March 2009. The model was built using data from April 2001 to March 2008 and validated using data from patients undergoing surgery from April 2008 to March 2009. The model was compared against the additive and logistic EuroSCORE models and a valve-specific risk-prediction model. RESULTS: The final multivariable model includes items describing cardiovascular risk status and procedural factors. Applying the model to the independent validation dataset provided a c-statistic (index of rank correlation) of 0.791, which was substantially better than that achieved by previously developed risk models in Europe, and significantly improved risk prediction in higher-risk patients. CONCLUSIONS: We have produced an accurate risk model to predict outcome following AVR surgery. It will be of use for patient selection and informed consent, and of particular interest in defining those patients at high risk who may benefit from novel approaches to AVR. © The Author 2012. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
    Original languageEnglish
    Article numberezs457
    Pages (from-to)704-708
    Number of pages4
    JournalEuropean Journal of Cardio-Thoracic Surgery
    Volume43
    Issue number4
    DOIs
    Publication statusPublished - Apr 2013

    Keywords

    • Aortic valve replacement
    • Prognostic model
    • Risk model
    • Risk score

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