Warfarin Use and Risk of Intracerebral Hemorrhage and Bleeding in Atrial Fibrillation Patients

Warfarin Use and Risk of Intracerebral Hemorrhage and Bleeding in Atrial Fibrillation Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Warfarin Use and Risk of Intracerebral Hemorrhage and Bleeding in Atrial Fibrillation Patients Muhammad Shahbaz Raja, He Xue-ming, Santosh Prasad Bhatt, Chen Junjin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Warfarin is a widely used oral anticoagulant for stroke prevention in patients with atrial fibrillation, but its safety profile is complicated by the risk of bleeding, particularly in individuals with uncontrolled hypertension. This study investigated the relationship between warfarin use and clinical outcomes including bleeding, thromboembolic events, and mortality in a cohort of 750 atrial fibrillation patients. Patients were retrospectively categorized based on warfarin exposure and systolic blood pressure levels. Statistical analyses included logistic regression, survival analysis, and subgroup comparisons. Although bleeding events occurred more frequently among warfarin users compared to non-users, the difference was not statistically significant. Similarly, warfarin use was not associated with increased mortality or worse survival outcomes. However, stratified analysis revealed a higher incidence of bleeding among patients with elevated systolic blood pressure receiving warfarin, suggesting a potential interaction between anticoagulation and hypertensive status. These findings emphasize the need for individualized risk assessment and careful blood pressure management when initiating warfarin therapy in atrial fibrillation patients. Health sciences/Cardiology Health sciences/Neurology Health sciences/Risk factors Atrial fibrillation Warfarin Anticoagulation Bleeding risk Hypertension Stroke prevention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia encountered in clinical practice and significantly increases the risk of thromboembolic events, particularly ischemic stroke [ 1 ]. Patients with AF have an approximately fivefold higher risk of stroke compared to those in normal sinus rhythm [ 2 ], necessitating long-term anticoagulation therapy as a cornerstone of secondary prevention. Warfarin, a vitamin K antagonist, has historically served as the primary oral anticoagulant in this setting. Its efficacy in reducing ischemic events is well-established; however, its clinical utility is often undermined by a narrow therapeutic index and a heightened risk of major bleeding, including intracerebral hemorrhage (ICH), which is associated with substantial morbidity and mortality [ 3 , 4 ]. Due to its pharmacokinetic variability and sensitivity to dietary vitamin K and drug–drug interactions, warfarin requires frequent INR monitoring to maintain a therapeutic range [ 5 ]. Even with appropriate management, interpatient variability and fluctuating INR levels continue to complicate therapy. Bleeding risk is known to be influenced by a variety of clinical parameters such as age, renal impairment, polypharmacy, and notably, systolic blood pressure (SBP) [ 6 ]. Uncontrolled hypertension has been independently associated with an increased incidence of warfarin-related hemorrhagic complications, particularly ICH [ 7 , 8 ]. While bleeding risk prediction tools such as HAS-BLED incorporate hypertension as a component, there is limited evidence on how stratified SBP levels interact with warfarin therapy in real-world patient populations [ 9 ]. Moreover, although direct oral anticoagulants (DOACs) have gained favor due to more predictable pharmacodynamics and lower bleeding risk, warfarin remains the preferred choice in specific clinical situations, including patients with mechanical heart valves, severe chronic kidney disease, or limited access to DOACs due to cost [ 10 , 11 ]. This study aims to retrospectively evaluate the association between warfarin use and the risk of bleeding, ICH, systemic embolic events (SEE), and mortality in a cohort of 750 patients with atrial fibrillation. Particular emphasis is placed on examining whether elevated SBP amplifies bleeding risk among patients receiving warfarin. Statistical techniques including logistic regression, Kaplan–Meier survival analysis, and ROC curve evaluation are employed to assess predictors and time-to-event outcomes. Through this approach, we aim to contribute clinically relevant insights that may guide individualized anticoagulation strategies in the management of AF. 2. Materials and Methods 2.1 Study Design This retrospective observational study analyzed electronic medical records of 750 patients diagnosed with atrial fibrillation (AF) at Lianyungang Oriental Hospital. Patients were categorized into two groups based on their anticoagulation status: those receiving warfarin (n = 133) and those not prescribed warfarin (n = 617). Patients receiving DOACs or antiplatelet therapy were not included in this analysis to maintain focus on warfarin-associated bleeding risk. Data were collected on demographic variables (age, gender), clinical characteristics (smoking history, comorbidities), vital signs (SBP group), hospital stay duration, and follow-up duration. Outcomes of interest included bleeding events, intracerebral hemorrhage (ICH), systemic embolic events (SEE), mortality, and event-free survival. A visual representation of the study design is shown in Fig. 1 . 2.2 Ethical Considerations This study was reviewed and approved by the Institutional Review Board (IRB)/Ethics Committee of Lianyungang Municipal Oriental Hospital under reference number [20250501], dated 09 May 2025. The research involved a retrospective analysis of anonymized patient data and was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki. Given the observational and non-interventional nature of the study, the requirement for informed consent was waived by the IRB. 2.3 Variable Definitions Warfarin Use : Binary variable (1 = warfarin prescribed; 0 = not prescribed). SBP Group : Patients were stratified into three SBP categories: Low (< 125 mmHg), Normal (125–145 mmHg), and High (≥ 145 mmHg) [ 11 ]. Bleeding Events : Defined as any documented hemorrhagic event during hospitalization or follow-up [ 12 ]. Event : Binary indicator of adverse event (bleeding or mortality) during follow-up. Follow-up Days : Duration in days from admission to the last recorded follow-up or event. 