<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "journalpublishing.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="2.0" xml:lang="en" article-type="research-article"><front><journal-meta><journal-id journal-id-type="nlm-ta">J Med Internet Res</journal-id><journal-id journal-id-type="publisher-id">jmir</journal-id><journal-id journal-id-type="index">1</journal-id><journal-title>Journal of Medical Internet Research</journal-title><abbrev-journal-title>J Med Internet Res</abbrev-journal-title><issn pub-type="epub">1438-8871</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">v28i1e93347</article-id><article-id pub-id-type="doi">10.2196/93347</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Gastrointestinal Bleeding Risk Associated With Pulse Pressure in Patients With Atrial Fibrillation: Retrospective Cohort Study</article-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name name-style="western"><surname>Abou Khalil</surname><given-names>Michel</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="equal-contrib1">*</xref></contrib><contrib contrib-type="author" equal-contrib="yes"><name name-style="western"><surname>El Khoury</surname><given-names>Carlo</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="equal-contrib1">*</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Jia</surname><given-names>Yishi</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Moersdorf</surname><given-names>Maximilian</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Massad</surname><given-names>Christian</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>El Darzi</surname><given-names>Alex</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Menassa</surname><given-names>Yara</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Atasi</surname><given-names>Mohammad Montaser</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Bidaoui</surname><given-names>Ghassan</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Younes</surname><given-names>Hadi</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Assaf</surname><given-names>Ala</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Mekhael</surname><given-names>Mario</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Tsakiris</surname><given-names>Eli</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Lim</surname><given-names>Chanho</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Noujaim</surname><given-names>Charbel</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Hassan</surname><given-names>Abboud</given-names></name><degrees>BS</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Liu</surname><given-names>Yingshuo</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Maleckar</surname><given-names>Mary Margot M</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Montiel Quintero</surname><given-names>Rodolfo A</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Kreidieh</surname><given-names>Omar</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Pandey</surname><given-names>Amitabh C</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="aff" rid="aff4">4</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Feng</surname><given-names>Han</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Marashly</surname><given-names>Qussay</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author" corresp="yes"><name name-style="western"><surname>Marrouche</surname><given-names>Nassir F</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref></contrib></contrib-group><aff id="aff1"><institution>Tulane Research Innovation for Arrhythmia Discovery, Tulane University</institution><addr-line>1430 Tulane Avenue</addr-line><addr-line>New Orleans</addr-line><addr-line>LA</addr-line><country>United States</country></aff><aff id="aff2"><institution>Department of Internal Medicine, Emory University</institution><addr-line>Atlanta</addr-line><addr-line>GA</addr-line><country>United States</country></aff><aff id="aff3"><institution>Department of Cardiology, Heart and Vascular Institute, Tulane University School of Medicine</institution><addr-line>New Orleans</addr-line><addr-line>LA</addr-line><country>United States</country></aff><aff id="aff4"><institution>Southeast Louisiana Veterans Health Care System</institution><addr-line>New Orleans</addr-line><addr-line>LA</addr-line><country>United States</country></aff><contrib-group><contrib contrib-type="editor"><name name-style="western"><surname>Brini</surname><given-names>Stefano</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type="reviewer"><name name-style="western"><surname>Kodani</surname><given-names>Eitaro</given-names></name></contrib><contrib contrib-type="reviewer"><name name-style="western"><surname>Fernandez-Saez</surname><given-names>Jose</given-names></name></contrib></contrib-group><author-notes><corresp>Correspondence to Nassir F Marrouche, MD, Tulane Research Innovation for Arrhythmia Discovery, Tulane University, 1430 Tulane Avenue, New Orleans, LA, 70112, United States, 1 504-988-3072; <email>nmarrouche@tulane.edu</email></corresp><fn fn-type="equal" id="equal-contrib1"><label>*</label><p>these authors contributed equally</p></fn></author-notes><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>20</day><month>7</month><year>2026</year></pub-date><volume>28</volume><elocation-id>e93347</elocation-id><history><date date-type="received"><day>11</day><month>02</month><year>2026</year></date><date date-type="rev-recd"><day>15</day><month>05</month><year>2026</year></date><date date-type="accepted"><day>20</day><month>05</month><year>2026</year></date></history><copyright-statement>&#x00A9; Michel Abou Khalil, Carlo El Khoury, Yishi Jia, Maximilian Moersdorf, Christian Massad, Alex El Darzi, Yara Menassa, Mohammad Montaser Atasi, Ghassan Bidaoui, Hadi Younes, Ala Assaf, Mario Mekhael, Eli Tsakiris, Chanho Lim, Charbel Noujaim, Abboud Hassan, Yingshuo Liu, Mary Margot M Maleckar, Rodolfo A Montiel Quintero, Omar Kreidieh, Amitabh C Pandey, Han Feng, Qussay Marashly, Nassir F Marrouche. Originally published in the Journal of Medical Internet Research (<ext-link ext-link-type="uri" xlink:href="https://www.jmir.org">https://www.jmir.org</ext-link>), 20.7.2026. </copyright-statement><copyright-year>2026</copyright-year><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type="uri" xlink:href="https://www.jmir.org/">https://www.jmir.org/</ext-link>, as well as this copyright and license information must be included.</p></license><self-uri xlink:type="simple" xlink:href="https://www.jmir.org/2026/1/e93347"/><abstract><sec><title>Background</title><p>Patients with atrial fibrillation (AF) face significant bleeding risks, particularly those receiving oral anticoagulation; however, existing risk scores such as HAS-BLED and ORBIT demonstrate limited predictive accuracy. Pulse pressure (PP), calculated as the difference between systolic blood pressure (SBP) and diastolic blood pressure, is a noninvasive marker of arterial stiffness that has been associated with cardiovascular outcomes. However, PP has not been evaluated as a predictor of bleeding in this population.</p></sec><sec><title>Objective</title><p>This study evaluated whether elevated PP independently predicts major bleeding events, overall and by subtype, in patients with AF after adjusting for established clinical risk factors.</p></sec><sec sec-type="methods"><title>Methods</title><p>We conducted a retrospective cohort study using electronic health records from REACHnet, a PCORnet-affiliated clinical data network in Louisiana. A total of 4935 adults (mean age 63.7, SD 11.0 y; n=1606, 32.5% female) with AF between 2010 and 2019 were included via consecutive sampling of all eligible patients. PP was derived from outpatient blood pressure measurements closest to AF diagnosis and analyzed in tertiles (low: &#x003C;46, middle: 46&#x2010;62, high: &#x003E;62 mm Hg) and continuously per 10 mm Hg. The primary outcome was time to the first bleeding event, a composite of gastrointestinal bleeding, intracranial hemorrhage, and other clinically significant bleeding, identified using <italic>ICD-9</italic>/<italic>ICD-10</italic> codes. Kaplan-Meier survival curves with log-rank testing were used for univariable analysis. Multivariable Cox proportional hazards regression was adjusted for age, sex, race, comorbidities, medications, and the ORBIT score. A sensitivity analysis applied multivariable logistic regression additionally incorporating SBP. Statistical significance was set at <italic>P</italic>&#x003C;.05.