Biomarkers of increased bleeding risk in patients with atrial fibrillation on oral anticoagulation: a narrative review
Review Article

Biomarkers of increased bleeding risk in patients with atrial fibrillation on oral anticoagulation: a narrative review

Abdalazeem Ibrahem1,2,3, Ahmed Abdalwahab2,4, Michael Gillan2, Mohaned Egred2, Mohammad Alkhalil2, Diana A. Gorog3,5*, Mohamed Farag2,3*

1Cardiology Department, North Bristol NHS Trust, Bristol, UK; 2Cardiothoracic Department, Freeman Hospital, Newcastle Upon Tyne, UK; 3School of Life and Medical Sciences, University of Hertfordshire, Hertfordshire, UK; 4Cardiovascular Department, Faculty of Medicine, Tanta University, Tanta, Egypt; 5National Heart and Lung Institute, Imperial College, London, UK

Contributions: (I) Conception and design: M Farag, A Ibrahem, DA Gorog; (II) Administrative support: None; (III) Provision of study materials or patients: A Abdalwahab, M Gillan, M Egred, M Alkhalil, DA Gorog; (IV) Collection and assembly of data: A Abdalwahab, M Gillan; (V) Data analysis and interpretation: None; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

*These authors contributed equally to this work as co-senior authors.

Correspondence to: Dr. Mohamed Farag, MSc, PhD. Cardiothoracic Department, Freeman Hospital, Freeman Road, High Heaton, Newcastle Upon Tyne, NE7 7DN, UK; School of Life and Medical Sciences, University of Hertfordshire, Hertfordshire, UK. Email: mohamedfarag@nhs.net.

Background and Objective: Atrial fibrillation (AF) is an independent risk factor for ischemic stroke and systemic thromboembolism. Oral anticoagulation (OAC) effectively reduces stroke risk but also increases bleeding risk. Current clinical risk scores for bleeding in AF patients have only modest predictive ability and overlapping stroke and bleeding risk factors complicate treatment decisions. This narrative review aims to review and evaluate current evidence on biomarkers that can predict bleeding risk in AF patients on OAC and assess their integration into risk-scoring systems to guide more personalised clinical decision-making.

Methods: This narrative review summarises data from major clinical trials and cohort studies evaluating bleeding-related biomarkers in AF patients on OAC, including growth differentiation factor 15 (GDF-15), high-sensitivity cardiac troponin (hs-cTn), N-terminal prohormone-brain natriuretic peptide (NT-pro-BNP), interleukin-6 (IL-6), von Willebrand factor (vWF), cystatin C, and D-dimer. The prognostic value of these biomarkers, their role in risk scores (e.g., ABC-bleeding), and their ability to improve predictive accuracy were examined.

Key Content and Findings: In recent years, several biomarkers have shown promise in predicting bleeding risk in patients with AF on OAC. GDF-15 has consistently emerged as a strong independent marker of significant bleeding and mortality, validated in trials such as Randomized Evaluation of Long-Term Anticoagulation Therapy (RE-LY), Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE), and Edoxaban Versus Warfarin in Patients with Atrial Fibrillation trial (ENGAGE AF-TIMI 48). hs-cTn and D-dimer levels are also independently associated with an increased bleeding risk and have been included in the ABC-bleeding score, which has shown superior predictive ability compared to traditional scores, such as HAS-BLED. Biomarkers such as cystatin C, which reflects renal dysfunction, vWF, and IL-6 have demonstrated associations with adverse outcomes, although their predictive abilities vary. The inclusion of these biomarkers in clinical tools has improved bleeding risk prediction. Although trials and cost-effectiveness models suggest clinical benefit, further real-world validation is required to confirm their place in everyday clinical practice.

