Dual Risk Prediction Model for Cardiovascular Events and Rebleeding in Patients with Antiplatelet Drug-Related Gastric Ulcer Bleeding

Dual Risk Prediction Model for Cardiovascular Events and Rebleeding in Patients with Antiplatelet Drug-Related Gastric Ulcer Bleeding | 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 Research Article Dual Risk Prediction Model for Cardiovascular Events and Rebleeding in Patients with Antiplatelet Drug-Related Gastric Ulcer Bleeding Rongrong Chen, Guixi Wu, Mengshi Chen, Xueyan Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8029710/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 Objective To develop a dual risk prediction model for cardiovascular events and rebleeding in patients with antiplatelet-related gastric ulcer bleeding, addressing the clinical dilemma of balancing thrombotic and hemorrhagic risks. Methods In a retrospective cohort of 213 patients followed for 12 months, we used Cox regression to identify independent predictors for each outcome. Model performance was assessed using AUC, calibration (Hosmer–Lemeshow test), and bootstrap validation. Results During follow-up, 48 (22.5%) patients had cardiovascular events and 56 (26.3%) experienced rebleeding. Independent predictors for cardiovascular events were age ≥ 70 years (HR = 2.48), heart failure (HR = 2.31), GRACE score > 140 (HR = 2.92), and albumin < 35 g/L (HR = 1.85). For rebleeding, Forrest Ia–Ib (HR = 3.15), Rockall score ≥ 6 (HR = 2.68), H. pylori infection (HR = 1.92), and antiplatelet discontinuation (HR = 2.58) were significant. The models showed AUCs of 0.758 and 0.781 for 12-month cardiovascular events and rebleeding, respectively, with good calibration (P > 0.05). Stratification into low-, medium-, and high-risk groups revealed graded outcomes: cardiovascular event rates were 8.2%, 22.6%, and 57.1%, and rebleeding rates were 10.5%, 28.4%, and 50.0% (P < 0.001). Conclusion This dual risk model effectively stratifies patients by cardiovascular and rebleeding risk using routinely available clinical variables, aiding individualized antiplatelet management. Antiplatelet drugs Gastric ulcer bleeding Cardiovascular events Rebleeding Risk prediction model GRACE score Forrest classification Helicobacter pylori Figures Figure 1 Figure 2 1 Introduction Antiplatelet drugs are the cornerstone of secondary prevention of cardiovascular disease and can significantly reduce the risk of major adverse cardiovascular events such as myocardial infarction and stroke [ 1 ]. However, while beneficial, antiplatelet therapy also significantly increases the risk of gastrointestinal bleeding, with gastric ulcer bleeding being one of the most common serious complications [ 2 ]. Epidemiological studies show that the risk of upper gastrointestinal bleeding increases 2–4 fold in patients on long-term aspirin, with dual antiplatelet therapy (DAPT) further elevating this risk [ 3 ]. For patients with antiplatelet drug-related gastric ulcer bleeding, clinicians face a therapeutic dilemma: continuing antiplatelet therapy may lead to rebleeding, while discontinuing or delaying reinitiation of antiplatelet drugs may increase cardiovascular event risk, both potentially life-threatening [ 4 ]. Currently, assessment tools for upper gastrointestinal bleeding risk such as the Glasgow-Blatchford Score (GBS) and Rockall score mainly focus on bleeding severity and rebleeding risk [ 5 ], while cardiovascular risk assessment tools such as the Global Registry of Acute Coronary Events (GRACE) score and CHADS₂ score emphasize cardiovascular event prediction [ 6 ]. However, these single-dimension scoring systems cannot comprehensively assess the dual risks in patients with antiplatelet drug-related gastric ulcer bleeding and cannot provide sufficient evidence for clinical decision-making. Previous studies have shown that prognosis in these patients is affected by multiple factors, including baseline cardiovascular status, bleeding severity, and antiplatelet drug management strategies [ 7 ], but prediction models comprehensively assessing both cardiovascular events and rebleeding risks have not been established. Therefore, this study aims to systematically analyze independent risk factors for cardiovascular events and rebleeding in patients with antiplatelet drug-related gastric ulcer bleeding through a retrospective cohort study, construct a dual risk prediction model for this special population, and provide scientific evidence for clinicians in antiplatelet drug management, individualized treatment decisions, and patient prognosis assessment [ 8 ]. 2 Materials and Methods 2.1 Study Design and Subjects This study was a single-center retrospective cohort study. Clinical data of patients with antiplatelet drug-related gastric ulcer bleeding hospitalized in our hospital from January 2021 to December 2023 were collected. This study was approved by our hospital's ethics committee and informed consent was waived due to its retrospective nature. Inclusion criteria (1) Age ≥ 18 years; (2) Long-term use of antiplatelet drugs (aspirin, clopidogrel, ticagrelor, etc.) for ≥ 1 month due to cardiovascular disease (coronary heart disease, cerebrovascular disease, peripheral vascular disease, etc.); (3) Gastric ulcer bleeding confirmed by endoscopy (Forrest classification Ia, Ib, IIa, IIb, or IIc grade, excluding grade III white ulcer base); (4) Clinical manifestations of hematemesis and/or melena, with hemoglobin decreased ≥ 20g/L compared to baseline stable values before admission; (5) Complete clinical data and available follow-up data. Exclusion criteria (1) Non-ulcerative upper gastrointestinal bleeding (such as esophageal and gastric variceal rupture bleeding, Mallory-Weiss syndrome, etc.); (2) Combined gastrointestinal bleeding from other causes (gastrointestinal tumors, vascular malformations, etc.); (3) Combined severe coagulation dysfunction or hematological diseases; (4) Combined active bleeding at other sites; (5) Regular use of non-steroidal anti-inflammatory drugs (NSAIDs) or glucocorticoids that may affect gastrointestinal bleeding; (6) Combined terminal malignancy or expected survival < 6 months; (7) Previous partial gastrectomy. A total of 213 patients meeting the criteria were finally included. 2.3 Data Collection The following data were collected through the hospital electronic medical record system (except as specifically noted, baseline data were patient data at admission): Demographic data Age, gender, body mass index (BMI), smoking history, alcohol consumption history. Cardiovascular disease-related data : (1) Types of cardiovascular disease: coronary heart disease, myocardial infarction history, heart failure, cerebrovascular disease, peripheral vascular disease; (2) Cardiovascular risk factors: hypertension, diabetes, dyslipidemia; (3) Cardiac function classification (New York Heart Association [NYHA] classification); (4) Cardiovascular risk scores: GRACE score, CHADS₂ score, etc. Antiplatelet drug use Drug types (single or dual antiplatelet therapy), duration of medication, dosage, whether combined with anticoagulants. Antiplatelet drug management strategies were decided by consulting cardiologists based on comprehensive assessment of patient bleeding risk and cardiovascular event risk, following Chinese and international guideline recommendations, recorded as discontinuation, continued use, or change to other drugs. Bleeding-related data : (1) Clinical manifestations: hematemesis, melena, hematochezia; (2) Vital signs: systolic blood pressure and heart rate at admission; (3) Laboratory indicators: hemoglobin, platelet count, coagulation function (prothrombin time [PT], activated partial thromboplastin time [APTT], international normalized ratio [INR]), liver and kidney function, serum albumin at admission; (4) Endoscopic examination: ulcer location, size, Forrest classification, Helicobacter pylori (Hp) infection status (determined by rapid urease test with or without histological examination, positive if either test positive); (5) Bleeding severity: GBS, Rockall score. Treatment-related data : (1) Endoscopic hemostatic treatment (injection, thermal coagulation, clips, etc.); (2) Drug treatment: proton pump inhibitor (PPI) administration method (intravenous, oral) and duration; (3) Transfusion: red blood cell and plasma transfusion volume; (4) Antiplatelet drug management: discontinuation, continued use, or change to other drugs. 2.4 Follow-up and Outcome Indicators 2.4.1 Follow-up Methods Patient prognosis was tracked through outpatient visits, telephone follow-up, and electronic medical record system. Follow-up period was 12 months after the bleeding event, with data collection ending on December 31, 2024. 2.4.2 Primary Outcome Indicators (1) Cardiovascular Events (CVE) Including: cardiac death; non-fatal myocardial infarction; non-fatal stroke; urgent revascularization (defined as percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG] performed within 24 hours after bleeding due to acute coronary syndrome [ACS] or severe myocardial ischemia); rehospitalization for unstable angina or heart failure. Observation time windows: 30 days, 90 days, 180 days, and 12 months after bleeding. If the same patient experienced multiple cardiovascular events in different time windows, only the first event was recorded for survival analysis, but all events were included in event counts and subgroup analyses. (2) Rebleeding Events Defined as recurrence of any of the following after initial bleeding was controlled by endoscopic or medical treatment with no active bleeding signs within 72 hours and stable vital signs: recurrence of hematemesis and/or melena; hemoglobin decrease ≥ 20g/L without other explanation; need for blood transfusion or repeat endoscopic intervention; death from gastrointestinal bleeding. Observation time windows: 30 days, 90 days, 180 days, and 12 months after bleeding. If the same patient experienced multiple rebleeding events in different time windows, only the first rebleeding event was recorded for survival analysis, but all rebleeding events were included in event counts. 2.4.3 Secondary Outcome Indicators Prespecified secondary outcome indicators : (1) All-cause mortality; (2) Composite endpoint events (defined as occurrence of both cardiovascular events and rebleeding during follow-up period, regardless of event sequence); (3) Endoscopic re-intervention rate; (4) Transfusion volume; (5) Bleeding-related complications: hemorrhagic shock, multiple organ dysfunction, aspiration pneumonia, etc. Exploratory secondary outcome indicators (6) Antiplatelet drug reinitiation time and adverse event rate within 30 days after reinitiation; (7) Ulcer healing rate (evaluated in patients who underwent repeat endoscopy during follow-up, patients without repeat endoscopy treated as missing data, analyzed using available case analysis); (8) Time required for hemoglobin recovery to normal or baseline levels. Results of exploratory secondary outcome indicators will be interpreted cautiously to avoid over-inference. 2.5 Related Definitions Successful hemostasis No active bleeding signs within 72 hours after endoscopic or medical treatment, stable vital signs, stable or rising hemoglobin. High-risk bleeding patients : Meeting any of the following criteria: Rockall score ≥ 6; GBS score ≥ 12; Forrest Ia-Ib grade on endoscopy. High-risk cardiovascular event patients : Meeting any of the following criteria: Recent (< 1 month) ACS or recent (< 3 months) post-PCI; recent ( 140; cardiac function NYHA class III-IV. 2.6 Statistical Methods Statistical analysis was performed using SPSS 26.0 and R 4.2.0 software. Continuous variables were assessed for normality using Shapiro-Wilk test. Those following normal distribution were expressed as mean ± standard deviation, with independent samples t-test for group comparisons; non-normally distributed variables were expressed as median (interquartile range) [M(IQR)], with Mann-Whitney U test for group comparisons. Categorical variables were expressed as number (percentage) [n(%)], with χ² test or Fisher's exact test (when theoretical frequency < 5) for group comparisons. Cox proportional hazards regression models were used to analyze risk factors for cardiovascular events and rebleeding. Variables with P 0.05) for multivariate analysis, and collinearity diagnosis performed (variance inflation factor [VIF] > 5 indicating collinearity). Prediction scoring models were constructed based on β coefficients of independent risk factors in multivariate analysis. Kaplan-Meier method was used to plot survival curves, with Log-rank test comparing cumulative event rates between different groups. Time-dependent receiver operating characteristic (ROC) curves were used to assess model discrimination, calculating area under the curve (AUC) and 95% confidence interval (95%CI) at different time points. Hosmer-Lemeshow test assessed model calibration (P > 0.05 indicating good calibration). Bootstrap method (1000 resampling iterations) was used for internal validation to assess model stability. Two-tailed tests were used, with P < 0.05 considered statistically significant. 