</p></sec><sec sec-type="results"><title>Results</title><p>Over a 5-year follow-up, 677 out of 4935 (13.7%) patients experienced a bleeding event (intracranial hemorrhage: n=60, 1.2%; gastrointestinal bleeding: n=195, 4.0%; and other bleeding: n=149, 3.0%). Gastrointestinal bleeding differed significantly across PP tertiles (<italic>P=</italic>.007). Kaplan-Meier analysis confirmed lower gastrointestinal bleeding-free survival in the highest tertile (log-rank <italic>P=</italic>.004). No significant differences were observed for intracranial (<italic>P=</italic>.08), other (<italic>P</italic>=.58), or composite bleeding (<italic>P</italic>=.22). In multivariable Cox regression, each 1 mm Hg increase in PP was independently associated with a 1.4% higher gastrointestinal bleeding risk (hazard ratio 1.014, 95% CI 1.001&#x2010;1.028; <italic>P=</italic>.04), approximately 15% per 10 mm Hg. This remained significant after adjusting for SBP and ORBIT score (odds ratio 1.013/mm Hg, 95% CI 1.001&#x2010;1.025; <italic>P</italic>=.03), while SBP was not independently significant (<italic>P</italic>=.13).</p></sec><sec sec-type="conclusions"><title>Conclusions</title><p>PP independently predicts gastrointestinal bleeding risk in patients with AF beyond established clinical risk factors and validated bleeding risk scores. Unlike prior investigations that examined SBP or diastolic blood pressure components in isolation, this is the first study to identify PP as a predictor of gastrointestinal bleeding in this population. As a readily available, low-cost hemodynamic parameter derived from routine clinical measurements, PP could enhance existing risk stratification tools and inform more personalized bleeding risk management strategies in patients with AF.</p></sec></abstract><kwd-group><kwd>atrial fibrillation</kwd><kwd>pulse pressure</kwd><kwd>gastrointestinal bleeding</kwd><kwd>anticoagulation</kwd><kwd>bleeding risk</kwd><kwd>arterial stiffness</kwd></kwd-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>Atrial fibrillation (AF) is the most common cardiac arrhythmia and a major contributor to global morbidity and mortality [<xref ref-type="bibr" rid="ref1">1</xref>,<xref ref-type="bibr" rid="ref2">2</xref>]. Its prevalence continues to rise, creating growing clinical and economic burdens on health care systems [<xref ref-type="bibr" rid="ref3">3</xref>]. AF increases the risk of thromboembolic stroke nearly 5-fold, necessitating the use of oral anticoagulation (OAC) as a primary strategy for stroke prevention [<xref ref-type="bibr" rid="ref4">4</xref>]. However, this therapeutic benefit is offset by a substantial risk of serious bleeding complications, particularly gastrointestinal and intracranial hemorrhages (ICHs), which can lead to hospitalization, premature discontinuation of therapy, and increased mortality [<xref ref-type="bibr" rid="ref5">5</xref>]. A 2026 meta-analysis of 83 studies (970,248 patients with AF treated with direct oral anticoagulation [DOAC]) confirmed that major bleeding remains a serious and multifactorial complication [<xref ref-type="bibr" rid="ref6">6</xref>].</p><p>To assist clinicians in balancing stroke prevention with bleeding risk, several clinical risk scores have been developed, including HAS-BLED (Hypertension, Abnormal renal and/or liver function, Stroke, Bleeding, Labile INR, Elderly, Drugs and/or alcohol), ORBIT (Older age, Reduced hemoglobin, Bleeding history, Insufficient kidney function, Treatment with antiplatelets), and ATRIA (Anticoagulation and Risk factors in Atrial Fibrillation) [<xref ref-type="bibr" rid="ref7">7</xref>,<xref ref-type="bibr" rid="ref8">8</xref>]. These tools incorporate clinical and laboratory parameters such as hypertension, renal dysfunction, age, and prior bleeding history [<xref ref-type="bibr" rid="ref7">7</xref>]. However, their ability to accurately stratify bleeding risk remains limited. The prospective Murcia Atrial Fibrillation Project-III cohort (2025) demonstrated that all commonly used scores achieved c-indexes below 0.7, with none showing clear superiority, and a 2026 meta-analysis confirmed only modest discrimination for the DOAC score (pooled c-index 0.68) versus HAS-BLED (0.63) [<xref ref-type="bibr" rid="ref8">8</xref>,<xref ref-type="bibr" rid="ref9">9</xref>,<xref ref-type="bibr" rid="ref10">10</xref>]. Therefore, there is a clear need for novel, readily available markers that can enhance bleeding risk stratification.</p><p>Pulse pressure (PP), defined as the difference between systolic blood pressure (SBP) and diastolic blood pressure (DBP), serves as a surrogate marker of arterial stiffness and vascular aging [<xref ref-type="bibr" rid="ref8">8</xref>]. Recent studies have demonstrated its association with adverse cardiovascular outcomes, including stroke, heart failure, and AF [<xref ref-type="bibr" rid="ref11">11</xref>-<xref ref-type="bibr" rid="ref13">13</xref>]. Arterial stiffness has also been linked to coagulation imbalance and endothelial injury, suggesting that elevated PP may directly influence hemostatic balance and bleeding susceptibility [<xref ref-type="bibr" rid="ref14">14</xref>,<xref ref-type="bibr" rid="ref15">15</xref>].</p><p>While previous studies have examined the association of SBP or DBP with bleeding risk in patients with AF, the independent role of PP remains underexplored [<xref ref-type="bibr" rid="ref16">16</xref>,<xref ref-type="bibr" rid="ref17">17</xref>]. The F-Create Project confirmed that the incidence of intracranial bleeding in anticoagulated patients with AF increased with higher blood pressure (BP) levels [<xref ref-type="bibr" rid="ref18">18</xref>]. Critically, the PICASSO (Proximal Internal Carotid Artery Acute Stroke Secondary to Tandem Lesion or Local Occlusion) trial demonstrated that the elevated PP (&#x2265;60 mm Hg) was an independent predictor of recurrent hemorrhagic stroke (adjusted HR 6.03, 95% CI 1.04&#x2010;34.99) in patients with cerebral microbleeds, suggesting that PP captures a dimension of vascular vulnerability not fully reflected by SBP or DBP alone [<xref ref-type="bibr" rid="ref15">15</xref>]. To our knowledge, no large-scale study has systematically evaluated the association between PP and major bleeding events, including gastrointestinal, intracranial, and other types, in a contemporary real-world population with AF.</p><p>In this study, we aim to evaluate the association between elevated PP and the risk of bleeding in patients with AF, using electronic health record (EHR) data from the REACHnet (Research Action for Health Network). We hypothesize that higher PP, as a marker of arterial stiffness and vascular vulnerability, is an independent predictor of bleeding and may improve risk stratification beyond existing clinical prediction models. If confirmed, PP could serve as a practical, cost-free addition to current bleeding risk assessment tools.</p></sec><sec id="s2" sec-type="methods"><title>Methods</title><sec id="s2-1"><title>Study Design</title><p>This was a retrospective cohort study conducted using EHR data. The study followed patients with AF, examining the association between PP and the risk of major bleeding events. OAC status was recorded as a baseline covariate.</p></sec><sec id="s2-2"><title>Setting</title><p>The study was conducted using data from REACHnet, a regional PCORnet-affiliated clinical data network based in Louisiana. REACHnet integrates longitudinal clinical data from multiple health systems across the region, capturing information on demographics, diagnoses, medications, vital signs, procedures, and outcomes. The data network draws from a diverse patient population receiving care across outpatient, inpatient, and specialty settings within these affiliated health systems.