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      Novel United Kingdom prognostic model for 30-day mortality following transcatheter aortic valve implantation

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          Abstract

          Objective

          Existing clinical prediction models (CPM) for short-term mortality after transcatheter aortic valve implantation (TAVI) have limited applicability in the UK due to moderate predictive performance and inconsistent recording practices across registries. The aim of this study was to derive a UK-TAVI CPM to predict 30-day mortality risk for benchmarking purposes.

          Methods

          A two-step modelling strategy was undertaken: first, data from the UK-TAVI Registry between 2009 and 2014 were used to develop a multivariable logistic regression CPM using backwards stepwise regression. Second, model-updating techniques were applied using the 2013–2014 data, thereby leveraging new approaches to include frailty and to ensure the model was reflective of contemporary practice. Internal validation was performed by bootstrapping to estimate in-sample optimism-corrected performance.

          Results

          Between 2009 and 2014, up to 6339 patients were included across 34 centres in the UK-TAVI Registry (mean age, 81.3; 2927 female (46.2%)). The observed 30-day mortality rate was 5.14%. The final UK-TAVI CPM included 15 risk factors, which included two variables associated with frailty. After correction for in-sample optimism, the model was well calibrated, with a calibration intercept of 0.02 (95% CI −0.17 to 0.20) and calibration slope of 0.79 (95% CI 0.55 to 1.03). The area under the receiver operating characteristic curve, after adjustment for in-sample optimism, was 0.66.

          Conclusion

          The UK-TAVI CPM demonstrated strong calibration and moderate discrimination in UK-TAVI patients. This model shows potential for benchmarking, but even the inclusion of frailty did not overcome the need for more wide-ranging data and other outcomes might usefully be explored.

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          Most cited references 14

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          Development and validation of a prediction model with missing predictor data: a practical approach.

          To illustrate the sequence of steps needed to develop and validate a clinical prediction model, when missing predictor values have been multiply imputed. We used data from consecutive primary care patients suspected of deep venous thrombosis (DVT) to develop and validate a diagnostic model for the presence of DVT. Missing values were imputed 10 times with the MICE conditional imputation method. After the selection of predictors and transformations for continuous predictors according to three different methods, we estimated regression coefficients and performance measures. The three methods to select predictors and transformations of continuous predictors showed similar results. Rubin's rules could easily be applied to estimate regression coefficients and performance measures, once predictors and transformations were selected. We provide a practical approach for model development and validation with multiply imputed data. Copyright 2010 Elsevier Inc. All rights reserved.
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            Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models

            We conducted an extensive set of empirical analyses to examine the effect of the number of events per variable (EPV) on the relative performance of three different methods for assessing the predictive accuracy of a logistic regression model: apparent performance in the analysis sample, split-sample validation, and optimism correction using bootstrap methods. Using a single dataset of patients hospitalized with heart failure, we compared the estimates of discriminatory performance from these methods to those for a very large independent validation sample arising from the same population. As anticipated, the apparent performance was optimistically biased, with the degree of optimism diminishing as the number of events per variable increased. Differences between the bootstrap-corrected approach and the use of an independent validation sample were minimal once the number of events per variable was at least 20. Split-sample assessment resulted in too pessimistic and highly uncertain estimates of model performance. Apparent performance estimates had lower mean squared error compared to split-sample estimates, but the lowest mean squared error was obtained by bootstrap-corrected optimism estimates. For bias, variance, and mean squared error of the performance estimates, the penalty incurred by using split-sample validation was equivalent to reducing the sample size by a proportion equivalent to the proportion of the sample that was withheld for model validation. In conclusion, split-sample validation is inefficient and apparent performance is too optimistic for internal validation of regression-based prediction models. Modern validation methods, such as bootstrap-based optimism correction, are preferable. While these findings may be unsurprising to many statisticians, the results of the current study reinforce what should be considered good statistical practice in the development and validation of clinical prediction models.
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              Impact of the Clinical Frailty Scale on Outcomes After Transcatheter Aortic Valve Replacement.

              The semiquantitative Clinical Frailty Scale (CFS) is a simple tool to assess patients' frailty and has been shown to correlate with mortality in elderly patients even when evaluated by nongeriatricians. The aim of the current study was to determine the prognostic value of CFS in patients who underwent transcatheter aortic valve replacement.
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                Author and article information

                Journal
                Heart
                Heart
                heartjnl
                heart
                Heart
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1355-6037
                1468-201X
                July 2018
                7 December 2017
                : 104
                : 13
                : 1109-1116
                Affiliations
                [1 ] departmentFaculty of Biology, Medicine and Health , Farr Institute, University of Manchester, Manchester Academic Health Science Centre , Manchester, UK
                [2 ] departmentCardiology Department , Queen Elizabeth Hospital , Birmingham, UK
                [3 ] departmentCardiology Department , James Cook University Hospital , Middlesbrough, UK
                [4 ] departmentCardiology Department , Guys and St Thomas' NHS Foundation Trust , London, UK
                [5 ] departmentKeele Cardiovascular Research Group , Keele University , Stoke-on-Trent, UK
                [6 ] departmentCardiology Department , Royal Brompton and Harefield National Health Service (NHS) Foundation Trust , London, UK
                [7 ] departmentCardiology Department , John Radcliffe Hospital , Oxford, UK
                Author notes
                [Correspondence to ] Prof. Mamas A Mamas, Keele Cardiovascular Research Group, Centre for Prognosis Research, Institute for Primary Care and Health Sciences, Keele University, Stoke-on-Trent, ST4 7QB, UK; mamasmamas1@ 123456yahoo.co.uk
                Article
                heartjnl-2017-312489
                10.1136/heartjnl-2017-312489
                6031259
                29217636
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

                Product
                Funding
                Funded by: North Staffordshire Heart Committee;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Categories
                Valvular Heart Disease
                1506
                Original research article
                Custom metadata
                unlocked

                Cardiovascular Medicine

                aortic stenosis, transcatheter valve interventions

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