Conclusions: Several biomarkers have demonstrated the ability to predict bleeding risk in patients with AF. Risk-scoring systems that incorporate biomarkers have improved the prediction of bleeding events. More accurate identification of patients at higher risk of bleeding allows clinicians and patients to better balance the risks of bleeding versus stroke in the setting of AF and create individualised care plans to lower the overall rate of both stroke and bleeding.

Keywords: Atrial fibrillation (AF); oral anticoagulation (OAC); bleeding risk; biomarkers; risk stratification

Submitted Dec 31, 2024. Accepted for publication Apr 27, 2025. Published online Aug 19, 2025.

doi: 10.21037/cdt-2024-696

Introduction

Atrial fibrillation (AF) is an independent risk factor for ischemic stroke and systemic thromboembolism (1,2). Oral anticoagulation (OAC) is routinely used for the prevention of stroke and systemic embolism in patients with AF (3,4). However, whilst significantly reducing the risk of stroke, OAC substantially increases the risk of bleeding complications (5-8), including intracranial or gastrointestinal hemorrhages. An individual’s bleeding risk is dynamic and is determined by various modifiable, potentially modifiable or non-modifiable factors (6). Some factors are patient-related (age, prior history of bleeding, anaemia, liver or kidney disease), whilst others relate to the type of OAC and concomitant medications which may exacerbate bleeding risk (such as antiplatelet agents, glucocorticoids, non-steroidal anti-inflammatory drugs and selective serotonin reuptake inhibitors) (6).

A large nationwide registry recently reported that 4.5% of patients with AF on OAC experienced a major bleeding event over an average follow-up of 403 days (7). With vitamin K antagonists (VKA), the estimated risk of major bleeding is 0.4–7.2% per year and as high as 15.4% for minor bleeding (9). The European Society of Cardiology and the American College of Cardiology/American Heart Association Joint Committee recommend using non-vitamin K oral anticoagulants (NOACs) over VKA for most individuals with non-valvular AF (3,10). Compared to VKA, NOAC are associated with a decreased risk of stroke and intracranial haemorrhage (ICH) but may increase the risk of gastrointestinal bleeding by up to 25%, particularly in women (9). The large trials comparing NOACs with VKA showed that the rate of major bleeding with NOACs was around 2–4% per annum (11-14). Although validated risk scoring systems are available to predict stroke and bleeding risk in patients with AF on OAC, there is a significant overlap between clinical risk factors for stroke and bleeding. Patients identified as having a high risk of stroke are often also found to have a high risk of bleeding, making risk-benefit decisions challenging (15). OAC is often under-prescribed in those patients at the highest risk of stroke (16), perhaps due to concerns about major bleeding, despite evidence that almost all patients with AF would benefit from anticoagulation (17).

The scoring systems used to assess bleeding risk have only a modest predictive ability for bleeding events. Improving their effectiveness would help differentiate the patients at the highest risk of stroke from bleeding and hopefully enhance the prevention of both stroke and bleeding complications. We present this article in accordance with the Narrative Review reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-696/rc).

Methods

A comprehensive literature search was conducted to identify studies evaluating biomarkers associated with bleeding risk in patients with atrial fibrillation (AF) on OAC (Table 1).

Table 1

Search strategy summary

Items Specification
Date of search 16 July 2024
Databases and other sources searched PubMed, EMBASE, Cochrane Library
Search terms used “Atrial Fibrillation”, “Oral Anticoagulants”, “Bleeding”, “Biomarkers”
Timeframe January 2000–July 2024
Inclusion and exclusion criteria Inclusion: studies evaluating the association between biomarkers and bleeding in AF patients on OACs
Study designs including: (I) randomized controlled trials (RCTs); (II) systematic reviews; (III) meta-analyses; (IV) observational studies
Exclusion: (I) studies that do not mention the use of anticoagulant therapy; (II) editorials, letters to the editor, and conference abstracts without full data
Selection process M.F. and A.I., independent reviewers, performed the selection; discrepancies were resolved through discussion. D.A.G. and M.E. were consulted when necessary

AF, atrial fibrillation; OAC, oral anticoagulation.