3 Results 3.1 Patient Baseline Characteristics This study finally included 213 patients with antiplatelet drug-related gastric ulcer bleeding, with mean age (68.4 ± 10.2) years and 64.8% males. Patients were divided into cardiovascular event group (n = 48) and no cardiovascular event group (n = 165) based on whether cardiovascular events occurred during follow-up, and into rebleeding group (n = 56) and no rebleeding group (n = 157) based on whether rebleeding occurred. Patients in the cardiovascular event group were older (P < 0.001), with higher proportions of myocardial infarction history (P = 0.002), heart failure (P < 0.001), and diabetes (P = 0.008), higher GRACE scores (P < 0.001) and CHADS₂ scores (P < 0.001), lower systolic blood pressure at admission (P = 0.015), and lower serum albumin levels (P = 0.003). Patients in the rebleeding group had higher GBS scores (P < 0.001) and Rockall scores (P < 0.001), higher proportion of Forrest Ia-Ib grade (P < 0.001), higher Hp infection rate (P = 0.006), and more patients discontinued antiplatelet drugs (P < 0.001) (Table 1 , Table 2 ). Table 1 Comparison of Baseline Characteristics in Patients with Different Cardiovascular Event Outcomes Variable Cardiovascular Event Group (n = 48) No Cardiovascular Event Group (n = 165) Test Statistic P value Demographic Data Age (years) 73.6 ± 8.9 66.8 ± 10.1 t = 4.29 < 0.001 Male [n(%)] 33(68.8) 105(63.6) χ²=0.45 0.502 BMI (kg/m²) 23.8 ± 3.2 24.3 ± 3.5 t = 0.92 0.358 Smoking history [n(%)] 22(45.8) 68(41.2) χ²=0.34 0.562 Alcohol consumption history [n(%)] 15(31.3) 45(27.3) χ²=0.30 0.586 Cardiovascular Disease-Related Data Coronary heart disease [n(%)] 42(87.5) 132(80.0) χ²=1.45 0.229 Myocardial infarction history [n(%)] 28(58.3) 58(35.2) χ²=9.14 0.002 Heart failure [n(%)] 22(45.8) 32(19.4) χ²=14.81 < 0.001 Cerebrovascular disease [n(%)] 18(37.5) 52(31.5) χ²=0.66 0.416 Peripheral vascular disease [n(%)] 8(16.7) 21(12.7) χ²=0.54 0.464 Hypertension [n(%)] 38(79.2) 118(71.5) χ²=1.23 0.268 Diabetes [n(%)] 26(54.2) 56(33.9) χ²=7.02 0.008 Dyslipidemia [n(%)] 32(66.7) 98(59.4) χ²=0.89 0.346 NYHA class III-IV [n(%)] 18(37.5) 28(17.0) χ²=10.17 0.001 GRACE score 156.3 ± 28.4 128.7 ± 32.5 t = 5.42 < 0.001 CHADS₂ score 3.2 ± 1.1 2.3 ± 1.2 t = 4.72 < 0.001 Antiplatelet Drug Use Dual antiplatelet therapy [n(%)] 35(72.9) 112(67.9) χ²=0.48 0.487 Aspirin dose 100mg [n(%)] 40(83.3) 138(83.6) χ²=0.00 0.956 Clopidogrel dose 75mg [n(%)] 32(66.7) 105(63.6) χ²=0.16 0.693 Combined anticoagulants [n(%)] 12(25.0) 28(17.0) χ²=1.71 0.191 Duration of medication (months) 8.5(4.0–18.0) 9.0(3.0–24.0) Z=-0.45 0.653 Discontinued antiplatelet drugs [n(%)] 15(31.3) 68(41.2) χ²=1.68 0.195 Table 2 Comparison of Baseline Characteristics in Patients with Different Rebleeding Outcomes Variable Rebleeding Group (n = 56) No Rebleeding Group (n = 157) Test Statistic P value Bleeding-Related Data Hematemesis [n(%)] 42(75.0) 98(62.4) χ²=3.07 0.08 Melena [n(%)] 52(92.9) 145(92.4) χ²=0.01 0.906 Hematochezia [n(%)] 8(14.3) 15(9.6) χ²=0.98 0.323 Systolic blood pressure at admission (mmHg) 102.5 ± 18.6 110.3 ± 20.4 t = 2.59 0.01 Heart rate at admission (beats/min) 96.8 ± 16.2 88.4 ± 14.7 t = 3.63 < 0.001 Hemoglobin (g/L) 68.4 ± 18.5 75.2 ± 20.3 t = 2.28 0.024 Platelet count (×10⁹/L) 198.5 ± 62.3 215.4 ± 68.7 t = 1.67 0.097 Alanine aminotransferase (ALT) (U/L) 35.6 ± 18.2 32.8 ± 16.5 t = 1.08 0.282 Aspartate aminotransferase (AST) (U/L) 38.4 ± 20.1 34.5 ± 17.8 t = 1.38 0.17 Serum albumin (g/L) 32.5 ± 5.8 35.2 ± 5.4 t = 3.20 0.002 Creatinine (µmol/L) 95.6 ± 28.4 88.3 ± 25.6 t = 1.81 0.072 PT (s) 13.2 ± 2.1 12.8 ± 1.8 t = 1.38 0.169 APTT (s) 34.6 ± 8.5 32.8 ± 7.2 t = 1.55 0.123 INR 1.18 ± 0.24 1.12 ± 0.21 t = 1.81 0.072 Endoscopic Examination Ulcer location [n(%)] χ²=2.18 0.536 Gastric antrum 32(57.1) 95(60.5) Gastric body 18(32.1) 48(30.6) Gastric fundus 4(7.1) 10(6.4) Multiple sites 2(3.6) 4(2.5) Ulcer diameter ≥ 2cm [n(%)] 32(57.1) 65(41.4) χ²=4.18 0.041 Forrest Ia-Ib grade [n(%)] 28(50.0) 42(26.8) χ²=10.61 0.001 Forrest IIa grade [n(%)] 20(35.7) 68(43.3) χ²=1.03 0.311 Forrest IIb-IIc grade [n(%)] 8(14.3) 47(29.9) χ²=5.41 0.02 Hp infection [n(%)] 38(67.9) 78(49.7) χ²=5.71 0.017 Bleeding Severity Scores GBS score 11.8 ± 3.2 8.6 ± 3.4 t = 6.28 < 0.001 Rockall score 6.5 ± 1.8 4.8 ± 1.9 t = 5.94 < 0.001 Treatment-Related Data Endoscopic hemostatic treatment [n(%)] 48(85.7) 110(70.1) χ²=5.67 0.017 Intravenous PPI treatment [n(%)] 56(100.0) 155(98.7) - 0.547* PPI treatment duration (days) 9.2 ± 2.8 7.8 ± 2.5 t = 3.52 < 0.001 Red blood cell transfusion (U) 4.5(2.0–6.0) 2.0(0–4.0) Z=-4.82 < 0.001 Plasma transfusion (ml) 400(0-600) 200(0-400) Z=-3.15 0.002 Discontinued antiplatelet drugs [n(%)] 35(62.5) 48(30.6) χ²=18.31 < 0.001 *Fisher's exact test used 3.2 Follow-up Results During follow-up, 48 patients (22.5%) experienced cardiovascular events, and 56 patients (26.3%) experienced rebleeding. Among them, 16 patients (7.5%) experienced both cardiovascular events and rebleeding during follow-up (composite endpoint events, regardless of event sequence), and 15 patients (7.0%) died from all causes. The occurrence of cardiovascular events was mainly concentrated within 30 days after bleeding (n = 22, 45.8%) and within 90 days (n = 35, 72.9%). Specific event types included: 8 cardiac deaths, 15 non-fatal myocardial infarctions, 12 non-fatal strokes, 9 urgent revascularizations, and 20 rehospitalizations for cardiovascular disease (total of 64 events, with some patients experiencing multiple cardiovascular events during follow-up). Among rebleeding events, 32 cases (57.1%) occurred within 30 days, 45 cases (80.4%) within 90 days, and 12 cases required repeat endoscopic intervention. Table 3 presents patient numbers based on first events, used for survival analysis. Table 3 shows cumulative numbers of patients experiencing first events in different time windows, consistent with survival analysis principles where each patient is counted only at first event occurrence (Table 3 ). Table 3 Occurrence of Major Outcome Events During Follow-up Time Window Cardiovascular Events [n(%)] Rebleeding Events [n(%)] Composite Endpoint [n(%)]* All-Cause Mortality [n(%)] 30 days 22(10.3) 32(15.0) 8(3.8) 5(2.3) 90 days 35(16.4) 45(21.1) 12(5.6) 9(4.2) 180 days 42(19.7) 51(23.9) 14(6.6) 12(5.6) 12 months 48(22.5) 56(26.3) 16(7.5) 15(7.0) *Composite endpoint defined as patients experiencing both cardiovascular events and rebleeding during follow-up period, regardless of event sequence. Data in table represent cumulative patient numbers reaching composite endpoint by each time point, based on time of first reaching composite endpoint. Kaplan-Meier survival analysis showed that 12-month cumulative cardiovascular event rate was 22.8% (95%CI: 17.3%-28.3%), and cumulative rebleeding rate was 26.6% (95%CI: 20.8%-32.4%). High-risk cardiovascular event patients had significantly higher cardiovascular event rates (38.2% vs 12.8%, Log-rank P < 0.001) and high-risk bleeding patients had significantly higher rebleeding rates (42.6% vs 15.9%, Log-rank P < 0.001). 3.3 Risk Factor Analysis for Cardiovascular Events Univariate Cox regression analysis showed that age, myocardial infarction history, heart failure, diabetes, NYHA class III-IV, GRACE score, CHADS₂ score, systolic blood pressure at admission, and serum albumin were associated with cardiovascular events (P < 0.10). Multivariate Cox regression analysis results showed that age ≥ 70 years (hazard ratio [HR] = 2.48, 95%CI: 1.35–4.56, P = 0.004), heart failure (HR = 2.31, 95%CI: 1.28–4.17, P = 0.006), GRACE score > 140 (HR = 2.92, 95%CI: 1.58–5.39, P = 0.001), and serum albumin < 35g/L (HR = 1.85, 95%CI: 1.04–3.29, P = 0.037) were independent risk factors for cardiovascular events (Table 4 ). Table 4 Cox Regression Analysis for Cardiovascular Events Variable Univariate Analysis Multivariate Analysis HR(95%CI) P value HR(95%CI) P value Age ≥ 70 years 2.86(1.58–5.18) 0.001 2.48(1.35–4.56) 0.004 Male 1.26(0.70–2.28) 0.444 - - BMI < 18.5kg/m² 1.42(0.65–3.11) 0.378 - - Myocardial infarction history 2.54(1.43–4.51) 0.001 Not included† Not included† Heart failure 3.52(2.02–6.13) < 0.001 2.31(1.28–4.17) 0.006 Diabetes 2.28(1.28–4.06) 0.005 Not included† Not included† NYHA class III-IV 2.84(1.58–5.09) 140 3.68(2.03–6.66) < 0.001 2.92(1.58–5.39) 0.001 CHADS₂ score ≥ 3 2.95(1.61–5.40) < 0.001 Not included† Not included† Systolic blood pressure at admission < 100mmHg 1.78(0.95–3.34) 0.072 Not included† Not included† Serum albumin < 35g/L 2.34(1.32–4.15) 0.004 1.85(1.04–3.29) 0.037 Dual antiplatelet therapy 1.26(0.69–2.30) 0.451 - - Discontinued antiplatelet drugs 0.68(0.36–1.29) 0.237 - - †Stepwise backward method used for variable selection (removal criterion P > 0.05). Reasons some univariately significant variables were not included in the final model include: (1) Collinearity with retained variables (collinearity diagnosis VIF > 5), preferentially retaining variables with stronger clinical significance or higher predictive efficacy; (2) No longer having independent predictive value in multivariate model (P > 0.05). For example, myocardial infarction history, diabetes, NYHA classification showed collinearity with GRACE score, and CHADS₂ score components overlapped with variables such as age, heart failure, and diabetes. 3.4 Risk Factor Analysis for Rebleeding Univariate Cox regression analysis showed that systolic blood pressure at admission, heart rate at admission, hemoglobin, serum albumin, ulcer diameter, Forrest classification, Hp infection, GBS score, Rockall score, endoscopic hemostatic treatment, transfusion volume, and discontinuation of antiplatelet drugs were associated with rebleeding (P < 0.10). Multivariate Cox regression analysis results showed that Forrest Ia-Ib grade (HR = 3.15, 95%CI: 1.76–5.64, P < 0.001), Rockall score ≥ 6 (HR = 2.68, 95%CI: 1.52–4.73, P = 0.001), Hp infection (HR = 1.92, 95%CI: 1.09–3.38, P = 0.024), and discontinuation of antiplatelet drugs (HR = 2.58, 95%CI: 1.48–4.50, P = 0.001) were independent risk factors for rebleeding (Table 5 ). Table 5 Cox Regression Analysis for Rebleeding Variable Univariate Analysis Multivariate Analysis HR(95%CI) P value HR(95%CI) P value Age ≥ 70 years 1.18(0.69–2.02) 0.549 - - Hematemesis 1.78(0.99–3.20) 0.054 Not included† Not included† Systolic blood pressure at admission 100 beats/min 1.85(1.08–3.17) 0.025 Not included† Not included† Hemoglobin < 70g/L 1.82(1.07–3.10) 0.027 Not included† Not included† Platelet count 1.2 1.64(0.94–2.86) 0.083 Not included† Not included† Serum albumin 110µmol/L 1.58(0.86–2.90) 0.14 - - Ulcer diameter ≥ 2cm 1.82(1.07–3.10) 0.027 Not included† Not included† Forrest Ia-Ib grade 3.86(2.25–6.62) < 0.001 3.15(1.76–5.64) < 0.001 Hp infection 2.14(1.24–3.69) 0.006 1.92(1.09–3.38) 0.024 GBS score ≥ 12 3.42(1.99–5.87) < 0.001 Not included† Not included† Rockall score ≥ 6 3.28(1.92–5.60) < 0.001 2.68(1.52–4.73) 0.001 Endoscopic hemostatic treatment 2.58(1.38–4.82) 0.003 Not included† Not included† Red blood cell transfusion ≥ 4U 2.45(1.44–4.17) 0.001 Not included† Not included† Discontinued antiplatelet drugs 3.72(2.18–6.35) 0.05). Reasons some univariately significant variables were not included in the final model include: (1) Collinearity with retained variables (collinearity diagnosis VIF > 5); (2) No longer having independent predictive value in multivariate model (P > 0.05). For example, systolic blood pressure at admission, heart rate, hemoglobin, serum albumin showed collinearity with GBS and Rockall scores, as these scores already incorporate multiple clinical indicators; ulcer diameter correlated with Forrest classification; transfusion volume correlated with bleeding severity scores. Comprehensive scoring indicators with stronger clinical application value and predictive efficacy were preferentially retained. 3.5 Construction and Validation of Dual Risk Prediction Model Based on multivariate Cox regression analysis results, dual risk prediction models for cardiovascular events and rebleeding were constructed. According to β coefficients of each independent risk factor, they were standardized and converted to integer scores. The cardiovascular event prediction model included 4 variables: age ≥ 70 years (2 points), heart failure (2 points), GRACE score > 140 (3 points), serum albumin < 35g/L (2 points), with total score 0–9 points. The rebleeding prediction model included 4 variables: Forrest Ia-Ib grade (3 points), Rockall score ≥ 6 (3 points), Hp infection (2 points), discontinuation of antiplatelet drugs (3 points), with total score 0–11 points. Based on scores, patients were classified into low-risk (cardiovascular events: 0–3 points; rebleeding: 0–4 points), medium-risk (cardiovascular events: 4–6 points; rebleeding: 5–8 points), and high-risk groups (cardiovascular events: ≥7 points; rebleeding: ≥9 points). The 12-month cardiovascular event rates in low-, medium-, and high-risk groups were 8.2%, 22.6%, and 57.1% (P < 0.001), respectively, while rebleeding rates were 10.5%, 28.4%, and 50.0% (P < 0.001), respectively (Table 6 、Figure 1 ). Table 6 Risk Stratification and Event Rates of Dual Risk Prediction Model Risk Stratification Cardiovascular Event Model Rebleeding Model Patient Number 12-Month Event Rate (%) Patient Number 12-Month Event Rate (%) Low-risk group 85 8.2(7/85) 76 10.5(8/76) Medium-risk group 93 22.6(21/93) 95 28.4(27/95) High-risk group 35 57.1(20/35) 42 50.0(21/42) Total 213 22.5(48/213) 213 26.3(56/213) P value - < 0.001 - < 0.001 ROC curve analysis showed that the cardiovascular event prediction model had AUCs of 0.782 (95%CI: 0.693–0.871), 0.765 (95%CI: 0.689–0.841), and 0.758 (95%CI: 0.687–0.829) for predicting cardiovascular events at 30 days, 90 days, and 12 months; the rebleeding prediction model had AUCs of 0.812 (95%CI: 0.738–0.886), 0.796 (95%CI: 0.729–0.863), and 0.781 (95%CI: 0.716–0.846) for predicting rebleeding at 30 days, 90 days, and 12 months (Table 7 、Figure 2 ). Table 7 ROC Curve Analysis of Dual Risk Prediction Model Prediction Time Cardiovascular Event Model Rebleeding Model AUC(95%CI) P value AUC(95%CI) P value 30 days 0.782(0.693–0.871) < 0.001 0.812(0.738–0.886) < 0.001 90 days 0.765(0.689–0.841) < 0.001 0.796(0.729–0.863) < 0.001 180 days 0.761(0.687–0.835) < 0.001 0.788(0.722–0.854) < 0.001 12 months 0.758(0.687–0.829) < 0.001 0.781(0.716–0.846) < 0.001 Hosmer-Lemeshow test showed both models had good calibration (cardiovascular event model: χ²=6.82, P = 0.556; rebleeding model: χ²=8.35, P = 0.400). Bootstrap internal validation (1000 resampling iterations) showed mean AUCs of 0.752 (95%CI: 0.731–0.773) for the cardiovascular event model and 0.776 (95%CI: 0.755–0.797) for the rebleeding model, demonstrating good model stability. 