Comparison of the H3B2A1 score with other risk models for upper gastrointestinal bleeding

Comparison of the H3B2A1 score with other risk models for upper gastrointestinal 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 Comparison of the H3B2A1 score with other risk models for upper gastrointestinal bleeding meliha fındık, muhammet çakas, ahmet buğra önler This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5925892/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 Background : Acute gastrointestinal (GI) bleeding is a critical emergency and a significant cause of morbidity and mortality worldwide, with rates ranging between 5% and 10%, despite advancements in treatment. Upper gastrointestinal bleeding (UGIB) accounts for a substantial proportion of cases, particularly in elderly populations with comorbid conditions. Methods : This retrospective study evaluated the predictive accuracy of the H3B2A1 scoring model for mortality and intensive care unit (ICU) admission in UGIB patients. The AIMS65, Glasgow-Blatchford score (GBS), modified GBS (mGBS), and H3B2 scores were calculated for all patients. Receiver operating characteristic (ROC) curves were used to assess the scores' predictive performance. Results : Compared with the AIMS65 score, the H3B2A1 score demonstrated comparable predictive accuracy for mortality (H3B2A1-AUC: 0.750; AIMS65-AUC: 0.754), with all the scores showing moderate accuracy (AUC = 0.7–0.9). In predicting ICU hospitalization, the AIMS65 score outperformed the other scores (AUC: 0.844; sensitivity: 96.1%), followed by H3B2 (82.4%). Conclusion : The H3B2A1 model (modified H3B2), which incorporates albumin into the H3B2 score, provides a practical tool for assessing mortality risk, ICU admission, and the need for emergency endoscopy in UGIB patients. Its simplicity and predictive power makes it a valuable addition to clinical practice. Critical Care & Emergency Medicine gastrointestinal bleeding mortality risk score elderly Introduction Despite advancements in diagnosis and treatment, the mortality rate remains between 5% and 10% because of the growing elderly population and comorbid conditions. Acute gastrointestinal bleeding (GIB) is one of the gastrointestinal emergencies that is the most important cause of mortality and morbidity in our country worldwide [ 1 , 2 ]. Upper gastrointestinal bleeding (UGIB) accounts for more than 50% of all gastrointestinal bleeding cases and hospitalizations. Despite advancements in endoscopic techniques and their widespread use, the mortality rate for UGIB ranges from 3–15% [ 3 , 4 ]. Therefore, evaluating the prognosis and rebleeding rate remains a serious problem. Numerous prognostic-risk scoring systems have been proposed to evaluate mortality risk, the need for intervention and transfusion, rebleeding, and length of hospitalization [ 4 ]. Risk scores are typically categorized into two groups: pre- and postendoscopy. Preendoscopy risk scores include the Glasgow-Blatchford score (GBS), AIMS65 score (AIMS65), ABC (age, blood tests, comorbidity), and preendoscopy Rockall score (pRS). Although the GBS, AIMS-65, and RS are frequently used, there are no widely used risk scores in the literature [ 5 ]. Since these scoring systems are not readily applicable in emergency departments and their memorability is difficult, new scoring systems that can be used as alternatives are needed. Sasaki et al. (2022) introduced the H3B2 score, a new scoring model that utilizes simple and objective indices. It is recommended for high-risk patients with UGIB who require emergency hemostatic interventions, including emergency endoscopy [ 3 ]. This study aimed to evaluate the ability of the H3B2A1(modified H3B2) scoring model to predict outcomes and mortality in patients with UGIB in clinical practice and to develop this scoring system. Materials and methods This was a retrospective study conducted at a single tertiary care center. Between 01.06.2022 and 31.07.2023, patients over 18 who presented with GI symptoms and were diagnosed with UGIB after endoscopy were analyzed. Patients with variceal bleeding, patients who did not undergo endoscopy, trauma patients, patients younger than 18 years of age, and patients whose patient records could not be accessed were excluded. Patients were categorized into two groups: survivors and nonsurvivors. The AIMS65, GBS, mGBS, H3B2, and H3B2A1 (modified H3B2) were calculated for each patient. Statistical analysis The study's data were analyzed via SPSS (Statistical Package for Social Sciences) for Windows 25.0. The receiver operating characteristic curve (AUC) analysis was performed for ICU admissions and mortality using the AIMS65, GBS, mGBS, H3B2, and H3B2A1 scores. The cutoff value and Youden J index were calculated for each score; we compared the prediction of mortality and ICU hospitalization via the AUC. A significance level of p < 0.05 was considered statistically significant. Ethics Committee Approval The Clinical Research Ethics Committee approved the study with decision number 2023/66. All procedures were conducted according to the ethical rules and principles of the Declaration of Helsinki. Results A total of 649 patients admitted to the emergency department with suspected GIB were retrospectively analyzed. A total of 233 patients who underwent an endoscopy and were diagnosed with UGIB were included in the study. Patients were categorized into two groups: survivors and nonsurvivors. The mortality rate was 10.7% (25 patients). The mean age of the patients was 70.28 years (± 14.96), and 67.38% were over 65 years of age ( Table 1 ). Table 1 Comparison of the demographic and clinical characteristics of patients between groups Survivor N (%) Nonsurvivor N (%) Total N (%) p Age 18–45 15 (%6,44) 0 15(%6,44) 0,57 46–65 58(%24,89) 3(%1,29) 61(%26,18) 66 ≤ 135(%57,94) 22(%9,44) 157(%67,38) Gender Female 88(%37,77) 7(%3) 95 (%40,8) 0,169 Male 120(%51,50) 18(%7,73) 138 (%59,2) Total 208(%89,27) 25(%10,73) 233(%100) Comorbidity Hypertension (HT) 108(%46,35) 18(%7,72) 126(%54,07) 0,057 Diyabetes Mellitus (DM) 53(%22,75) 4(%1,71) 57(%24,46) 0,297 Cerebrovascular Disease (CVD) 23(%9,87) 7(%3) 30(%12,87) 0,017 Coronary Artery Disease (KAD) 73(%31,33) 14(%6,01) 87(%37,34) 0,041 Alzheimer Disease 10(%4,29) 6(%2,8) 16(%6,87) 0,00 Malignancy 19(%8,16) 8(%3,43) 27(%11,59) 0,001 Drug used Acetylsalicylic acid 64(%27,48) 10(%4,29) 74(%31,76) 0,349 