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      Large-scale international validation of the ADO index in subjects with COPD: an individual subject data analysis of 10 cohorts

      research-article
      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 2 , 11 , 15 , 12 , 11 , 13 , 14 , 15 , 16 , 17 , 11 , 13 , 14 , 15 , for the International COPD Cohorts Collaboration Working Group
      (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab), (Collab)
      BMJ Open
      BMJ Publishing Group
      Pulmonary Disease, Chronic Obstructive, Mortality, Prognosis, Validation Studies

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          Abstract

          Background

          Little evidence on the validity of simple and widely applicable tools to predict mortality in patients with chronic obstructive pulmonary disease (COPD) exists.

          Objective

          To conduct a large international study to validate the ADO index that uses age, dyspnoea and FEV 1 to predict 3-year mortality and to update it in order to make prediction of mortality in COPD patients as generalisable as possible.

          Design

          Individual subject data analysis of 10 European and American cohorts (n=13 914).

          Setting

          Population-based, primary, secondary and tertiary care.

          Patients

          COPD GOLD stages I–IV.

          Measurements

          We validated the original ADO index. We then obtained an updated ADO index in half of our cohorts to improve its predictive accuracy, which in turn was validated comprehensively in the remaining cohorts using discrimination, calibration and decision curve analysis and a number of sensitivity analyses.

          Results

          1350 (9.7%) of all subjects with COPD (60% male, mean age 61 years, mean FEV 1 66% predicted) had died at 3 years. The original ADO index showed high discrimination but poor calibration (p<0.001 for difference between predicted and observed risk). The updated ADO index (scores from 0 to 14) preserved excellent discrimination (area under curve 0.81, 95% CI 0.80 to 0.82) but showed much improved calibration with predicted 3-year risks from 0.7% (95% CI 0.6% to 0.9%, score of 0) to 64.5% (61.2% to 67.7%, score of 14). The ADO index showed higher net benefit in subjects at low-to-moderate risk of 3-year mortality than FEV 1 alone.

          Interpretation

          The updated 15-point ADO index accurately predicts 3-year mortality across the COPD severity spectrum and can be used to inform patients about their prognosis, clinical trial study design or benefit harm assessment of medical interventions.

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          Most cited references17

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          Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper.

          W MacNee, , B Celli (2004)
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            Using the outcome for imputation of missing predictor values was preferred.

            Epidemiologic studies commonly estimate associations between predictors (risk factors) and outcome. Most software automatically exclude subjects with missing values. This commonly causes bias because missing values seldom occur completely at random (MCAR) but rather selectively based on other (observed) variables, missing at random (MAR). Multiple imputation (MI) of missing predictor values using all observed information including outcome is advocated to deal with selective missing values. This seems a self-fulfilling prophecy. We tested this hypothesis using data from a study on diagnosis of pulmonary embolism. We selected five predictors of pulmonary embolism without missing values. Their regression coefficients and standard errors (SEs) estimated from the original sample were considered as "true" values. We assigned missing values to these predictors--both MCAR and MAR--and repeated this 1,000 times using simulations. Per simulation we multiple imputed the missing values without and with the outcome, and compared the regression coefficients and SEs to the truth. Regression coefficients based on MI including outcome were close to the truth. MI without outcome yielded very biased--underestimated--coefficients. SEs and coverage of the 90% confidence intervals were not different between MI with and without outcome. Results were the same for MCAR and MAR. For all types of missing values, imputation of missing predictor values using the outcome is preferred over imputation without outcome and is no self-fulfilling prophecy.
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              Prognosis and prognostic research: validating a prognostic model.

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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2012
                13 December 2012
                : 2
                : 6
                : e002152
                Affiliations
                [1 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, Maryland, USA
                [2 ]Horten Centre, University of Zurich , Zurich, Switzerland
                [3 ]Division of Pulmonary & Critical Care Medicine, Johns Hopkins School of Medicine , Baltimore, Maryland, USA
                [4 ]CIBER Enfermedades Respiratorias (CIBERES), Fundación Caubet-Cimera , Mallorca, Spain
                [5 ]College of Public Health, The University of Arizona , Tucson, Arizona, USA
                [6 ]Pulmonary Division, Hvidovre Hospital , Hvidovre, Danmark
                [7 ]Jackson Heart Study, Coordinating Center, Jackson State University , Jackson, Mississippi, USA
                [8 ]Department of Medicine, University of Mississippi Medical Center , Jackson, Mississippi, USA
                [9 ]Federal University of Pelotas , Pelotas, Brazil
                [10 ]Department of General Practice, Academic Medical Centre, University of Amsterdam , Amsterdam, The Netherlands
                [11 ]Hospital del Mar Research Institute (IMIM) , Barcelona, Spain
                [12 ]Cedars Sinai Medical Center, David Geffen School of Medicine at UCLA , Los Angeles, California, USA
                [13 ]Center for Research in Environmental Epidemiology (CREAL) , Barcelona, Spain
                [14 ]Universitat Pompeu Fabra , Barcelona, Spain
                [15 ]CIBER de Epidemiologia y Salud Pública (CIBERESP) , Barcelona, Spain
                [16 ]Department of Epidemiology,Julius Centre for Health Sciences and General Practice , University Medical Center , Utrecht, The Netherlands
                [17 ]Clinical Epidemiology, and Medical Technology Assessment, University Hospital Maastricht , Maastricht, The Netherlands
                Author notes
                [Correspondence to ] Dr Judith Garcia-Aymerich; jgarcia@ 123456creal.cat
                Article
                bmjopen-2012-002152
                10.1136/bmjopen-2012-002152
                3533065
                23242246
                e056a9df-26f7-41db-9c6b-4362f785120c
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

                History
                : 22 September 2012
                : 6 November 2012
                : 12 November 2012
                Categories
                Epidemiology
                Research
                1506
                1692
                1731

                Medicine
                pulmonary disease, chronic obstructive,mortality,prognosis,validation studies
                Medicine
                pulmonary disease, chronic obstructive, mortality, prognosis, validation studies

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