The need for biomarkers to predict bleeding in AF patients

Identification of biomarkers to predict bleeding and/or distinguish stroke from bleeding risk in AF patients would be very important to identify individuals at high bleeding risk and help personalise their treatment to reduce the net risk (18-21).

Biomarkers of bleeding risk

In the past decade, many studies have investigated the potential role of biomarkers in the prediction, risk stratification and assessment of bleeding risk in patients with AF taking OAC and showed some promising results (15,18,21-24). Among those biomarkers, some have been shown to be useful in predicting bleeding risk, with a few incorporated into the biomarker-based risk-scoring tools and described as markers of risk for anticoagulant-associated bleeding (6,23). Commonly used biomarkers are discussed in detail in the below section.

Growth differentiation factor 15 (GDF-15)

Inflammation, hypoxia or any oxidative stress to organs such as the liver, kidney, lungs or heart causing secretion of pro-inflammatory cytokines can result in the expression of a protein bearing the structural characteristics of a transforming growth factor β (TGF-β) superfamily cytokine, known as GDF-15 (25-27). Cardiovascular diseases, including heart failure, AF and atherosclerosis, are a major driver of GDF-15 production from cardiac and extracardiac tissues (28).

The Randomized Evaluation of Long-Term Anticoagulation Therapy (RE-LY) trial evaluated the prognostic value of GDF-15 beyond clinical characteristics and other biomarkers concerning bleeding and stroke outcomes in 8,474 patients with AF (29). This showed that a raised level of GDF-15 is an independent risk indicator for major bleeding and all-cause mortality but not for stroke. Furthermore, GDF-15 improved the C-index of both the HAS-BLED (0.62–0.69) and ORBIT (0.68–0.71) bleeding risk scores (29).

The Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial 11, in which 14,789 patients with non-valvular AF were randomised to OAC with apixaban or VKA, showed that elevated levels of GDF-15 was an independent risk factor for major bleeding, mortality, and stroke. However, when adjusted for other cardiac biomarkers, the increased risk of stroke was no longer significant, but the association with increased bleeding risk remained. These findings validated the independent association of GDF-15 with the risk of major bleeding in AF patients on OAC therapy (21,28). Higher levels of GDF-15 were associated with 3.5 times higher rates of major bleeding (P<0.001). Incorporation of this biomarker into the HAS-BLED score resulted in a net reclassification index (NRI) of 33% (21).

The incorporation of GDF-15 in risk scoring systems, such as the biomarker-based ABC-bleeding risk score, has shown promising results over other contemporary bleeding risk scores (23,29). This has been further validated in the Edoxaban Versus Warfarin in Patients with Atrial Fibrillation trial (ENGAGE AF-TIMI 48) (30).

GDF-15 is mainly useful for subsequent bleeding event estimation and is most associated with fatal bleeding (31). A raised level of GDF-15 is associated with various adverse outcomes, including bleeding, stroke, myocardial infarction, and death. When adjusted for other comorbidities and biomarkers, it remained an independent risk factor for major bleeding and death (32). This suggests that GDF-15 can be useful to improve risk assessment of bleeding and mortality in patients with AF.

High-sensitivity cardiac troponin (hs-cTn)

In the last decade, the development of hs-cTn assays provided a precise quantitative assessment of cardiac myocyte injury (18,33,34). Cardiac troponin levels have been linked to increasing mortality, stroke and major bleeding, as demonstrated in various cardiac conditions, including AF (33,34). Circulating high-sensitivity troponin-T levels were shown to be significantly greater in individuals with AF than those without (34).

The biomarker sub-studies of the RE-LY trial showed the significance of cTn as an independent marker of increased thromboembolic and major bleeding events (20,35). In 6,189 patients with AF treated with either warfarin or dabigatran, when adjusted for other risk factors, increased cardiac troponin I (cTnI) concentration was associated with a 1.9-fold increased risk of major bleeding and a 1.7-fold increase in risk of stroke over a median follow-up duration of 1.8 years (18).

