A biomarker-based risk score to predict death in patients with atrial fibrillation: the ABC (age, biomarkers, clinical history) death risk score

Abstract Aims In atrial fibrillation (AF), mortality remains high despite effective anticoagulation. A model predicting the risk of death in these patients is currently not available. We developed and validated a risk score for death in anticoagulated patients with AF including both clinical information and biomarkers. Methods and results The new risk score was developed and internally validated in 14 611 patients with AF randomized to apixaban vs. warfarin for a median of 1.9 years. External validation was performed in 8548 patients with AF randomized to dabigatran vs. warfarin for 2.0 years. Biomarker samples were obtained at study entry. Variables significantly contributing to the prediction of all-cause mortality were assessed by Cox-regression. Each variable obtained a weight proportional to the model coefficients. There were 1047 all-cause deaths in the derivation and 594 in the validation cohort. The most important predictors of death were N-terminal pro B-type natriuretic peptide, troponin-T, growth differentiation factor-15, age, and heart failure, and these were included in the ABC (Age, Biomarkers, Clinical history)-death risk score. The score was well-calibrated and yielded higher c-indices than a model based on all clinical variables in both the derivation (0.74 vs. 0.68) and validation cohorts (0.74 vs. 0.67). The reduction in mortality with apixaban was most pronounced in patients with a high ABC-death score. Conclusion A new biomarker-based score for predicting risk of death in anticoagulated AF patients was developed, internally and externally validated, and well-calibrated in two large cohorts. The ABC-death risk score performed well and may contribute to overall risk assessment in AF. ClinicalTrials.gov identifier NCT00412984 and NCT00262600

[1]  A. Siegbahn,et al.  Application of Biomarkers for Risk Stratification in Patients with Atrial Fibrillation. , 2017, Clinical chemistry.

[2]  S. Yusuf,et al.  Performance and Validation of a Novel Biomarker-Based Stroke Risk Score for Atrial Fibrillation , 2016, Circulation.

[3]  P. Kirchhof,et al.  2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. , 2016, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[4]  P. Kirchhof,et al.  2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. , 2016, European heart journal.

[5]  Robert P. Giugliano,et al.  Sudden Cardiac Death in Patients With Atrial Fibrillation: Insights From the ENGAGE AF‐TIMI 48 Trial , 2016, Journal of the American Heart Association.

[6]  D. Fitzmaurice,et al.  Two-year outcomes of patients with newly diagnosed atrial fibrillation: results from GARFIELD-AF , 2016, European heart journal.

[7]  S. Yusuf,et al.  The novel biomarker-based ABC (age, biomarkers, clinical history)-bleeding risk score for patients with atrial fibrillation: a derivation and validation study , 2016, The Lancet.

[8]  M. Turakhia,et al.  Comparative Effectiveness of Cardiac Resynchronization Therapy Among Patients With Heart Failure and Atrial Fibrillation: Findings From the National Cardiovascular Data Registry's Implantable Cardioverter-Defibrillator Registry. , 2016, Circulation. Heart failure.

[9]  James E. Helmreich.  Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis (2nd Edition) , 2016 .

[10]  G. Breithardt,et al.  Cause of Death and Predictors of All‐Cause Mortality in Anticoagulated Patients With Nonvalvular Atrial Fibrillation: Data From ROCKET AF , 2016, Journal of the American Heart Association.

[11]  Claes Held,et al.  The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation , 2016, European heart journal.

[12]  Ulrika Andersson,et al.  Biomarkers of inflammation and risk of cardiovascular events in anticoagulated patients with atrial fibrillation , 2016, Heart.

[13]  Emily C. O'Brien,et al.  The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation , 2015, European heart journal.

[14]  D. Atar,et al.  Comparison of cardiac troponins I and T measured with high-sensitivity methods for evaluation of prognosis in atrial fibrillation: an ARISTOTLE substudy. , 2015, Clinical chemistry.

[15]  Gary S Collins,et al.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration , 2015, Annals of Internal Medicine.

[16]  M. Ezekowitz,et al.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. , 2014, Circulation.

[17]  M. Ezekowitz,et al.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. , 2014, Circulation.

[18]  R. de Caterina,et al.  Growth Differentiation Factor 15, a Marker of Oxidative Stress and Inflammation, for Risk Assessment in Patients With Atrial Fibrillation: Insights From the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) Trial , 2014, Circulation.

[19]  E. Hylek,et al.  D‐dimer and risk of thromboembolic and bleeding events in patients with atrial fibrillation – observations from the ARISTOTLE trial , 2014, Journal of thrombosis and haemostasis : JTH.

[20]  Yvonne Vergouwe,et al.  Towards better clinical prediction models: seven steps for development and an ABCD for validation. , 2014, European heart journal.