3.6 Secondary Outcome Indicators During follow-up, 15 patients (7.0%) died from all causes, including 8 cardiac deaths, 5 bleeding-related deaths, and 2 deaths from other causes. 16 patients (7.5%) experienced both cardiovascular events and rebleeding during follow-up (composite endpoint events). 12 patients (5.6%) required endoscopic re-intervention. The incidence of bleeding-related complications was 18.3% (39/213), including 22 cases of hemorrhagic shock, 9 cases of multiple organ dysfunction, and 8 cases of aspiration pneumonia. Regarding antiplatelet drug management, 83 patients (39.0%) discontinued antiplatelet drugs, of which 62 (74.7%) reinitiated after successful hemostasis, with median reinitiation time of 5 days (IQR: 3–7 days). Within 30 days after reinitiation, cardiovascular event rate was 8.1% (5/62) and rebleeding rate was 12.9% (8/62). Among 108 patients who underwent repeat endoscopy during follow-up, 89 (82.4%) had ulcer healing and 19 (17.6%) had unhealed or recurrent ulcers. Median ulcer healing time was 8 weeks (IQR: 6–12 weeks). Median time for hemoglobin recovery to normal or baseline levels was 4 weeks (IQR: 3–6 weeks). Median transfusion volume for all patients was 2U (IQR: 0-4U), with 78 patients (36.6%) requiring transfusion; median transfusion volume for transfused patients was 4U (IQR: 2-6U) (Table 8 ). Table 8 Secondary Outcome Indicators Indicator Results All-cause mortality [n(%)] 15(7.0) Cardiac death 8(3.8) Bleeding-related death 5(2.3) Other causes of death 2(0.9) Composite endpoint events [n(%)]* 16(7.5) Endoscopic re-intervention [n(%)] 12(5.6) Bleeding-related complications [n(%)] 39(18.3) Hemorrhagic shock 22(10.3) Multiple organ dysfunction 9(4.2) Aspiration pneumonia 8(3.8) Discontinued antiplatelet drugs [n(%)] 83(39.0) Antiplatelet drug reinitiation rate [n(%)]† 62/83(74.7) Reinitiation time (days)† 5(3–7) Cardiovascular events within 30 days after reinitiation [n(%)]† 5/62(8.1) Rebleeding within 30 days after reinitiation [n(%)]† 8/62(12.9) Ulcer healing rate [n(%)]‡ 89/108(82.4) Ulcer healing time (weeks)‡ 8(6–12) Hemoglobin recovery time (weeks) 4(3–6) Required transfusion [n(%)] 78(36.6) Red blood cell transfusion for all patients (U) 2(0–4) Red blood cell transfusion for transfused patients (U)† 4(2–6) Plasma transfusion (ml)§ 200(0-400) Patients receiving plasma transfusion [n(%)] 52(24.4) *Composite endpoint defined as patients experiencing both cardiovascular events and rebleeding during follow-up period, regardless of event sequence; †Based on 83 patients who discontinued antiplatelet drugs or 62 patients who reinitiated antiplatelet drugs; ‡Based on 108 patients who underwent repeat endoscopy during follow-up; §Median plasma transfusion volume for all patients 4 Discussion This study found that age ≥ 70 years, heart failure, GRACE score > 140, and serum albumin < 35g/L were independent risk factors for cardiovascular events. This is largely consistent with previous research results on prognosis in patients with ACS [ 9 ]. Elderly patients often have multiple cardiovascular risk factors, impaired vascular endothelial function, and greater atherosclerotic burden, making them more susceptible to cardiovascular events under acute bleeding stress [ 10 ]. Patients with heart failure have decreased cardiac reserve function, and acute blood loss resulting in reduced blood volume and decreased hemoglobin can exacerbate myocardial ischemia and hypoxia, triggering heart failure decompensation or acute coronary events [ 11 ]. The GRACE score, as a classic tool for risk assessment in ACS patients, integrates multiple prognostic factors including age, heart rate, systolic blood pressure, creatinine level, cardiac arrest history, cardiac markers, and ST-segment changes [ 12 ]. This study confirmed that patients with GRACE score > 140 had nearly 3-fold increased cardiovascular event risk, indicating this scoring system is equally applicable for cardiovascular risk assessment in patients with antiplatelet drug-related bleeding. Notably, serum albumin level, as a comprehensive indicator of nutritional status and inflammatory response, is closely related to cardiovascular disease prognosis [ 13 ]. Hypoalbuminemia reflects chronic consumption state and systemic inflammatory response, potentially increasing cardiovascular event risk through multiple mechanisms affecting vascular endothelial function, coagulation system, and immune function [ 14 ]. Independent risk factors for rebleeding identified in this study included Forrest Ia-Ib grade, Rockall score ≥ 6, Hp infection, and discontinuation of antiplatelet drugs. Forrest classification is an important endoscopic indicator for assessing peptic ulcer bleeding risk, with Ia grade (spurting bleeding) and Ib grade (oozing bleeding) representing active bleeding states with significantly increased rebleeding risk [ 15 ]. The Rockall score comprehensively considers patient age, shock state, comorbidities, endoscopic diagnosis, and bleeding stigmata, and is an established scoring system for predicting gastrointestinal bleeding prognosis [ 16 ]. This study's results are consistent with international guideline recommendations, confirming the predictive value of these scoring tools in patients with antiplatelet drug-related bleeding. The association between Hp infection and rebleeding was confirmed in this study, with infected patients having 92% increased rebleeding risk. Hp infection increases the risk of peptic ulcer and its complications through mechanisms including destroying gastric mucosal barrier, inducing chronic inflammatory response, and inhibiting ulcer healing [ 17 ]. Multiple studies have shown that Hp eradication can significantly reduce peptic ulcer recurrence rate and rebleeding risk [ 18 ]. Therefore, active screening and eradication of Hp has important clinical significance for patients with antiplatelet drug-related ulcer bleeding. Notably, this study found that discontinuation of antiplatelet drugs paradoxically increased rebleeding risk (HR = 2.58). This seemingly contradictory result may reflect the "confounding by indication" phenomenon in clinical decision-making [ 19 ]: patients with higher bleeding risk are more likely to have antiplatelet drugs discontinued, and these patients inherently have higher rebleeding tendency. Additionally, discontinuation of antiplatelet drugs may increase rebleeding risk through the following mechanisms: (1) Rapid recovery of platelet function followed by "rebound" thrombosis leading to ulcer local microcirculatory disturbance, affecting ulcer healing [ 20 ]; (2) Patients who discontinue drugs often have more severe bleeding (such as Forrest Ia-Ib grade) and more complications; (3) Physicians may adopt more conservative PPI treatment strategies for patients who discontinue drugs due to concerns about cardiovascular risk. This finding emphasizes the complexity of antiplatelet drug management decisions, requiring comprehensive consideration of bleeding and thrombotic risks. Unlike previous single-dimension risk assessment tools, this study is the first to establish a dual risk model simultaneously predicting cardiovascular events and rebleeding [ 21 ]. This model has the following innovative aspects and clinical application value: First, the model is constructed based on routinely available clinical indicators, including demographic characteristics, cardiovascular scores, endoscopic examination results, and laboratory indicators, requiring no additional tests and facilitating clinical application. Second, through risk stratification (low, medium, high risk), patient populations with different risk characteristics can be identified. Low-risk group patients have low cardiovascular event and rebleeding rates (8.2% and 10.5%), allowing consideration of early antiplatelet therapy reinitiation; high-risk group patients have significantly elevated event rates (57.1% and 50.0%), requiring more aggressive monitoring and intervention measures [ 22 ]. Furthermore, dual risk assessment helps guide individualized antiplatelet drug management strategies. For patients at high cardiovascular risk but low bleeding risk, antiplatelet therapy should be resumed as early as possible; for patients at high bleeding risk but low cardiovascular risk, drug discontinuation time can be appropriately extended or regimens adjusted [ 23 ]. For dual high-risk patients, multidisciplinary team collaboration is needed, balancing treatment options while strengthening monitoring. The model's good predictive performance (AUC > 0.75) and calibration provide reliable evidence for clinical decision-making. Bootstrap internal validation showed the model has good stability, but external cohort validation is still needed to confirm its generalizability [ 24 ]. In this study, 39.0% of patients discontinued antiplatelet drugs, of which 74.7% reinitiated after successful hemostasis, with median reinitiation time of 5 days. Cardiovascular event rate within 30 days after reinitiation was 8.1%, and rebleeding rate was 12.9%, indicating antiplatelet drug management still faces challenges. Recent studies suggest that for patients with acute gastrointestinal bleeding combined with high-risk cardiovascular disease, early (within 3–7 days) reinitiation of antiplatelet therapy may be relatively safe under effective hemostasis [ 25 ]. However, reinitiation timing and strategy require individualized decision-making, comprehensively considering bleeding control status, cardiovascular disease severity, patient compliance, and other factors [ 26 ]. This study found that discontinuation of antiplatelet drugs was associated with increased rebleeding risk, suggesting clinicians need to be more cautious in decision-making. For patients who must discontinue drugs, PPI treatment should be strengthened, Hp actively eradicated, ulcer healing closely monitored, and timing for reinitiation of antiplatelet therapy assessed as early as possible [ 27 ]. Strengths of this study include: (1) Relatively large sample size with sufficient follow-up time (12 months) to capture key clinical events; (2) Simultaneous assessment of dual outcomes of cardiovascular events and rebleeding, filling gaps in previous research; (3) Strict inclusion and exclusion criteria ensuring study population homogeneity; (4) Identification of independent risk factors through multivariate analysis, with constructed prediction models having good statistical performance. However, this study also has limitations. First, as a single-center retrospective study, there may be selection bias and information bias, with study result generalizability requiring verification. Second, some secondary outcome indicators (such as ulcer healing rate) have missing data to some extent, potentially affecting result reliability. Third, antiplatelet drug management strategy formulation is influenced by multiple factors, and this study could not completely control confounding factors such as physician decision preferences. Additionally, the study did not include factors such as genetic polymorphisms and platelet function testing that may affect prognosis. Finally, the model has not yet been externally validated, and its predictive performance in different medical institutions and patient populations requires further evaluation. Based on this study's results, future research can be conducted in the following directions: (1) Conduct multicenter prospective cohort studies, expand sample size and perform external validation to improve model generalizability; (2) Explore incremental value of novel biomarkers (such as circulating microRNAs, inflammatory factors) for prognosis prediction; (3) Conduct randomized controlled trials comparing effects of different antiplatelet drug management strategies (such as early reinitiation vs. delayed reinitiation, drug dose adjustment) on dual outcomes; (4) Optimize prediction models using advanced statistical methods such as machine learning; (5) Develop clinical decision support systems based on prediction models and evaluate their actual impact on clinical practice and patient prognosis. The dual risk prediction model established in this study can effectively identify high-risk populations for cardiovascular events and rebleeding in patients with antiplatelet drug-related gastric ulcer bleeding, providing scientific evidence for clinicians' individualized treatment decisions and antiplatelet drug management. This model has good discrimination and calibration, and being constructed based on routine clinical indicators, has good clinical application prospects. Future multicenter prospective studies are needed for external validation and exploration of clinical decision support systems based on this model to improve patient prognosis. Declarations Funding: This work was supported by the Natural Science Foundation of Fujian Province, China (2024J01122172). Ethics approval and consent to participate Not applicable. Competing interests No conflict of interest exits in this manuscript. Consent for publication Manuscript is approved by all authors for publication. Availability of data and materials The data and materials of this experiment are available. Competing interests No conflict of interest exits in this manuscript. Acknowledgements Not applicable. Authors' contributions Rongrong Chen and Guixi Wu were responsible for the design of the whole study. Mengshi Chen were in charge of experimental operation and drawing.Xueyan Lin submitted the manuscripts.All authors read and approved the final manuscript References Patel PP, Fanaroff AC. Optimal Medical Therapy for Chronic Coronary Disease in 2024: Focus on Antithrombotic Therapy. Med Clin North Am. 2024;108(3):489–507. doi: 10.1016/j.mcna.2023.11.004. Epub 2023 Dec 27. PMID: 38548459. Huang J, Liao F, Tang J, Shu X. Risk factors for gastrointestinal bleeding in patients with cerebral infarction after dual antiplatelet therapy. Clin Neurol Neurosurg. 