</p><p>The study period spanned from 2010 to 2019. Patients were identified, and their index dates (the date of AF diagnosis) were established within this window. Data collection for baseline covariates occurred prior to the index date, and follow-up continued from the index date until the occurrence of a primary outcome event, loss to follow-up, death, or December 31, 2019, whichever came first.</p></sec><sec id="s2-3"><title>Participants</title><sec id="s2-3-1"><title>Inclusion and Exclusion Criteria</title><p>Eligible patients were required to meet all of the following inclusion criteria: (1) a documented diagnosis of AF, defined using <italic>ICD-9</italic> or <italic>ICD-10</italic> (<italic>International Classification of Diseases</italic>) codes, (2) aged &#x2265;18 years at the index date, and (3) available baseline PP data recorded proximal to the index date.</p><p>Patients were excluded if they had (1) missing baseline PP data or (2) a documented bleeding event prior to the index date.</p></sec><sec id="s2-3-2"><title>Participant Characteristics</title><p>Baseline characteristics were extracted from EHR data at or preceding the index date and included demographic variables (age, sex, and race/ethnicity), comorbid conditions (hypertension, diabetes mellitus, chronic kidney disease [CKD], congestive heart failure, prior stroke or transient ischemic attack, peripheral artery disease, liver disease, anemia, and thrombocytopenia), and concurrent medication use (OAC type, antiplatelet agents, nonsteroidal anti-inflammatory drugs, beta-blockers, angiotensin-converting enzyme [ACE] inhibitors/angiotensin receptor blockers, calcium channel blockers, and proton pump inhibitors). The ORBIT bleeding risk score was calculated for each patient and incorporated as a composite covariate in adjusted models.</p></sec></sec><sec id="s2-4"><title>Sampling Procedures</title><p>Patient identification followed a nonprobability, consecutive sampling approach. All patients within the REACHnet who met the eligibility criteria during the study period (2010&#x2010;2019) were included in the analysis; no random or purposive sampling was applied. This census-type approach was chosen to maximize sample size and minimize selection bias within the available data source. The index date was defined as the date AF diagnosis had been recorded during the study window.</p></sec><sec id="s2-5"><title>Sample Size, Power, and Precision</title><p>A total of 4935 patients with AF were identified and included after applying all eligibility criteria. No formal a priori sample size or power calculation was performed. The study enrolled all eligible patients identified within the REACHnet during the study period (2010&#x2010;2019). Statistical precision of the primary estimates is reflected in the 95% CIs reported in the <italic>Results</italic> section.</p></sec><sec id="s2-6"><title>Measures and Covariates</title><sec id="s2-6-1"><title>Exposure: PP</title><p>The primary exposure was PP, defined as the arithmetic difference between SBP and DBP: PP = SBP &#x2013; DBP. PP values were derived from outpatient BP measurements recorded in the EHR closest to the index date. For the primary analysis, patients were categorized into tertiles of PP (low, middle, and high) based on the distribution across the full study cohort. In prespecified sensitivity analyses, PP was modeled as a continuous variable, with estimates expressed per 10 mm Hg increment.</p></sec><sec id="s2-6-2"><title>Primary Outcomes</title><p>The primary outcome was the time to the first bleeding event occurring after the index date. Any bleeding was defined as a composite end point comprising (1) gastrointestinal bleeding, (2) ICH, and (3) other clinically significant bleeding. All bleeding events were identified using <italic>ICD-9</italic> and <italic>ICD-10</italic> diagnosis codes recorded in the EHR. Patients were followed from the index date until the first bleeding event, death, loss to follow-up, or the study end date.</p></sec><sec id="s2-6-3"><title>Secondary Outcomes</title><p>Prespecified secondary analyses evaluated the association between PP tertiles and each individual bleeding subtype (gastrointestinal bleeding, ICH, and other clinically significant bleeding) as separate outcomes.</p></sec><sec id="s2-6-4"><title>Data Sources</title><p>All study variables, including the exposure, outcomes, and covariates, were derived exclusively from structured EHR data captured within the REACHnet clinical data network. Diagnoses were ascertained using <italic>ICD-9</italic> and <italic>ICD-10</italic> code mappings applied to the problem list and encounter diagnosis fields. Medication exposures were extracted from prescription records. BP measurements were obtained from vital signs recorded during outpatient encounters. Comorbidity variables were defined using previously validated code sets applied to the longitudinal encounter and diagnosis records.</p></sec></sec><sec id="s2-7"><title>Data Analysis</title><sec id="s2-7-1"><title>Descriptive Statistics</title><p>Baseline characteristics were summarized descriptively. Continuous variables were reported as means with SDs or medians with IQRs as appropriate. Categorical variables were reported as frequencies and percentages. Differences in baseline characteristics across PP tertiles were assessed using the Kruskal-Wallis test for continuous variables and chi-square tests for categorical variables. No missing data were identified across the primary exposure, outcome variables, or baseline covariates. Complete case analysis was performed on the full study sample of 4935 patients.</p></sec><sec id="s2-7-2"><title>Incidence Analysis</title><p>Bleeding event frequencies and proportions were summarized for each PP tertile. Kaplan-Meier survival curves were constructed to depict cumulative bleeding-free survival over the follow-up period across tertiles, and differences were assessed using the log-rank test.</p></sec><sec id="s2-7-3"><title>Multivariable Analysis</title><p>Multivariable Cox proportional hazards regression models were used to estimate the association between PP and time to the first bleeding event, adjusting for potential confounders, including age, sex, race, comorbid conditions, concurrent medication use, and the ORBIT bleeding risk score. PP was modeled continuously (per 10 mm Hg increase).</p></sec><sec id="s2-7-4"><title>Sensitivity Analyses</title><p>In sensitivity analyses, a multivariable logistic regression model was additionally fitted, incorporating SBP and the ORBIT score as covariates, to evaluate whether PP retained an independent association with bleeding risk beyond that attributable to SBP alone.</p></sec><sec id="s2-7-5"><title>Software and Significance Threshold</title><p>All statistical analyses were conducted using R (R Development Core Team, version 4.5.1). A 2-sided <italic>P</italic> value of &#x003C;.05 was considered statistically significant for all tests.</p></sec></sec><sec id="s2-8"><title>Ethical Considerations</title><p>This study involved a secondary analysis of deidentified EHR data obtained through REACHnet and did not constitute human subjects research requiring institutional review board review under 45 CFR &#x00A7;46.101(b) [<xref ref-type="bibr" rid="ref19">19</xref>]. Accordingly, institutional review board approval and informed consent were not required. Informed consent for primary data collection was obtained by the participating health systems as part of routine clinical care, and the applicable data use agreements permitted secondary research use without additional patient consent. All data were deidentified prior to analysis in accordance with the HIPAA (Health Insurance Portability and Accountability Act) privacy rule, and no direct patient identifiers were available to the research team at any stage. Data were accessed and stored in a secure, access-controlled environment, consistent with REACHnet governance requirements. No compensation was provided, as no direct participant contact occurred. This paper and all supplementary materials present results in aggregate form only, and no images or descriptions that could identify individual participants are included.