Literature DB >> 32252601

Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke.

Christopher D Anderson1,2, Steven A Lubitz3,4, Shaan Khurshid5,4, Ludovic Trinquart6,7, Lu-Chen Weng4, Olivia L Hulme8, Wyliena Guan4, Darae Ko7, Kristin Schwab9, Natalia S Rost9, Mostafa A Al-Alusi10, Emelia J Benjamin11,7,12, Patrick T Ellinor3,4.   

Abstract

Background and Purpose- Classification of stroke as cardioembolic in etiology can be challenging, particularly since the predominant cause, atrial fibrillation (AF), may not be present at the time of stroke. Efficient tools that discriminate cardioembolic from noncardioembolic strokes may improve care as anticoagulation is frequently indicated after cardioembolism. We sought to assess and quantify the discriminative power of AF risk as a classifier for cardioembolism in a real-world population of patients with acute ischemic stroke. Methods- We performed a cross-sectional analysis of a multi-institutional sample of patients with acute ischemic stroke. We systematically adjudicated stroke subtype and examined associations between AF risk using CHA2DS2-VASc, Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score, and the recently developed Electronic Health Record-Based AF score, and cardioembolic stroke using logistic regression. We compared the ability of AF risk to discriminate cardioembolism by calculating C statistics and sensitivity/specificity cutoffs for cardioembolic stroke. Results- Of 1431 individuals with ischemic stroke (age, 65±15; 40% women), 323 (22.6%) had cardioembolism. AF risk was significantly associated with cardioembolism (CHA2DS2-VASc: odds ratio [OR] per SD, 1.69 [95% CI, 1.49-1.93]; Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score: OR, 2.22 [95% CI, 1.90-2.60]; electronic Health Record-Based AF: OR, 2.55 [95% CI, 2.16-3.04]). Discrimination was greater for Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score (C index, 0.695 [95% CI, 0.663-0.726]) and Electronic Health Record-Based AF score (0.713 [95% CI, 0.681-0.744]) versus CHA2DS2-VASc (C index, 0.651 [95% CI, 0.619-0.683]). Examination of AF scores across a range of thresholds indicated that AF risk may facilitate identification of individuals at low likelihood of cardioembolism (eg, negative likelihood ratios for Electronic Health Record-Based AF score ranged 0.31-0.10 at sensitivity thresholds 0.90-0.99). Conclusions- AF risk scores associate with cardioembolic stroke and exhibit moderate discrimination. Utilization of AF risk scores at the time of stroke may be most useful for identifying individuals at low probability of cardioembolism. Future analyses are warranted to assess whether stroke subtype classification can be enhanced to improve outcomes in undifferentiated stroke.

Entities:  

Keywords:  atrial fibrillation; heart; humans; odds ratio; stroke

Mesh:

Substances:

Year:  2020        PMID: 32252601      PMCID: PMC7188588          DOI: 10.1161/STROKEAHA.120.028837

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  30 in total

Review 1.  Role of Outpatient Cardiac Rhythm Monitoring in Cryptogenic Stroke: A Systematic Review and Meta-Analysis.

Authors:  Muhammad R Afzal; Sampath Gunda; Salman Waheed; Nandhini Sehar; Ryan J Maybrook; Buddhadeb Dawn; Dhanunjaya Lakkireddy
Journal:  Pacing Clin Electrophysiol       Date:  2015-08-27       Impact factor: 1.976

2.  Usefulness of CHADS2 and CHA2DS2-VASc Scores in the Prediction of New-Onset Atrial Fibrillation: A Population-Based Study.

Authors:  Walid Saliba; Naomi Gronich; Ofra Barnett-Griness; Gad Rennert
Journal:  Am J Med       Date:  2016-03-21       Impact factor: 4.965

Review 3.  Detection of atrial fibrillation after ischemic stroke or transient ischemic attack: a systematic review and meta-analysis.

Authors:  Amit Kishore; Andy Vail; Arshad Majid; Jesse Dawson; Kennedy R Lees; Pippa J Tyrrell; Craig J Smith
Journal:  Stroke       Date:  2014-01-02       Impact factor: 7.914

4.  Ejection fraction and outcomes in patients with atrial fibrillation and heart failure: the Loire Valley Atrial Fibrillation Project.

