Literature DB >> 31084333

Refining Stroke and Bleeding Prediction in Atrial Fibrillation by Adding Consecutive Biomarkers to Clinical Risk Scores.

José Miguel Rivera-Caravaca1, Francisco Marín1, Juan Antonio Vilchez2, Josefa Gálvez3, María Asunción Esteve-Pastor1, Vicente Vicente3, Gregory Y H Lip4,5, Vanessa Roldán3.   

Abstract

Background and Purpose- Current European guidelines for the management of atrial fibrillation suggest using biomarkers to refine the risk stratification process. However, it is unclear whether ≥2 biomarkers incrementally improve risk prediction beyond 1 biomarker alone. We investigated whether the predictive performance of CHA2DS2-VASc and HAS-BLED scores could be enhanced by incrementally adding consecutive different biomarkers in real-world atrial fibrillation patients taking vitamin K antagonists therapy. Methods- We included 940 atrial fibrillation patients stable on vitamin K antagonists (international normalized ratio, 2.0-3.0) for at least the previous 6 months. At inclusion, VWF (von Willebrand factor), high-sensitivity troponin T, NT-proBNP (N-terminal pro-B-type natriuretic peptide), high-sensitivity IL (interleukin)-6, fibrin monomers, and BTP (β-trace protein) concentrations were quantified. During follow-up, all adverse events were recorded, and biomarkers were added to CHA2DS2-VASc and HAS-BLED scores depending on the C index. Results- During 6.5 (4.3-7.9) years, there were 98 ischemic strokes (1.60% per year) and 172 major bleeds (1.60% per year). After the addition of biomarkers, the predictive performance of CHA2DS2-VASc was not significantly increased, although the model with 3 biomarkers (ie, NT-proBNP+BTP+VWF) showed a low gain in sensitivity (integrated discrimination improvement, 2.70%; P<0.001). The predictive performance of HAS-BLED was enhanced in all biomarker-based models, with the best prediction shown by the model with 3 biomarkers (ie, VWF+NT-proBNP+high-sensitivity IL-6; C index, 0.600 [95% CI, 0.561-0.625] versus 0.639 [95% CI, 0.607-0.669]; P=0.025). This model also confirmed an increased sensitivity (integrated discrimination improvement, 5.20%; P<0.001) and positive reclassification (net reclassification improvement, 19.20%; P=0.020). Conclusions- By adding consecutive biomarkers, the predictive ability of CHA2DS2-VASc for ischemic stroke was not increased, whereas the predictive ability of HAS-BLED for major bleeding was only slightly enhanced. The net benefit and clinical usefulness of the biomarker-based models were marginal in comparison to the original scores based on clinical factors.

Entities:  

Keywords:  atrial fibrillation; biomarkers; hemorrhage; risk assessment; stroke

Year:  2019        PMID: 31084333     DOI: 10.1161/STROKEAHA.118.024305

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


  10 in total

Review 1.  The Atrium in Atrial Fibrillation - A Clinical Review on How to Manage Atrial Fibrotic Substrates.

Authors:  Pedro Silva Cunha; Sérgio Laranjo; Jordi Heijman; Mário Martins Oliveira
Journal:  Front Cardiovasc Med       Date:  2022-07-04

Review 2.  Stroke prevention strategies in high-risk patients with atrial fibrillation.

Authors:  Agnieszka Kotalczyk; Michał Mazurek; Zbigniew Kalarus; Tatjana S Potpara; Gregory Y H Lip
Journal:  Nat Rev Cardiol       Date:  2020-10-27       Impact factor: 32.419

3.  European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population.

Authors:  Jens Cosedis Nielsen; Yenn-Jiang Lin; Marcio Jansen de Oliveira Figueiredo; Alireza Sepehri Shamloo; Alberto Alfie; Serge Boveda; Nikolaos Dagres; Dario Di Toro; Lee L Eckhardt; Kenneth Ellenbogen; Carina Hardy; Takanori Ikeda; Aparna Jaswal; Elizabeth Kaufman; Andrew Krahn; Kengo Kusano; Valentina Kutyifa; Han S Lim; Gregory Y H Lip; Santiago Nava-Townsend; Hui-Nam Pak; Gerardo Rodríguez Diez; William Sauer; Anil Saxena; Jesper Hastrup Svendsen; Diego Vanegas; Marmar Vaseghi; Arthur Wilde; T Jared Bunch; Alfred E Buxton; Gonzalo Calvimontes; Tze-Fan Chao; Lars Eckardt; Heidi Estner; Anne M Gillis; Rodrigo Isa; Josef Kautzner; Philippe Maury; Joshua D Moss; Gi-Byung Nam; Brian Olshansky; Luis Fernando Pava Molano; Mauricio Pimentel; Mukund Prabhu; Wendy S Tzou; Philipp Sommer; Janice Swampillai; Alejandro Vidal; Thomas Deneke; Gerhard Hindricks; Christophe Leclercq
Journal:  Europace       Date:  2020-08-01       Impact factor: 5.214

4.  The role of biomarkers and neuroimaging in ischemic/hemorrhagic risk assessment for cardiovascular/cerebrovascular disease prevention.

Authors:  Elif Gokcal; Mitchell J Horn; M Edip Gurol
Journal:  Handb Clin Neurol       Date:  2021

Review 5.  Optimizing indices of atrial fibrillation susceptibility and burden to evaluate atrial fibrillation severity, risk and outcomes.

Authors:  Giuseppe Boriani; Marco Vitolo; Igor Diemberger; Marco Proietti; Anna Chiara Valenti; Vincenzo Livio Malavasi; Gregory Y H Lip
Journal:  Cardiovasc Res       Date:  2021-06-16       Impact factor: 13.081

6.  Elevated plasma syndecan-1 as glycocalyx injury marker predicts unfavorable outcomes after rt-PA intravenous thrombolysis in acute ischemic stroke.

Authors:  Fangfang Zhao; Rongliang Wang; Yuyou Huang; Lingzhi Li; Liyuan Zhong; Yue Hu; Ziping Han; Junfen Fan; Ping Liu; Yangmin Zheng; Yumin Luo
Journal:  Front Pharmacol       Date:  2022-07-15       Impact factor: 5.988

Review 7.  A Review of Biomarkers for Ischemic Stroke Evaluation in Patients With Non-valvular Atrial Fibrillation.

Authors:  Luxiang Shang; Ling Zhang; Yankai Guo; Huaxin Sun; Xiaoxue Zhang; Yakun Bo; Xianhui Zhou; Baopeng Tang
Journal:  Front Cardiovasc Med       Date:  2021-07-01

8.  Prognostic significance of plasma IL-2 and sIL-2Rα in patients with first-ever ischaemic stroke.

Authors:  Haiping Zhao; Fangfang Li; Yuyou Huang; Sijia Zhang; Lingzhi Li; Zhenhong Yang; Rongliang Wang; Zhen Tao; Ziping Han; Junfen Fan; Yangmin Zheng; Qingfeng Ma; Yumin Luo
Journal:  J Neuroinflammation       Date:  2020-08-14       Impact factor: 8.322

Review 9.  Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care.

Authors:  Jordi Heijman; Henry Sutanto; Harry J G M Crijns; Stanley Nattel; Natalia A Trayanova
Journal:  Cardiovasc Res       Date:  2021-06-16       Impact factor: 10.787

10.  Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms.

Authors:  Gregory Y H Lip; George Tran; Ash Genaidy; Patricia Marroquin; Cara Estes; Jeremy Landsheft
Journal:  Eur Heart J Qual Care Clin Outcomes       Date:  2022-08-17
  10 in total

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