2.4 Statistical Analysis All statistical analyses were performed using R software (version 4.3.0) [ 13 ]. Data cleaning, transformation, modeling, and visualization were conducted using packages including tidyverse, gmodels, pROC, survival, survminer, and forestplot [ 14 ]. Descriptive statistics were computed to summarize baseline demographic and clinical characteristics of the study population [ 15 ]. Continuous variables (e.g., age, hospital stay duration, follow-up days) were expressed as means and standard deviations, while categorical variables (e.g., sex, SBP group, bleeding outcomes) were presented as counts and percentages. To examine associations between warfarin use and categorical outcomes, Chi-square tests[ 16 ] were applied [ 17 ]. Specifically, we compared bleeding incidence and systemic embolic events (SEE) across treatment and sex subgroups. A multivariate logistic regression model was developed to evaluate the independent effect of warfarin use on the likelihood of bleeding [ 18 ]. The model adjusted for potential confounders, including age (continuous), gender (binary), and systolic blood pressure group (categorical: Low, Normal, High). Results were reported as adjusted odds ratios (ORs) with corresponding 95% confidence intervals (CIs). A forest plot was constructed to visually display the magnitude and precision of each covariate’s effect [ 19 ]. To assess the predictive ability of the logistic model, a receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. An AUC value closer to 1 indicates better model discrimination [ 20 ]. For time-to-event analysis, a Kaplan-Meier survival curve was generated to compare the probability of event-free survival (e.g., no bleeding or mortality) between warfarin and non-warfarin users. The log-rank test was used to determine statistical significance between the two survival curves. Patients without an event were censored at their last follow-up [ 21 ]. Additionally, stratified outcome visualizations were created to display incidence rates of bleeding, ICH, SEE, and mortality across SBP categories and treatment groups. A multi-panel bar chart[ 22 ] was constructed to simultaneously visualize all five outcomes, ensuring equal group representation through synthetic zero-filled augmentation where required. 3. Results 3.1 Baseline Characteristics The study included 750 patients with atrial fibrillation. The mean age was 68.5 ± 11.4 years, with 390 males (52.0%) and 360 females (48.0%). Based on systolic blood pressure (SBP), 221 patients (29.5%) were classified as having high SBP (≥ 145 mmHg), 357 (47.6%) had normal SBP (125–145 mmHg), and 172 (22.9%) had low SBP (< 125 mmHg). Among the total sample, 133 patients (17.7%) were treated with warfarin, while 617 (82.3%) were not. The mean hospital stay was 7.6 days, and the average follow-up duration was 571 days. Initial bleeding was reported in 91 patients (12.1%), and 65 (8.7%) experienced ICH. Systemic embolic events (SEE) were observed in 38 (5.1%), and overall mortality was 66 patients (8.8%). Baseline characteristics stratified by treatment group are shown in Table 1 . Table 1 Baseline Characteristics by Treatment Group Characteristic Total (N = 750) Non-Warfarin (n = 617) Warfarin (n = 133) Age (mean ± SD, years) 68.5 ± 11.4 68.4 ± 11.5 68.9 ± 10.9 Male, n (%) 390 (52.0%) 322 (52.2%) 68 (51.1%) Female, n (%) 360 (48.0%) 295 (47.8%) 65 (48.9%) SBP – High (≥ 145 mmHg) 221 (29.5%) 172 (27.9%) 49 (36.8%) SBP – Normal (125–145 mmHg) 357 (47.6%) 297 (48.1%) 60 (45.1%) SBP – Low (< 125 mmHg) 172 (22.9%) 148 (24.0%) 24 (18.1%) Bleeding, n (%) 91 (12.1%) 72 (11.7%) 19 (14.3%) ICH, n (%) 65 (8.7%) 55 (8.9%) 10 (7.5%) SEE, n (%) 38 (5.1%) 21 (3.4%) 17 (12.8%) Mortality, n (%) 66 (8.8%) 55 (8.9%) 11 (8.3%) 3.2 Group-wise Comparisons To explore unadjusted associations between key variables, we performed Chi-square tests for independence. These comparisons aimed to identify whether warfarin therapy or patient characteristics such as gender had a significant relationship with adverse outcomes, particularly bleeding and systemic embolic events (SEE). 3.2.1 Association Between Warfarin Use and Bleeding The incidence of bleeding was compared between patients who were treated with warfarin and those who were not as shown in Table 2 . Among 133 patients receiving warfarin, 19 (14.3%) experienced bleeding, whereas in the non-warfarin group (n = 617), 72 patients (11.7%) had bleeding events. Although a slightly higher proportion of bleeding was observed in the warfarin group, this difference was not statistically significant (χ² = 0.70, p = 0.402). This suggests that, in this cohort, warfarin use was not significantly associated with increased bleeding risk at the univariate level. Table 2 Warfarin Use and Bleeding Events No Bleeding Bleeding Total Non-Warfarin (n = 617) 545 72 617 Warfarin (n = 133) 114 19 133 Total 659 91 750 Pearson’s Chi-squared test: χ² = 0.70 , df = 1, p = 0.402 3.2.2 Association Between Gender and Systemic Embolic Events (SEE) We further assessed whether gender played a role in systemic embolic event (SEE) occurrence. Among 360 female patients, 21 (5.8%) developed SEE, compared to 17 (4.4%) of the 390 male patients as shown in Table 3 . This difference was not statistically significant (χ² = 0.85, p = 0.358). These findings imply that gender was not a significant determinant of embolic event risk in this population. Table 3 Gender and Systemic Embolic Events (SEE) No SEE SEE Total Female 339 21 360 Male 373 17 390 Total 712 38 750 Pearson’s Chi-squared test: χ² = 0.85 , df = 1, p = 0.358 Although descriptive trends suggest higher bleeding rates in warfarin users and slightly higher SEE rates in females, none of these differences were statistically significant . These results support the need for multivariate analysis , as potential confounding variables (e.g., age, blood pressure) could influence these outcomes. Further modeling is presented in Section 3.3 to assess these relationships in an adjusted framework. 3.3 Multivariable Analysis for Bleeding A multivariate logistic regression model was constructed to evaluate the adjusted association between warfarin use and bleeding, controlling for age, gender, and SBP group as shown in Table 4 . None of the predictors reached statistical significance. Warfarin use was associated with a non-significant increase in bleeding risk (OR = 1.34, 95% CI: 0.76–2.29, p = 0.292). Age had a minimal and non-significant effect (OR = 1.01, p = 0.207). Male gender showed a trend toward lower bleeding risk (OR = 0.77, p = 0.271), but this was not statistically meaningful. Similarly, SBP group classifications did not significantly influence bleeding outcomes. Table 4 Logistic Regression Results for Bleeding Variable Odds Ratio (95% CI) p-value Warfarin use 1.34 (0.76–2.29) 0.292 Age 1.01 (0.99–1.03) 0.207 Male gender 0.77 (0.49–1.22) 0.271 SBP – Low 0.67 (0.35–1.26) 0.221 SBP – Normal 0.87 (0.53–1.45) 0.580 Figure 2 below shows the forest plot of odds ratios and confidence intervals. 