Using Blood Biomarkers to Identify Atrial Fibrillation–Related Stroke

HomeStrokeVol. 50, No. 8Using Blood Biomarkers to Identify Atrial Fibrillation–Related Stroke Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBUsing Blood Biomarkers to Identify Atrial Fibrillation–Related StrokeBalancing Simplicity and Practicality Monika Kozieł, MD, PhD, Tatjana S. Potpara, MD, PhD and Gregory Y.H. Lip, MD Monika KoziełMonika Kozieł From the Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, United Kingdom (M.K., G.Y.H.L.) Department of Cardiology, Congenital Heart Diseases and Electrotherapy, Medical University of Silesia, Silesian Centre for Heart Diseases, Zabrze, Poland (M.K., G.Y.H.L.) , Tatjana S. PotparaTatjana S. Potpara School of Medicine, Belgrade University, Serbia (T.S.P., G.Y.H.L.) Cardiology Clinic, Clinical Centre of Serbia, Belgrade (T.S.P., G.Y.H.L.) and Gregory Y.H. LipGregory Y.H. Lip Correspondence to Gregory Y.H. Lip, MD, University of Liverpool, Liverpool, United Kingdom. Email E-mail Address: [email protected] From the Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, United Kingdom (M.K., G.Y.H.L.) Department of Cardiology, Congenital Heart Diseases and Electrotherapy, Medical University of Silesia, Silesian Centre for Heart Diseases, Zabrze, Poland (M.K., G.Y.H.L.) School of Medicine, Belgrade University, Serbia (T.S.P., G.Y.H.L.) Cardiology Clinic, Clinical Centre of Serbia, Belgrade (T.S.P., G.Y.H.L.) Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Denmark, (G.Y.H.L.). Originally published20 Jun 2019https://doi.org/10.1161/STROKEAHA.119.026185Stroke. 2019;50:1956–1957This article is a commentary on the followingBlood Biomarkers of Heart Failure and Hypercoagulation to Identify Atrial Fibrillation–Related StrokeOther version(s) of this articleYou are viewing the most recent version of this article. Previous versions: June 20, 2019: Ahead of Print See related article, p 2223Stroke risk in patients with atrial fibrillation (AF) is heterogenous, being characterized by different stroke risk factors. Given the need to use anticoagulants for stroke prevention, much focus has been on improving risk stratification, to target patients who would benefit the most from thromboprophylaxis.1 The more common and validated risk factors for stroke and bleeding in AF have been used to formulate clinical risk prediction scores.2 With the intention to improve risk prediction, attention has been directed to using biomarkers (biological markers) whether from blood (eg, troponin and natriuretic peptide), urine, or imaging (cardiac or cerebral), while balancing the need for simplicity of use and everyday practical application.3Why the interest in these biomarkers? Numerous biomarkers have been related to stroke and bleeding in AF and have contributed to our understanding of pathophysiological mechanisms involved in complications of AF or to be used as surrogate markers of thrombosis in clinical studies.4 Many proposed biomarkers in AF have provided independent prognostic value in AF and have generally improved risk stratification over an approach based on clinical factors alone.In the current issue of Stroke, Kneihsl et al5 report findings regarding the association between blood biomarkers and AF-related stroke. The investigators used data from one stroke unit and analyzed prospectively all consecutive ischemic stroke patients admitted over 1 year. The authors found that AF-related stroke patients had higher NT-proBNP (N-terminal pro-B-type natriuretic peptide) and D-dimer levels and lower antithrombin III when compared with patients with a noncardiac stroke pathogenesis. Moreover, they observed that NT-proBNP level was independently associated with AF-related stroke on multivariate analysis. Limitations include the small number of patients and single center cohort, as well as baseline one-off sampling to predict a remote event occurring many years later. The lack of laboratory follow-up samples would not adequately account for the dynamic nature of clinical risk.In many biomarker studies, the incremental predictive value of biomarker(s) over clinical factor–based risk scores still remains marginal, although may be statistically significant with large cohorts. Another limitation is the interpatient and intrapatient (and assay) variability, diurnal variation, and influence of concomitant diseases and drug therapies. Moreover, stroke