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      Value of risk scores in the decision to palliate patients with ruptured abdominal aortic aneurysm

      , 1 , 2 , 5 , 5 , 6 , 6 , 4 , 3 , 1 , 2 , the Ruptured Aneurysm Collaborators, including IMPROVE, AJAX, ECAR and STAR collaborators

      The British Journal of Surgery

      John Wiley & Sons, Ltd

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          The aim of this study was to develop a 48‐h mortality risk score, which included morphology data, for patients with ruptured abdominal aortic aneurysm presenting to an emergency department, and to assess its predictive accuracy and clinical effectiveness in triaging patients to immediate aneurysm repair, transfer or palliative care.


          Data from patients in the IMPROVE (Immediate Management of the Patient With Ruptured Aneurysm: Open Versus Endovascular Repair) randomized trial were used to develop the risk score. Variables considered included age, sex, haemodynamic markers and aortic morphology. Backwards selection was used to identify relevant predictors. Predictive performance was assessed using calibration plots and the C‐statistic. Validation of the newly developed and other previously published scores was conducted in four external populations. The net benefit of treating patients based on a risk threshold compared with treating none was quantified.


          Data from 536 patients in the IMPROVE trial were included. The final variables retained were age, sex, haemoglobin level, serum creatinine level, systolic BP, aortic neck length and angle, and acute myocardial ischaemia. The discrimination of the score for 48‐h mortality in the IMPROVE data was reasonable (C‐statistic 0·710, 95 per cent c.i. 0·659 to 0·760), but varied in external populations (from 0·652 to 0·761). The new score outperformed other published risk scores in some, but not all, populations. An 8 (95 per cent c.i. 5 to 11) per cent improvement in the C‐statistic was estimated compared with using age alone.


          The assessed risk scores did not have sufficient accuracy to enable potentially life‐saving decisions to be made regarding intervention. Focus should therefore shift to offering repair to more patients and reducing non‐intervention rates, while respecting the wishes of the patient and family.


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          Most cited references 26

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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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            Decision curve analysis: a novel method for evaluating prediction models.

            Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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              The use of fractional polynomials to model continuous risk variables in epidemiology.

              The traditional method of analysing continuous or ordinal risk factors by categorization or linear models may be improved. We propose an approach based on transformation and fractional polynomials which yields simple regression models with interpretable curves. We suggest a way of presenting the results from such models which involves tabulating the risks estimated from the model at convenient values of the risk factor. We discuss how to incorporate several continuous risk and confounding variables within a single model. The approach is exemplified with data from the Whitehall I study of British Civil Servants. We discuss the approach in relation to categorization and non-parametric regression models. We show that non-linear risk models fit the data better than linear models. We discuss the difficulties introduced by categorization and the advantages of the new approach. Our approach based on fractional polynomials should be considered as an important alternative to the traditional approaches for the analysis of continuous variables in epidemiological studies.