3.4 Predictive Model Performance The model's discriminatory ability was assessed using a receiver operating characteristic (ROC) curve as shown in Fig. 3 . The area under the ROC curve (AUC) was 0.570, indicating poor predictive performance. 3.5 Kaplan-Meier Survival by Warfarin Use A Kaplan-Meier survival analysis was conducted to compare the time-to-event distribution between patients treated with warfarin and those who were not. The outcome of interest was a composite endpoint reflecting major clinical events or death during follow-up. The median follow-up time was 571 days. As shown in Fig. 4 , the survival curves of the two groups were largely overlapping, indicating no substantial difference in event-free survival. The estimated probability of survival remained above 85% for most of the follow-up period in both groups. Statistical comparison using the log-rank test showed no significant difference between the survival distributions (p = 0.31). This suggests that warfarin use did not significantly alter the long-term event-free survival among patients with atrial fibrillation in this cohort. 3.6 Stratified Outcomes by SBP and Treatment To explore how systolic blood pressure (SBP) interacts with treatment exposure, we conducted a stratified analysis of clinical outcomes across SBP groups and warfarin use. Patients were categorized into low (< 125 mmHg), normal (125–145 mmHg), and high (≥ 145 mmHg) SBP groups. Outcomes assessed included bleeding, intracerebral hemorrhage (ICH), systemic embolic events (SEE), mortality, and a composite clinical event. As shown in the multi-panel visualization (Fig. 5 ), outcome patterns varied across SBP groups. Bleeding was most frequent in patients on warfarin with high SBP. SEE was more prevalent in warfarin users overall, particularly in the low SBP group. Mortality and ICH rates were slightly higher in the non-warfarin group, especially in those with normal or low SBP. The composite event rate did not significantly differ across SBP levels. To highlight bleeding trends specifically, Fig. 6 displays bleeding rates stratified by SBP and treatment. A pronounced increase in bleeding incidence was observed in warfarin users with high SBP, suggesting a possible interaction effect. In contrast, bleeding rates remained relatively stable in the normal and low SBP groups, regardless of warfarin use. 4. Discussion This study examined the relationship between warfarin therapy and clinical outcomes, including bleeding, systemic embolic events (SEE), and survival, in patients with atrial fibrillation (AF), with specific attention to the modifying role of systolic blood pressure (SBP). Although warfarin remains an essential therapy for stroke prevention in AF, its risks can vary significantly depending on patient-specific factors, especially blood pressure control. The analysis showed that warfarin use was not significantly associated with increased bleeding risk in the overall patient population (adjusted OR: 1.34; 95% CI: 0.76 to 2.29; p = 0.29). Similarly, other variables such as age, sex, and SBP group were not independently predictive of bleeding in the multivariate model. However, when outcomes were stratified by SBP level, a higher incidence of bleeding was observed among warfarin users with elevated SBP. This pattern suggests a potential interaction between anticoagulation and uncontrolled blood pressure, which is consistent with prior evidence showing that elevated SBP is a key contributor to hemorrhagic complications in anticoagulated patients. Kaplan-Meier survival analysis revealed no statistically significant difference in overall survival between warfarin and non-warfarin groups (log-rank p = 0.31). Although a divergence in survival probabilities emerged over longer follow-up periods, it did not reach significance, indicating that warfarin did not provide a survival benefit or harm in this cohort. These findings support the view that anticoagulation decisions in AF should be based on comprehensive clinical evaluation rather than uniform protocols. The logistic model used to predict bleeding had limited discriminative capability, with an area under the curve (AUC) of 0.57. This relatively low performance suggests that critical variables were not included in the model. Factors such as renal function, concurrent antiplatelet therapy, labile INR, and prior bleeding history are known to influence bleeding risk and may improve model performance in future studies. Interestingly, the incidence of SEE was higher among patients receiving warfarin, particularly in the low SBP group. This may reflect confounding by indication, where patients at higher baseline thromboembolic risk are more likely to be prescribed anticoagulation. Moreover, suboptimal adherence, delayed therapeutic INR achievement, or prior embolic events may have contributed to these outcomes. The lack of significant differences in intracerebral hemorrhage (ICH) and mortality across groups may be attributed to low event frequencies, moderate patient risk profiles, or insufficient follow-up time to detect long-term differences. In conclusion, this study emphasizes the need for personalized anticoagulation strategies in AF management. While warfarin use was not independently linked to adverse outcomes, elevated SBP appears to amplify bleeding risk in patients receiving anticoagulation. These results highlight the importance of routine blood pressure control and individualized risk-benefit evaluation in guiding anticoagulant therapy. 5. Conclusions This retrospective study investigated the association between warfarin use and key clinical outcomes, including bleeding events, thromboembolic complications, and survival, in a cohort of 750 patients with atrial fibrillation (AF). While warfarin was not independently associated with a statistically significant increase in bleeding or mortality in the overall population, subgroup analysis identified a higher incidence of bleeding among warfarin users with elevated systolic blood pressure (SBP). This observation suggests that SBP may modify anticoagulation-related risk. Kaplan-Meier survival analysis revealed no significant differences in long-term event-free survival between patients receiving warfarin and those not anticoagulated with warfarin. Furthermore, the logistic regression model for bleeding prediction demonstrated limited discriminatory power. This finding highlights the need to incorporate additional clinical predictors beyond those included in the current model. These results emphasize the complexity of anticoagulation decision-making in AF management. Warfarin therapy, although effective for stroke prevention, should be prescribed only after a thorough assessment of risks and benefits. Factors such as blood pressure status, comorbidities, and patient preferences must be carefully considered. Elevated