</p></sec></sec><sec id="s3" sec-type="results"><title>Results</title><sec id="s3-1"><title>Baseline Characteristics</title><p>The participant flowchart is presented in <xref ref-type="fig" rid="figure1">Figure 1</xref>. Among 4935 patients with AF, 1560 (n=472, 30.3% female) were in the lowest tertile of PP (T1), 1669 (n=542, 32.5% female) in the middle tertile (T2), and 1706 (n=592, 34.7% female) in the highest tertile (T3).</p><p>Patients in the highest PP tertile were significantly older (mean age 66.6, SD 9.3 y) than those in T2 (mean age 63.0, SD 11.2 y) and T1 (mean age 61.3, SD 11.7 y; <italic>P</italic>&#x003C;.001). Comorbidities, including hypertension, diabetes, CKD, congestive heart failure, peripheral artery disease, anemia, and prior stroke, were more prevalent in the highest PP tertile (all <italic>P</italic>&#x003C;.05). The use of &#x03B2;-blockers and ACE inhibitors was also more common in higher PP tertiles (<xref ref-type="table" rid="table1">Table 1</xref>).</p><fig position="float" id="figure1"><label>Figure 1.</label><caption><p>Participant flowchart depicting the selection and follow-up of 4935 adults with atrial fibrillation across each stage of the study, from initial eligibility assessment to final analysis, stratified by pulse pressure tertile (REACHnet clinical data network, Louisiana, United States, 2010&#x2010;2019). AF: atrial fibrillation; PP: pulse pressure; REACHnet: Research Action for Health Network.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="jmir_v28i1e93347_fig01.png"/></fig><table-wrap id="t1" position="float"><label>Table 1.</label><caption><p>Baseline demographic and clinical characteristics of 4935 adults with atrial fibrillation, stratified by the tertile of pulse pressure (low: &#x003C;46 mm Hg, middle: 46&#x2010;62 mm Hg, high: &#x003E;62 mm Hg), derived from electronic health records in the REACHnet (Research Action for Health Network) clinical data network (Louisiana, United States, 2010&#x2010;2019)<sup><xref ref-type="table-fn" rid="table1fn1">a</xref></sup>.</p></caption><table id="table1" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Characteristic</td><td align="left" valign="bottom">Low (n=1560)</td><td align="left" valign="bottom">Middle (n=1669)</td><td align="left" valign="bottom">High (n=1706)</td><td align="left" valign="bottom">Total (n=4935)</td><td align="left" valign="bottom"><italic>P</italic> value</td></tr></thead><tbody><tr><td align="left" valign="top">Age (y), mean (SD)</td><td align="left" valign="top">61.3 (11.7)</td><td align="left" valign="top">63.0 (11.2)</td><td align="left" valign="top">66.6 (9.3)</td><td align="left" valign="top">63.7 (11.0)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Female, n (%)</td><td align="left" valign="top">472 (30.3)</td><td align="left" valign="top">542 (32.5)</td><td align="left" valign="top">592 (34.7)</td><td align="left" valign="top">1606 (32.5)</td><td align="left" valign="top">.03</td></tr><tr><td align="left" valign="top">Hypertension, n (%)</td><td align="left" valign="top">690 (44.2)</td><td align="left" valign="top">803 (48.1)</td><td align="left" valign="top">926 (54.3)</td><td align="left" valign="top">2419 (49.0)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Diabetes mellitus, n (%)</td><td align="left" valign="top">350 (22.4)</td><td align="left" valign="top">441 (26.4)</td><td align="left" valign="top">598 (35.1)</td><td align="left" valign="top">1389 (28.1)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Chronic kidney disease, n (%)</td><td align="left" valign="top">244 (15.6)</td><td align="left" valign="top">251 (15.0)</td><td align="left" valign="top">353 (20.7)</td><td align="left" valign="top">848 (17.2)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Liver disease, n (%)</td><td align="left" valign="top">50 (3.2)</td><td align="left" valign="top">54 (3.2)</td><td align="left" valign="top">53 (3.1)</td><td align="left" valign="top">157 (3.2)</td><td align="left" valign="top">.98</td></tr><tr><td align="left" valign="top">Congestive heart failure, n (%)</td><td align="left" valign="top">402 (25.8)</td><td align="left" valign="top">356 (21.3)</td><td align="left" valign="top">416 (24.4)</td><td align="left" valign="top">1174 (23.8)</td><td align="left" valign="top">.01</td></tr><tr><td align="left" valign="top">Peripheral artery disease, n (%)</td><td align="left" valign="top">268 (17.2)</td><td align="left" valign="top">283 (17.0)</td><td align="left" valign="top">372 (21.8)</td><td align="left" valign="top">923 (18.7)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Malignant tumor, n (%)</td><td align="left" valign="top">12 (0.8)</td><td align="left" valign="top">17 (1.0)</td><td align="left" valign="top">14 (0.8)</td><td align="left" valign="top">43 (0.9)</td><td align="left" valign="top">.72</td></tr><tr><td align="left" valign="top">Anemia, n (%)</td><td align="left" valign="top">120 (7.7)</td><td align="left" valign="top">107 (6.4)</td><td align="left" valign="top">166 (9.7)</td><td align="left" valign="top">393 (8.0)</td><td align="left" valign="top">.002</td></tr><tr><td align="left" valign="top">Thrombocytopenia, n (%)</td><td align="left" valign="top">7 (0.4)</td><td align="left" valign="top">11 (0.7)</td><td align="left" valign="top">6 (0.4)</td><td align="left" valign="top">24 (0.5)</td><td align="left" valign="top">.43</td></tr><tr><td align="left" valign="top">Stroke, n (%)</td><td align="left" valign="top">152 (9.7)</td><td align="left" valign="top">187 (11.2)</td><td align="left" valign="top">255 (14.9)</td><td align="left" valign="top">594 (12.0)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Direct oral anticoagulant, n (%)</td><td align="left" valign="top">226 (14.5)</td><td align="left" valign="top">215 (12.9)</td><td align="left" valign="top">242 (14.2)</td><td align="left" valign="top">683 (13.8)</td><td align="left" valign="top">.37</td></tr><tr><td align="left" valign="top">Anticoagulant use, n (%)</td><td align="left" valign="top">553 (35.4)</td><td align="left" valign="top">525 (31.5)</td><td align="left" valign="top">661 (38.7)</td><td align="left" valign="top">1739 (35.2)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Antiplatelet therapy, n (%)</td><td align="left" valign="top">1 (0.1)</td><td align="left" valign="top">4 (0.2)</td><td align="left" valign="top">5 (0.3)</td><td align="left" valign="top">10 (0.2)</td><td align="left" valign="top">.32</td></tr><tr><td align="left" valign="top">NSAIDs<sup><xref ref-type="table-fn" rid="table1fn2">b</xref></sup> use, n (%)</td><td align="left" valign="top">266 (17.1)</td><td align="left" valign="top">292 (17.5)</td><td align="left" valign="top">341 (20.0)</td><td align="left" valign="top">899 (18.2)</td><td align="left" valign="top">.06</td></tr><tr><td align="left" valign="top">&#x03B2;-Blocker use, n (%)</td><td align="left" valign="top">228 (14.6)</td><td align="left" valign="top">222 (13.3)</td><td align="left" valign="top">295 (17.3)</td><td align="left" valign="top">745 (15.1)</td><td align="left" valign="top">.004</td></tr><tr><td align="left" valign="top">ACE<sup><xref ref-type="table-fn" rid="table1fn3">c</xref></sup> inhibitor use, n (%)</td><td align="left" valign="top">270 (17.3)</td><td align="left" valign="top">286 (17.1)</td><td align="left" valign="top">377 (22.1)</td><td align="left" valign="top">933 (18.9)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Calcium channel blocker use, n (%)</td><td align="left" valign="top">185 (11.9)</td><td align="left" valign="top">164 (9.8)</td><td align="left" valign="top">200 (11.7)</td><td align="left" valign="top">549 (11.1)</td><td align="left" valign="top">.12</td></tr><tr><td align="left" valign="top">Warfarin use, n (%)</td><td align="left" valign="top">134 (8.6)</td><td align="left" valign="top">157 (9.4)</td><td align="left" valign="top">194 (11.4)</td><td align="left" valign="top">485 (9.8)</td><td align="left" valign="top">.02</td></tr></tbody></table><table-wrap-foot><fn id="table1fn1"><p><sup>a</sup>Continuous variables are reported as mean (SD); categorical variables as n (%). Group differences were assessed using the Kruskal-Wallis test for continuous variables and chi-square tests for categorical variables.