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      On-line risk prediction models for acute type A aortic dissection surgery: validation of the German Registry of Acute Aortic Dissection Type A score and the European System for Cardiac Operative Risk Evaluation II

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          Abstract

          OBJECTIVES

          The German Registry of Acute Aortic Dissection Type A (GERAADA) on-line score calculator to predict 30-day mortality in patients undergoing surgery for acute type A aortic dissection (ATAAD) was recently launched. Using the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II), it is also possible to predict operative mortality for the same type of surgery. The goal of our study was to validate the prediction accuracy of these 2 on-line risk prediction models.

          METHODS

          Prospectively collected data for EuroSCORE II risk factors as well as all data for GERAADA scoring were extracted from an institutional database for 147 patients who underwent surgery for ATAAD between April 2018 and April 2021. The discriminative power was assessed using area under the receiver operating characteristic curve. The calibration of the models was tested by the Hosmer–Lemeshow statistics and by using the observed-to-expected (O/E) mortality ratio with the 95% confidence interval.

          RESULTS

          The observed operative mortality was 14.3%. The mean predicted mortality rates for the GERAADA score and the EuroSCORE II were 15.6% and 10.6%, respectively. The EuroSCORE II discriminative power (area under the curve = 0.799) significantly outperformed the discriminatory power of the GERAADA score (area under the curve = 0.550). The Hosmer–Lemeshow statistics confirmed good calibration for both models (P-values of 0.49 and 0.29 for the GERAADA score and the EuroSCORE II, respectively). The O/E mortality ratio certified good calibration for both scores [GERAADA score (O/E ratio of 0.93, 95% confidence interval: 0.53–1.33); EuroSCORE II (O/E ratio of 1.35, 95% confidence interval: 0.77–1.93)].

          CONCLUSIONS

          The EuroSCORE II has better discriminative power for predicting operative mortality in ATAAD surgery than the GERAADA score. Both scores confirmed good calibration ability.

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          Most cited references29

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          Assessing the performance of prediction models: a framework for traditional and novel measures.

          The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
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            EuroSCORE II.

            To update the European System for Cardiac Operative Risk Evaluation (EuroSCORE) risk model. A dedicated website collected prospective risk and outcome data on 22,381 consecutive patients undergoing major cardiac surgery in 154 hospitals in 43 countries over a 12-week period (May-July 2010). Completeness and accuracy were validated during data collection using mandatory field entry, error and range checks and after data collection using summary feedback confirmation by responsible officers and multiple logic checks. Information was obtained on existing EuroSCORE risk factors and additional factors proven to influence risk from research conducted since the original model. The primary outcome was mortality at the base hospital. Secondary outcomes were mortality at 30 and 90 days. The data set was divided into a developmental subset for logistic regression modelling and a validation subset for model testing. A logistic risk model (EuroSCORE II) was then constructed and tested. Compared with the original 1995 EuroSCORE database (in brackets), the mean age was up at 64.7 (62.5) with 31% females (28%). More patients had New York Heart Association class IV, extracardiac arteriopathy, renal and pulmonary dysfunction. Overall mortality was 3.9% (4.6%). When applied to the current data, the old risk models overpredicted mortality (actual: 3.9%; additive predicted: 5.8%; logistic predicted: 7.57%). EuroSCORE II was well calibrated on testing in the validation data subset of 5553 patients (actual mortality: 4.18%; predicted: 3.95%). Very good discrimination was maintained with an area under the receiver operating characteristic curve of 0.8095. Cardiac surgical mortality has significantly reduced in the last 15 years despite older and sicker patients. EuroSCORE II is better calibrated than the original model yet preserves powerful discrimination. It is proposed for the future assessment of cardiac surgical risk.
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              A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

              Receiver operating characteristic (ROC) curves are used to describe and compare the performance of diagnostic technology and diagnostic algorithms. This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data. The correspondence between the area under an ROC curve and the Wilcoxon statistic is used and underlying Gaussian distributions (binormal) are assumed to provide a table that converts the observed correlations in paired ratings of images into a correlation between the two ROC areas. This between-area correlation can be used to reduce the standard error (uncertainty) about the observed difference in areas. This correction for pairing, analogous to that used in the paired t-test, can produce a considerable increase in the statistical sensitivity (power) of the comparison. For studies involving multiple readers, this method provides a measure of a component of the sampling variation that is otherwise difficult to obtain.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                European Journal of Cardio-Thoracic Surgery
                Oxford University Press (OUP)
                1010-7940
                1873-734X
                May 01 2022
                May 02 2022
                December 16 2021
                May 01 2022
                May 02 2022
                December 16 2021
                : 61
                : 5
                : 1068-1075
                Affiliations
                [1 ]Department of Cardiac Surgery, “Dedinje” Cardiovascular Institute, Belgrade, Serbia
                [2 ]Department of Pediatric Cardiology, Mother and Child Health Care Institute of Serbia, Belgrade, Serbia
                Article
                10.1093/ejcts/ezab517
                03d288af-34a4-4dc3-91ff-c619780cdfd5
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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