Clopidogrel 37(%15,88) 10(%4,29) 47(%20,17) 0,009 Proton pump inhibitörs (PPI) 37(%15,88) 3(%1,29) 40(%17,18) 0,468 Nonsteroidal anti-inflammatory drugs (NSAIDs) 35(%15,02) 3(%1,29) 38(%16,31) 0,537 Rivaroxaban 20(%8,58) 2(%0,86) 22(%9,44) 0,794 Warfarin 13(%5,58) 0 13(%5,58) 0,194 Ticagrelor 5(%2,15) 0 5(%2,15) 0,433 *P < 0.05 The mean hemoglobin (Hb) levels of the patients (8.89 ± 2.32) were similar in both surviving patients (8.99 ± 2.34) and nonsurviving patients (8.10 ± 1.98) ( p = 0.068 ). The mean platelet (Plt) value was significantly lower in nonsurviving patients than in surviving patients ( p < 0.05 ). The mean value of serum ALB (Alb) was significantly lower in nonsurviving patients (26.84 ± 4.45) than in surviving patients ( p < 0.01 ). Table 2 Comparison of laboratory parameters between groups Survivor (MEAN ± STD) Nonsurvivor (MEAN ± STD) Total (MEAN ± STD) 95% CI P Lower Upper Hemoglobin(g/dl) 8,99 ± 2,34 8,10 ± 1,98 8,89 ± 2,32 -0,07 1,86 0,068 Hematocrit (%) 27,49 ± 6,90 24,71 ± 6,02 27,19 ± 6,85 -0,06 5,62 0,055 Platelet count(10 3 /µL) 265,25 ± 121,72 219,96 ± 90,88 236,25 ± 76,30 -4,30 94,86 0,030 aPTT(sn) 25,97 ± 9,28 26,86 ± 7,92 26,07 ± 9,14 -4,70 2,93 0,649 International Normalized Ratio (INR)(sn) 1,25 ± 0,47 1,26 ± 0,63 1,25 ± 0,49 -0,22 0,19 0,895 AST(IU/L) 25,92 ± 32,61 22,56 ± 13,43 25,56 ± 31,12 -9,64 16,36 0,611 ALT(IU/L) 19,62 ± 26,37 18,32 ± 10,69 19,48 ± 25,14 -9,21 11,81 0,808 GGT(IU/L) 36,89 ± 62,36 72,20 ± 104,26 40,68 ± 68,66 -79,08 8,45 0,015 BUN (mg/dL) 38,88 ± 24,53 41,03 ± 28,66 39,11 ± 24,95 -12,57 8,28 0,686 KREATİNİN (mg/dL) 1,13 ± 0,76 1,26 ± 0,46 1,15 ± 0,74 -0,44 0,18 0,409 ALBUMİN (g/dL) 31,84 ± 5,43 26,84 ± 4,45 31,30 ± 5,54 2,77 7,22 0,00 BUN/ALB 1,29 ± 0,90 1,54 ± 0,98 1,32 ± 0,91 -0,63 0,13 0,191 BUN/KR 37,08 ± 18,66 31,40 ± 12,68 36,47 ± 18,17 -1,88 13,24 0,14 CRP (mg/L) 21,95 ± 37,69 16,64 ± 36,16 21,39 ± 37,49 -10,34 20,97 0,504 The associations between mortality and intensive care unit (ICU) hospitalization among the five analyzed scores were evaluated, and the AUC, cutoff point, sensitivity (%), specificity (%), p, and Youden j index values are given in Table 3 . All the scoring systems were statistically significant in terms of their ability to predict mortality ( p < 0.001 ), and the H3B2A1 score was as highly predictive as the AIMS65 score was. In predicting ICU hospitalization, the AIMS65 score had greater predictive accuracy (AUC: 0.844) and a high sensitivity of 96.1%, while H3B2 was second-highest with a sensitivity of 82.4%. All the scores were not significantly different. Table 3 The area under the receiver operating characteristic (ROC) curve represents the score for mortality. SCORE SYSTEMS Mortality H3B2 AİMS65 GBS mGBS Model-H3B2A1 AUROC (95%) 0,725 (0,620-0,830) 0,754 (0,655-0,852) 0,706 (0,614-0,797) 0,731 (0,640-0,822) 0,750 (0,653-0,846) Cutt off 3,5 1,5 11,5 10,5 3,5 Youden J İndex 0,454 0,366 0,265 0,374 0,414 P value < 0,001 < 0,001 0,001 < 0,001 < 0,001 Sensitivity (%) 80,0 88,0 64,0 84,0 88,0 Specificity (%) 65,4 48,6 62,5 53,4 53,4 ICU hospitalization H3B2 AİMS65 GBS mGBS Model-H3B2A1 AUROC (95%) 0,624 (0,535-0,713) 0,844 (0,790-0,899) 0,606 (0,519-0,692) 0,613 (0,525-0,701) 0,645 (0,563-0,727) Cutt off 2,5 1,5 10,5 9,5 3,5 Youden J İndex 0,137 0,521 0,17 0,08 0,175 P value 0,007 0,001 0,021 0,014 0,002 Sensitivity (%) 82,4 96,1 68,6 56,9 64,7 Specificity (%) 31,3 56 48,4 51,1 52,7 Discussion Gastrointestinal bleeding is one of the gastroenterology emergencies requiring medical intervention and one of the most common causes of hospitalization [ 6 ]. Although the incidence varies according to age, elderly patients are more frequently affected, and the frequency of hospitalization increases with age. The most common cause of UGIB in the elderly population is gastric and duodenal ulcers or esophagitis, which is observed in 80% of cases [ 7 ]. In a 2007 study in England, 63% of patients diagnosed with UGIB were found to be over 60 years of age [ 8 ]. In our study, consistent with the literature, elderly individuals presented with UGIB more frequently; the mean age was 70.28 years (± 14.96), and 67.38% of the patients were over 65 years. The mortality rate was 10.7%, similar to that reported in the literature, and mortality increased with age. Since acute UGIB has a significant morbidity and mortality rate, early evaluation and prediction are of utmost clinical importance to improve patient prognosis. The severity of acute GIB ranges from mild to life-threatening. Therefore, various scoring systems are used to evaluate the condition and prognosis of patients [ 9 ]. Accurate risk stratification in patients is vital for improving clinical outcomes and making treatment decisions. However, risk scoring in emergency medicine should be more practical and easier to apply. In particular, the use of risk scoring methods such as GBS and mGBS is challenging because they include a wide variety of parameters, and it is even known that they are not used [ 4 ]. In our study, all the scoring systems were similar in terms of their ability to predict mortality (AUC: 0.70–0.93; p < 0.001). However, AIMS65 and H3B2A1 performed well, with AUCs of 0.754 and 0.750, respectively. The mGBS, H3B2, and GBS had AUCs of 0.731, 0.725, and 0.706, respectively. Sasaki et al. reported AUCs of 0.707 for the AIMS65 and 0.685 for H3B2, which are more significant than those of the other scores (GBS, AUC: 0.587; mGBS, AUC: 0.594; MAP, AUC: 0.665; and Lino: AUC: 0.549) are more significant, whereas, in our study, all the scoring systems yielded similar results in terms of mortality prediction; however, the AIMS65 and H3B2A1 scores performed better. In the study by Cacazu et al. 2023 in patients with and without endoscopy, similar to our research, all scores were significant in predicting mortality. According to the AUC, the International Bleeding score (INBS, 0.844), GBS (0.783), MAP score (0.78), Iino (0.766), AIM65, and modified N-score (0.745 each), mGBS (0.73), H3B2 and N-score (0.701), Rockall, Baylor, and T score reported AUC values below 0.7. However, unlike our study, they included variceal bleeding. As suggested in the study by Sasaki et al., the H3B2 score is more practical for predicting mortality. In addition, while parameters such as impaired consciousness and age are available in AIMS65, many parameters need to be evaluated in GBS, and liver and heart diseases should