In the biomarker sub-studies of the ARISTOTLE trial, in which 14,897 patients with AF were randomised to apixaban or warfarin, raised cTn concentration was associated with a 2-fold increased risk of major bleeding (11,23). Cardiac troponins T and I both provide valuable prognostic information regarding cardiovascular, thromboembolic and bleeding risks, and the risk of these events increases when both troponins are above the median concentration (36-38).

N-terminal prohormone-brain natriuretic peptide (NT-pro-BNP)

NT-pro-BNP is secreted mainly from the ventricular myocardium in response to excessive myocyte stretch or injury and, to a lesser extent, from the atria (39,40). The long half-life makes this inactive protein a suitable serologic marker of cardiovascular disease (41,42). A large meta-analysis looking at 25,715 individuals across 11 studies showed that all-cause mortality was significantly higher in those with elevated NT-pro-BNP [hazard ratio (HR) 2.44; 95% confidence interval (CI): 2.11–2.83] (43). AF in acute or chronic form is associated with a rise in natriuretic peptides (44-49). This biomarker is also associated with a higher risk of stroke and thromboembolic events than bleeding events in comparison to other useful biomarkers (20,31,50-52). Results from the ARISTOTLE sub-study showed no association between higher NT-pro-BNP levels and risk of major bleeding (adjusted HR: 1.07; 95% CI: 0.82–1.40; P=0.067) (53). A biomarker substudy of the RE-LY trial also demonstrated no link between NT-pro-BNP levels and bleeding events (20). It again showed that higher levels of NT-pro-BNP are associated with increased risk of stroke and mortality (20).

Interleukin-6 (IL-6)

IL-6 is a protein produced by endothelial cells, activated monocytes and macrophages, and some lymphocytes in response to infection and tissue injury. After IL-6 is produced in the lesion, it moves to the liver and increases the expression of tissue factor, fibrinogen, factor VIII, C-reactive protein and von Willebrand factor (vWF) and reduces the levels of haemostasis inhibitors such as antithrombin and protein S. Higher levels of IL-6 have previously been associated with an increased risk of thromboembolism, major bleeding and vascular death in patients with AF, although sub-studies of the ARISTOTLE trial demonstrated that IL-6 was significantly associated with all-cause mortality (53,54). Another study looking at 930 patients with AF on OAC showed that IL-6 was an independent predictor for death and cardiovascular events, including stroke (55). One meta-analysis, which included 22,928 patients with AF, demonstrated an association between higher levels of IL-6 and increased risk of stroke, bleeding and death (56). However, there was no independent association between IL-6 and stroke when adjusted for clinical risk factors, and no statistically significant association between IL-6 and major bleeding (57).

vWF

vWF is a crucial glycoprotein with a pivotal role in haemostasis and coagulation cascade, promoting platelet adhesion and stabilising factor VIII. Higher levels of vWF have been found in patients with AF compared to healthy individuals (58). New models of risk assessment scoring schemes incorporating biomarkers have recently been validated (23,59,60), such as the ABC-bleeding risk score (23). The ABC biomarker-based risk score has outperformed many of the current clinical risk assessment tools but still requires real-world data for validation (23,60,61).

Although clinical characteristics may be similar, the risk of bleeding can be very different in individuals and may further vary within an individual over time. A personalised medical approach to delivering optimal care has the significant advantages of reducing risk for an individual as well as the ability to weigh up the pros and cons of any intervention carefully. This would be particularly important to identify patients at risk of bleeding on OAC and to determine which patient is at increased risk of bleeding or stroke. A meta-analysis of 12 studies, comprising 7,119 individuals with AF, showed that higher levels of vWF were associated with all-cause mortality: relative risk (RR) 1.5 (95% CI: 1.16–2.11), stroke: RR 1.69 (95% CI: 1.08–2.64), bleeding: RR 2.01 (95% CI: 1.65–2.45) (62). However, there was significant variability in the use of OAC drugs and the cut-off value for vWF used in the studies included. Another study, including 1,215 patients with AF, investigated the impact of adding vWF levels to CHA2DS2-VASc and HAS-BLED scores. This study showed that adding vWF levels to these scoring systems did improve their predictive ability, but overall, the effect was minimal (63).