2023;231:107802. doi: 10.1016/j.clineuro.2023.107802 . Epub 2023 May 25. PMID: 37295199. 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Re-starting anticoagulation and antiplatelets after gastrointestinal bleeding: A systematic review. F1000Res. 2023;12:806. doi: 10.12688/f1000research.135132.1 . PMID: 38966192; PMCID: PMC11222779. Liu CH, Wu YL, Hsu CC, Lee TH. Early Antiplatelet Resumption and the Risks of Major Bleeding After Intracerebral Hemorrhage. Stroke. 2023;54(2):537–545. doi: 10.1161/STROKEAHA.122.040500 . Epub 2023 Jan 9. PMID: 36621820. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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12:34:12","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166313,"visible":true,"origin":"","legend":"","description":"","filename":"0f3023c87e794a80bead33bc96400d4e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8029710/v1/11f89178c3190baa3f279a7b.xml"},{"id":97794170,"identity":"5667f0c5-24d0-4d38-a4ce-b0a454789e4b","added_by":"auto","created_at":"2025-12-09 12:34:12","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171641,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8029710/v1/031f79a46c844ac23ab5e1bb.html"},{"id":97898323,"identity":"568ace0d-73d8-4c0d-bf5b-cad2af489e10","added_by":"auto","created_at":"2025-12-10 15:39:01","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":250115,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival Curves for Cardiovascular Events and Rebleeding in Patients with Different Risk Stratifications.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8029710/v1/9baf2dfcb06e610199275ffe.jpeg"},{"id":97794165,"identity":"3d58233f-011d-4a60-821c-3c82da0431dd","added_by":"auto","created_at":"2025-12-09 12:34:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":378325,"visible":true,"origin":"","legend":"\u003cp\u003eshows the calibration curves for both prediction models\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8029710/v1/a1cabeeeeb1e6bf81b57883e.jpeg"},{"id":106402381,"identity":"d76e8aa7-3d26-4b7e-b975-d7212d741084","added_by":"auto","created_at":"2026-04-08 09:11:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2193460,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8029710/v1/b0382d9f-0864-448d-9597-96e726b6f8fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual Risk Prediction Model for Cardiovascular Events and Rebleeding in Patients with Antiplatelet Drug-Related Gastric Ulcer Bleeding","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAntiplatelet drugs are the cornerstone of secondary prevention of cardiovascular disease and can significantly reduce the risk of major adverse cardiovascular events such as myocardial infarction and stroke [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, while beneficial, antiplatelet therapy also significantly increases the risk of gastrointestinal bleeding, with gastric ulcer bleeding being one of the most common serious complications [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Epidemiological studies show that the risk of upper gastrointestinal bleeding increases 2\u0026ndash;4 fold in patients on long-term aspirin, with dual antiplatelet therapy (DAPT) further elevating this risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For patients with antiplatelet drug-related gastric ulcer bleeding, clinicians face a therapeutic dilemma: continuing antiplatelet therapy may lead to rebleeding, while discontinuing or delaying reinitiation of antiplatelet drugs may increase cardiovascular event risk, both potentially life-threatening [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrently, assessment tools for upper gastrointestinal bleeding risk such as the Glasgow-Blatchford Score (GBS) and Rockall score mainly focus on bleeding severity and rebleeding risk [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], while cardiovascular risk assessment tools such as the Global Registry of Acute Coronary Events (GRACE) score and CHADS₂ score emphasize cardiovascular event prediction [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these single-dimension scoring systems cannot comprehensively assess the dual risks in patients with antiplatelet drug-related gastric ulcer bleeding and cannot provide sufficient evidence for clinical decision-making. Previous studies have shown that prognosis in these patients is affected by multiple factors, including baseline cardiovascular status, bleeding severity, and antiplatelet drug management strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but prediction models comprehensively assessing both cardiovascular events and rebleeding risks have not been established.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to systematically analyze independent risk factors for cardiovascular events and rebleeding in patients with antiplatelet drug-related gastric ulcer bleeding through a retrospective cohort study, construct a dual risk prediction model for this special population, and provide scientific evidence for clinicians in antiplatelet drug management, individualized treatment decisions, and patient prognosis assessment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Subjects\u003c/h2\u003e\u003cp\u003eThis study was a single-center retrospective cohort study. Clinical data of patients with antiplatelet drug-related gastric ulcer bleeding hospitalized in our hospital from January 2021 to December 2023 were collected. This study was approved by our hospital's ethics committee and informed consent was waived due to its retrospective nature.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003e(1) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) Long-term use of antiplatelet drugs (aspirin, clopidogrel, ticagrelor, etc.) for \u0026ge;\u0026thinsp;1 month due to cardiovascular disease (coronary heart disease, cerebrovascular disease, peripheral vascular disease, etc.); (3) Gastric ulcer bleeding confirmed by endoscopy (Forrest classification Ia, Ib, IIa, IIb, or IIc grade, excluding grade III white ulcer base); (4) Clinical manifestations of hematemesis and/or melena, with hemoglobin decreased\u0026thinsp;\u0026ge;\u0026thinsp;20g/L compared to baseline stable values before admission; (5) Complete clinical data and available follow-up data.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003cp\u003e(1) Non-ulcerative upper gastrointestinal bleeding (such as esophageal and gastric variceal rupture bleeding, Mallory-Weiss syndrome, etc.); (2) Combined gastrointestinal bleeding from other causes (gastrointestinal tumors, vascular malformations, etc.); (3) Combined severe coagulation dysfunction or hematological diseases; (4) Combined active bleeding at other sites; (5) Regular use of non-steroidal anti-inflammatory drugs (NSAIDs) or glucocorticoids that may affect gastrointestinal bleeding; (6) Combined terminal malignancy or expected survival\u0026thinsp;\u0026lt;\u0026thinsp;6 months; (7) Previous partial gastrectomy.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eA total of 213 patients meeting the criteria were finally included.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Collection\u003c/h2\u003e\u003cp\u003eThe following data were collected through the hospital electronic medical record system (except as specifically noted, baseline data were patient data at admission):\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDemographic data\u003c/strong\u003e\u003cp\u003eAge, gender, body mass index (BMI), smoking history, alcohol consumption history.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCardiovascular disease-related data\u003c/b\u003e: (1) Types of cardiovascular disease: coronary heart disease, myocardial infarction history, heart failure, cerebrovascular disease, peripheral vascular disease; (2) Cardiovascular risk factors: hypertension, diabetes, dyslipidemia; (3) Cardiac function classification (New York Heart Association [NYHA] classification); (4) Cardiovascular risk scores: GRACE score, CHADS₂ score, etc.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAntiplatelet drug use\u003c/strong\u003e\u003cp\u003eDrug types (single or dual antiplatelet therapy), duration of medication, dosage, whether combined with anticoagulants. Antiplatelet drug management strategies were decided by consulting cardiologists based on comprehensive assessment of patient bleeding risk and cardiovascular event risk, following Chinese and international guideline recommendations, recorded as discontinuation, continued use, or change to other drugs.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBleeding-related data\u003c/b\u003e: (1) Clinical manifestations: hematemesis, melena, hematochezia; (2) Vital signs: systolic blood pressure and heart rate at admission; (3) Laboratory indicators: hemoglobin, platelet count, coagulation function (prothrombin time [PT], activated partial thromboplastin time [APTT], international normalized ratio [INR]), liver and kidney function, serum albumin at admission; (4) Endoscopic examination: ulcer location, size, Forrest classification, Helicobacter pylori (Hp) infection status (determined by rapid urease test with or without histological examination, positive if either test positive); (5) Bleeding severity: GBS, Rockall score.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTreatment-related data\u003c/b\u003e: (1) Endoscopic hemostatic treatment (injection, thermal coagulation, clips, etc.); (2) Drug treatment: proton pump inhibitor (PPI) administration method (intravenous, oral) and duration; (3) Transfusion: red blood cell and plasma transfusion volume; (4) Antiplatelet drug management: discontinuation, continued use, or change to other drugs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Follow-up and Outcome Indicators\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Follow-up Methods\u003c/h2\u003e\u003cp\u003ePatient prognosis was tracked through outpatient visits, telephone follow-up, and electronic medical record system. Follow-up period was 12 months after the bleeding event, with data collection ending on December 31, 2024.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Primary Outcome Indicators\u003c/h2\u003e\u003cp\u003e\u003cb\u003e(1) Cardiovascular Events (CVE)\u003c/b\u003e Including: cardiac death; non-fatal myocardial infarction; non-fatal stroke; urgent revascularization (defined as percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG] performed within 24 hours after bleeding due to acute coronary syndrome [ACS] or severe myocardial ischemia); rehospitalization for unstable angina or heart failure. Observation time windows: 30 days, 90 days, 180 days, and 12 months after bleeding. If the same patient experienced multiple cardiovascular events in different time windows, only the first event was recorded for survival analysis, but all events were included in event counts and subgroup analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003e(2) Rebleeding Events\u003c/b\u003e Defined as recurrence of any of the following after initial bleeding was controlled by endoscopic or medical treatment with no active bleeding signs within 72 hours and stable vital signs: recurrence of hematemesis and/or melena; hemoglobin decrease\u0026thinsp;\u0026ge;\u0026thinsp;20g/L without other explanation; need for blood transfusion or repeat endoscopic intervention; death from gastrointestinal bleeding. Observation time windows: 30 days, 90 days, 180 days, and 12 months after bleeding. If the same patient experienced multiple rebleeding events in different time windows, only the first rebleeding event was recorded for survival analysis, but all rebleeding events were included in event counts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Secondary Outcome Indicators\u003c/h2\u003e\u003cp\u003e\u003cb\u003ePrespecified secondary outcome indicators\u003c/b\u003e: (1) All-cause mortality; (2) Composite endpoint events (defined as occurrence of both cardiovascular events and rebleeding during follow-up period, regardless of event sequence); (3) Endoscopic re-intervention rate; (4) Transfusion volume; (5) Bleeding-related complications: hemorrhagic shock, multiple organ dysfunction, aspiration pneumonia, etc.