[21]  C. Murray,et al.  Worldwide Epidemiology of Atrial Fibrillation: A Global Burden of Disease 2010 Study , 2014, Circulation.

[22]  D. Atar,et al.  High-sensitivity troponin T and risk stratification in patients with atrial fibrillation during treatment with apixaban or warfarin. , 2014, Journal of the American College of Cardiology.

[23]  S. Yusuf,et al.  Efficacy and Safety of Dabigatran Compared With Warfarin in Relation to Baseline Renal Function in Patients With Atrial Fibrillation: A RE-LY (Randomized Evaluation of Long-term Anticoagulation Therapy) Trial Analysis , 2013, Circulation.

[24]  D. Atar,et al.  High-Sensitivity Troponin I for Risk Assessment in Patients With Atrial Fibrillation: Insights From the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) Trial , 2013, Circulation.

[25]  Hugh Calkins,et al.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. , 2014, Journal of the American College of Cardiology.

[26]  J. Le Heuzey,et al.  Causes of Death and Influencing Factors in Patients With Atrial Fibrillation: A Competing-Risk Analysis From the Randomized Evaluation of Long-Term Anticoagulant Therapy Study , 2013, Circulation.

[27]  S. Hohnloser,et al.  N-terminal pro-B-type natriuretic peptide for risk assessment in patients with atrial fibrillation: insights from the ARISTOTLE Trial (Apixaban for the Prevention of Stroke in Subjects With Atrial Fibrillation). , 2013, Journal of the American College of Cardiology.

[28]  C. Granger,et al.  Biomarkers in atrial fibrillation: a clinical review. , 2013, European heart journal.

[29]  P. Royston,et al.  External validation of a Cox prognostic model: principles and methods , 2013, BMC Medical Research Methodology.

[30]  Tommy Andersson,et al.  All-cause mortality in 272 186 patients hospitalized with incident atrial fibrillation 1995–2008: a Swedish nationwide long-term case–control study , 2013, European heart journal.

[31]  S. Yusuf,et al.  Cardiac Biomarkers Are Associated With an Increased Risk of Stroke and Death in Patients With Atrial Fibrillation: A Randomized Evaluation of Long-Term Anticoagulation Therapy (RE-LY) Substudy , 2012, Circulation.

[32]  Robby Nieuwlaat,et al.  Revised A novel user-friendly score (HAS-BLED) to assess one-year risk of major bleeding in atrial fibrillation patients: The Euro Heart Survey , 2012 .

[33]  D. Atar,et al.  Apixaban versus warfarin in patients with atrial fibrillation. , 2011, The New England journal of medicine.

[34]  J. Healey,et al.  Cardiac resynchronization therapy: a meta-analysis of randomized controlled trials , 2011, Canadian Medical Association Journal.

[35]  John L Sapp,et al.  Cardiac-resynchronization therapy for mild-to-moderate heart failure. , 2010, The New England journal of medicine.

[36]  Gregory Y H Lip,et al.  A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. , 2010, Chest.

[37]  B. Gersh,et al.  Apixaban for reduction in stroke and other ThromboemboLic events in atrial fibrillation (ARISTOTLE) trial: design and rationale. , 2010, American heart journal.

[38]  Gregory Y H Lip,et al.  Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. , 2010, Chest.

[39]  S. Yusuf,et al.  Dabigatran versus warfarin in patients with atrial fibrillation. , 2009, The New England journal of medicine.

[40]  S. Yusuf,et al.  Rationale and design of RE-LY: randomized evaluation of long-term anticoagulant therapy, warfarin, compared with dabigatran. , 2009, American heart journal.

[41]  Elena B. Elkin,et al.  Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers , 2008, BMC Medical Informatics Decis. Mak..

[42]  Jagmeet P. Singh,et al.  Cardiac resynchronization in patients with atrial fibrillation: a meta-analysis of prospective cohort studies. , 2008, Journal of the American College of Cardiology.

[43]  Ben Vandermeer,et al.  Cardiac resynchronization therapy for patients with left ventricular systolic dysfunction: a systematic review. , 2007, JAMA.

[44]  G. Brabant,et al.  Circulating concentrations of growth-differentiation factor 15 in apparently healthy elderly individuals and patients with chronic heart failure as assessed by a new immunoradiometric sandwich assay. , 2007, Clinical chemistry.

[45]  N. Crepaz,et al.  Higher incidence of clear cell adenocarcinoma of the cervix and vagina among women born between 1947 and 1971 in the United States , 2011, Cancer Causes & Control.

[46]  D. Singer,et al.  Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. , 2001, JAMA.

[47]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[48]  D. Levy,et al.  Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. , 1998, Circulation.