Markers of renal function: cystatin C, creatinine and glomerular filtration rate (GFR)

Chronic kidney disease (CKD) is a common comorbidity in patients with AF and is associated with increased risk of stroke, bleeding and mortality (64-67). Cystatin C is a non-glycosylated basic protein found in almost all nucleated cells, which is produced at a constant rate and freely filtered by the renal glomeruli (68). Unlike creatinine, it is not affected by muscle mass or habitus, so it may be a more accurate measure of renal function (69).

A prospective cohort study, including 3,865 patients with AF, investigated the association of cystatin-based and creatinine-based GFR equations with major adverse cardiac outcomes and bleeding events. It found that when using cystatin-based GFR calculations, lower GFR (and therefore higher levels of cystatin c) was associated with major adverse cardiac outcomes (HR 0.68, 95% CI: 0.58–0.78, P<0.001) and with bleeding (HR 0.73, 95% CI: 0.60–0.88, P=0.001) (69).

Another study including 180 patients with AF and stage 4 CKD, all on OAC, found that raised cystatin-C (but not estimated GFR) was an independent predictor of major bleeding (HR 9.24, 95% CI: 2.15–39.67, P=0.003) and all-cause mortality (HR 3.95, 95% CI: 1.08–14.37, P=0.04) (70).

Reduced renal function has been shown to increase bleeding risk in patients with AF on OAC. One large cohort study including 12,403 participants demonstrated that bleeding events increased as GFR decreased (P=0.001) (65). In the first 30 days of starting OAC, bleeding events were 10-fold higher in individuals with GFR <15 mL/min/1.73 m2, compared with those with GFR >90 mL/min/1.73 m2 (65). Creatinine-based GFR equations were associated with major adverse cardiovascular events (HR 0.87, 95% CI: 0.77–0.97, P=0.01), but not with major bleeding (HR 0.91, 95% CI: 0.78–1.07, P=0.25) (69). Another small study suggested that AF patients with CKD stage 4 receiving reduced-dose NOAC or warfarin have a similar risk of thromboembolism and bleeding in everyday clinical practice (71).

D-dimer

D-dimer is a small protein fragment formed on degradation of fibrin, and so elevated levels usually reflect increased fibrin turnover. AF is associated with raised levels of d-dimer (72). Data from the ARISTOTLE trial showed that patients with d-dimer levels in the highest quartile had an increased risk of stroke and bleeding (73). Incorporating d-dimer into clinical scoring systems improved their predictive ability for stroke (NRI of 11%) and bleeding events (NRI of 28%). A substudy of the RE-LY trial also showed that higher baseline d-dimer levels were associated with an increased risk of stroke and bleeding and that adding d-dimer levels to clinical risk factors slightly improved the prediction of stroke (C-index increased from 0.66 to 0.68) and bleeding (C-index improved from 0.66 to 0.67) (74).

Discussion

Multiple biomarkers have been associated with an increased risk of bleeding in patients with AF. Sub-studies of the RE-LY and ARISTOTLE trials have found higher GDF-15 (11,21,28,29,31), hs-cTN (11,19,20,23,35-38) and d-dimer (73,74) levels to be independently associated with increased bleeding risk. Also, Cystatin C, as a useful measure of renal function, has been associated with increased bleeding risk in patients with AF (64,66,70,75). Further, higher levels of vWF have been linked to increased bleeding risk (62,63). These biomarkers have been shown to improve bleeding risk prediction when incorporated into commonly used scoring tools. GDF-15 and d-dimer, in particular, have been shown to significantly enhance the ability of common scoring systems to predict bleeding events (21,23,29,63,74).