or bleeding risk is a continuum and is not a one-off evaluation at baseline to determine events many years later; indeed, risk is a dynamic (rather than static) process modified by aging, incident risk factors, or changing comorbidities.6Moreover, many biomarkers are nonspecifically associated with multiple outcomes, cardiovascular and noncardiovascular, whether in AF or non-AF settings and are also elevated in many comorbidities, for example, renal failure, pulmonary embolism, decompensated heart failure, severe infection, or inflammatory disorders. Indeed, biomarkers predicting stroke are also predictive of bleeding and even glaucoma, as well as death, heart failure, etc.7,8 Thus, many biomarkers simply reflect a sick patient or a sick heart. Another hurdle includes the variations in availability, use of specific biomarker assays, and access to laboratories in different healthcare systems.Some biomarker-based approaches have been proposed and validated in highly selected anticoagulated clinical trial cohorts, statistically improving on stroke and bleeding risk prediction using baseline assessments.9–11 Even then, the C indexes as measures of prediction were modest, approximately 0.7; however, in real-world cohorts, especially with long-term follow-up, the inclusion of multiple biomarkers did not confer added predictive advantage over the risk prediction based on clinical scores.12,13 Also, the patient pathway often encounters a newly diagnosed patient on no antithrombotic therapy or aspirin, where clinicians need to risk stratify such patient and, after initiation of anticoagulation, reassess the risk regularly. As mentioned, stroke risk is a dynamic process, and regular reassessment is needed given the changing clinical profile of the patient.14 Also, bleeding risk prediction should be used to address modifiable bleeding risk factors and to flag up the high-risk patients for early and more frequent review (eg, 4 weeks rather than 4 to 6 months).15Perhaps biomarkers may be better used to rule out (rather than rule in) in relation to management decision-making. Further investigation of biomarkers for stroke or bleeding risk stratification in AF should balance the practical usefulness, costs, and daily use in clinical practice and provide data on the patient pathway from the nonanticoagulated inception stage to follow-up during long-term oral anticoagulant therapy, including possible treatment changes over time.16 Adding more and more biomarkers would certainly improve risk stratification (at least statistically) but have less practicality and clinical usefulness. However, biomarkers could aid risk stratification and treatment decision-making in patients who are borderline with regard to initiation of oral anticoagulants, for example, the population with CHA2DS2-VASc score 0 to 1.For many clinicians, simplicity and practicality matter, especially in busy clinics and ward settings. Given that the default should be to offer stroke prevention (ie, anticoagulation) to AF patients unless they are low risk, the initial focus should be to identify those low-risk patients as the first step,16 rather than the continued obsession to focus on identifying high-risk patients (whether using biomarkers or not) that led to underutilization of anticoagulants over the last decades.DisclosuresDr Lip is a consultant for Bayer/Janssen, BMS/Pfizer, Medtronic, Boehringer Ingelheim, Novartis, Verseon, and Daiichi-Sankyo and speaker for Bayer, BMS/Pfizer, Medtronic, Boehringer Ingelheim, and Daiichi-Sankyo. No fees are directly received personally. Dr Potpara declares personal fees from Bayer. The other author reports no conflicts.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to Gregory Y.H. Lip, MD, University of Liverpool, Liverpool, United Kingdom. Email gregory.[email protected]ac.ukReferences1. Lip GYH, Freedman B, De Caterina R, Potpara TS. Stroke prevention in atrial fibrillation: past, present and future. Comparing the guidelines and practical decision-making.Thromb Haemost. 2017; 117:1230–1239. doi: 10.1160/TH16-11-0876CrossrefMedlineGoogle Scholar2. Borre ED, Goode A, Raitz G, Shah B, Lowenstern A, Chatterjee R, et al. Predicting thromboembolic and bleeding event risk in patients with non-valvular atrial fibrillation: a systematic review.Thrombosis and haemostasis. 