</p></fn><fn id="table1fn2"><p><sup>b</sup>NSAIDs: nonsteroidal anti-inflammatory drugs.</p></fn><fn id="table1fn3"><p><sup>c</sup>ACE: angiotensin-converting enzyme.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s3-2"><title>Bleeding Events and Survival Analysis</title><p>During a median follow-up of 5 years, among 4935 patients, 677 (13.7%) experienced a bleeding event, while 4258 (86.3%) remained event-free. Overall, intracranial bleeding occurred in 60 (1.2%) patients, gastrointestinal bleeding in 195 (4.0%), and other bleeding events in 149 (3.0%).</p><p>Among the specific bleeding subtypes, only gastrointestinal bleeding showed a statistically significant difference across PP tertiles (<italic>P</italic>=.007), with the highest incidence observed in the upper tertile group. In contrast, rates of intracranial and other bleeding events did not differ significantly between groups (<italic>P</italic>=.08 and <italic>P</italic>=.32, respectively). Full subgroup counts are presented in <xref ref-type="table" rid="table2">Table 2</xref>.</p><p>Kaplan-Meier survival analysis demonstrated a statistically significant association between higher PP tertiles and an increased risk of gastrointestinal bleeding (log-rank <italic>P</italic>=.004), with patients in the highest tertile (T3) experiencing the lowest bleeding-free survival over time. In contrast, no significant differences were observed in intracranial bleeding-free survival across tertiles (<italic>P</italic>=.08), nor in the risk of other bleeding events (<italic>P</italic>=.58). For the composite outcome of any bleeding, although some separation of curves was noted, the difference did not reach statistical significance (<italic>P</italic>=.22; <xref ref-type="fig" rid="figure2">Figures 2</xref><xref ref-type="fig" rid="figure3"/><xref ref-type="fig" rid="figure4"/>-<xref ref-type="fig" rid="figure5">5</xref>).</p><p>In <xref ref-type="fig" rid="figure2">Figures 2</xref><xref ref-type="fig" rid="figure3"/><xref ref-type="fig" rid="figure4"/>-<xref ref-type="fig" rid="figure5">5</xref>, Kaplan-Meier curves depicting bleeding-free survival by PP tertile (T1: low &#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg) in 4935 adults with AF (REACHnet, Louisiana, United States, 2010&#x2010;2019). Follow-up extended from the date of AF diagnosis to the first bleeding event, death, loss to follow-up, or December 31, 2019. Outcomes depicted are as follows: <xref ref-type="fig" rid="figure2">Figure 2</xref>, composite bleeding (any gastrointestinal, intracranial, or other clinically significant bleeding; log-rank <italic>P</italic>=.22); <xref ref-type="fig" rid="figure3">Figure 3</xref>, gastrointestinal bleeding (log-rank <italic>P</italic>=.004); <xref ref-type="fig" rid="figure4">Figure 4</xref>, ICH (log-rank <italic>P</italic>=.08); and <xref ref-type="fig" rid="figure5">Figure 5</xref>, other clinically significant bleeding (log-rank <italic>P</italic>=.58). Group differences were assessed using the log-rank test.</p><table-wrap id="t2" position="float"><label>Table 2.</label><caption><p>Distribution of bleeding events by the pulse pressure tertile in 4935 patients with atrial fibrillation (REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019)<sup><xref ref-type="table-fn" rid="table2fn1">a</xref></sup>.</p></caption><table id="table2" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Outcome</td><td align="left" valign="bottom">Low (&#x003C;46 mm Hg; n=1560), n (%)</td><td align="left" valign="bottom">Middle (46&#x2010;62 mm Hg; n=1669), n (%)</td><td align="left" valign="bottom">High (&#x003E;62 mm Hg; n=1706), n (%)</td><td align="left" valign="bottom">Total (n=4935), n (%)</td><td align="left" valign="bottom"><italic>P</italic> value</td></tr></thead><tbody><tr><td align="left" valign="top">Intracranial hemorrhage</td><td align="left" valign="top">14 (0.9)</td><td align="left" valign="top">17 (1.0)</td><td align="left" valign="top">29 (1.7)</td><td align="left" valign="top">60 (1.2)</td><td align="left" valign="top">.08</td></tr><tr><td align="left" valign="top">Gastrointestinal bleeding</td><td align="left" valign="top">53 (3.4)</td><td align="left" valign="top">54 (3.2)</td><td align="left" valign="top">88 (5.2)</td><td align="left" valign="top">195 (4.0)</td><td align="left" valign="top">.007</td></tr><tr><td align="left" valign="top">Other bleeding</td><td align="left" valign="top">43 (2.8)</td><td align="left" valign="top">59 (3.5)</td><td align="left" valign="top">47 (2.8)</td><td align="left" valign="top">149 (3.0)</td><td align="left" valign="top">.32</td></tr><tr><td align="left" valign="top">Any bleeding (composite)</td><td align="left" valign="top">218 (14.0)</td><td align="left" valign="top">218 (13.1)</td><td align="left" valign="top">241 (14.1)</td><td align="left" valign="top">677 (13.7)</td><td align="left" valign="top">.22</td></tr></tbody></table><table-wrap-foot><fn id="table2fn1"><p><sup>a</sup>Values are presented as n (%). Group differences were assessed using chi-square tests.</p></fn></table-wrap-foot></table-wrap><fig position="float" id="figure2"><label>Figure 2.</label><caption><p>Kaplan-Meier curves depicting composite bleeding-free survival (gastrointestinal bleeding, intracranial hemorrhage, and other clinically significant bleeding) by pulse pressure tertiles (T1: low &#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg) in 4935 adults with atrial fibrillation (REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019); log-rank <italic>P</italic>=.22. (A) Time to any bleeding outcome; (B) number at risk.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="jmir_v28i1e93347_fig02.png"/></fig><fig position="float" id="figure3"><label>Figure 3.</label><caption><p>Kaplan-Meier curves depicting gastrointestinal bleeding-free survival by pulse pressure tertiles (T1: low&#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg) in 4935 adults with atrial fibrillation (REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019); log-rank <italic>P</italic>=.004. (A) Time to gastrointestinal bleeding; (B) number at risk.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="jmir_v28i1e93347_fig03.png"/></fig><fig position="float" id="figure4"><label>Figure 4.</label><caption><p>Kaplan-Meier curves depicting intracranial hemorrhage-free survival by pulse pressure tertiles (T1: low&#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg) in 4935 adults with atrial fibrillation (REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019); log-rank <italic>P</italic>=.08. (A) Time to intracranial bleeding; (B) number at risk.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="jmir_v28i1e93347_fig04.png"/></fig><fig position="float" id="figure5"><label>Figure 5.</label><caption><p>Kaplan-Meier curves depicting other clinically significant bleeding-free survival by pulse pressure tertiles (T1: low &#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg) in 4935 adults with atrial fibrillation (REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019); log-rank <italic>P</italic>=.58. (A) Time to other bleeding; (B) number at risk.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="jmir_v28i1e93347_fig05.png"/></fig></sec><sec id="s3-3"><title>Multivariable Analysis</title><p>In multivariable Cox proportional hazards models, PP emerged as a significant independent predictor of gastrointestinal bleeding. Each 10 mm Hg increase in PP was independently associated with a 14.9% higher risk of gastrointestinal bleeding (hazard ratio [HR] 1.149, 95% CI 1.010&#x2010;1.318; <italic>P</italic>=.04), even after adjusting for SBP, OAC usage, and a comprehensive set of clinical covariates, including age, sex, race, hypertension, diabetes, CKD, heart failure, anemia, and medication use (HR per 1 mm Hg=1.014, 95% CI 1.001&#x2010;1.028; <italic>P</italic>=.04; <xref ref-type="table" rid="table3">Table 3</xref>). PP remained not significantly associated with intracranial bleeding (HR 1.008, 95% CI 0.986&#x2010;1.030; <italic>P</italic>=.50), other bleeding (HR 0.990, 95% CI 0.976&#x2010;1.005; <italic>P</italic>=.19), or any bleeding (HR 1.003, 95% CI 0.996&#x2010;1.011; <italic>P</italic>=.36).