The ABC-Bleeding score is a newer scoring tool which incorporates clinical risk factors and biomarkers (haemoglobin, GDF-15 and hs-cTN) to predict bleeding risk in patients with AF (23,59-61). It outperformed HAS-BLED in the ENGAGE-AF trial cohort (C-index 0.69 vs. 0.61) (30), but the real-world data remains controversial. In a study of 1,120 patients, all stable on VKA treatment for AF, HAS-BLED was better able to predict major bleeding when compared to the ABC-Bleeding score (C-index 0.583 vs. 0.518) (76).

A cost-effectiveness analysis of the ABC-stroke and ABC-bleeding scores looked at a hypothetical cohort of 1,000 patients with AF, using the baseline event rates from the ARISTOTLE trial, found that the biomarker-based scoring systems reduced per-patient cost and increased quality-adjusted life years, mainly by reducing the incidence of bleeding events in the model (77). However, this analysis has limitations, as it used a cohort of hypothetical patients with data derived from large-scale clinical trials and may not accurately reflect real-world populations.

Some other potential biomarkers of increased bleeding risk have been identified with new technologies. One study used Proximity Extension Assay to screen plasma from 5,568 patients from the ARISTOTLE and RE-LY trials. It identified nine biomarkers independently associated with increased bleeding risk, including GDF-15, hs-cTn and seven novel biomarkers (TNF-R1, EphB4, suPAR, OPN, OPG, TNF-R2, and TRAIL-R2) (78). It concluded that further investigation is needed to determine if these novel biomarkers can improve the prediction of bleeding events in patients with AF. Overall, several validated risk prediction models have been developed to estimate the risk of bleeding in patients with AF who are on oral anticoagulants (Table 2). Emerging evidence suggests that incorporating data from biomarkers of bleeding (Table 3) could enhance the predictive accuracy of this model and improve individualised decision-making, ultimately leading to better patient management.

