Lactate to albumin ratio as a prognosis predictor in gastrointestinal bleeding in the emergency department

Lactate to albumin ratio as a prognosis predictor in gastrointestinal bleeding in the emergency department | 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 Lactate to albumin ratio as a prognosis predictor in gastrointestinal bleeding in the emergency department SungJin Bae, Myeong Namgung, Kwang Yul Jung, Dong Hoon Lee, Yoon Hee Choi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4013025/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jul, 2024 Read the published version in Internal and Emergency Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background Gastrointestinal bleeding (GIB) is a common cause of emergency department (ED) visits and has a variety of prognoses. This study aimed to verify the prognosis prediction ability of the lactate/albumin ratio (L/A ratio) in GIB patients compared to the AIMS65 score and the blood urea nitrogen/albumin ratio (B/A ratio). Methods This retrospective study was conducted among patients complaining of GIB symptoms who visited an ED in 2019. Baseline characteristics and laboratory data were obtained to calculate the L/A ratio, B/A ratio, and AIMS65 score. Each score was evaluated as a predictor of ICU admission and in-hospital mortality using the area under the receiver operating characteristic (AUROC) curve. Results Multivariate logistic regression revealed that the L/A ratio significantly predicted ICU admission and in-hospital mortality. The AUROC scores for predicting ICU admission were 0.788 for the L/A ratio, 0.695 for the B/A ratio, and 0.586 for the AIMS65 score. For predicting in-hospital mortality, the scores were 0.807 for the L/A ratio, 0.799 for the B/A ratio, and 0.683 for AIMS65. Conclusions The L/A ratio, consisting of serum lactate and albumin levels, had superior performance relative to the other tools (B/A and AIMS65) in predicting the prognosis of GIB patients. Albumins Emergency department Gastrointestinal Hemorrhage Lactates Figures Figure 1 Figure 2 Figure 3 Introduction Gastrointestinal bleeding (GIB) is the fifth most common cause of emergency department (ED) visits in the category of gastrointestinal problems [ 1 ]. Despite advances in treatment strategy, including endoscopic and radiologic technology, GIB is still associated with morbidity and mortality, resulting in economic burdens [ 2 , 3 ]. The overall mortality rate of GIB varies from 5–10% [ 4 , 5 ]. Additionally, the medication and follow-up alone range from treating minor bleeding to severe bleeding that requires an urgent procedure. Therefore, early risk stratification in the ED based on clinical parameters helps to establish a patient's treatment plan and identify and reduce the risk of adverse outcomes [ 6 ]. There are several tools for predicting the prognosis of GIB patients. These include the Glasgow-Blatchford score (GBS), Rockall score (RS), and the AIMS65 score [ 7 ]. However, they are difficult to apply to all GIB patients in an ED because they have been developed specifically for upper GIB and are complicated [ 8 , 9 ]. Therefore, a simpler indicator that can predict the prognosis of patients is needed. According to a recent study, the ratio of blood urea nitrogen (BUN) and albumin can be used as an equivalent predictive tool for elderly GIB patients [ 10 ]. However, BUN is also known as a more useful predictor of upper GIB than lower GIB since its level increases when patients ingest a lot of protein or blood [ 11 ]. Additionally, BUN is also affected by dehydration, and it is difficult to determine whether it was caused by acute GIB in patients with existing kidney disease [ 12 ]. In contrast, lactate is known as a useful prognostic marker that can predict tissue hypoxia and hypoperfusion. According to earlier studies, the lactate level helped screen severe patients and predict mortality associated with GIB [ 13 , 14 ]. However, several conditions such as sepsis, septic shock, hypovolemia, hepatic or renal dysfunctions, or even certain medications can increase lactate levels [ 15 ]. Additionally, since the blood concentration of lactic acid is affected by both the production rate and the removal rate, it is difficult to estimate the patient's prognosis by simply measuring it once or twice [ 16 ]. Thus, predicting severity using only lactate levels may be inadequate and inaccurate. Some