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExploratory secondary outcome indicators\u003c/strong\u003e\u003cp\u003e(6) Antiplatelet drug reinitiation time and adverse event rate within 30 days after reinitiation; (7) Ulcer healing rate (evaluated in patients who underwent repeat endoscopy during follow-up, patients without repeat endoscopy treated as missing data, analyzed using available case analysis); (8) Time required for hemoglobin recovery to normal or baseline levels. Results of exploratory secondary outcome indicators will be interpreted cautiously to avoid over-inference.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Related Definitions\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eSuccessful hemostasis\u003c/strong\u003e\u003cp\u003eNo active bleeding signs within 72 hours after endoscopic or medical treatment, stable vital signs, stable or rising hemoglobin.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh-risk bleeding patients\u003c/b\u003e: Meeting any of the following criteria: Rockall score\u0026thinsp;\u0026ge;\u0026thinsp;6; GBS score\u0026thinsp;\u0026ge;\u0026thinsp;12; Forrest Ia-Ib grade on endoscopy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh-risk cardiovascular event patients\u003c/b\u003e: Meeting any of the following criteria: Recent (\u0026lt;\u0026thinsp;1 month) ACS or recent (\u0026lt;\u0026thinsp;3 months) post-PCI; recent (\u0026lt;\u0026thinsp;6 months) stroke; GRACE score\u0026thinsp;\u0026gt;\u0026thinsp;140; cardiac function NYHA class III-IV.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Methods\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS 26.0 and R 4.2.0 software. Continuous variables were assessed for normality using Shapiro-Wilk test. Those following normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with independent samples t-test for group comparisons; non-normally distributed variables were expressed as median (interquartile range) [M(IQR)], with Mann-Whitney U test for group comparisons. Categorical variables were expressed as number (percentage) [n(%)], with χ\u0026sup2; test or Fisher's exact test (when theoretical frequency\u0026thinsp;\u0026lt;\u0026thinsp;5) for group comparisons.\u003c/p\u003e\u003cp\u003eCox proportional hazards regression models were used to analyze risk factors for cardiovascular events and rebleeding. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate analysis were included, with stepwise backward method (removal criterion P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for multivariate analysis, and collinearity diagnosis performed (variance inflation factor [VIF]\u0026thinsp;\u0026gt;\u0026thinsp;5 indicating collinearity). Prediction scoring models were constructed based on β coefficients of independent risk factors in multivariate analysis. Kaplan-Meier method was used to plot survival curves, with Log-rank test comparing cumulative event rates between different groups.\u003c/p\u003e\u003cp\u003eTime-dependent receiver operating characteristic (ROC) curves were used to assess model discrimination, calculating area under the curve (AUC) and 95% confidence interval (95%CI) at different time points. Hosmer-Lemeshow test assessed model calibration (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating good calibration). Bootstrap method (1000 resampling iterations) was used for internal validation to assess model stability. Two-tailed tests were used, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Patient Baseline Characteristics\u003c/h2\u003e\u003cp\u003eThis study finally included 213 patients with antiplatelet drug-related gastric ulcer bleeding, with mean age (68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2) years and 64.8% males. Patients were divided into cardiovascular event group (n\u0026thinsp;=\u0026thinsp;48) and no cardiovascular event group (n\u0026thinsp;=\u0026thinsp;165) based on whether cardiovascular events occurred during follow-up, and into rebleeding group (n\u0026thinsp;=\u0026thinsp;56) and no rebleeding group (n\u0026thinsp;=\u0026thinsp;157) based on whether rebleeding occurred.\u003c/p\u003e\u003cp\u003ePatients in the cardiovascular event group were older (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher proportions of myocardial infarction history (P\u0026thinsp;=\u0026thinsp;0.002), heart failure (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and diabetes (P\u0026thinsp;=\u0026thinsp;0.008), higher GRACE scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CHADS₂ scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower systolic blood pressure at admission (P\u0026thinsp;=\u0026thinsp;0.015), and lower serum albumin levels (P\u0026thinsp;=\u0026thinsp;0.003). Patients in the rebleeding group had higher GBS scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Rockall scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher proportion of Forrest Ia-Ib grade (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher Hp infection rate (P\u0026thinsp;=\u0026thinsp;0.006), and more patients discontinued antiplatelet drugs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eComparison of Baseline Characteristics in Patients with Different Cardiovascular Event Outcomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eCardiovascular Event Group (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Cardiovascular Event Group (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003eDemographic Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e33(68.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105(63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22(45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68(41.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption history [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15(31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45(27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular Disease-Related Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(87.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132(80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyocardial infarction history [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58(35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=9.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22(45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32(19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=14.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52(31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral vascular disease [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38(79.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118(71.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26(54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=7.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98(59.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNYHA class III-IV [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28(17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=10.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRACE score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e156.3\u0026thinsp;\u0026plusmn;\u0026thinsp;28.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128.7\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;5.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHADS₂ score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;4.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiplatelet Drug Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDual antiplatelet therapy [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(72.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112(67.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspirin dose 100mg [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40(83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138(83.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.956\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClopidogrel dose 75mg [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105(63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined anticoagulants [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28(17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of medication (months)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.5(4.0\u0026ndash;18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.0(3.0\u0026ndash;24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ=-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscontinued antiplatelet drugs [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15(31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68(41.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.195\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\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\u003eComparison of Baseline Characteristics in Patients with Different Rebleeding Outcomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eRebleeding Group (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Rebleeding Group (n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\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\u003eBleeding-Related Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematemesis [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98(62.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMelena [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52(92.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145(92.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematochezia [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure at admission (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate at admission (beats/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198.5\u0026thinsp;\u0026plusmn;\u0026thinsp;62.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e215.4\u0026thinsp;\u0026plusmn;\u0026thinsp;68.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlanine aminotransferase (ALT) (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspartate aminotransferase (AST) (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.6\u0026thinsp;\u0026plusmn;\u0026thinsp;28.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.3\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndoscopic Examination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlcer location [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastric antrum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95(60.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastric body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48(30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastric fundus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple sites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlcer diameter\u0026thinsp;\u0026ge;\u0026thinsp;2cm [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32(57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65(41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForrest Ia-Ib grade [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42(26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=10.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForrest IIa grade [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68(43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForrest IIb-IIc grade [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47(29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=5.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHp infection [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38(67.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78(49.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=5.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBleeding Severity Scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGBS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;6.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRockall score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment-Related Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndoscopic hemostatic treatment [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48(85.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110(70.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntravenous PPI treatment [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56(100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155(98.