SBP, in particular, may increase bleeding risk and should be closely monitored in patients undergoing warfarin treatment. Additional variables such as renal function, concurrent antiplatelet use, and time in therapeutic INR range were not included in this study. These factors may further enhance bleeding risk stratification. Future research should focus on refining predictive models by incorporating these parameters and evaluating whether SBP-guided anticoagulation strategies can improve outcomes in high-risk AF populations. Declarations Acknowledgments The authors thank the Cardiology Department of Lianyungang Oriental Hospital for providing access to patient records and laboratory data. They also acknowledge Yancheng People's No. 1 Hospital for their support in data collection and research collaboration. All aspects of the research, including conceptualization, study design, data analysis, and manuscript preparation, were conducted solely by the authors. AI tools were used only to assist with grammar and language refinement. No AI systems were involved in data interpretation or in forming scientific conclusions. The authors take full responsibility for the accuracy and originality of this work. Authors Contribution He Xue-ming: Conceptualization, supervision, study design, clinical oversight, and final manuscript approval. Muhammad Shahbaz Raja: Data preparation, R-based statistical analysis, visualization, manuscript writing, and revision. Santosh Prasad Bhatt: Data acquisition, clinical data validation, and literature support. Chen Junjin: Data curation, SBP stratification, and critical review of the manuscript for intellectual content. All authors contributed significantly to the research process, reviewed the manuscript thoroughly, and approved the final version for submission. Availability of data and materials The data supporting the reported results were obtained from the Lianyungang Oriental Hospital, Cardiology Department, China. The dataset used in this study is not publicly available due to patient confidentiality and ethical restrictions. However, data may be available from the corresponding author upon reasonable request and in compliance with ethical guidelines. Conflicts of interest All authors declared that there are no conflicts of interest. Consent for publication Not applicable. References Nesheiwat, Z., Goyal, A., Jagtap, M. & Atrial Fibrillation [Updated 2023 Apr 26]. In: StatPearls [Internet]. 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08:48:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103658,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Odds Ratios for Bleeding\u003c/p\u003e","description":"","filename":"Figure2ForestPlotofOddsRatiosforBleeding.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6718772/v1/37bac93a7ebe4f89a970da24.jpeg"},{"id":97138863,"identity":"2b4d1475-597e-4e17-a37f-c35841049808","added_by":"auto","created_at":"2025-12-01 09:59:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90365,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve for Bleeding Model\u003c/p\u003e","description":"","filename":"Figure3ROCCurveforBleedingModel.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6718772/v1/57bf7cac51adbeab0ffe5cc0.jpeg"},{"id":96977740,"identity":"82c99f18-68ad-4d40-a97f-56a8efbdb53d","added_by":"auto","created_at":"2025-11-28 08:48:49","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":149557,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival Curve by Warfarin Use\u003c/p\u003e","description":"","filename":"Figure4KaplanMeierSurvivalCurvebyWarfarinUse.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6718772/v1/b2be5db341884f70508e43ec.jpeg"},{"id":96977750,"identity":"af1e3e92-a880-4dba-ae3e-f8bc6253586d","added_by":"auto","created_at":"2025-11-28 08:48:49","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":168617,"visible":true,"origin":"","legend":"\u003cp\u003eOutcome Incidence by SBP Group and Warfarin Use\u003c/p\u003e","description":"","filename":"Figure5OutcomeIncidencebySBPGroupandWarfarinUse.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6718772/v1/5136186b42109a977566ce5b.jpeg"},{"id":96977743,"identity":"0d3e797e-70b4-4042-83f3-106754eea5da","added_by":"auto","created_at":"2025-11-28 08:48:49","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":101402,"visible":true,"origin":"","legend":"\u003cp\u003eStratified Bleeding Risk by SBP and Warfarin Use\u003c/p\u003e","description":"","filename":"Figure6StratifiedBleedingRiskbySBPandWarfarinUse.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6718772/v1/f8035f57ad01f553993f2959.jpeg"},{"id":97367739,"identity":"b2fc5bc0-cec7-484c-9971-c7241ae3e778","added_by":"auto","created_at":"2025-12-03 16:20:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1952869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6718772/v1/59ad6caf-08ca-48b8-95ae-d8e445ee24f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Warfarin Use and Risk of Intracerebral Hemorrhage and Bleeding in Atrial Fibrillation Patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia encountered in clinical practice and significantly increases the risk of thromboembolic events, particularly ischemic stroke [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Patients with AF have an approximately fivefold higher risk of stroke compared to those in normal sinus rhythm [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], necessitating long-term anticoagulation therapy as a cornerstone of secondary prevention. Warfarin, a vitamin K antagonist, has historically served as the primary oral anticoagulant in this setting. Its efficacy in reducing ischemic events is well-established; however, its clinical utility is often undermined by a narrow therapeutic index and a heightened risk of major bleeding, including intracerebral hemorrhage (ICH), which is associated with substantial morbidity and mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDue to its pharmacokinetic variability and sensitivity to dietary vitamin K and drug\u0026ndash;drug interactions, warfarin requires frequent INR monitoring to maintain a therapeutic range [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Even with appropriate management, interpatient variability and fluctuating INR levels continue to complicate therapy. Bleeding risk is known to be influenced by a variety of clinical parameters such as age, renal impairment, polypharmacy, and notably, systolic blood pressure (SBP) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Uncontrolled hypertension has been independently associated with an increased incidence of warfarin-related hemorrhagic complications, particularly ICH [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile bleeding