Authors:  Amitava Banerjee; Sophie Taillandier; Jonas Bjerring Olesen; Deirdre A Lane; Benedicte Lallemand; Gregory Y H Lip; Laurent Fauchier
Journal:  Eur J Heart Fail       Date:  2012-01-30       Impact factor: 15.534

5.  Predictors for atrial fibrillation detection after cryptogenic stroke: Results from CRYSTAL AF.

Authors:  Vincent N Thijs; Johannes Brachmann; Carlos A Morillo; Rod S Passman; Tommaso Sanna; Richard A Bernstein; Hans-Christoph Diener; Vincenzo Di Lazzaro; Marilyn M Rymer; Laurence Hogge; Tyson B Rogers; Paul D Ziegler; Manish D Assar
Journal:  Neurology       Date:  2015-12-18       Impact factor: 9.910

Review 6.  Epidemiology of stroke and its subtypes in Chinese vs white populations: a systematic review.

Authors:  Chung-Fen Tsai; Brenda Thomas; Cathie L M Sudlow
Journal:  Neurology       Date:  2013-07-16       Impact factor: 9.910

7.  Diagnostic accuracy of STAF, LADS, and iPAB scores for predicting paroxysmal atrial fibrillation in patients with acute cerebral infarction.

Authors:  Xingdong Chen; Weiliang Luo; Jiming Li; Mei Li; Lizhi Wang; Yu Rao; Bosheng Li; Wei Zeng
Journal:  Clin Cardiol       Date:  2018-11-23       Impact factor: 2.882

8.  Efficacy and safety of rivaroxaban in patients with heart failure and nonvalvular atrial fibrillation: insights from ROCKET AF.

Authors:  Sean van Diepen; Anne S Hellkamp; Manesh R Patel; Richard C Becker; Günter Breithardt; Werner Hacke; Jonathan L Halperin; Graeme J Hankey; Christopher C Nessel; Daniel E Singer; Scott D Berkowitz; Robert M Califf; Keith A A Fox; Kenneth W Mahaffey
Journal:  Circ Heart Fail       Date:  2013-05-30       Impact factor: 8.790

9.  Non-cardioembolic risk factors in atrial fibrillation-associated ischemic stroke.

Authors:  Pil-Sung Yang; Hui-Nam Pak; Dong-Hyuk Park; Joonsang Yoo; Tae-Hoon Kim; Jae-Sun Uhm; Young Dae Kim; Hyo Suk Nam; Boyoung Joung; Moon-Hyoung Lee; Ji Hoe Heo
Journal:  PLoS One       Date:  2018-07-20       Impact factor: 3.240

10.  Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes.

Authors:  Sara L Pulit; Lu-Chen Weng; Patrick F McArdle; Ludovic Trinquart; Seung Hoan Choi; Braxton D Mitchell; Jonathan Rosand; Paul I W de Bakker; Emelia J Benjamin; Patrick T Ellinor; Steven J Kittner; Steven A Lubitz; Christopher D Anderson
Journal:  Neurol Genet       Date:  2018-12-03
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  3 in total

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Journal:  CNS Neurosci Ther       Date:  2022-03-04       Impact factor: 5.243

2.  Risk of New-Diagnosed Atrial Fibrillation After Transient Ischemic Attack.

Authors:  Francisco Purroy; Mikel Vicente-Pascual; Gloria Arque; Robert Begue; Joan Farre; Yhovany Gallego; Maria Pilar Gil-Villar; Gerard Mauri; Nuria Montalà; Cristina Pereira; Coral Torres-Querol; Daniel Vazquez-Justes
Journal:  Front Neurol       Date:  2022-07-14       Impact factor: 4.086

3.  Identification of magnetic resonance imaging features for the prediction of unrecognized atrial fibrillation in acute ischemic stroke.

Authors:  Chao-Hui Chen; Meng Lee; Hsu-Huei Weng; Jiann-Der Lee; Jen-Tsung Yang; Yuan-Hsiung Tsai; Yen-Chu Huang
Journal:  Front Neurol       Date:  2022-09-13       Impact factor: 4.086

  3 in total

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