</p><table-wrap id="t3" position="float"><label>Table 3.</label><caption><p>Multivariable Cox proportional hazards regression model estimating the association between pulse pressure (per 1 mm Hg increase) and time to the first gastrointestinal bleeding event in 4935 adults with atrial fibrillation (REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019)<sup><xref ref-type="table-fn" rid="table3fn1">a</xref></sup>.</p></caption><table id="table3" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Variable</td><td align="left" valign="bottom">Hazard ratio (95% CI)</td><td align="left" valign="bottom"><italic>P</italic> value</td></tr></thead><tbody><tr><td align="left" valign="top">Pulse pressure</td><td align="left" valign="top">1.014 (1.001-1.028)</td><td align="left" valign="top">.04</td></tr><tr><td align="left" valign="top">Systolic blood pressure</td><td align="left" valign="top">0.994 (0.983-1.005)</td><td align="left" valign="top">.31</td></tr><tr><td align="left" valign="top">Age</td><td align="left" valign="top">1.011 (0.996-1.026)</td><td align="left" valign="top">.15</td></tr><tr><td align="left" valign="top">Sex: female</td><td align="left" valign="top">0.908 (0.666-1.237)</td><td align="left" valign="top">.54</td></tr><tr><td align="left" valign="top">Race</td><td align="left" valign="top">1.109 (0.814-1.512)</td><td align="left" valign="top">.51</td></tr><tr><td align="left" valign="top">Hypertension</td><td align="left" valign="top">2.914 (1.783-4.762)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">Diabetes</td><td align="left" valign="top">0.935 (0.679-1.287)</td><td align="left" valign="top">.68</td></tr><tr><td align="left" valign="top">Chronic kidney disease</td><td align="left" valign="top">1.220 (0.871-1.709)</td><td align="left" valign="top">.25</td></tr><tr><td align="left" valign="top">Congestive heart failure</td><td align="left" valign="top">0.739 (0.523-1.046)</td><td align="left" valign="top">.09</td></tr><tr><td align="left" valign="top">Peripheral artery disease</td><td align="left" valign="top">0.979 (0.685-1.399)</td><td align="left" valign="top">.91</td></tr><tr><td align="left" valign="top">Anemia</td><td align="left" valign="top">1.190 (0.815-1.738)</td><td align="left" valign="top">.37</td></tr><tr><td align="left" valign="top">Thrombocytopenia</td><td align="left" valign="top">0.494 (0.068-3.571)</td><td align="left" valign="top">.49</td></tr><tr><td align="left" valign="top">Stroke</td><td align="left" valign="top">0.927 (0.634-1.355)</td><td align="left" valign="top">.70</td></tr><tr><td align="left" valign="top">Anticoagulation</td><td align="left" valign="top">4.287 (2.733-6.724)</td><td align="left" valign="top">&#x003C;.001</td></tr><tr><td align="left" valign="top">NSAID<sup><xref ref-type="table-fn" rid="table3fn2">b</xref></sup></td><td align="left" valign="top">0.603 (0.432-0.841)</td><td align="left" valign="top">.003</td></tr><tr><td align="left" valign="top">&#x03B2;-Blockers</td><td align="left" valign="top">1.493 (1.052-2.118)</td><td align="left" valign="top">.03</td></tr><tr><td align="left" valign="top">ACE<sup><xref ref-type="table-fn" rid="table3fn3">c</xref></sup> inhibitors</td><td align="left" valign="top">0.717 (0.506-1.014)</td><td align="left" valign="top">.06</td></tr></tbody></table><table-wrap-foot><fn id="table3fn1"><p><sup>a</sup>Results are expressed as hazard ratios (HRs) with 95% CI and 2-sided <italic>P</italic> values. In a multivariable logistic regression model adjusting for systolic blood pressure and ORBIT score, pulse pressure (PP) remained a statistically significant predictor of gastrointestinal bleeding (odds ratio 1.013 per mm Hg increase, 95% CI 1.001&#x2010;1.025; <italic>P</italic>=.03). In contrast, systolic blood pressure was not significantly associated with bleeding risk (<italic>P</italic>=.13).</p></fn><fn id="table3fn2"><p><sup>b</sup>NSAID: nonsteroidal anti-inflammatory drug.</p></fn><fn id="table3fn3"><p><sup>c</sup>ACE: angiotensin-converting enzyme.</p></fn></table-wrap-foot></table-wrap></sec><sec id="s3-4"><title>Subgroup Analysis by the Anticoagulant Type</title><p>Given the known differential association between the anticoagulant type and gastrointestinal bleeding risk, Kaplan-Meier analyses were performed separately for individuals treated with warfarin and DOAC. Among patients treated with warfarin (T1: n=134, T2: n=157, and T3: n=194), no significant differences in gastrointestinal bleeding-free survival were observed across PP tertiles (log-rank <italic>P</italic>=.96). Similarly, among patients treated with DOAC (T1: n=226, T2: n=215, and T3: n=242), gastrointestinal bleeding-free survival did not differ significantly across tertiles (log-rank <italic>P</italic>=.29; <xref ref-type="fig" rid="figure6">Figures 6</xref> and <xref ref-type="fig" rid="figure7">7</xref>).</p><p>In <xref ref-type="fig" rid="figure6">Figures 6</xref> and <xref ref-type="fig" rid="figure7">7</xref>, Kaplan-Meier curves depict gastrointestinal bleeding-free survival by PP tertiles (T1: low &#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg), stratified by the anticoagulant type in patients with AF (REACHnet, Louisiana, United States, 2010&#x2010;2019). The follow-up extended from the date of AF diagnosis to the first gastrointestinal bleeding event, death, loss to follow-up, or December 31, 2019. <xref ref-type="fig" rid="figure6">Figure 6</xref> depicts patients treated with DOAC (T1: n=134, T2: n=157, and T3: n=194; log-rank <italic>P</italic>=.96). <xref ref-type="fig" rid="figure7">Figure 7</xref> depicts patients treated with warfarin (T1: n=226, T2: n=215, and T3: n=242; log-rank <italic>P</italic>=.29). Group differences were assessed using the log-rank test.</p><fig position="float" id="figure6"><label>Figure 6.</label><caption><p>Kaplan-Meier curves depicting gastrointestinal bleeding-free survival by pulse pressure tertiles (T1: low &#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg) among patients with atrial fibrillation treated with direct oral anticoagulant (DOAC) (T1: n=226, T2: n=215, T3: n=242; REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019); log-rank <italic>P</italic>=.29. (A) Time to gastrointestinal bleeding for DOAC; (B) number at risk.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="jmir_v28i1e93347_fig06.png"/></fig><fig position="float" id="figure7"><label>Figure 7.</label><caption><p>Kaplan-Meier curves depicting gastrointestinal bleeding-free survival by pulse pressure tertiles (T1: low &#x003C;46 mm Hg, T2: middle 46&#x2010;62 mm Hg, T3: high &#x003E;62 mm Hg) among patients with atrial fibrillation treated with warfarin (T1: n=134, T2: n=157, T3: n=194; REACHnet [Research Action for Health Network], Louisiana, United States, 2010&#x2010;2019); log-rank <italic>P</italic>=.96. (A) Time to gastrointestinal bleeding for warfarin; (B) number at risk.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="jmir_v28i1e93347_fig07.png"/></fig></sec></sec><sec id="s4" sec-type="discussion"><title>Discussion</title><sec id="s4-1"><title>Principal Findings</title><p>This study aimed to evaluate whether elevated PP independently predicts major bleeding events in patients with AF. Consistent with our hypothesis, higher PP was independently associated with an increased risk of gastrointestinal bleeding after adjusting for established clinical risk factors, including SBP and the ORBIT bleeding score. This association was specific to gastrointestinal bleeding and was not observed for ICH, other bleeding subtypes, or the composite bleeding outcome, suggesting a potentially organ-specific hemodynamic mechanism.