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.547*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPI treatment duration (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;3.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell transfusion (U)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5(2.0\u0026ndash;6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0(0\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ=-4.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma transfusion (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400(0-600)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200(0-400)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ=-3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscontinued antiplatelet drugs [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48(30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=18.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Fisher's exact test used\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Follow-up Results\u003c/h2\u003e\u003cp\u003eDuring follow-up, 48 patients (22.5%) experienced cardiovascular events, and 56 patients (26.3%) experienced rebleeding. Among them, 16 patients (7.5%) experienced both cardiovascular events and rebleeding during follow-up (composite endpoint events, regardless of event sequence), and 15 patients (7.0%) died from all causes.\u003c/p\u003e\u003cp\u003eThe occurrence of cardiovascular events was mainly concentrated within 30 days after bleeding (n\u0026thinsp;=\u0026thinsp;22, 45.8%) and within 90 days (n\u0026thinsp;=\u0026thinsp;35, 72.9%). Specific event types included: 8 cardiac deaths, 15 non-fatal myocardial infarctions, 12 non-fatal strokes, 9 urgent revascularizations, and 20 rehospitalizations for cardiovascular disease (total of 64 events, with some patients experiencing multiple cardiovascular events during follow-up). Among rebleeding events, 32 cases (57.1%) occurred within 30 days, 45 cases (80.4%) within 90 days, and 12 cases required repeat endoscopic intervention.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents patient numbers based on first events, used for survival analysis. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows cumulative numbers of patients experiencing first events in different time windows, consistent with survival analysis principles where each patient is counted only at first event occurrence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eOccurrence of Major Outcome Events During Follow-up\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime Window\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCardiovascular Events [n(%)]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRebleeding Events [n(%)]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComposite Endpoint [n(%)]*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAll-Cause Mortality [n(%)]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22(10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8(3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5(2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35(16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45(21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9(4.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e180 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42(19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51(23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12(5.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48(22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56(26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16(7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15(7.0)\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\u003e*Composite endpoint defined as patients experiencing both cardiovascular events and rebleeding during follow-up period, regardless of event sequence. Data in table represent cumulative patient numbers reaching composite endpoint by each time point, based on time of first reaching composite endpoint.\u003c/p\u003e\u003cp\u003eKaplan-Meier survival analysis showed that 12-month cumulative cardiovascular event rate was 22.8% (95%CI: 17.3%-28.3%), and cumulative rebleeding rate was 26.6% (95%CI: 20.8%-32.4%). High-risk cardiovascular event patients had significantly higher cardiovascular event rates (38.2% vs 12.8%, Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and high-risk bleeding patients had significantly higher rebleeding rates (42.6% vs 15.9%, Log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Risk Factor Analysis for Cardiovascular Events\u003c/h2\u003e\u003cp\u003eUnivariate Cox regression analysis showed that age, myocardial infarction history, heart failure, diabetes, NYHA class III-IV, GRACE score, CHADS₂ score, systolic blood pressure at admission, and serum albumin were associated with cardiovascular events (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Multivariate Cox regression analysis results showed that age\u0026thinsp;\u0026ge;\u0026thinsp;70 years (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;2.48, 95%CI: 1.35\u0026ndash;4.56, P\u0026thinsp;=\u0026thinsp;0.004), heart failure (HR\u0026thinsp;=\u0026thinsp;2.31, 95%CI: 1.28\u0026ndash;4.17, P\u0026thinsp;=\u0026thinsp;0.006), GRACE score\u0026thinsp;\u0026gt;\u0026thinsp;140 (HR\u0026thinsp;=\u0026thinsp;2.92, 95%CI: 1.58\u0026ndash;5.39, P\u0026thinsp;=\u0026thinsp;0.001), and serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35g/L (HR\u0026thinsp;=\u0026thinsp;1.85, 95%CI: 1.04\u0026ndash;3.29, P\u0026thinsp;=\u0026thinsp;0.037) were independent risk factors for cardiovascular events (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eCox Regression Analysis for Cardiovascular Events\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eUnivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;70 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.86(1.58\u0026ndash;5.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.48(1.35\u0026ndash;4.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26(0.70\u0026ndash;2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5kg/m\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.42(0.65\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyocardial infarction history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.54(1.43\u0026ndash;4.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.52(2.02\u0026ndash;6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.31(1.28\u0026ndash;4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.28(1.28\u0026ndash;4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNYHA class III-IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.84(1.58\u0026ndash;5.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRACE score\u0026thinsp;\u0026gt;\u0026thinsp;140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.68(2.03\u0026ndash;6.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.92(1.58\u0026ndash;5.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHADS₂ score\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.95(1.61\u0026ndash;5.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure at admission\u0026thinsp;\u0026lt;\u0026thinsp;100mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78(0.95\u0026ndash;3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.34(1.32\u0026ndash;4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.85(1.04\u0026ndash;3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDual antiplatelet therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26(0.69\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscontinued antiplatelet drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68(0.36\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\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\u003e\u0026dagger;Stepwise backward method used for variable selection (removal criterion P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Reasons some univariately significant variables were not included in the final model include: (1) Collinearity with retained variables (collinearity diagnosis VIF\u0026thinsp;\u0026gt;\u0026thinsp;5), preferentially retaining variables with stronger clinical significance or higher predictive efficacy; (2) No longer having independent predictive value in multivariate model (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For example, myocardial infarction history, diabetes, NYHA classification showed collinearity with GRACE score, and CHADS₂ score components overlapped with variables such as age, heart failure, and diabetes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Risk Factor Analysis for Rebleeding\u003c/h2\u003e\u003cp\u003eUnivariate Cox regression analysis showed that systolic blood pressure at admission, heart rate at admission, hemoglobin, serum albumin, ulcer diameter, Forrest classification, Hp infection, GBS score, Rockall score, endoscopic hemostatic treatment, transfusion volume, and discontinuation of antiplatelet drugs were associated with rebleeding (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Multivariate Cox regression analysis results showed that Forrest Ia-Ib grade (HR\u0026thinsp;=\u0026thinsp;3.15, 95%CI: 1.76\u0026ndash;5.64, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Rockall score\u0026thinsp;\u0026ge;\u0026thinsp;6 (HR\u0026thinsp;=\u0026thinsp;2.68, 95%CI: 1.52\u0026ndash;4.73, P\u0026thinsp;=\u0026thinsp;0.001), Hp infection (HR\u0026thinsp;=\u0026thinsp;1.92, 95%CI: 1.09\u0026ndash;3.38, P\u0026thinsp;=\u0026thinsp;0.024), and discontinuation of antiplatelet drugs (HR\u0026thinsp;=\u0026thinsp;2.58, 95%CI: 1.48\u0026ndash;4.50, P\u0026thinsp;=\u0026thinsp;0.001) were independent risk factors for rebleeding (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCox Regression Analysis for Rebleeding\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eUnivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultivariate Analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;70 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18(0.69\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematemesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.78(0.99\u0026ndash;3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure at admission\u0026thinsp;\u0026lt;\u0026thinsp;100mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.12(1.23\u0026ndash;3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate at admission\u0026thinsp;\u0026gt;\u0026thinsp;100 beats/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85(1.08\u0026ndash;3.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;70g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.82(1.07\u0026ndash;3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count\u0026thinsp;\u0026lt;\u0026thinsp;100\u0026times;10⁹/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.56(0.72\u0026ndash;3.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u0026thinsp;\u0026gt;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.64(0.94\u0026ndash;2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.28(1.31\u0026ndash;3.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine\u0026thinsp;\u0026gt;\u0026thinsp;110\u0026micro;mol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58(0.86\u0026ndash;2.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlcer diameter\u0026thinsp;\u0026ge;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.82(1.07\u0026ndash;3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForrest Ia-Ib grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.86(2.25\u0026ndash;6.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.15(1.76\u0026ndash;5.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHp infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.14(1.24\u0026ndash;3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92(1.09\u0026ndash;3.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGBS score\u0026thinsp;\u0026ge;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.42(1.99\u0026ndash;5.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRockall score\u0026thinsp;\u0026ge;\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.28(1.92\u0026ndash;5.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.68(1.52\u0026ndash;4.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndoscopic hemostatic treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.58(1.38\u0026ndash;4.