Table 2

Risk scoring tools to predict bleeding risk in patients with AF

Scoring tool, year (Ref.) Description Scoring parameters Risk estimation categories Category population Major bleeding rates Major bleeding definition
OBRI, 1989 (79) Population =556. Follow-up period =48 months. Data = ORBIT-AF registry. Analysis = prospective Age ≥65 years, prior stroke, prior gastrointestinal bleeding (1 point each). Recent myocardial infarction, diabetes mellitus, haematocrit <30%, creatinine >1.5 mg/dL (1 point if any) Low [0]. Intermediate [1–2]. High [3–4] 164 (31%)/331 (58%)/61 (11%) 3% at 12 months. 11% at 12 months. 17% at 12 months Fatal, life threatening, potentially life threatening, led to severe blood loss, led to surgical treatment or led to moderate blood loss
mOBRI, 1998 (80) Population =556. Follow-up period =48 months. Data = ORBIT-AF registry. Analysis = prospective Age ≥65 years, prior stroke, prior gastrointestinal bleeding (1 point each). Recent myocardial infarction, diabetes mellitus, haematocrit <30%, creatinine >1.5 mg/dL (1 point if any) Low [0]. Intermediate [1–2]. High [3–4] 186 (33%)/336 (59%)/34 (6%) 3% at 12 months. 12% at 12 months. 48% at 12 months Overt bleeding leading to a loss of at least 2 units in 7 days or less, or life-threatening
HEMORR2HAGES, 2006 (81) Population =3,791. Follow-up period =36 months. Data = NRAF. Analysis = retrospective Liver/renal disease, ethanol abuse, malignancy, age >75 years, low platelet count or function, uncontrolled hypertension, anaemia, genetic factors (CYP2C9), risk of fall or stroke (1 point each). Re-bleeding risk (2 points) Low [0–1]. Intermediate [2–3]. High [≥4] 717 (45%)/694 (43%)/193 (19%) 2% at 12 months. 5% at 12 months. 9% at 12 months ICD-9-CM codes
HAS-BLED, 2010 (82) Population =3,456. Follow-up period =1 year. Data = Euro Heart Survey. Analysis = retrospective Hypertension, abnormal renal (CrCl <50 mL/min) or liver function, stroke, bleeding history or predisposition, labile INR (TTR <60%), age >65 years, drugs of interest/alcohol (1 point each) Low [0]. Intermediate [1–2]. High [3] 798 (n/a)/2,030 (n/a)/187 (n/a) 1.13 per 100 person-years. 2.90 per 100 person-years. 3.74 per 100 person-years Any bleed requiring hospitalization or causing a decrease in haemoglobin level of 2 g/L or requiring blood transfusion that was not a haemorrhagic stroke
ATRIA, 2011 (83) Population =9,186. Follow-up period =6 years. Data = large integrated healthcare system data of ATRIA cohort. Analysis = retrospective Anaemia, renal disease (CrCl <30 mL/min) (3 points each), age ≥75 years (2 points), any prior bleeding, hypertension (1 point each) Low [0–3]. Intermediate [4]. High [5–10] 83% person years. 7% person years. 10% person years 0.76% per 100 person-years. 2.62% per 100 person-years. 5.76% per 100 person-years ICD-9-CM codes. fatal, requiring transfusion of ≥2 units blood, or haemorrhage into a critical site
ORBIT, 2015 (84) Population =7,411. Follow-up period =2 years. Data = ORBIT-AF registry. Analysis = retrospective Older age ≥75 years, insufficient renal function, treatment with antiplatelets (1 point each). Reduced Hb/HCT/anaemia, bleeding history (2 points each) Low [0–2]. Medium [3]. High [≥4] 4,341 (58%)/1,351 (18%)/1,719 (23%) 2.4% per 100 person-years. 4.7% per 100 person-years. 8.1% per 100 person-years (I) Fatal bleeding and/or (II) symptomatic bleeding in a critical area or organ (intracranial, intraspinal, intraocular, retroperitoneal, intra-articular or pericardial, or intramuscular with compartment syndrome), and/or (III) bleeding causing a fall in haemoglobin level of 20 g·L−1 (1.24 mmol·L−1) or more, or leading to transfusion of two or more units of whole blood or red cells
ABC, 2016 (23) Population =23,005. Follow-up period =1.7–1.9 years. Data = ARISTOTLE and RE-LY trials. Analysis = retrospective. Internally validated in 14,537 patients in the ARISTOTLE trial and externally validated in 8,468 patients from RE-LY trial Age, biomarkers (GDF-15, cTnThs, and haemoglobin), and clinical history (previous bleeding) (Software base calculation) Low (<1%). Medium (1–2%). High (>2%) 4,170 (46%)/7,154 (39%)/3,377 (18%) 0.56 per 100 person-years. 1.37 per 100 person-years. 2.63 per 100 person-years ISTH criteria
DOAC score (85) Population =5,682. Follow-up period =1.74 years. Data = RE-LY trial. Analysis = retrospective Age, creatinine clearance/glomerular filtration rate, underweight status, stroke/TIA/embolism history, diabetes, hypertension, antiplatelet use, nonsteroidal anti-inflammatory use, liver disease, and bleeding history Very low risk [0–3]. Low risk [4–5]. Moderate risk [6–7]. High risk [8–9]. Very high risk [10] 767 (2%)/1,249 (21%)/1,727 (53%)/1,296 (87%)/645 (73%) 0.8% at 12 months. 1.6% at 12 months. 3.4% at 12 months. 6.9% at 12 months. 13.9% at 12 months A 20-g/L reduction in hemoglobin, 2 units transfusion need, fatal bleeding, or symptomatic bleeding in a critical area or organ

