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      Inadequacy of existing clinical prediction models for predicting mortality after transcatheter aortic valve implantation

      , MSc a , , PhD a , , MA, MD, FRCP, FESC b , , MA, MD, FRCP c , , PhD, FRCP, FESC d , , MD, FRCP, FESC e , f , , MBBS, MS g , , MBBS h , , MD, FFPH a , , MA, DPhil, FRCP a , i , *

      American Heart Journal


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          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 analyzed 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 analyzed 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.

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          Most cited references 23

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          Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

          The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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            Prognosis and prognostic research: validating a prognostic model.

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              Decision-making in elderly patients with severe aortic stenosis: why are so many denied surgery?

              To analyse decision-making in elderly patients with severe, symptomatic aortic stenosis (AS). In the Euro Heart Survey on valvular heart disease, 216 patients aged > or =75 had severe AS (valve area or =50 mmHg) and angina or New York Heart Association class III or IV. Patient characteristics were analysed according to the decision to operate or not. A decision not to operate was taken in 72 patients (33%). In multivariable analysis, left ventricular (LV) ejection fraction [OR = 2.27, 95% CI (1.32-3.97) for ejection fraction 30-50, OR = 5.15, 95% CI (1.73-15.35) for ejection fraction 50%, P = 0.003] and age [OR = 1.84, 95% CI (1.18-2.89) for 80-85 years, OR=3.38, 95% CI (1.38-8.27) for > or =85 vs. 75-80 years, P = 0.008] were significantly associated with the decision not to operate; however, the Charlson comorbidity index was not [OR = 1.72, 95% CI (0.83-3.50), P = 0.14 for index > or =2 vs. <2]. Neurological dysfunction was the only comorbidity significantly linked with the decision not to operate. Surgery was denied in 33% of elderly patients with severe, symptomatic AS. Older age and LV dysfunction were the most striking characteristics of patients who were denied surgery, whereas comorbidity played a less important role.

                Author and article information

                Am Heart J
                Am. Heart J
                American Heart Journal
                1 February 2017
                February 2017
                : 184
                : 97-105
                [a ]Health e-Research Centre, University of Manchester, Manchester, United Kingdom
                [b ]Queen Elizabeth Hospital, Birmingham, United Kingdom
                [c ]James Cook University Hospital, Middlesbrough, United Kingdom
                [d ]MRC Bioinformatics Unit, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds
                [e ]Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield General Hospital, Leicester, United Kingdom
                [f ]NIHR Leicester Cardiovascular Biomedical Research Unit, Leicester, United Kingdom
                [g ]Royal Brompton and Harefield National Health Service (NHS) Foundation Trust, London, United Kingdom
                [h ]Sussex Cardiac Centre, Brighton and Sussex University Hospitals, Brighton,United Kingdom
                [i ]Keele Cardiovascular Research Group, Keele University, Stoke-on-Trent, UK, Royal Stoke Hospital, University Hospitals North Midlands, Stoke-on-Trent, United Kingdom
                Author notes
                [* ]Reprint requests: Mamas A. Mamas, MA, DPhil, FRCP, Professor of Cardiology/Honorary Consultant Cardiologist, Keele Cardiovascular Research Group, University of Keele, Stoke-on-Trent, ST4 7QB, United Kingdom.Keele Cardiovascular Research GroupUniversity of KeeleStoke-on-TrentUnited Kingdom mamasmamas1@ 123456yahoo.co.uk
                © 2016 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                Clinical Investigation

                Cardiovascular Medicine


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