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell transfusion\u0026thinsp;\u0026ge;\u0026thinsp;4U\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.45(1.44\u0026ndash;4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot included\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscontinued antiplatelet drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.72(2.18\u0026ndash;6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.58(1.48\u0026ndash;4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\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\u003e\u0026dagger;Stepwise backward method used for variable selection (removal criterion P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Reasons some univariately significant variables were not included in the final model include: (1) Collinearity with retained variables (collinearity diagnosis VIF\u0026thinsp;\u0026gt;\u0026thinsp;5); (2) No longer having independent predictive value in multivariate model (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For example, systolic blood pressure at admission, heart rate, hemoglobin, serum albumin showed collinearity with GBS and Rockall scores, as these scores already incorporate multiple clinical indicators; ulcer diameter correlated with Forrest classification; transfusion volume correlated with bleeding severity scores. Comprehensive scoring indicators with stronger clinical application value and predictive efficacy were preferentially retained.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Construction and Validation of Dual Risk Prediction Model\u003c/h2\u003e\u003cp\u003eBased on multivariate Cox regression analysis results, dual risk prediction models for cardiovascular events and rebleeding were constructed. According to β coefficients of each independent risk factor, they were standardized and converted to integer scores. The cardiovascular event prediction model included 4 variables: age\u0026thinsp;\u0026ge;\u0026thinsp;70 years (2 points), heart failure (2 points), GRACE score\u0026thinsp;\u0026gt;\u0026thinsp;140 (3 points), serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35g/L (2 points), with total score 0\u0026ndash;9 points. The rebleeding prediction model included 4 variables: Forrest Ia-Ib grade (3 points), Rockall score\u0026thinsp;\u0026ge;\u0026thinsp;6 (3 points), Hp infection (2 points), discontinuation of antiplatelet drugs (3 points), with total score 0\u0026ndash;11 points.\u003c/p\u003e\u003cp\u003eBased on scores, patients were classified into low-risk (cardiovascular events: 0\u0026ndash;3 points; rebleeding: 0\u0026ndash;4 points), medium-risk (cardiovascular events: 4\u0026ndash;6 points; rebleeding: 5\u0026ndash;8 points), and high-risk groups (cardiovascular events: \u0026ge;7 points; rebleeding: \u0026ge;9 points). The 12-month cardiovascular event rates in low-, medium-, and high-risk groups were 8.2%, 22.6%, and 57.1% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, while rebleeding rates were 10.5%, 28.4%, and 50.0% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e、Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRisk Stratification and Event Rates of Dual Risk Prediction Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Stratification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCardiovascular Event Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRebleeding Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatient Number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12-Month Event Rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePatient Number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12-Month Event Rate (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.2(7/85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.5(8/76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.6(21/93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.4(27/95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.1(20/35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.0(21/42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.5(48/213)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.3(56/213)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e\u003c/p\u003e\u003cp\u003eROC curve analysis showed that the cardiovascular event prediction model had AUCs of 0.782 (95%CI: 0.693\u0026ndash;0.871), 0.765 (95%CI: 0.689\u0026ndash;0.841), and 0.758 (95%CI: 0.687\u0026ndash;0.829) for predicting cardiovascular events at 30 days, 90 days, and 12 months; the rebleeding prediction model had AUCs of 0.812 (95%CI: 0.738\u0026ndash;0.886), 0.796 (95%CI: 0.729\u0026ndash;0.863), and 0.781 (95%CI: 0.716\u0026ndash;0.846) for predicting rebleeding at 30 days, 90 days, and 12 months (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e、Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC Curve Analysis of Dual Risk Prediction Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrediction Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCardiovascular Event Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRebleeding Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAUC(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.782(0.693\u0026ndash;0.871)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.812(0.738\u0026ndash;0.886)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.765(0.689\u0026ndash;0.841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.796(0.729\u0026ndash;0.863)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e180 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.761(0.687\u0026ndash;0.835)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.788(0.722\u0026ndash;0.854)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.758(0.687\u0026ndash;0.829)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.781(0.716\u0026ndash;0.846)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eHosmer-Lemeshow test showed both models had good calibration (cardiovascular event model: χ\u0026sup2;=6.82, P\u0026thinsp;=\u0026thinsp;0.556; rebleeding model: χ\u0026sup2;=8.35, P\u0026thinsp;=\u0026thinsp;0.400). Bootstrap internal validation (1000 resampling iterations) showed mean AUCs of 0.752 (95%CI: 0.731\u0026ndash;0.773) for the cardiovascular event model and 0.776 (95%CI: 0.755\u0026ndash;0.797) for the rebleeding model, demonstrating good model stability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Secondary Outcome Indicators\u003c/h2\u003e\u003cp\u003eDuring follow-up, 15 patients (7.0%) died from all causes, including 8 cardiac deaths, 5 bleeding-related deaths, and 2 deaths from other causes. 16 patients (7.5%) experienced both cardiovascular events and rebleeding during follow-up (composite endpoint events). 12 patients (5.6%) required endoscopic re-intervention. The incidence of bleeding-related complications was 18.3% (39/213), including 22 cases of hemorrhagic shock, 9 cases of multiple organ dysfunction, and 8 cases of aspiration pneumonia.\u003c/p\u003e\u003cp\u003eRegarding antiplatelet drug management, 83 patients (39.0%) discontinued antiplatelet drugs, of which 62 (74.7%) reinitiated after successful hemostasis, with median reinitiation time of 5 days (IQR: 3\u0026ndash;7 days). Within 30 days after reinitiation, cardiovascular event rate was 8.1% (5/62) and rebleeding rate was 12.9% (8/62).\u003c/p\u003e\u003cp\u003eAmong 108 patients who underwent repeat endoscopy during follow-up, 89 (82.4%) had ulcer healing and 19 (17.6%) had unhealed or recurrent ulcers. Median ulcer healing time was 8 weeks (IQR: 6\u0026ndash;12 weeks). Median time for hemoglobin recovery to normal or baseline levels was 4 weeks (IQR: 3\u0026ndash;6 weeks). Median transfusion volume for all patients was 2U (IQR: 0-4U), with 78 patients (36.6%) requiring transfusion; median transfusion volume for transfused patients was 4U (IQR: 2-6U) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSecondary Outcome Indicators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15(7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiac death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBleeding-related death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5(2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther causes of death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(0.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComposite endpoint events [n(%)]*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(7.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndoscopic re-intervention [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12(5.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBleeding-related complications [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39(18.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemorrhagic shock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22(10.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple organ dysfunction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(4.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspiration pneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscontinued antiplatelet drugs [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83(39.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiplatelet drug reinitiation rate [n(%)]\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62/83(74.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReinitiation time (days)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5(3\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular events within 30 days after reinitiation [n(%)]\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5/62(8.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRebleeding within 30 days after reinitiation [n(%)]\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/62(12.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlcer healing rate [n(%)]\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89/108(82.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlcer healing time (weeks)\u0026Dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(6\u0026ndash;12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin recovery time (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(3\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRequired transfusion [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78(36.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell transfusion for all patients (U)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(0\u0026ndash;4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell transfusion for transfused patients (U)\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(2\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma transfusion (ml)\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200(0-400)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatients receiving plasma transfusion [n(%)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52(24.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e*Composite endpoint defined as patients experiencing both cardiovascular events and rebleeding during follow-up period, regardless of event sequence; \u0026dagger;Based on 83 patients who discontinued antiplatelet drugs or 62 patients who reinitiated antiplatelet drugs; \u0026Dagger;Based on 108 patients who underwent repeat endoscopy during follow-up; \u0026sect;Median plasma transfusion volume for all patients\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study found that age\u0026thinsp;\u0026ge;\u0026thinsp;70 years, heart failure, GRACE score\u0026thinsp;\u0026gt;\u0026thinsp;140, and serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;35g/L were independent risk factors for cardiovascular events. This is largely consistent with previous research results on prognosis in patients with ACS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Elderly patients often have multiple cardiovascular risk factors, impaired vascular endothelial function, and greater atherosclerotic burden, making them more susceptible to cardiovascular events under acute bleeding stress [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Patients with heart failure have decreased cardiac reserve function, and acute blood loss resulting in reduced blood volume and decreased hemoglobin can exacerbate myocardial ischemia and hypoxia, triggering heart failure decompensation or acute coronary events [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe GRACE score, as a classic tool for risk assessment in ACS patients, integrates multiple prognostic factors including age, heart rate, systolic blood pressure, creatinine level, cardiac arrest history, cardiac markers, and ST-segment changes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This study confirmed that patients with GRACE score\u0026thinsp;\u0026gt;\u0026thinsp;140 had nearly 3-fold increased cardiovascular event risk, indicating this scoring system is equally applicable for cardiovascular risk assessment in patients with antiplatelet drug-related bleeding. Notably, serum albumin level, as a comprehensive indicator of nutritional status and inflammatory response, is closely related to cardiovascular disease prognosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hypoalbuminemia reflects chronic consumption state and systemic inflammatory response, potentially increasing cardiovascular event risk through multiple mechanisms affecting vascular endothelial function, coagulation system, and immune function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIndependent risk factors for rebleeding identified in this study included Forrest Ia-Ib grade, Rockall score\u0026thinsp;\u0026ge;\u0026thinsp;6, Hp infection, and discontinuation of antiplatelet drugs. Forrest classification is an important endoscopic indicator for assessing peptic ulcer bleeding risk, with Ia grade (spurting bleeding) and Ib grade (oozing bleeding) representing active bleeding states with significantly increased rebleeding risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Rockall score comprehensively considers patient age, shock state, comorbidities, endoscopic diagnosis, and bleeding stigmata, and is an established scoring system for predicting gastrointestinal bleeding prognosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This study's results are consistent with international guideline recommendations, confirming the predictive value of these scoring tools in patients with antiplatelet drug-related bleeding.