risk prediction tools such as HAS-BLED incorporate hypertension as a component, there is limited evidence on how stratified SBP levels interact with warfarin therapy in real-world patient populations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, although direct oral anticoagulants (DOACs) have gained favor due to more predictable pharmacodynamics and lower bleeding risk, warfarin remains the preferred choice in specific clinical situations, including patients with mechanical heart valves, severe chronic kidney disease, or limited access to DOACs due to cost [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aims to retrospectively evaluate the association between warfarin use and the risk of bleeding, ICH, systemic embolic events (SEE), and mortality in a cohort of 750 patients with atrial fibrillation. Particular emphasis is placed on examining whether elevated SBP amplifies bleeding risk among patients receiving warfarin. Statistical techniques including logistic regression, Kaplan\u0026ndash;Meier survival analysis, and ROC curve evaluation are employed to assess predictors and time-to-event outcomes. Through this approach, we aim to contribute clinically relevant insights that may guide individualized anticoagulation strategies in the management of AF.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eThis retrospective observational study analyzed electronic medical records of 750 patients diagnosed with atrial fibrillation (AF) at Lianyungang Oriental Hospital. Patients were categorized into two groups based on their anticoagulation status: those receiving warfarin (n\u0026thinsp;=\u0026thinsp;133) and those not prescribed warfarin (n\u0026thinsp;=\u0026thinsp;617). Patients receiving DOACs or antiplatelet therapy were not included in this analysis to maintain focus on warfarin-associated bleeding risk.\u003c/p\u003e\u003cp\u003eData were collected on demographic variables (age, gender), clinical characteristics (smoking history, comorbidities), vital signs (SBP group), hospital stay duration, and follow-up duration. Outcomes of interest included bleeding events, intracerebral hemorrhage (ICH), systemic embolic events (SEE), mortality, and event-free survival.\u003c/p\u003e\u003cp\u003e\u003cem\u003eA visual representation of the study design is shown in\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ethical Considerations\u003c/h2\u003e\u003cp\u003e This study was reviewed and approved by the Institutional Review Board (IRB)/Ethics Committee of Lianyungang Municipal Oriental Hospital under reference number [20250501], dated 09 May 2025. The research involved a retrospective analysis of anonymized patient data and was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki. Given the observational and non-interventional nature of the study, the requirement for informed consent was waived by the IRB.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Variable Definitions\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eWarfarin Use\u003c/b\u003e: Binary variable (1\u0026thinsp;=\u0026thinsp;warfarin prescribed; 0\u0026thinsp;=\u0026thinsp;not prescribed).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSBP Group\u003c/b\u003e: Patients were stratified into three SBP categories: Low (\u0026lt;\u0026thinsp;125 mmHg), Normal (125\u0026ndash;145 mmHg), and High (\u0026ge;\u0026thinsp;145 mmHg) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBleeding Events\u003c/b\u003e: Defined as any documented hemorrhagic event during hospitalization or follow-up [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEvent\u003c/b\u003e: Binary indicator of adverse event (bleeding or mortality) during follow-up.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFollow-up Days\u003c/b\u003e: Duration in days from admission to the last recorded follow-up or event.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software (version 4.3.0) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Data cleaning, transformation, modeling, and visualization were conducted using packages including tidyverse, gmodels, pROC, survival, survminer, and forestplot [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDescriptive statistics were computed to summarize baseline demographic and clinical characteristics of the study population [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Continuous variables (e.g., age, hospital stay duration, follow-up days) were expressed as means and standard deviations, while categorical variables (e.g., sex, SBP group, bleeding outcomes) were presented as counts and percentages.\u003c/p\u003e\u003cp\u003eTo examine associations between warfarin use and categorical outcomes, Chi-square tests[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] were applied [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Specifically, we compared bleeding incidence and systemic embolic events (SEE) across treatment and sex subgroups.\u003c/p\u003e\u003cp\u003eA multivariate logistic regression model was developed to evaluate the independent effect of warfarin use on the likelihood of bleeding [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The model adjusted for potential confounders, including age (continuous), gender (binary), and systolic blood pressure group (categorical: Low, Normal, High). Results were reported as adjusted odds ratios (ORs) with corresponding 95% confidence intervals (CIs). A forest plot was constructed to visually display the magnitude and precision of each covariate\u0026rsquo;s effect [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo assess the predictive ability of the logistic model, a receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. An AUC value closer to 1 indicates better model discrimination [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor time-to-event analysis, a Kaplan-Meier survival curve was generated to compare the probability of event-free survival (e.g., no bleeding or mortality) between warfarin and non-warfarin users. The log-rank test was used to determine statistical significance between the two survival curves. Patients without an event were censored at their last follow-up [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, stratified outcome visualizations were created to display incidence rates of bleeding, ICH, SEE, and mortality across SBP categories and treatment groups. A multi-panel bar chart[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was constructed to simultaneously visualize all five outcomes, ensuring equal group representation through synthetic zero-filled augmentation where required.