studies have recently suggested that lactate/albumin (L/A) ratios help predict the prognosis of severely ill patients [ 17 ]. Albumin is known as a negative acute phase protein, and its levels are affected by nutritional status and chronic inflammatory conditions, both additional parameters for mortality and prognosis. Serum albumin levels are also affected by several conditions including liver cirrhosis, malnutrition, and inflammations which may also cause GIB. We hypothesized that the lactate and albumin levels had a reverse correlation in GIB patients and that the L/A ratio would help predict the prognoses of GIB patients. Therefore, this study aims to investigate the usefulness of the L/A ratio as a predictor of mortality and intensive care unit (ICU) admission among GIB patients. Material and methods Study design and population This single-center, retrospective study was conducted among GIB patients who visited an academic tertiary university hospital from 1 January to 31 December 2019. Utilizing electronic medical records, we collected clinical data by selecting GIB patients who visited the ED complaining of GIB symptoms, including hematemesis, melena, and hematochezia. Patients over the age of 18 years whose diagnoses were confirmed by a certified emergency physician or gastroenterologist were included in the study analysis. Patients who visited the ED for non-medical purposes or trauma or had incomplete EMR data due to being discharged against medical advice or transferred to another hospital were excluded from the study. The study was approved by the hospital's institutional review board, and the requirement for written informed consent was waived. Data collection and outcome measurement Collected data included demographics such as age, sex, and initial vital signs, including systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), respiratory rate (RR), body temperature (BT), and mental status. Additionally, laboratory tests including BUN, albumin, and lactic acid, which were initially performed after the initial ED visit, were collected for evaluation as independent factors for prognosis prediction. Data on clinical outcomes including the final disposition of patients (discharge, general ward (GW) admission, ICU admission) and in-hospital mortality was collected. Patients who were admitted to the ICU were defined as the ICU group and patients who were discharged from the ED or admitted to the GW were defined as the non-ICU group. Additionally, patients who died in the ED or after admission were defined as the mortality group, and those who did not die were defined as the non-mortality group. The primary outcome was ICU admission, and the secondary outcome was in-hospital mortality. Statistical analysis Categorical variables are expressed as counts and percentages, and continuous variables are expressed as means ± standard deviation. For continuous variables, the independent t -test was used for normally distributed data, and the Mann–Whitney U test was used for skewed data. The Pearson chi-square test or the Fisher exact test was used for nominal variables, and particularly, the Fisher exact test was applied when more than 20% of cells had expected frequencies less than five. Continuous variables are expressed as means ± standard deviation or medians (interquartile range), and categorical variables are expressed as counts and percentages. Logistic regression was used to investigate the clinical factors associated with ICU admission and mortality. Differences of p < .05 were considered statistically significant. All statistical analyses were performed using SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA). The analysis of the AUROC utilized the DeLong method using MedCalc Statistical Software version 19 (MedCalc Software bvba, Ostend, Belgium). Result Baseline characteristics A total of 306 patients were enrolled in this study (Fig. 1 ). The mean age was 63.2 ± 17.5 years, and among the 306 patients, 67.9% ( n = 208) were male. Of all the patients, 44.8% ( n = 137) were admitted to the ICU, and in-hospital mortality was 9.8% ( n = 30). The average BUN/albumin ratio (B/A ratio) was 12.77 ± 12.34, and the average L/A ratio was 15.8 ± 26.0. Clinical factors associated with ICU admission of GI bleeding patients The ICU group had significantly lower SBP, DBP, and BT compared to the non-ICU group. The ICU group had a significantly higher