also be known [ 3 ]. In addition, other underlying causes should be differentiated in most patients presenting with impaired consciousness of ASs. In conclusion, early recognition of high-risk patients in patients presenting with UGIB and early planning for endoscopy are important, and predicting mortality during follow-up is also critical [ 10 ]. The literature shows that early risk classifications are inadequate in the clinical decision-making process before endoscopy in high-risk patients, especially in deciding on ICU admission or hospitalization in the relevant service. Generally, risk stratifications have been developed against the possibility of mortality and rebleeding [ 11 , 12 ]. Khread et al. 2023 evaluated ICU admission rates with the AIMS65, GBS, ABC, and Rockall scores. They reported that ICU admission rates increased significantly for all high-risk patient scores predicted for all scores except the Rockall score, but AUC curves showed poor discriminatory ability for all scores (ABC: 0.55 (51%; 60%), AIMS65: 0.61 (56%; 66%), GBS:0.61 (55%; 0.66%), RS: 0.51 (53%; 64%)). In our study, in patients admitted to the ICU, in addition to AIMS 65 (AUC: 0.844 (95% CI 0.790–0.899)), other scoring systems (GBS; AUC: 0.606 (0.519–0.69), mGBS; AUC: 0.613 (0.525–0.701), H3B2; AUC: 0.624 (0.535–0.713), H3B2A1; AUC: 0.645 (0.563–0.727)) showed similar characteristics in predicting ICU admission (p < 0.001). In conclusion, all scoring systems have been developed to predict the need for early endoscopic treatment, mortality, and transfusion [ 12 ]. However, the primary purpose of risk stratification in emergency departments is to identify patients who can be discharged [ 13 ]. Conclusion We propose a new scoring model called H3B2A1, which enhances the existing H3B2 score by incorporating Albumin (A). This model is designed for emergency departments to identify high-risk patients with suspected upper gastrointestinal bleeding (GIB). It can aid in determining the need for emergency endoscopy and assessing the risk of intensive care unit admission and mortality. Declarations Authorship Contributions : All authors had access to data and a role in writing the manuscript. Meliha Fındık: conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing—original draft, supervision, project administration, writing—review and editing. Ramazan Kıyak: conceptualization, methodology, writing original draft, writing—review and editing. Muhammet Çakas: conceptualization, resources, data curation, writing—review and editing. Ahmet Buğra Önler: conceptualization, resources, data curation, writing—review, and editing. Murat Başcı: conceptualization, resources, data curation, writing—review and editing. Selman Gümüş: conceptualization, resources, data curation, writing—review and editing. Data availability : The data supporting this study's findings are available from the corresponding author upon reasonable request. Funding : The authors declared that this study received no financial support. Conflict of Interest : The authors declare no conflict of interest. Informed Consent : Informed consent is not required for the patients concerned. References Bae SJ, Kim KK, Yun SJ, Lee SH (2021) Predictive performance of blood urea nitrogen to serum albumin ratio in elderly patients with gastrointestinal bleeding. Am J Emerg Med 41:152–157. https://doi:10.1016/j.ajem.2020.12.022 Lee YJ, Min BR, Kim ES, Park KS, Cho KB, Jang BK, Chung WJ, Hwang JS, Jeon SW (2016) Predictive factors of mortality within 30 days in patients with nonvariceal upper gastrointestinal bleeding. Korean J Intern Med 31(1):54–64. https://doi:10.1186/s12876-022-02413-8 Sasaki Y, Abe T, Kawamura N, Keitoku T, Shibata I, Ohno S, Ono K, Makishima M (2022) Prediction of emergency endoscopic treatment for upper gastrointestinal bleeding and new score model: a retrospective study. BMC Gastroenterol 22:337. https://doi:10.1186/s12876-022-02413-8 Cazacu SM, Alexandru DO, Statie RC, Lordache S, Ungureanu BS, Popa P, Sacerdotianu VM, Neagoe CD, Florescu MM (2023) The accuracy of pre-endoscopic scores for mortality prediction in patients with upper GI bleeding and no endoscopy performed. Diagnostics 13(6):1188. https://doi:10.3390/diagnostics13061188 Palmer JO, Stanley AJ (2023) A review of risk scores within upper gastrointestinal bleeding. J Clin Med 12(11):3678. https://doi:10.3390/jcm12113678 Wasserman RD, Abel W, Monkemuller K, Yeaton P, Kesar V, Kesar V (2024) Non-variceal Upper Gastrointestinal Bleeding and Its Endoscopic Management. Turk J Gastroenterol 35(8):599–608. https://doi.org/10.5152/tjg.2024.23507 Menichelli D, Gazzaniga G, Del Sole F, Pani A, Pignatelli P, Pastori D (2024) Acute upper and lower gastrointestinal bleeding management in older people taking or not taking anticoagulants: a literature review. Front Med(Lausanne) 3:11. https://doi.org/10.3389/fmed.2024.1399429 Hearnshaw SA, Logan RF, Lowe D, Travis SP, Murphy MF, Palmer KR (2011) Acute upper gastrointestinal bleeding in the UK: patient characteristics, diagnoses and outcomes in the 2007 UK audit. Gut 60(10):1327–1335. https://doi.org/10.1136/gut.2010.228437 Lee HA, Jung HK, Kim TO, Byeon JR, Jeong ES, Cho HJ, Tae CH, Moon CM, Kim SE, Shim KN, Jung SA (2022) Clinical outcomes of acute upper gastrointestinal bleeding according to the risk indicated by Glasgow-Blatchford risk score-computed tomography score in the emergency room. Korean J Intern Med 37(6):1176–1185. https://doi.org/10.3904/kjim.2022.099 Rivieri S, Carron PN, Schoepfer A, François XA (2022) External validation and comparison of the Glasgow-Blatchford score, modified Glasgow-Blatchford score, Rockall score, and AIMS65 score in patients with upper gastrointestinal bleeding. Eur J Emerg Med 30(1):32–39. https://doi.org/10.1097/MEJ.0000000000000983 Kherad O, Restellini S, Almadi M, Martel M, Barkun AN (2023) Comparative Evaluation of the ABC Score to Other Risk Stratification Scales in Managing High-risk Patients Presenting With Acute Upper Gastrointestinal Bleeding. J Clin Gastroenterol 57(5):479–485. https://doi.org/10.1097/MCG.0000000000001720 Rao VL, Gupta N, Swei E, Wagner T, Aronsohn A, Reddy KG, Sengupta N (2020) Predictors of mortality and endoscopic intervention in patients with upper gastrointestinal bleeding in the intensive care unit. Gastroenterol Rep 8(4):299–305. https://doi.org/10.1093/gastro/goaa009 Venkat A, Cattamanchi S, Madali A, Farook AR, Trichur RV (2017) Comparison of the AIMS-65 score with the Glasgow-Blatchford Score in Upper Gastrointestinal Bleed in the Emergency Department. Eurasian. J Emerg Med 16(2):70–78 https://doi org/ 10.5152/eajem.2017.38258 Additional Declarations The authors declare no competing interests. 