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      Effectiveness and Safety of Oral Anticoagulants Among Nonvalvular Atrial Fibrillation Patients : The ARISTOPHANES Study

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          Abstract

          Background and Purpose—

          This ARISTOPHANES study (Anticoagulants for Reduction in Stroke: Observational Pooled Analysis on Health Outcomes and Experience of Patients) used multiple data sources to compare stroke/systemic embolism (SE) and major bleeding (MB) among a large number of nonvalvular atrial fibrillation patients on non–vitamin K antagonist oral anticoagulants (NOACs) or warfarin.

          Methods—

          A retrospective observational study of nonvalvular atrial fibrillation patients initiating apixaban, dabigatran, rivaroxaban, or warfarin from January 1, 2013, to September 30, 2015, was conducted pooling Centers for Medicare and Medicaid Services Medicare data and 4 US commercial claims databases. After 1:1 NOAC-warfarin and NOAC-NOAC propensity score matching in each database, the resulting patient records were pooled. Cox models were used to evaluate the risk of stroke/SE and MB across matched cohorts.

          Results—

          A total of 434 046 patients were included in the 6 matched cohorts: 100 977 apixaban-warfarin, 36 990 dabigatran-warfarin, 125 068 rivaroxaban-warfarin, 37 314 apixaban-dabigatran, 107 236 apixaban-rivaroxaban, and 37 693 dabigatran-rivaroxaban patient pairs. Apixaban (hazard ratio [HR], 0.64; 95% CI, 0.58–0.70), dabigatran (HR, 0.82; 95% CI, 0.71–0.95), and rivaroxaban (HR, 0.79; 95% CI, 0.73–0.85) were associated with lower rates of stroke/SE compared with warfarin. Apixaban (HR, 0.60; 95% CI, 0.56–0.63) and dabigatran (HR, 0.71; 95% CI, 0.65–0.78) had lower rates of MB, and rivaroxaban (HR, 1.06; 95% CI, 1.02–1.10) had a higher rate of MB compared with warfarin. Differences exist in rates of stroke/SE and MB across NOACs.

          Conclusions—

          In this largest observational study to date on NOACs and warfarin, the NOACs had lower rates of stroke/SE and variable comparative rates of MB versus warfarin. The findings from this study may help inform the discussion on benefit and risk in the shared decision-making process for stroke prevention between healthcare providers and nonvalvular atrial fibrillation patients.

          Clinical Trial Registration—

          URL: https://www.clinicaltrials.gov/ . Unique identifier: NCT03087487.

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          Most cited references40

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples

            The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity-score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity-score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five-number summaries; and graphical methods such as quantile–quantile plots, side-by-side boxplots, and non-parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity-score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.
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              2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS.

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                Author and article information

                Journal
                Stroke
                Stroke
                Ovid Technologies (Wolters Kluwer Health)
                0039-2499
                1524-4628
                December 2018
                December 2018
                : 49
                : 12
                : 2933-2944
                Affiliations
                [1 ]From the Institute of Cardiovascular Sciences, University of Birmingham, United Kingdom (G.Y.H.L.)
                [2 ]Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, United Kingdom (G.Y.H.L.)
                [3 ]Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Denmark (G.Y.H.L.)
                [4 ]Health Economics and Outcomes Research, STATinMED Research, Ann Arbor, MI (A.K.)
                [5 ]Worldwide Health Economics and Outcomes Research, Bristol-Myers Squibb Company, Lawrenceville, NJ (X. Li, M.H.)
                [6 ]Patient Health & Impact, Outcomes & Evidence, Pfizer, Inc, New York, NY (C.M., J.M.)
                [7 ]US Health Economics and Outcomes Research, Bristol-Myers Squibb Company, Lawrenceville, NJ (K.G., A.N.)
                [8 ]Patient Health & Impact, Outcomes & Evidence, Pfizer, Inc, Groton, CT (X. Luo)
                [9 ]Worldwide Medical, Bristol-Myers Squibb Company, Lawrenceville, NJ (K.F.)
                [10 ]Center for Observational Research and Data Sciences, Bristol-Myers Squibb Company, Lawrenceville, NJ (X.P.)
                [11 ]Deparment of Internal Medicine, University of Michigan, Ann Arbor (O.B.)
                [12 ]Department of Hospital Medicine, Ochsner Clinic Foundation, New Orleans, LA; and Ochsner Clinical School, University of Queensland School of Medicine, New Orleans, LA (S.D.).
                Article
                10.1161/STROKEAHA.118.020232
                3f46a33a-9a8e-497b-8673-bf3bdd5f7fd6
                © 2018
                History

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