ABC, age, biomarkers, clinical history; AF, atrial fibrillation; ARISTOTLE, Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation; ATRIA, Anticoagulation and Risk Factors in Atrial Fibrillation; CrCl, creatinine clearance; cTnThs, cardiac troponin T high-sensitivity; CYP2C9, cytochrome P450 2C9; DOAC, direct oral anticoagulant; GDF-15, growth differentiation factor 15; HAS-BLED, Hypertension, Abnormal renal/liver function, Stroke, Bleeding history or predisposition, Labile international normalised ratio, Elderly (>65 years), Drugs/alcohol concomitantly; Hb, hemoglobin; HCT, hematocrit; HEMORR2HAGES, Hepatic or renal disease, Ethanol abuse, Malignancy, Older (age >75 years), Reduced platelet count or function, Re-bleeding risk (2 points), Hypertension (uncontrolled), Anaemia, Genetic factors, Excessive fall risk, Stroke; ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; INR, international normalized ratio; ISTH, International Society on Thrombosis and Haemostasis criteria; mOBRI, Modified Outpatient Bleeding Risk Index; NRAF, National Registry of Atrial Fibrillation; n/a, not applicable; OBRI, Outpatient Bleeding Risk Index; ORBIT, Older age, Reduced hemoglobin/hematocrit or history of anemia, Bleeding history, Insufficient kidney function, and Treatment with antiplatelet; RE-LY, Randomized Evaluation of Long-Term Anticoagulation Therapy; TIA, transient ischemic attack; TTR, time in therapeutic range.

Table 3

Biomarkers of bleeding risk

Type Biomarker
Cardiac biomarkers hs-cTn (18,20,36-38)
Haematological markers Hb (86)
Platelets (87,88)
Markers of renal function Creatinine (75)
GFR (65,67)
Cystatin C (70,75)
Markers of inflammation, oxidative stress and fibrosis CRP (89)
IL-6 (54)
GDF-15 (21,28,32)
Markers of endothelial function vWF (62)
Markers of coagulation D-dimer (73,74)
Others Vitamin E (90,91)
Genetic polymorphisms (92)

CRP, C-reactive protein; GDF-15, growth differentiation factor 15; GFR, glomerular filtration rate; Hb, haemoglobin; hs-cTn, high-sensitivity cardiac troponin; IL-6, interleukin-6; vWF, von Willebrand factor.

Conclusions

Several biomarkers have been shown to predict the risk of bleeding events in patients with AF. Risk-scoring systems incorporating biomarkers have improved the ability to predict bleeding events. More accurate identification of individuals at higher risk of bleeding allows clinicians and patients to better weigh up risks of bleeding versus stroke in the context of AF and create individualised care plans to reduce the net incidence of stroke and bleeding events. The dynamic nature of biomarkers would also allow clinicians to more easily identify changes in the individual patient’s bleeding and stroke risk profile. More real-world validation of biomarker-based bleeding scores would be useful to prove that they can reliably outperform currently used systems and demonstrate cost-effectiveness in real-world populations.

Acknowledgments

We would like to thank Sarah Stockton and libraries staff at Queens Elizabeth Hospital, Gateshead, Newcastle Upon Tyne, UK for their support.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-696/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-696/prf

Funding: This work was supported by North Bristol NHS Trust, Cardiology Department, Southmead Road, Westbury-on-Trym, Bristol, United Kingdom (No. B09148) for the publication fee (ACP) of this article.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-696/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Cite this article as: Ibrahem A, Abdalwahab A, Gillan M, Egred M, Alkhalil M, Gorog DA, Farag M. Biomarkers of increased bleeding risk in patients with atrial fibrillation on oral anticoagulation: a narrative review. Cardiovasc Diagn Ther 2025;15(4):876-887. doi: 10.21037/cdt-2024-696

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