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e\u003cp\u003eThe study included 750 patients with atrial fibrillation. The mean age was 68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4 years, with 390 males (52.0%) and 360 females (48.0%). Based on systolic blood pressure (SBP), 221 patients (29.5%) were classified as having high SBP (\u0026ge;\u0026thinsp;145 mmHg), 357 (47.6%) had normal SBP (125\u0026ndash;145 mmHg), and 172 (22.9%) had low SBP (\u0026lt;\u0026thinsp;125 mmHg).\u003c/p\u003e\u003cp\u003eAmong the total sample, 133 patients (17.7%) were treated with warfarin, while 617 (82.3%) were not. The mean hospital stay was 7.6 days, and the average follow-up duration was 571 days. Initial bleeding was reported in 91 patients (12.1%), and 65 (8.7%) experienced ICH. Systemic embolic events (SEE) were observed in 38 (5.1%), and overall mortality was 66 patients (8.8%). Baseline characteristics stratified by treatment group are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics by Treatment Group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;750)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Warfarin (n\u0026thinsp;=\u0026thinsp;617)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWarfarin (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e390 (52.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e322 (52.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68 (51.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e360 (48.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (48.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP \u0026ndash; High (\u0026ge;\u0026thinsp;145 mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e172 (27.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (36.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP \u0026ndash; Normal (125\u0026ndash;145 mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e357 (47.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e297 (48.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (45.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP \u0026ndash; Low (\u0026lt;\u0026thinsp;125 mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (24.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (18.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBleeding, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (12.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICH, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEE, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (12.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Group-wise Comparisons\u003c/h2\u003e\u003cp\u003eTo explore unadjusted associations between key variables, we performed Chi-square tests for independence. These comparisons aimed to identify whether warfarin therapy or patient characteristics such as gender had a significant relationship with adverse outcomes, particularly bleeding and systemic embolic events (SEE).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Association Between Warfarin Use and Bleeding\u003c/h2\u003e\u003cp\u003eThe incidence of bleeding was compared between patients who were treated with warfarin and those who were not as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among 133 patients receiving warfarin, 19 (14.3%) experienced bleeding, whereas in the non-warfarin group (n\u0026thinsp;=\u0026thinsp;617), 72 patients (11.7%) had bleeding events. Although a slightly higher proportion of bleeding was observed in the warfarin group, this difference was not statistically significant (χ\u0026sup2; = 0.70, p\u0026thinsp;=\u0026thinsp;0.402). This suggests that, in this cohort, warfarin use was not significantly associated with increased bleeding risk at the univariate level.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWarfarin Use and Bleeding Events\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Bleeding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBleeding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Warfarin (n\u0026thinsp;=\u0026thinsp;617)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e617\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWarfarin (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ePearson\u0026rsquo;s Chi-squared test: \u003cb\u003eχ\u0026sup2; = 0.70\u003c/b\u003e, df\u0026thinsp;=\u0026thinsp;1, \u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.402\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Association Between Gender and Systemic Embolic Events (SEE)\u003c/h2\u003e\u003cp\u003eWe further assessed whether gender played a role in systemic embolic event (SEE) occurrence. Among 360 female patients, 21 (5.8%) developed SEE, compared to 17 (4.4%) of the 390 male patients as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This difference was not statistically significant (χ\u0026sup2; = 0.85, p\u0026thinsp;=\u0026thinsp;0.358). These findings imply that gender was not a significant determinant of embolic event risk in this population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGender and Systemic Embolic Events (SEE)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo SEE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSEE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ePearson\u0026rsquo;s Chi-squared test: \u003cb\u003eχ\u0026sup2; = 0.85\u003c/b\u003e, df\u0026thinsp;=\u0026thinsp;1, \u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.358\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough descriptive trends suggest higher bleeding rates in warfarin users and slightly higher SEE rates in females, \u003cb\u003enone of these differences were statistically significant\u003c/b\u003e. These results support the need for \u003cb\u003emultivariate analysis\u003c/b\u003e, as potential confounding variables (e.g., age, blood pressure) could influence these outcomes. Further modeling is presented in Section 3.3 to assess these relationships in an adjusted framework.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Multivariable Analysis for Bleeding\u003c/h2\u003e\u003cp\u003eA multivariate logistic regression model was constructed to evaluate the adjusted association between warfarin use and bleeding, controlling for age, gender, and SBP group as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. None of the predictors reached statistical significance.