2018; 118:2171–2187.CrossrefMedlineGoogle Scholar3. Lip GY. Stroke and bleeding risk assessment in atrial fibrillation: when, how, and why?Eur Heart J. 2013; 34:1041–1049. doi: 10.1093/eurheartj/ehs435CrossrefMedlineGoogle Scholar4. Khan AA, Lip GYH. The prothrombotic state in atrial fibrillation: pathophysiological and management implications.Cardiovasc Res. 2019; 115:31–45. doi: 10.1093/cvr/cvy272CrossrefMedlineGoogle Scholar5. Kneihsl M, Gattringer T, Bisping E, Scherr D, Raggam R, Mangge H, et al. Blood biomarkers of heart failure and hypercoagulation to identify atrial fibrillation-related stroke.Stroke. 2019; 50:2223–2226. doi: 10.1161/STROKEAHA.119.025339LinkGoogle Scholar6. Chang TY, Lip GYH, Chen SA, Chao TF. Importance of risk reassessment in patients with atrial fibrillation in guidelines: assessing risk as a dynamic process.Can J Cardiol. 2019; 35:611–618. doi: 10.1016/j.cjca.2019.01.018Google Scholar7. Ban N, Siegfried CJ, Lin JB, Shui YB, Sein J, Pita-Thomas W, et al. Gdf15 is elevated in mice following retinal ganglion cell death and in glaucoma patients.JCI Insight. 2017; 2:pii: 91455.Google Scholar8. Sharma A, Stevens SR, Lucas J, Fiuzat M, Adams KF, Whellan DJ, et al. Utility of growth differentiation factor-15, a marker of oxidative stress and inflammation, in chronic heart failure: insights from the HF-ACTION study.JACC Heart Fail. 2017; 5:724–734. doi: 10.1016/j.jchf.2017.07.013CrossrefMedlineGoogle Scholar9. Hijazi Z, Lindbäck J, Alexander JH, Hanna M, Held C, Hylek EM, et al; ARISTOTLE and STABILITY Investigators. The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation.Eur Heart J. 2016; 37:1582–1590. doi: 10.1093/eurheartj/ehw054CrossrefMedlineGoogle Scholar10. Oldgren J, Hijazi Z, Lindbäck J, Alexander JH, Connolly SJ, Eikelboom JW, et al; RE-LY and ARISTOTLE Investigators. Performance and validation of a novel biomarker-based stroke risk score for atrial fibrillation.Circulation. 2016; 134:1697–1707. doi: 10.1161/CIRCULATIONAHA.116.022802LinkGoogle Scholar11. Berg DD, Ruff CT, Jarolim P, Giugliano RP, Nordio F, Lanz HJ, et al. Performance of the ABC scores for assessing the risk of stroke or systemic embolism and bleeding in patients with atrial fibrillation in ENGAGE AF-TIMI 48.Circulation. 2019; 139:760–771. doi: 10.1161/CIRCULATIONAHA.118.038312LinkGoogle Scholar12. Rivera-Caravaca JM, Roldan V, Esteve-Pastor MA, Valdes M, Vicente V, Lip GYH, et al. Long-term stroke risk prediction in patients with atrial fibrillation: comparison of the abc-stroke and cha2ds2-vasc scores.J Am Heart Assoc. 2017; 6:pii: e006490.Google Scholar13. Roldán V, Rivera-Caravaca JM, Shantsila A, García-Fernández A, Esteve-Pastor MA, Vilchez JA, et al. Enhancing the 'real world' prediction of cardiovascular events and major bleeding with the CHA2DS2-VASc and HAS-BLED scores using multiple biomarkers.Ann Med. 2018; 50:26–34. doi: 10.1080/07853890.2017.1378429CrossrefMedlineGoogle Scholar14. Chao TF, Liao JN, Tuan TC, Lin YJ, Chang SL, Lo LW, et al. Incident co-morbidities in patients with atrial fibrillation initially with a cha2ds2-vasc score of 0 (males) or 1 (females): implications for reassessment of stroke risk in initially 'low-risk' patients [published online March 21, 2019].Thrombosis and haemostasis. doi: 10.1055/s-0039-1683933Google Scholar15. Chao TF, Lip GYH, Lin YJ, Chang SL, Lo LW, Hu YF, et al. Incident risk factors and major bleeding in patients with atrial fibrillation treated with oral anticoagulants: a comparison of baseline, follow-up and delta HAS-BLED scores with an approach focused on modifiable bleeding risk factors.Thromb Haemost. 2018; 118:768–777. doi: 10.1055/s-0038-1636534CrossrefMedlineGoogle Scholar16. Lip GYH, Banerjee A, Boriani G, Chiang CE, Fargo R, Freedman B, et al. Antithrombotic therapy for atrial fibrillation: CHEST guideline and expert panel report.Chest. 2018; 154:1121–1201. doi: 10.1016/j.chest.2018.07.040CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesBlood Biomarkers of Heart Failure and Hypercoagulation to Identify Atrial Fibrillation–Related StrokeMarkus Kneihsl, et al. Stroke. 2019;50:2223-2226 August 2019Vol 50, Issue 8 Advertisement Article InformationMetrics © 2019 American Heart Association, Inc.https://doi.org/10.1161/STROKEAHA.119.026185PMID: 31216960 Originally publishedJune 20, 2019 Keywordsrisk factorsstrokeatrial fibrillationEditorialsbiomarkersPDF download Advertisement SubjectsAtrial FibrillationIschemic StrokeRisk Factors