</p></sec><sec id="s4-2"><title>Pulse Pressure and Bleeding Mechanisms</title><p>Hypertension is a well-established contributor to bleeding risk in patients with AF. The HAS-BLED score assigns 1 point for SBP &#x003E;160 mm Hg [<xref ref-type="bibr" rid="ref20">20</xref>]. High SBP has been linked to intracerebral hemorrhage in patients on warfarin and to higher overall bleeding rates in older patients with AF cohorts [<xref ref-type="bibr" rid="ref21">21</xref>]. In a recent analysis of Japanese octogenarians with AF, those with poorly controlled home SBP (&#x2265;145 mm Hg) had a significantly higher incidence of major bleeding and stroke than those with SBP &#x003C;125 mm Hg [<xref ref-type="bibr" rid="ref21">21</xref>]. However, these conventional approaches treat BP as a static or binary variable and do not account for the pulsatile hemodynamic forces that may independently contribute to vascular injury [<xref ref-type="bibr" rid="ref22">22</xref>]. PP, defined as the difference between SBP and DBP, reflects arterial stiffness and the magnitude of pulsatile stress transmitted to end-organ vasculature, a dimension of hemodynamic risk that existing bleeding scores do not capture [<xref ref-type="bibr" rid="ref23">23</xref>].</p><p>To our knowledge, no prior large-scale study has examined PP in relation to bleeding outcomes in patients with AF. Previous investigations of PP have focused predominantly on its role as a predictor of cardiovascular events such as myocardial infarction, stroke, and cardiovascular mortality, particularly in older adults and hypertensive populations [<xref ref-type="bibr" rid="ref24">24</xref>]. The extension of PP as a risk marker to bleeding outcomes represents a novel application of this readily available hemodynamic parameter, and this study was designed to address this gap in the literature.</p><p>In this large cohort of 4935 patients with AF, higher PP was independently associated with an increased risk of gastrointestinal bleeding. Patients in the highest PP tertile were older and had a higher burden of vascular comorbidities. In multivariable Cox regression, a clinically meaningful and statistically robust increase in gastrointestinal bleeding hazard was observed per 10 mm Hg increment in PP, even after adjusting for SBP, OAC usage, and the ORBIT bleeding score in logistic regression. The consistent direction and statistical significance of this association across both Cox and logistic regression models, with CIs excluding the null in both analyses, strengthen confidence in PP as an independent predictor of gastrointestinal bleeding. The magnitude of effect is clinically plausible, given the hemodynamic burden that elevated PP imposes on the gastrointestinal vasculature. To our knowledge, this is the first study to demonstrate PP as an independent predictor of gastrointestinal bleeding in patients with AF.</p><p>Gastrointestinal bleeding remains one of the most common and clinically impactful bleeding complications among patients with AF [<xref ref-type="bibr" rid="ref25">25</xref>,<xref ref-type="bibr" rid="ref26">26</xref>]. Among older patients with AF, the incidence of gastrointestinal bleeding was 1.92 per 100 person-years, highlighting the real-world burden and recurrence potential of this outcome [<xref ref-type="bibr" rid="ref27">27</xref>]. A recent large international study (INTERBLEED) identified age as the strongest predictor of gastrointestinal bleeding in patients with cardiovascular disease, with those aged &#x2265;71 years having more than 4-fold higher risk than those &#x2264;60 years (odds ratio 4.16, <italic>P</italic>&#x003C;.001) [<xref ref-type="bibr" rid="ref28">28</xref>]. While aging remains a robust risk factor, its effects are likely mediated through vascular stiffening and elevated PP, a hemodynamic consequence of reduced arterial compliance. In our analysis, PP remained independently associated with gastrointestinal bleeding even after adjusting for age, suggesting that PP may reflect cumulative subclinical vascular damage beyond chronological aging. Mechanistically, PP is a surrogate for arterial stiffness; as arteries stiffen with age and hypertension, they lose elasticity and the ability to dampen the pulsatile output of the heart [<xref ref-type="bibr" rid="ref29">29</xref>]. This loss of arterial buffering capacity results in the greater transmission of pulsatile energy to downstream microvascular beds, including those of the gastrointestinal tract [<xref ref-type="bibr" rid="ref23">23</xref>]. The splanchnic circulation, which receives a substantial proportion of cardiac output, may be particularly vulnerable to this augmented pulsatile stress [<xref ref-type="bibr" rid="ref30">30</xref>].</p><p>Our finding that PP was not significantly related to ICH risk hints at possible organ-specific differences. Intracerebral microvessels, which are also sensitive to hypertension, might be more acutely affected by absolute BP spikes or long-term hypertensive remodeling rather than by pulsatile pressure per se [<xref ref-type="bibr" rid="ref31">31</xref>]. The cerebral vasculature possesses unique autoregulatory mechanisms, including the myogenic response and neurovascular coupling, which actively modulate blood flow across a range of perfusion pressures [<xref ref-type="bibr" rid="ref32">32</xref>]. These protective mechanisms may partially attenuate the impact of pulsatile stress on cerebral microvessels, whereas the gastrointestinal vasculature lacks comparably robust autoregulatory defenses [<xref ref-type="bibr" rid="ref33">33</xref>]. In contrast, gastrointestinal bleeding sources (such as submucosal arterioles or angiodysplastic vessels) may be more directly influenced by the ongoing pulsatile stress that PP represents [<xref ref-type="bibr" rid="ref34">34</xref>]. This pathophysiologic distinction warrants further investigation, but it aligns with our results showing PP&#x2019;s impact on gastrointestinal bleeds and the relative lack of effect on ICH.</p></sec><sec id="s4-3"><title>Clinical Implications</title><p>Our findings have several practical implications. Bleeding risk scores such as HAS-BLED and ORBIT could be enhanced by incorporating PP or related measures of arterial stiffness. Current scoring systems typically treat hypertension as a binary variable, without explicitly capturing the hemodynamic burden of vascular stiffness [<xref ref-type="bibr" rid="ref21">21</xref>]. Given that PP is derived from standard BP measurements already obtained in routine clinical practice, its integration into existing risk models would impose no additional cost or procedural burden [<xref ref-type="bibr" rid="ref24">24</xref>]. A refined scoring system that incorporates PP as a continuous variable, or as a categorical threshold, could improve the discrimination of patients at the highest risk for gastrointestinal bleeding, thereby enabling more targeted surveillance and intervention strategies.</p><p>In our analysis, PP remained an independent predictor of gastrointestinal bleeding even after accounting for SBP and overall bleeding risk as captured by the ORBIT score, whereas SBP alone did not demonstrate a significant association. This underscores that the pulsatile component of BP, rather than absolute systolic values, may better reflect vascular fragility. Since PP can be easily calculated from routine BP readings, its incorporation into clinical workflows may enhance bleeding-risk discrimination, particularly for gastrointestinal events. Clinically, patients with elevated PP might warrant closer surveillance and more stringent BP control. Specifically, clinicians managing patients with AF and elevated PP could consider more frequent hemoglobin monitoring, lower thresholds for endoscopic evaluation of gastrointestinal symptoms, and optimization of antihypertensive regimens with agents known to reduce arterial stiffness, such as ACE inhibitors, angiotensin receptor blockers, or calcium channel blockers, which may preferentially lower PP relative to other drug classes [<xref ref-type="bibr" rid="ref35">35</xref>,<xref ref-type="bibr" rid="ref36">36</xref>]. Whether pharmacologic reduction of PP translates into a measurable decrease in gastrointestinal bleeding events remains an important question for future interventional studies [<xref ref-type="bibr" rid="ref37">37</xref>].