                Author and article information

                Br J Surg
                Br J Surg
                The British Journal of Surgery
                John Wiley & Sons, Ltd (Chichester, UK )
                06 April 2018
                August 2018
                : 105
                : 9 ( doiID: 10.1002/bjs.2018.105.issue-9 )
                : 1135-1144
                [ 1 ] Department of Public Health and Primary Care University of Cambridge Cambridge UK
                [ 2 ] Vascular Surgery Research Group, Imperial College London London UK
                [ 3 ] Bristol Centre for Surgical Research, University of Bristol Bristol UK
                [ 4 ] Stanford School of Medicine Stanford California USA
                [ 5 ] Department of Molecular Medicine and Surgery Karolinska Institute Stockholm Sweden
                [ 6 ] Department of Vascular Surgery Academic Medical Centre Amsterdam The Netherlands
                [ 7 ] Academic Medical Centre Amsterdam TheNetherlands
                [ 8 ] Academic Medical Centre Amsterdam The Netherlands
                [ 9 ] Onze LieveVrouwe Gasthuis Amsterdam TheNetherlands
                [ 10 ] VU University Medical Centre Amsterdam The Netherlands
                [ 11 ] Sint Lucas Andreas Ziekenhuis Amsterdam The Netherlands
                [ 12 ] Slotervaartziekenhuis Amsterdam TheNetherlands
                [ 13 ] Boven IJ Ziekenhuis Amsterdam The Netherlands
                [ 14 ] Ziekenhuis Amstelland Amsterdam The Netherlands
                [ 15 ] Waterlandziekenhuis Purmerend The Netherlands
                [ 16 ] Zaans Medisch Centrum Zaandam The Netherlands
                [ 17 ] Spaarne Ziekenhuis Hoofddorp The Netherlands
                [ 18 ] regional ambulance services
                [ 19 ] CentreHospitalier Universitaire (CHU) Henri Mondor Créteil France
                [ 20 ] CHU Bichat Paris France
                [ 21 ] CHU Dijon Dijon France
                [ 22 ] Centre Hospitalier Régional Universitaire (CHRU) Lille Lille France
                [ 23 ] Hôpital Nord Marseille Marseille France
                [ 24 ] CHUMarseille Marseille France
                [ 25 ] CHU Montpellier Montpellier France
                [ 26 ] CHU Hôpital Européen Georges‐Pompidou Paris France
                [ 27 ] CHU Brest Brest France
                [ 28 ] CHU Saint Etienne Saint Etienne France
                [ 29 ] CHRU Tours Tours France
                [ 30 ] CHU Nice Nice France
                [ 31 ] Centre Hospitalier Régional Annecy Annecy France
                [ 32 ] CHU Lyon Lyon France
                [ 33 ] Chair; Imperial College London London UK
                [ 34 ] University Hospital of South Manchester Manchester UK
                [ 35 ] London School of Hygiene and Tropical Medicine London UK
                [ 36 ] Imperial College London London UK
                [ 37 ] London School of Hygiene and Tropical Medicine London UK
                [ 38 ] St George's Hospital London UK
                [ 39 ] University of Cambridge Cambridge UK
                [ 40 ] St George's Hospital London UK
                [ 41 ] University of Cambridge Cambridge UK
                [ 42 ] ImperialCollege London London UK
                [ 43 ] Imperial College Healthcare NHS Trust London UK
                [ 44 ] Addenbrooke's Hospital Cambridge UK
                [ 45 ] Manchester Royal Infirmary Manchester UK
                [ 46 ] St George's Hospital London UK
                [ 47 ] Guy's and St Thomas’ Hospital London UK
                [ 48 ] Kent and Canterbury Hospital Canterbury UK
                [ 49 ] Leicester Royal Infirmary Leicester UK
                [ 50 ] Royal Free Hospital London UK
                [ 51 ] University Hospital of South Manchester Manchester UK
                [ 52 ] Leeds General Infirmary Leeds UK
                [ 53 ] Freeman Hospital Newcastle upon Tyne UK
                [ 54 ] King's College Hospital London UK
                [ 55 ] Aberdeen Royal Infirmary Aberdeen UK
                [ 56 ] JamesCook University Hospital Middlesbrough UK
                [ 57 ] Queen's Medical Centre Nottingham UK
                [ 58 ] Royal Cornwall Hospital Truro UK
                [ 59 ] Royal BournemouthHospital Bournemouth UK
                [ 60 ] Royal Sussex County Hospital Brighton UK
                [ 61 ] Queen Elizabeth Hospital Gateshead UK
                [ 62 ] Hull Royal Infirmary Hull UK
                [ 63 ] Colchester General Hospital Colchester UK
                [ 64 ] Frimley Park Hospital Frimley UK
                [ 65 ] Ninewells Hospital Dundee UK
                [ 66 ] University Hospital of Wales Cardiff UK
                [ 67 ] New Cross Hospital Wolverhampton UK
                [ 68 ] Doncaster Royal Infirmary Doncaster UK
                [ 69 ] Royal Derby Hospital Derby UK
                [ 70 ] University Hospital of North Staffordshire Stoke‐on‐Trent UK
                [ 71 ] York Hospital York UK
                [ 72 ] London Health Sciences Centre, University ofWestern Ontario London Ontario Canada
                [ 73 ] Karolinska Institute Stockholm Sweden
                Author notes
                [* ] Correspondence to: Dr M. J. Sweeting, Department of Health Sciences, University of Leicester, George Davies Centre, University Road, Leicester LE1 7RH, UK (e‐mail: michael.sweeting@ )
                © 2018 The Authors. BJS published by John Wiley & Sons Ltd on behalf of BJS Society Ltd.

                This is an open access article under the terms of the License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                Page count
                Figures: 4, Tables: 3, Pages: 10, Words: 5880
                Funded by: UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) programme
                Award ID: 07/37/64
                Original Article
                Original Articles
                Custom metadata
                August 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.3 mode:remove_FC converted:23.07.2018



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