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5925892","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":408765212,"identity":"9e56ff16-fc56-4096-9ca7-7a9c510fb196","order_by":0,"name":"meliha fındık","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYFAC5obDDAxyDAxnmA8AeRIyRGhhBGkxBmphSwBp4SFKCzNEC48BiEtYi257Y+PhghqDPL4zZz6/ulFjwcPAfvjoBnxazM4cbDg845hBseTZ3m3WOceADuNJS7uBV8uNxIbDPGx/Ejec591mnMMG1CLBY4Zfy/2HQC3/DIBaeJ4Z5/wjRssNYIjxtgG1nO1hfpzbRoyWM0CH8fYZJM48c8yMObdPgoeNoF+OHz78meebQWLfmeTHn3O+1cnxsx8+hlcLMmCTAJPEKgcB5g+kqB4Fo2AUjIKRAwD9llAFfn4kFAAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"meliha","middleName":"","lastName":"fındık","suffix":""},{"id":408765213,"identity":"8b0c6879-8504-43c6-8d09-1c958d25c4cf","order_by":1,"name":"muhammet çakas","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"muhammet","middleName":"","lastName":"çakas","suffix":""},{"id":408765214,"identity":"3c82b68a-9a88-4e1c-bb98-25b6e15daca3","order_by":2,"name":"ahmet buğra önler","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"ahmet","middleName":"buğra","lastName":"önler","suffix":""}],"badges":[],"createdAt":"2025-01-29 17:40:22","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5925892/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5925892/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75083897,"identity":"f2561234-9c84-4543-8ead-1aa8826afdd0","added_by":"auto","created_at":"2025-01-30 09:39:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":646753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5925892/v1/123b04f1-66eb-44f0-8984-708231c7a987.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComparison of the H3B2A1 score with other risk models for upper gastrointestinal bleeding\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite advancements in diagnosis and treatment, the mortality rate remains between 5% and 10% because of the growing elderly population and comorbid conditions. Acute gastrointestinal bleeding (GIB) is one of the gastrointestinal emergencies that is the most important cause of mortality and morbidity in our country worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Upper gastrointestinal bleeding (UGIB) accounts for more than 50% of all gastrointestinal bleeding cases and hospitalizations. Despite advancements in endoscopic techniques and their widespread use, the mortality rate for UGIB ranges from 3\u0026ndash;15% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, evaluating the prognosis and rebleeding rate remains a serious problem. Numerous prognostic-risk scoring systems have been proposed to evaluate mortality risk, the need for intervention and transfusion, rebleeding, and length of hospitalization [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Risk scores are typically categorized into two groups: pre- and postendoscopy. Preendoscopy risk scores include the Glasgow-Blatchford score (GBS), AIMS65 score (AIMS65), ABC (age, blood tests, comorbidity), and preendoscopy Rockall score (pRS). Although the GBS, AIMS-65, and RS are frequently used, there are no widely used risk scores in the literature [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Since these scoring systems are not readily applicable in emergency departments and their memorability is difficult, new scoring systems that can be used as alternatives are needed. Sasaki et al. (2022) introduced the H3B2 score, a new scoring model that utilizes simple and objective indices. It is recommended for high-risk patients with UGIB who require emergency hemostatic interventions, including emergency endoscopy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This study aimed to evaluate the ability of the H3B2A1(modified H3B2) scoring model to predict outcomes and mortality in patients with UGIB in clinical practice and to develop this scoring system.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis was a retrospective study conducted at a single tertiary care center. Between 01.06.2022 and 31.07.2023, patients over 18 who presented with GI symptoms and were diagnosed with UGIB after endoscopy were analyzed. Patients with variceal bleeding, patients who did not undergo endoscopy, trauma patients, patients younger than 18 years of age, and patients whose patient records could not be accessed were excluded. Patients were categorized into two groups: survivors and nonsurvivors. The AIMS65, GBS, mGBS, H3B2, and H3B2A1 (modified H3B2) were calculated for each patient.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe study's data were analyzed via SPSS (Statistical Package for Social Sciences) for Windows 25.0. The receiver operating characteristic curve (AUC) analysis was performed for ICU admissions and mortality using the AIMS65, GBS, mGBS, H3B2, and H3B2A1 scores. The cutoff value and Youden J index were calculated for each score; we compared the prediction of mortality and ICU hospitalization via the AUC. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Committee Approval\u003c/strong\u003e \u003cp\u003eThe Clinical Research Ethics Committee approved the study with decision number 2023/66. All procedures were conducted according to the ethical rules and principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 649 patients admitted to the emergency department with suspected GIB were retrospectively analyzed. A total of 233 patients who underwent an endoscopy and were diagnosed with UGIB were included in the study. Patients were categorized into two groups: survivors and nonsurvivors. The mortality rate was 10.7% (25 patients). The mean age of the patients was 70.28 years (\u0026plusmn;\u0026thinsp;14.96), and 67.38% were over 65 years of age \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of the demographic and clinical characteristics of patients between groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurvivor\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNonsurvivor\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (%6,44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(%6,44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0,57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u0026ndash;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(%24,89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(%1,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(%26,18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 \u0026le;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135(%57,94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(%9,44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157(%67,38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(%37,77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(%3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (%40,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0,169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120(%51,50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(%7,73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (%59,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e208(%89,27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e25(%10,73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e233(%100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHypertension (HT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108(%46,35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(%7,72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126(%54,07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDiyabetes Mellitus (DM)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53(%22,75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(%1,71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(%24,46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,297\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCerebrovascular Disease (CVD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(%9,87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(%3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(%12,87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCoronary Artery Disease (KAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73(%31,33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(%6,01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87(%37,34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAlzheimer Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(%4,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(%2,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(%6,87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(%8,16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(%3,43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(%11,59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eDrug used\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAcetylsalicylic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(%27,48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(%4,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(%31,76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eClopidogrel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(%15,88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(%4,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(%20,17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eProton pump inhibit\u0026ouml;rs (PPI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(%15,88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(%1,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(%17,18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNonsteroidal anti-inflammatory drugs (NSAIDs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(%15,02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(%1,29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(%16,31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRivaroxaban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(%8,58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(%0,86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(%9,44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWarfarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(%5,58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(%5,58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTicagrelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(%2,15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(%2,15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n\u003cp\u003eThe mean hemoglobin (Hb) levels of the patients (8.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32) were similar in both surviving patients (8.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34) and nonsurviving patients (8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98) (\u003cstrong\u003ep\u0026thinsp;=\u0026thinsp;0.068\u003c/strong\u003e). The mean platelet (Plt) value was significantly lower in nonsurviving patients than in surviving patients (\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/strong\u003e). The mean value of serum ALB (Alb) was significantly lower in nonsurviving patients (26.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45) than in surviving patients (\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/strong\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of laboratory parameters between groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSurvivor\u003c/p\u003e\n \u003cp\u003e(MEAN\u0026thinsp;\u0026plusmn;\u0026thinsp;STD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNonsurvivor\u003c/p\u003e\n \u003cp\u003e(MEAN\u0026thinsp;\u0026plusmn;\u0026thinsp;STD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(MEAN\u0026thinsp;\u0026plusmn;\u0026thinsp;STD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin(g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,99\u0026thinsp;\u0026plusmn;\u0026thinsp;2,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,10\u0026thinsp;\u0026plusmn;\u0026thinsp;1,98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,89\u0026thinsp;\u0026plusmn;\u0026thinsp;2,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematocrit (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27,49\u0026thinsp;\u0026plusmn;\u0026thinsp;6,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,71\u0026thinsp;\u0026plusmn;\u0026thinsp;6,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27,19\u0026thinsp;\u0026plusmn;\u0026thinsp;6,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatelet count(10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265,25\u0026thinsp;\u0026plusmn;\u0026thinsp;121,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219,96\u0026thinsp;\u0026plusmn;\u0026thinsp;90,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236,25\u0026thinsp;\u0026plusmn;\u0026thinsp;76,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4,30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eaPTT(sn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,97\u0026thinsp;\u0026plusmn;\u0026thinsp;9,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26,86\u0026thinsp;\u0026plusmn;\u0026thinsp;7,92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26,07\u0026thinsp;\u0026plusmn;\u0026thinsp;9,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational Normalized Ratio (INR)(sn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,25\u0026thinsp;\u0026plusmn;\u0026thinsp;0,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,26\u0026thinsp;\u0026plusmn;\u0026thinsp;0,63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,25\u0026thinsp;\u0026plusmn;\u0026thinsp;0,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,92\u0026thinsp;\u0026plusmn;\u0026thinsp;32,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,56\u0026thinsp;\u0026plusmn;\u0026thinsp;13,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,56\u0026thinsp;\u0026plusmn;\u0026thinsp;31,12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,62\u0026thinsp;\u0026plusmn;\u0026thinsp;26,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,32\u0026thinsp;\u0026plusmn;\u0026thinsp;10,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,48\u0026thinsp;\u0026plusmn;\u0026thinsp;25,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGT(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,89\u0026thinsp;\u0026plusmn;\u0026thinsp;62,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72,20\u0026thinsp;\u0026plusmn;\u0026thinsp;104,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,68\u0026thinsp;\u0026plusmn;\u0026thinsp;68,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-79,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38,88\u0026thinsp;\u0026plusmn;\u0026thinsp;24,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41,03\u0026thinsp;\u0026plusmn;\u0026thinsp;28,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39,11\u0026thinsp;\u0026plusmn;\u0026thinsp;24,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKREATİNİN (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,13\u0026thinsp;\u0026plusmn;\u0026thinsp;0,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,26\u0026thinsp;\u0026plusmn;\u0026thinsp;0,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,15\u0026thinsp;\u0026plusmn;\u0026thinsp;0,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALBUMİN (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,84\u0026thinsp;\u0026plusmn;\u0026thinsp;5,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26,84\u0026thinsp;\u0026plusmn;\u0026thinsp;4,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,30\u0026thinsp;\u0026plusmn;\u0026thinsp;5,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN/ALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,29\u0026thinsp;\u0026plusmn;\u0026thinsp;0,90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,54\u0026thinsp;\u0026plusmn;\u0026thinsp;0,98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,32\u0026thinsp;\u0026plusmn;\u0026thinsp;0,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN/KR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,08\u0026thinsp;\u0026plusmn;\u0026thinsp;18,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,40\u0026thinsp;\u0026plusmn;\u0026thinsp;12,68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,47\u0026thinsp;\u0026plusmn;\u0026thinsp;18,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,95\u0026thinsp;\u0026plusmn;\u0026thinsp;37,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,64\u0026thinsp;\u0026plusmn;\u0026thinsp;36,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,39\u0026thinsp;\u0026plusmn;\u0026thinsp;37,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe associations between mortality and intensive care unit (ICU) hospitalization among the five analyzed scores were evaluated, and the AUC, cutoff point, sensitivity (%), specificity (%), p, and Youden j index values are given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. All the scoring systems were statistically significant in terms of their ability to predict mortality (\u003cstrong\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e), and the H3B2A1 score was as highly predictive as the AIMS65 score was. In predicting ICU hospitalization, the AIMS65 score had greater predictive accuracy (AUC: 0.844) and a high sensitivity of 96.1%, while H3B2 was second-highest with a sensitivity of 82.4%. All the scores were not significantly different.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe area under the receiver operating characteristic (ROC) curve represents the score for mortality.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSCORE SYSTEMS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMortality\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3B2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAİMS65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003emGBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-H3B2A1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUROC (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,725\u003c/p\u003e\n \u003cp\u003e(0,620-0,830)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,754\u003c/p\u003e\n \u003cp\u003e(0,655-0,852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,706\u003c/p\u003e\n \u003cp\u003e(0,614-0,797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,731\u003c/p\u003e\n \u003cp\u003e(0,640-0,822)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,750\u003c/p\u003e\n \u003cp\u003e(0,653-0,846)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCutt off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYouden J İndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eICU hospitalization\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3B2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAİMS65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003emGBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel-H3B2A1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUROC (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,624\u003c/p\u003e\n \u003cp\u003e(0,535-0,713)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,844\u003c/p\u003e\n \u003cp\u003e(0,790-0,899)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,606\u003c/p\u003e\n \u003cp\u003e(0,519-0,692)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,613\u003c/p\u003e\n \u003cp\u003e(0,525-0,701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,645\u003c/p\u003e\n \u003cp\u003e(0,563-0,727)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCutt off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYouden J İndex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGastrointestinal bleeding is one of the gastroenterology emergencies requiring medical intervention and one of the most common causes of hospitalization [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although the incidence varies according to age, elderly patients are more frequently affected, and the frequency of hospitalization increases with age. The most common cause of UGIB in the elderly population is gastric and duodenal ulcers or esophagitis, which is observed in 80% of cases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a 2007 study in England, 63% of patients diagnosed with UGIB were found to be over 60 years of age [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In our study, consistent with the literature, elderly individuals presented with UGIB more frequently; the mean age was 70.28 years (\u0026plusmn;\u0026thinsp;14.96), and 67.38% of the patients were over 65 years. The mortality rate was 10.7%, similar to that reported in the literature, and mortality increased with age.\u003c/p\u003e \u003cp\u003eSince acute UGIB has a significant morbidity and mortality rate, early evaluation and prediction are of utmost clinical importance to improve patient prognosis. The severity of acute GIB ranges from mild to life-threatening. Therefore, various scoring systems are used to evaluate the condition and prognosis of patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Accurate risk stratification in patients is vital for improving clinical outcomes and making treatment decisions. However, risk scoring in emergency medicine should be more practical and easier to apply. In particular, the use of risk scoring methods such as GBS and mGBS is challenging because they include a wide variety of parameters, and it is even known that they are not used [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, all the scoring systems were similar in terms of their ability to predict mortality (AUC: 0.70\u0026ndash;0.93; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, AIMS65 and H3B2A1 performed well, with AUCs of 0.754 and 0.750, respectively. The mGBS, H3B2, and GBS had AUCs of 0.731, 0.725, and 0.706, respectively.\u003c/p\u003e \u003cp\u003eSasaki et al. reported AUCs of 0.707 for the AIMS65 and 0.685 for H3B2, which are more significant than those of the other scores (GBS, AUC: 0.587; mGBS, AUC: 0.594; MAP, AUC: 0.665; and Lino: AUC: 0.549) are more significant, whereas, in our study, all the scoring systems yielded similar results in terms of mortality prediction; however, the AIMS65 and H3B2A1 scores performed better.\u003c/p\u003e \u003cp\u003eIn the study by Cacazu et al. 2023 in patients with and without endoscopy, similar to our research, all scores were significant in predicting mortality. According to the AUC, the International Bleeding score (INBS, 0.844), GBS (0.783), MAP score (0.78), Iino (0.766), AIM65, and modified N-score (0.745 each), mGBS (0.73), H3B2 and N-score (0.701), Rockall, Baylor, and T score reported AUC values below 0.7. However, unlike our study, they included variceal bleeding.\u003c/p\u003e \u003cp\u003eAs suggested in the study by Sasaki et al., the H3B2 score is more practical for predicting mortality. In addition, while parameters such as impaired consciousness and age are available in AIMS65, many parameters need to be evaluated in GBS, and liver and heart diseases should also be known [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, other underlying causes should be differentiated in most patients presenting with impaired consciousness of ASs. In conclusion, early recognition of high-risk patients in patients presenting with UGIB and early planning for endoscopy are important, and predicting mortality during follow-up is also critical [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe literature shows that early risk classifications are inadequate in the clinical decision-making process before endoscopy in high-risk patients, especially in deciding on ICU admission or hospitalization in the relevant service. Generally, risk stratifications have been developed against the possibility of mortality and rebleeding [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Khread et al. 2023 evaluated ICU admission rates with the AIMS65, GBS, ABC, and Rockall scores. They reported that ICU admission rates increased significantly for all high-risk patient scores predicted for all scores except the Rockall score, but AUC curves showed poor discriminatory ability for all scores (ABC: 0.55 (51%; 60%), AIMS65: 0.61 (56%; 66%), GBS:0.61 (55%; 0.66%), RS: 0.51 (53%; 64%)). In our study, in patients admitted to the ICU, in addition to AIMS 65 (AUC: 0.844 (95% CI 0.790\u0026ndash;0.899)), other scoring systems (GBS; AUC: 0.606 (0.519\u0026ndash;0.69), mGBS; AUC: 0.613 (0.525\u0026ndash;0.701), H3B2; AUC: 0.624 (0.535\u0026ndash;0.713), H3B2A1; AUC: 0.645 (0.563\u0026ndash;0.727)) showed similar characteristics in predicting ICU admission (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn conclusion, all scoring systems have been developed to predict the need for early endoscopic treatment, mortality, and transfusion [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the primary purpose of risk stratification in emergency departments is to identify patients who can be discharged [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe propose a new scoring model called H3B2A1, which enhances the existing H3B2 score by incorporating Albumin (A). This model is designed for emergency departments to identify high-risk patients with suspected upper gastrointestinal bleeding (GIB). It can aid in determining the need for emergency endoscopy and assessing the risk of intensive care unit admission and mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthorship Contributions\u003c/strong\u003e: All authors had access to data and a role in writing the manuscript. Meliha Fındık: conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing\u0026mdash;original draft, supervision, project administration, writing\u0026mdash;review and editing. Ramazan Kıyak: conceptualization, methodology, writing original draft, writing\u0026mdash;review and editing. Muhammet \u0026Ccedil;akas: conceptualization, resources, data curation, writing\u0026mdash;review and editing. Ahmet Buğra \u0026Ouml;nler: conceptualization, resources, data curation, writing\u0026mdash;review, and editing. Murat Başcı: conceptualization, resources, data curation, writing\u0026mdash;review and editing. Selman G\u0026uuml;m\u0026uuml;ş: conceptualization, resources, data curation, writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The data supporting this study\u0026apos;s findings are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The authors declared that this study received no financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: The authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e: Informed consent is not required for the patients concerned.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBae SJ, Kim KK, Yun SJ, Lee SH (2021) Predictive performance of blood urea nitrogen to serum albumin ratio in elderly patients with gastrointestinal bleeding. 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Front Med(Lausanne) 3:11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmed.2024.1399429\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2024.1399429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHearnshaw SA, Logan RF, Lowe D, Travis SP, Murphy MF, Palmer KR (2011) Acute upper gastrointestinal bleeding in the UK: patient characteristics, diagnoses and outcomes in the 2007 UK audit. Gut 60(10):1327\u0026ndash;1335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/gut.2010.228437\u003c/span\u003e\u003cspan address=\"10.1136/gut.2010.228437\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HA, Jung HK, Kim TO, Byeon JR, Jeong ES, Cho HJ, Tae CH, Moon CM, Kim SE, Shim KN, Jung SA (2022) Clinical outcomes of acute upper gastrointestinal bleeding according to the risk indicated by Glasgow-Blatchford risk score-computed tomography score in the emergency room. 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Eur J Emerg Med 30(1):32\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MEJ.0000000000000983\u003c/span\u003e\u003cspan address=\"10.1097/MEJ.0000000000000983\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKherad O, Restellini S, Almadi M, Martel M, Barkun AN (2023) Comparative Evaluation of the ABC Score to Other Risk Stratification Scales in Managing High-risk Patients Presenting With Acute Upper Gastrointestinal Bleeding. J Clin Gastroenterol 57(5):479\u0026ndash;485. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/MCG.0000000000001720\u003c/span\u003e\u003cspan address=\"10.1097/MCG.0000000000001720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao VL, Gupta N, Swei E, Wagner T, Aronsohn A, Reddy KG, Sengupta N (2020) Predictors of mortality and endoscopic intervention in patients with upper gastrointestinal bleeding in the intensive care unit. Gastroenterol Rep 8(4):299\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gastro/goaa009\u003c/span\u003e\u003cspan address=\"10.1093/gastro/goaa009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkat A, Cattamanchi S, Madali A, Farook AR, Trichur RV (2017) Comparison of the AIMS-65 score with the Glasgow-Blatchford Score in Upper Gastrointestinal Bleed in the Emergency Department. Eurasian. J Emerg Med 16(2):70\u0026ndash;78\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ehttps://doi org/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5152/eajem.2017.38258\u003c/span\u003e\u003cspan address=\"10.5152/eajem.2017.38258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"gastrointestinal bleeding, mortality, risk score, elderly","lastPublishedDoi":"10.21203/rs.3.rs-5925892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5925892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Acute gastrointestinal (GI) bleeding is a critical emergency and a significant cause of morbidity and mortality worldwide, with rates ranging between 5% and 10%, despite advancements in treatment. Upper gastrointestinal bleeding (UGIB) accounts for a substantial proportion of cases, particularly in elderly populations with comorbid conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This retrospective study evaluated the predictive accuracy of the H3B2A1 scoring model for mortality and intensive care unit (ICU) admission in UGIB patients. The AIMS65, Glasgow-Blatchford score (GBS), modified GBS (mGBS), and H3B2 scores were calculated for all patients. Receiver operating characteristic (ROC) curves were used to assess the scores' predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Compared with the AIMS65 score, the H3B2A1 score demonstrated comparable predictive accuracy for mortality (H3B2A1-AUC: 0.750; AIMS65-AUC: 0.754), with all the scores showing moderate accuracy (AUC = 0.7–0.9). In predicting ICU hospitalization, the AIMS65 score outperformed the other scores (AUC: 0.844; sensitivity: 96.1%), followed by H3B2 (82.4%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The H3B2A1 model (modified H3B2), which incorporates albumin into the H3B2 score, provides a practical tool for assessing mortality risk, ICU admission, and the need for emergency endoscopy in UGIB patients. Its simplicity and predictive power makes it a valuable addition to clinical practice.\u003c/p\u003e","manuscriptTitle":"Comparison of the H3B2A1 score with other risk models for upper gastrointestinal bleeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 09:38:36","doi":"10.21203/rs.3.rs-5925892/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":"1a9b46cc-eee6-4125-9701-1749ccc2f599","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43589295,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2025-01-30T09:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-30 09:38:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5925892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5925892","identity":"rs-5925892","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}