</p><p>Another forward-looking implication involves the potential role of technology in monitoring PP and arterial health. The proliferation of wearable BP devices enables near-continuous tracking of both systolic SBP and DBP in real-world settings [<xref ref-type="bibr" rid="ref38">38</xref>]. Frequent out-of-office BP measurements using smartwatch sensors can detect patterns such as episodic hypertension or increased PP with much greater resolution than periodic clinic visits [<xref ref-type="bibr" rid="ref39">39</xref>]. Integrating these technologies with clinical risk models could facilitate real-time, individualized bleeding-risk assessment. For example, machine-learning algorithms applied to continuous PP data streams could identify patients whose PP trajectories are trending upward, triggering clinical alerts before a bleeding event occurs [<xref ref-type="bibr" rid="ref40">40</xref>]. Collectively, these approaches highlight the promise of PP as an accessible biomarker bridging traditional risk stratification and digital precision medicine.</p></sec><sec id="s4-4"><title>Limitations</title><p>This study has several limitations that should be considered when interpreting the findings. PP was derived from a single peripheral measurement, which may not fully capture the dynamic or central hemodynamic burden. However, this proof-of-concept analysis supports the need for future studies employing continuous monitoring or central PP estimation. Second, the retrospective observational design inherently carries risks of residual confounding and bias, despite multivariable adjustment. Prospective validation in diverse cohorts is warranted. Third, the quality of anticoagulation in patients treated with warfarin, specifically prothrombin time&#x2013;international normalized ratio and time in therapeutic range, was not available in the EHR data and could not be accounted for in the analysis. As anticoagulation intensity is a known determinant of bleeding risk, this represents a potential source of residual confounding that may have influenced the observed bleeding outcomes.</p></sec><sec id="s4-5"><title>Conclusion</title><p>Our study identifies elevated PP as a significant and independent predictor of gastrointestinal bleeding among patients with AF, representing the first evidence of this association in this population. The organ-specific nature of this finding, with PP predicting gastrointestinal but not intracranial or overall bleeding, suggests that distinct hemodynamic pathways underlie different bleeding subtypes, and that subtype-specific risk modeling may be more informative than composite bleeding end points alone. Unlike existing risk scores that treat hypertension as a binary variable, PP captures the pulsatile hemodynamic burden of arterial stiffness and may therefore improve bleeding risk discrimination when incorporated into clinical prediction models such as HAS-BLED or ORBIT. In our sensitivity analysis, PP remained independently predictive of gastrointestinal bleeding after adjusting for SBP, which itself was not significant, underscoring that the pulsatile rather than the absolute pressure component drives this risk. Patients with AF and elevated PP may represent a high-risk subgroup warranting heightened gastrointestinal surveillance, proactive gastroprotection, and careful selection of anticoagulant agents with favorable gastrointestinal safety profiles. Future research should aim to validate these findings in broader settings and explore how best to incorporate PP into clinical practice, whether by refining risk scores, targeting arterial stiffness reduction pharmacologically, guiding BP targets, or leveraging wearable technology for continuous vascular monitoring.</p></sec></sec></body><back><ack><p>The authors would like to thank the Tulane Research Innovation for Arrhythmia Discovery (TRIAD) team for their continuous support and collaboration. The authors also acknowledge the data partners and participating institutions for providing access to the datasets used in this study.</p><p>No generative artificial intelligence tools were used in any portion of the manuscript generation.</p></ack><notes><sec><title>Funding</title><p>The authors declared no financial support was received for this work.</p></sec><sec><title>Data Availability</title><p>The datasets analyzed during this study were obtained from the REACHnet (Research Action for Health Network) database in Louisiana. The data are not publicly available due to data use agreements and institutional restrictions; however, they may be accessed by qualified researchers upon approval through the data request process of REACHnet.</p></sec></notes><fn-group><fn fn-type="con"><p>Conceptualization: NFM (lead), MAK (equal), CEK (equal). Data curation: MAK (lead), CEK (equal), YJ (supporting). Formal analysis: YJ (lead), MAK (supporting). Investigation: MAK, CEK, M Moersdorf, CM, AED, YM, MMA, GB, HY, AA, M Mekhael, ET, CL, CN, AH, YL, RAMQ, OK, ACP. Methodology: MAK (lead), CEK (equal), NFM (supporting). Project administration: MAK, NFM. Resources: NFM, ACP. Supervision: NFM (lead), QM (supporting). Validation: MAK, CEK, HF. Visualization: MAK (lead), CEK (supporting). Writing &#x2013; original draft: MAK (equal), CEK (equal). Writing &#x2013; review &#x0026;amp; editing: MAK, CEK, YJ, M Moersdorf, CM, AED, YM, MMA, GB, HY, AA, M Mekhael, ET, CL, CN, AH, YL, MMMM, RAMQ, OK, ACP, HF, QM, NFM</p></fn><fn fn-type="conflict"><p>NFM reports having received consulting fees from Biosense Webster, Boston Scientific, and AtriCure; being a speaker for Abbott, Biosense Webster, AtriCure, and Sanofi; receiving research support from Abbott, Medtronic, Biosense Webster, Siemens, GE, Boston Scientific, Sanofi, and Samsung; having a family member who is the CEO of Cardiac Designs; being the founder of Marrek, being named in a patent issued for MRI fibrosis imaging; and being a previous shareholder of Cardiac Designs.</p><p>ACP reports having served on an advisory board for Novartis (completed).</p><p>MMoersdorf received a research grant from the German Heart Foundation.</p><p>All other coauthors report no relevant conflict of interest.</p></fn></fn-group><glossary><title>Abbreviations</title><def-list><def-item><term id="abb1">ACE</term><def><p>angiotensin-converting enzyme</p></def></def-item><def-item><term id="abb2">AF</term><def><p>atrial fibrillation</p></def></def-item><def-item><term id="abb3">ATRIA</term><def><p>Anticoagulation and Risk Factors in Atrial Fibrillation</p></def></def-item><def-item><term id="abb4">BP</term><def><p>blood pressure</p></def></def-item><def-item><term id="abb5">CKD</term><def><p>chronic kidney disease</p></def></def-item><def-item><term id="abb6">DBP</term><def><p>diastolic blood pressure</p></def></def-item><def-item><term id="abb7">DOAC</term><def><p>direct oral anticoagulant</p></def></def-item><def-item><term id="abb8">EHR</term><def><p>electronic health record</p></def></def-item><def-item><term id="abb9">HAS-BLED</term><def><p>Hypertension, Abnormal renal and/or liver function, Stroke, Bleeding, Labile INR, Elderly, Drugs and/or alcohol</p></def></def-item><def-item><term id="abb10">HIPAA</term><def><p> Health Insurance Portability and Accountability Act</p></def></def-item><def-item><term id="abb11">HR</term><def><p>hazard ratio</p></def></def-item><def-item><term id="abb12"><italic>ICD</italic></term><def><p><italic>International Classification of Diseases</italic></p></def></def-item><def-item><term id="abb13">OAC</term><def><p>oral anticoagulation</p></def></def-item><def-item><term id="abb14">ORBIT</term><def><p>Older age, Reduced hemoglobin, Bleeding history, Insufficient kidney function, Treatment with antiplatelets</p></def></def-item><def-item><term id="abb15">PICASSO</term><def><p>Proximal Internal Carotid Artery Acute Stroke Secondary to Tandem Lesion or Local Occlusion</p></def></def-item><def-item><term id="abb16">PP</term><def><p>pulse pressure</p></def></def-item><def-item><term id="abb17">REACHnet</term><def><p>Research Action for Health Network</p></def></def-item><def-item><term id="abb18">SBP</term><def><p>systolic blood pressure</p></def></def-item></def-list></glossary><ref-list><title>References</title><ref 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