Literature DB >> 23212720

Renal dysfunction as a predictor of stroke and systemic embolism in patients with nonvalvular atrial fibrillation: validation of the R(2)CHADS(2) index in the ROCKET AF (Rivaroxaban Once-daily, oral, direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation) and ATRIA (AnTicoagulation and Risk factors In Atrial fibrillation) study cohorts.

Jonathan P Piccini1, Susanna R Stevens, YuChiao Chang, Daniel E Singer, Yuliya Lokhnygina, Alan S Go, Manesh R Patel, Kenneth W Mahaffey, Jonathan L Halperin, Günter Breithardt, Graeme J Hankey, Werner Hacke, Richard C Becker, Christopher C Nessel, Keith A A Fox, Robert M Califf.   

Abstract

BACKGROUND: We sought to define the factors associated with the occurrence of stroke and systemic embolism in a large, international atrial fibrillation (AF) trial. METHODS AND
RESULTS: In ROCKET AF (Rivaroxaban Once-daily, oral, direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation), 14 264 patients with nonvalvular AF and creatinine clearance ≥30 mL/min were randomized to rivaroxaban or dose-adjusted warfarin. Cox proportional hazards modeling was used to identify factors at randomization independently associated with the occurrence of stroke or non-central nervous system embolism based on intention-to-treat analysis. A risk score was developed in ROCKET AF and validated in ATRIA (AnTicoagulation and Risk factors In Atrial fibrillation), an independent AF patient cohort. Over a median follow-up of 1.94 years, 575 patients (4.0%) experienced primary end-point events. Reduced creatinine clearance was a strong, independent predictor of stroke and systemic embolism, second only to prior stroke or transient ischemic attack. Additional factors associated with stroke and systemic embolism included elevated diastolic blood pressure and heart rate, as well as vascular disease of the heart and limbs (C-index 0.635). A model that included creatinine clearance (R(2)CHADS(2)) improved net reclassification index by 6.2% compared with CHA(2)DS(2)VASc (C statistic=0.578) and by 8.2% compared with CHADS(2) (C statistic=0.575). The inclusion of creatinine clearance <60 mL/min and prior stroke or transient ischemic attack in a model with no other covariates led to a C statistic of 0.590.Validation of R(2)CHADS(2) in an external, separate population improved net reclassification index by 17.4% (95% confidence interval, 12.1%-22.5%) relative to CHADS(2).
CONCLUSIONS: In patients with nonvalvular AF at moderate to high risk of stroke, impaired renal function is a potent predictor of stroke and systemic embolism. Stroke risk stratification in patients with AF should include renal function. CLINICAL TRIAL REGISTRATION: URL: http://www.ClinicalTrials.gov. Unique identifier: NCT00403767.

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Year:  2012        PMID: 23212720     DOI: 10.1161/CIRCULATIONAHA.112.107128

Source DB:  PubMed          Journal:  Circulation        ISSN: 0009-7322            Impact factor:   29.690


  139 in total

Review 1.  Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights.

Authors:  Jelena Kornej; Christin S Börschel; Emelia J Benjamin; Renate B Schnabel
Journal:  Circ Res       Date:  2020-06-18       Impact factor: 17.367

2.  Impact of renal function deterioration on adverse events during anticoagulation therapy using non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation.

Authors:  Koji Miyamoto; Takeshi Aiba; Shoji Arihiro; Makoto Watanabe; Yoshihiro Kokubo; Kohei Ishibashi; Sayako Hirose; Mitsuru Wada; Ikutaro Nakajima; Hideo Okamura; Takashi Noda; Kazuyuki Nagatsuka; Teruo Noguchi; Toshihisa Anzai; Satoshi Yasuda; Hisao Ogawa; Shiro Kamakura; Wataru Shimizu; Yoshihiro Miyamoto; Kazunori Toyoda; Kengo Kusano
Journal:  Heart Vessels       Date:  2015-08-15       Impact factor: 2.037

3.  Usefulness of transesophageal echocardiography before cardioversion in atrial arrhythmias.

Authors:  Katarzyna Kosmalska; Małgorzata Rzyman; Paweł Miękus; Natasza Gilis-Malinowska; Radosław Nowak; Marcin Fijałkowski
Journal:  Cardiol J       Date:  2019-06-21       Impact factor: 2.737

4.  Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Authors:  Lichy Han; Mariam Askari; Russ B Altman; Susan K Schmitt; Jun Fan; Jason P Bentley; Sanjiv M Narayan; Mintu P Turakhia
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2019-10-15

5.  Application of net reclassification index to non-nested and point-based risk prediction models: a review.

Authors:  Laine E Thomas; Emily C O'Brien; Jonathan P Piccini; Ralph B D'Agostino; Michael J Pencina
Journal:  Eur Heart J       Date:  2019-06-14       Impact factor: 29.983

6.  Paroxysmal Atrial Fibrillation in a Patient on Hemodialysis.

Authors:  Charmaine E Lok
Journal:  Clin J Am Soc Nephrol       Date:  2017-06-09       Impact factor: 8.237

7.  Renal function assessment in atrial fibrillation: Usefulness of chronic kidney disease epidemiology collaboration vs re-expressed 4 variable modification of diet in renal disease.

Authors:  Rami Riziq-Yousef Abumuaileq; Emad Abu-Assi; Andrea López-López; Sergio Raposeiras-Roubin; Moisés Rodríguez-Mañero; Luis Martínez-Sande; Francisco Javier García-Seara; Xesus Alberte Fernandez-López; Jose Ramón González-Juanatey
Journal:  World J Cardiol       Date:  2015-10-26

8.  Incident Atrial Fibrillation and the Risk of Stroke in Adults with Chronic Kidney Disease: The Stockholm CREAtinine Measurements (SCREAM) Project.

Authors:  Juan Jesus Carrero; Marco Trevisan; Manish M Sood; Peter Bárány; Hong Xu; Marie Evans; Leif Friberg; Karolina Szummer
Journal:  Clin J Am Soc Nephrol       Date:  2018-07-20       Impact factor: 8.237

9.  Glomerular filtration rate: A prognostic marker in atrial fibrillation-A subanalysis of the AntiThrombotic Agents Atrial Fibrillation.

Authors:  Riccardo Proietti; Lucio Gonzini; Giovanni Pizzimenti; Antonietta Ledda; Pietro Sanna; Ahmed AlTurki; Vincenzo Russo; Mauro Lencioni
Journal:  Clin Cardiol       Date:  2018-12-04       Impact factor: 2.882

10.  Kinetics methods for clinical epidemiology problems.

Authors:  Alexandru Dan Corlan; John Ross
Journal:  Proc Natl Acad Sci U S A       Date:  2015-11-02       Impact factor: 11.205

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