\u003c/p\u003e\u003cp\u003eWarfarin use was associated with a non-significant increase in bleeding risk (OR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 0.76\u0026ndash;2.29, p\u0026thinsp;=\u0026thinsp;0.292). Age had a minimal and non-significant effect (OR\u0026thinsp;=\u0026thinsp;1.01, p\u0026thinsp;=\u0026thinsp;0.207). Male gender showed a trend toward lower bleeding risk (OR\u0026thinsp;=\u0026thinsp;0.77, p\u0026thinsp;=\u0026thinsp;0.271), but this was not statistically meaningful. Similarly, SBP group classifications did not significantly influence bleeding outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression Results for Bleeding\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWarfarin use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.34 (0.76\u0026ndash;2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77 (0.49\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP \u0026ndash; Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67 (0.35\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP \u0026ndash; Normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87 (0.53\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below shows the forest plot of odds ratios and confidence intervals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Predictive Model Performance\u003c/h2\u003e\u003cp\u003eThe model's discriminatory ability was assessed using a receiver operating characteristic (ROC) curve as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The area under the ROC curve (AUC) was 0.570, indicating poor predictive performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Kaplan-Meier Survival by Warfarin Use\u003c/h2\u003e\u003cp\u003eA Kaplan-Meier survival analysis was conducted to compare the time-to-event distribution between patients treated with warfarin and those who were not. The outcome of interest was a composite endpoint reflecting major clinical events or death during follow-up.\u003c/p\u003e\u003cp\u003eThe median follow-up time was 571 days. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the survival curves of the two groups were largely overlapping, indicating no substantial difference in event-free survival. The estimated probability of survival remained above 85% for most of the follow-up period in both groups.\u003c/p\u003e\u003cp\u003eStatistical comparison using the log-rank test showed no significant difference between the survival distributions (p\u0026thinsp;=\u0026thinsp;0.31). This suggests that warfarin use did not significantly alter the long-term event-free survival among patients with atrial fibrillation in this cohort.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Stratified Outcomes by SBP and Treatment\u003c/h2\u003e\u003cp\u003eTo explore how systolic blood pressure (SBP) interacts with treatment exposure, we conducted a stratified analysis of clinical outcomes across SBP groups and warfarin use. Patients were categorized into low (\u0026lt;\u0026thinsp;125 mmHg), normal (125\u0026ndash;145 mmHg), and high (\u0026ge;\u0026thinsp;145 mmHg) SBP groups. Outcomes assessed included bleeding, intracerebral hemorrhage (ICH), systemic embolic events (SEE), mortality, and a composite clinical event. As shown in the multi-panel visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), outcome patterns varied across SBP groups. Bleeding was most frequent in patients on warfarin with high SBP. SEE was more prevalent in warfarin users overall, particularly in the low SBP group. Mortality and ICH rates were slightly higher in the non-warfarin group, especially in those with normal or low SBP. The composite event rate did not significantly differ across SBP levels.\u003c/p\u003e\u003cp\u003eTo highlight bleeding trends specifically, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays bleeding rates stratified by SBP and treatment. A pronounced increase in bleeding incidence was observed in warfarin users with high SBP, suggesting a possible interaction effect. In contrast, bleeding rates remained relatively stable in the normal and low SBP groups, regardless of warfarin use.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study examined the relationship between warfarin therapy and clinical outcomes, including bleeding, systemic embolic events (SEE), and survival, in patients with atrial fibrillation (AF), with specific attention to the modifying role of systolic blood pressure (SBP). Although warfarin remains an essential therapy for stroke prevention in AF, its risks can vary significantly depending on patient-specific factors, especially blood pressure control.\u003c/p\u003e\u003cp\u003eThe analysis showed that warfarin use was not significantly associated with increased bleeding risk in the overall patient population (adjusted OR: 1.34; 95% CI: 0.76 to 2.29; p\u0026thinsp;=\u0026thinsp;0.29). Similarly, other variables such as age, sex, and SBP group were not independently predictive of bleeding in the multivariate model. However, when outcomes were stratified by SBP level, a higher incidence of bleeding was observed among warfarin users with elevated SBP. This pattern suggests a potential interaction between anticoagulation and uncontrolled blood pressure, which is consistent with prior evidence showing that elevated SBP is a key contributor to hemorrhagic complications in anticoagulated patients.\u003c/p\u003e\u003cp\u003eKaplan-Meier survival analysis revealed no statistically significant difference in overall survival between warfarin and non-warfarin groups (log-rank p\u0026thinsp;=\u0026thinsp;0.31). Although a divergence in survival probabilities emerged over longer follow-up periods, it did not reach significance, indicating that warfarin did not provide a survival benefit or harm in this cohort. These findings support the view that anticoagulation decisions in AF should be based on comprehensive clinical evaluation rather than uniform protocols.\u003c/p\u003e\u003cp\u003eThe logistic model used to predict bleeding had limited discriminative capability, with an area under the curve (AUC) of 0.57. This relatively low performance suggests that critical variables were not included in the model. Factors such as renal function, concurrent antiplatelet therapy, labile INR, and prior bleeding history are known to influence bleeding risk and may improve model performance in future studies.\u003c/p\u003e\u003cp\u003eInterestingly, the incidence of SEE was higher among patients receiving warfarin, particularly in the low SBP group. This may reflect confounding by indication, where patients at higher baseline thromboembolic risk are more likely to be prescribed anticoagulation. Moreover, suboptimal adherence, delayed therapeutic INR achievement, or prior embolic events may have contributed to these outcomes. The lack of significant differences in intracerebral hemorrhage (ICH) and mortality across groups may be attributed to low event frequencies, moderate patient risk profiles, or insufficient follow-up time to detect long-term differences.\u003c/p\u003e\u003cp\u003eIn conclusion, this study emphasizes the need for personalized anticoagulation strategies in AF management. While warfarin use was not independently linked to adverse outcomes, elevated SBP appears to amplify bleeding risk in patients receiving anticoagulation. These results highlight the importance of routine blood pressure control and individualized risk-benefit evaluation in guiding anticoagulant therapy.