\u003c/p\u003e\u003cp\u003eThe association between Hp infection and rebleeding was confirmed in this study, with infected patients having 92% increased rebleeding risk. Hp infection increases the risk of peptic ulcer and its complications through mechanisms including destroying gastric mucosal barrier, inducing chronic inflammatory response, and inhibiting ulcer healing [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Multiple studies have shown that Hp eradication can significantly reduce peptic ulcer recurrence rate and rebleeding risk [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, active screening and eradication of Hp has important clinical significance for patients with antiplatelet drug-related ulcer bleeding.\u003c/p\u003e\u003cp\u003eNotably, this study found that discontinuation of antiplatelet drugs paradoxically increased rebleeding risk (HR\u0026thinsp;=\u0026thinsp;2.58). This seemingly contradictory result may reflect the \"confounding by indication\" phenomenon in clinical decision-making [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]: patients with higher bleeding risk are more likely to have antiplatelet drugs discontinued, and these patients inherently have higher rebleeding tendency. Additionally, discontinuation of antiplatelet drugs may increase rebleeding risk through the following mechanisms: (1) Rapid recovery of platelet function followed by \"rebound\" thrombosis leading to ulcer local microcirculatory disturbance, affecting ulcer healing [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; (2) Patients who discontinue drugs often have more severe bleeding (such as Forrest Ia-Ib grade) and more complications; (3) Physicians may adopt more conservative PPI treatment strategies for patients who discontinue drugs due to concerns about cardiovascular risk. This finding emphasizes the complexity of antiplatelet drug management decisions, requiring comprehensive consideration of bleeding and thrombotic risks.\u003c/p\u003e\u003cp\u003eUnlike previous single-dimension risk assessment tools, this study is the first to establish a dual risk model simultaneously predicting cardiovascular events and rebleeding [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This model has the following innovative aspects and clinical application value:\u003c/p\u003e\u003cp\u003eFirst, the model is constructed based on routinely available clinical indicators, including demographic characteristics, cardiovascular scores, endoscopic examination results, and laboratory indicators, requiring no additional tests and facilitating clinical application. Second, through risk stratification (low, medium, high risk), patient populations with different risk characteristics can be identified. Low-risk group patients have low cardiovascular event and rebleeding rates (8.2% and 10.5%), allowing consideration of early antiplatelet therapy reinitiation; high-risk group patients have significantly elevated event rates (57.1% and 50.0%), requiring more aggressive monitoring and intervention measures [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, dual risk assessment helps guide individualized antiplatelet drug management strategies. For patients at high cardiovascular risk but low bleeding risk, antiplatelet therapy should be resumed as early as possible; for patients at high bleeding risk but low cardiovascular risk, drug discontinuation time can be appropriately extended or regimens adjusted [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For dual high-risk patients, multidisciplinary team collaboration is needed, balancing treatment options while strengthening monitoring.\u003c/p\u003e\u003cp\u003eThe model's good predictive performance (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.75) and calibration provide reliable evidence for clinical decision-making. Bootstrap internal validation showed the model has good stability, but external cohort validation is still needed to confirm its generalizability [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, 39.0% of patients discontinued antiplatelet drugs, of which 74.7% reinitiated after successful hemostasis, with median reinitiation time of 5 days. Cardiovascular event rate within 30 days after reinitiation was 8.1%, and rebleeding rate was 12.9%, indicating antiplatelet drug management still faces challenges. Recent studies suggest that for patients with acute gastrointestinal bleeding combined with high-risk cardiovascular disease, early (within 3\u0026ndash;7 days) reinitiation of antiplatelet therapy may be relatively safe under effective hemostasis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, reinitiation timing and strategy require individualized decision-making, comprehensively considering bleeding control status, cardiovascular disease severity, patient compliance, and other factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study found that discontinuation of antiplatelet drugs was associated with increased rebleeding risk, suggesting clinicians need to be more cautious in decision-making. For patients who must discontinue drugs, PPI treatment should be strengthened, Hp actively eradicated, ulcer healing closely monitored, and timing for reinitiation of antiplatelet therapy assessed as early as possible [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStrengths of this study include: (1) Relatively large sample size with sufficient follow-up time (12 months) to capture key clinical events; (2) Simultaneous assessment of dual outcomes of cardiovascular events and rebleeding, filling gaps in previous research; (3) Strict inclusion and exclusion criteria ensuring study population homogeneity; (4) Identification of independent risk factors through multivariate analysis, with constructed prediction models having good statistical performance.\u003c/p\u003e\u003cp\u003eHowever, this study also has limitations. First, as a single-center retrospective study, there may be selection bias and information bias, with study result generalizability requiring verification. Second, some secondary outcome indicators (such as ulcer healing rate) have missing data to some extent, potentially affecting result reliability. Third, antiplatelet drug management strategy formulation is influenced by multiple factors, and this study could not completely control confounding factors such as physician decision preferences. Additionally, the study did not include factors such as genetic polymorphisms and platelet function testing that may affect prognosis. Finally, the model has not yet been externally validated, and its predictive performance in different medical institutions and patient populations requires further evaluation.\u003c/p\u003e\u003cp\u003eBased on this study's results, future research can be conducted in the following directions: (1) Conduct multicenter prospective cohort studies, expand sample size and perform external validation to improve model generalizability; (2) Explore incremental value of novel biomarkers (such as circulating microRNAs, inflammatory factors) for prognosis prediction; (3) Conduct randomized controlled trials comparing effects of different antiplatelet drug management strategies (such as early reinitiation vs. delayed reinitiation, drug dose adjustment) on dual outcomes; (4) Optimize prediction models using advanced statistical methods such as machine learning; (5) Develop clinical decision support systems based on prediction models and evaluate their actual impact on clinical practice and patient prognosis.\u003c/p\u003e\u003cp\u003eThe dual risk prediction model established in this study can effectively identify high-risk populations for cardiovascular events and rebleeding in patients with antiplatelet drug-related gastric ulcer bleeding, providing scientific evidence for clinicians' individualized treatment decisions and antiplatelet drug management. This model has good discrimination and calibration, and being constructed based on routine clinical indicators, has good clinical application prospects. Future multicenter prospective studies are needed for external validation and exploration of clinical decision support systems based on this model to improve patient prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Fujian Province, China (2024J01122172).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest exits in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManuscript is approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials of this experiment are available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest exits in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRongrong Chen and Guixi Wu were responsible for the design of the whole study. Mengshi Chen were in charge of experimental operation and drawing.Xueyan Lin submitted the manuscripts.All authors read and approved the final manuscript\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePatel PP, Fanaroff AC. Optimal Medical Therapy for Chronic Coronary Disease in 2024: Focus on Antithrombotic Therapy. Med Clin North Am. 2024;108(3):489\u0026ndash;507. doi: 10.1016/j.mcna.2023.11.004. Epub 2023 Dec 27. PMID: 38548459.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang J, Liao F, Tang J, Shu X. Risk factors for gastrointestinal bleeding in patients with cerebral infarction after dual antiplatelet therapy. 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Early Antiplatelet Resumption and the Risks of Major Bleeding After Intracerebral Hemorrhage. Stroke. 2023;54(2):537\u0026ndash;545. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/STROKEAHA.122.040500\u003c/span\u003e\u003cspan address=\"10.1161/STROKEAHA.122.040500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2023 Jan 9. PMID: 36621820.\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":"Antiplatelet drugs, Gastric ulcer bleeding, Cardiovascular events, Rebleeding, Risk prediction model, GRACE score, Forrest classification, Helicobacter pylori","lastPublishedDoi":"10.21203/rs.3.rs-8029710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8029710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo develop a dual risk prediction model for cardiovascular events and rebleeding in patients with antiplatelet-related gastric ulcer bleeding, addressing the clinical dilemma of balancing thrombotic and hemorrhagic risks.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn a retrospective cohort of 213 patients followed for 12 months, we used Cox regression to identify independent predictors for each outcome. Model performance was assessed using AUC, calibration (Hosmer\u0026ndash;Lemeshow test), and bootstrap validation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDuring follow-up, 48 (22.5%) patients had cardiovascular events and 56 (26.3%) experienced rebleeding. Independent predictors for cardiovascular events were age\u0026thinsp;\u0026ge;\u0026thinsp;70 years (HR\u0026thinsp;=\u0026thinsp;2.48), heart failure (HR\u0026thinsp;=\u0026thinsp;2.31), GRACE score\u0026thinsp;\u0026gt;\u0026thinsp;140 (HR\u0026thinsp;=\u0026thinsp;2.92), and albumin\u0026thinsp;\u0026lt;\u0026thinsp;35 g/L (HR\u0026thinsp;=\u0026thinsp;1.85). For rebleeding, Forrest Ia\u0026ndash;Ib (HR\u0026thinsp;=\u0026thinsp;3.15), Rockall score\u0026thinsp;\u0026ge;\u0026thinsp;6 (HR\u0026thinsp;=\u0026thinsp;2.68), H. pylori infection (HR\u0026thinsp;=\u0026thinsp;1.92), and antiplatelet discontinuation (HR\u0026thinsp;=\u0026thinsp;2.58) were significant. The models showed AUCs of 0.758 and 0.781 for 12-month cardiovascular events and rebleeding, respectively, with good calibration (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Stratification into low-, medium-, and high-risk groups revealed graded outcomes: cardiovascular event rates were 8.2%, 22.6%, and 57.1%, and rebleeding rates were 10.5%, 28.4%, and 50.0% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis dual risk model effectively stratifies patients by cardiovascular and rebleeding risk using routinely available clinical variables, aiding individualized antiplatelet management.\u003c/p\u003e","manuscriptTitle":"Dual Risk Prediction Model for Cardiovascular Events and Rebleeding in Patients with Antiplatelet Drug-Related Gastric Ulcer Bleeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 12:34:07","doi":"10.21203/rs.3.rs-8029710/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":"261846a6-7d6f-4d65-acd7-f3e79d21c507","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-04T01:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 12:34:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8029710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8029710","identity":"rs-8029710","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}