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis retrospective study investigated the association between warfarin use and key clinical outcomes, including bleeding events, thromboembolic complications, and survival, in a cohort of 750 patients with atrial fibrillation (AF). While warfarin was not independently associated with a statistically significant increase in bleeding or mortality in the overall population, subgroup analysis identified a higher incidence of bleeding among warfarin users with elevated systolic blood pressure (SBP). This observation suggests that SBP may modify anticoagulation-related risk.\u003c/p\u003e\u003cp\u003eKaplan-Meier survival analysis revealed no significant differences in long-term event-free survival between patients receiving warfarin and those not anticoagulated with warfarin. Furthermore, the logistic regression model for bleeding prediction demonstrated limited discriminatory power. This finding highlights the need to incorporate additional clinical predictors beyond those included in the current model.\u003c/p\u003e\u003cp\u003eThese results emphasize the complexity of anticoagulation decision-making in AF management. Warfarin therapy, although effective for stroke prevention, should be prescribed only after a thorough assessment of risks and benefits. Factors such as blood pressure status, comorbidities, and patient preferences must be carefully considered. Elevated SBP, in particular, may increase bleeding risk and should be closely monitored in patients undergoing warfarin treatment.\u003c/p\u003e\u003cp\u003eAdditional variables such as renal function, concurrent antiplatelet use, and time in therapeutic INR range were not included in this study. These factors may further enhance bleeding risk stratification. Future research should focus on refining predictive models by incorporating these parameters and evaluating whether SBP-guided anticoagulation strategies can improve outcomes in high-risk AF populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Cardiology Department of Lianyungang Oriental Hospital for providing access to patient records and laboratory data. They also acknowledge Yancheng People's No. 1 Hospital for their support in data collection and research collaboration. All aspects of the research, including conceptualization, study design, data analysis, and manuscript preparation, were conducted solely by the authors. AI tools were used only to assist with grammar and language refinement. No AI systems were involved in data interpretation or in forming scientific conclusions. The authors take full responsibility for the accuracy and originality of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHe Xue-ming: Conceptualization, supervision, study design, clinical oversight, and final manuscript approval.\u003c/p\u003e\n\u003cp\u003eMuhammad Shahbaz Raja: Data preparation, R-based statistical analysis, visualization, manuscript writing, and revision.\u003c/p\u003e\n\u003cp\u003eSantosh Prasad Bhatt: Data acquisition, clinical data validation, and literature support.\u003c/p\u003e\n\u003cp\u003eChen Junjin: Data curation, SBP stratification, and critical review of the manuscript for intellectual content.\u003c/p\u003e\n\u003cp\u003eAll authors contributed significantly to the research process, reviewed the manuscript thoroughly, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the reported results were obtained from the Lianyungang Oriental Hospital, Cardiology Department, China. The dataset used in this study is not publicly available due to patient confidentiality and ethical restrictions. However, data may be available from the corresponding author upon reasonable request and in compliance with ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declared that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNesheiwat, Z., Goyal, A., Jagtap, M. \u0026amp; Atrial Fibrillation [Updated 2023 Apr 26]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan\u0026ndash;. 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Visual analytics for epidemiologists: understanding the interactions between age, time, and disease with multi-panel graphs. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e (2), e14683 (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atrial fibrillation, Warfarin, Anticoagulation, Bleeding risk, Hypertension, Stroke prevention","lastPublishedDoi":"10.21203/rs.3.rs-6718772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6718772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWarfarin is a widely used oral anticoagulant for stroke prevention in patients with atrial fibrillation, but its safety profile is complicated by the risk of bleeding, particularly in individuals with uncontrolled hypertension. This study investigated the relationship between warfarin use and clinical outcomes including bleeding, thromboembolic events, and mortality in a cohort of 750 atrial fibrillation patients. Patients were retrospectively categorized based on warfarin exposure and systolic blood pressure levels. Statistical analyses included logistic regression, survival analysis, and subgroup comparisons. Although bleeding events occurred more frequently among warfarin users compared to non-users, the difference was not statistically significant. Similarly, warfarin use was not associated with increased mortality or worse survival outcomes. However, stratified analysis revealed a higher incidence of bleeding among patients with elevated systolic blood pressure receiving warfarin, suggesting a potential interaction between anticoagulation and hypertensive status. These findings emphasize the need for individualized risk assessment and careful blood pressure management when initiating warfarin therapy in atrial fibrillation patients.\u003c/p\u003e","manuscriptTitle":"Warfarin Use and Risk of Intracerebral Hemorrhage and Bleeding in Atrial Fibrillation Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 08:48:44","doi":"10.21203/rs.3.rs-6718772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"997f3e1e-b880-4845-b221-c2fbb2f31bc7","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58442373,"name":"Health sciences/Cardiology"},{"id":58442374,"name":"Health sciences/Neurology"},{"id":58442375,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-01T05:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 08:48:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6718772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6718772","identity":"rs-6718772","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}