proportion of altered mental status. Significant intergroup differences were observed in white blood cell, hemoglobin, hematocrit, platelet, AST, ALT, glucose, PT, and PTT values. The B/A ratio and L/A ratio were significantly higher in the ICU group. Multivariate logistic regression analysis using variables that were statistically significant in univariate regression analysis showed that DBP, AST, glucose, and L/A ratios were significant independent variables in predicting ICU admission (Table 1 ). Table 1 Logistic regression analysis of ICU admission predictors Variable Univariate analysis a Multivariate analysis + non-ICU group ICU group n = 169 n = 137 P-value OR B P-value Age (years) 62.1 ± 19.2 64.5 ± 15.0 0.220 Sex ; Male 108 (63.9) 100 (73.0) 0.091 Systolic Blood Pressure (mmHg) 123.6 ± 25.4 104.0 ± 32.6 < 0.001 1.012 (0.997–1.026) 0.012 0.107 Diastolic Blood Pressure (mmHg) 71.7 ± 19.5 58.0 ± 21.2 < 0.001 1.036 (1.021–1.051) 0.035 < 0.001 Pulse rate (beats/min) 92.6 ± 18.4 97.6 ± 27.3 0.065 Respiratory rate (breath/min) 20.0 ± 2.2 19.7 ± 3.9 0.486 Body temperature ( o C) 36.4 ± 3.0 35.1 ± 7.1 0.068 Altered mental status 4 (2.4) 16 (11.7) 0.003 0.569 (0.140–2.309) 0.565 0.430 Laboratory test White blood cell (10 9 /L) 10.3 ± 7.3 12.1 ± 6.2 0.027 0.980 (0.939–1.022) -0.021 0.346 Hemoglobin (g/dL) 10.5 ± 3.4 8.6 ± 3.1 < 0.001 1.074 (0.978–1.180) 0.072 0.134 Hematocrit (%) 30.0 ± 9.4 24.9 ± 8.7 < 0.001 0.923 (0.722–1.180) -0.080 0.523 Platelet(10 9 /L) 232.0 ± 157.5 177.8 ± 99.9 < 0.001 1.002 (1.000–1.005) 0.002 0.103 C-reactive protein (mg/dL) 2.2 ± 4.9 3.0 ± 5.4 0.179 Creatinine (mg/dL) 1.4 ± 1.7 1.7 ± 1.5 0.102 Aspartate aminotransferase (IU/L) 49.8 ± 71.7 126.3 ± 316.6 0.002 0.997 (0.995–1.000) -0.003 0.030 Alanine aminotransferase (IU/L) 31.7 ± 56.1 50.2 ± 84.3 0.037 1.004 (0.996–1.012) 0.004 0.362 Amylase (IU/L) 74.8 ± 57.9 88.8 ± 108.5 0.169 Glucose (mg/dL) 166.9 ± 101.2 215.6 ± 131.3 0.001 0.997 (0.994–1.000) -0.003 0.028 Troponin-T (ng/mL) 0.04 ± 0.13 0.1 ± 0.5 0.270 PT (INR) 1.2 ± 0.9 1.7 ± 1.2 0.002 0.843 (0.484–1.469) -0.171 0.547 PTT (sec) 26.5 ± 10.5 31.9 ± 16.2 0.003 1.006 (0.976–1.038) 0.006 0.682 BUN/Albumin ratio 9.6 ± 9.1 16.7 ± 14.5 < 0.001 0.991 (0.962–1.020) -0.010 0.523 Lactate/Albumin ratio 8.1 ± 13.1 25.5 ± 33.9 < 0.001 0.965 (0.947–0.983) -0.035 < 0.001 a Data are mean (standard deviation) or number (%) † Data in parentheses are 95% confidence intervals, conducted on variables with a p value of < 0.05 on univariate analysis OR odds ratio, B regression coefficient Boldface type indicates statistical significance (p < 0.05) Clinical factors associated with in-hospital mortality of GIB patients The mortality group had significantly lower SBP, DBP, and BT compared to the non-mortality group. The mortality group also had a significantly higher portion of altered mental status. In laboratory tests, significant differences between the two groups were observed in hemoglobin, hematocrit, platelet, C-reactive protein, creatinine, ALT, PT, and PTT values. The B/A ratio and L/A ratio were significantly higher in the mortality group. The multivariate logistic regression analysis demonstrated that PTT, B/A ratio, and L/A ratio were significant independent variables for predicting mortality (Table 2 ). Table 2 Logistic regression analysis of mortality predictors Variable Univariate analysis a Multivariate analysis + Non-Mortality group Mortality group n = 276 n = 30 P-value OR B P-value Age (years) 62.5 ± 17.4 68.8 ± 17.3 0.065 Sex ; Male 185 (67.0) 23 (76.7) 0.286 Systolic Blood Pressure (mmHg) 116.5 ± 28.1 99.0 ± 43.8 0.003 0.996 (0.969–1.024) -0.004 0.797 Diastolic Blood Pressure (mmHg) 66.7 ± 19.2 54.7 ± 33.9 0.004 1.003 (0.977–1.030) 0.003 0.816 Pulse rate (beats/min) 95.2 ± 21.3 91.2 ± 34.8 0.360 Respiratory rate (breath/min) 19.9 ± 2.5 19.2 ± 5.9 0.212 Body temperature ( o C) 36.1 ± 3.9 32.4 ± 11.3 0.003 1.064 (0.892–1.268) 0.062 0.491 Altered mental status 12 (4.3) 8 (26.7) < 0.001 2.152(0.355–13.065) 0.767 0.405 Laboratory test White blood cell (10 9 /L) 10.9 ± 6.9 12.6 ± 6.0 0.212 Hemoglobin (g/dL) 9.8 ± 3.4 7.7 ± 2.5 0.003 2.613 (0.685–9.964) 0.961 0.160 Hematocrit (%) 28.2 ± 9.4 22.7 ± 7.3 0.003 0.678 (0.418–1.099) -0.389 0.115 Platelet(10 9 /L) 216.5 ± 140.1 127.9 ± 70.6 < 0.001 0.999 (0.992–1.006) -0.001 0.698 C-reactive protein (mg/dL) 2.3 ± 4.9 4.4 ± 6.2 0.038 1.005 (0.924–1.094) 0.005 0.899 Creatinine (mg/dL) 1.4 ± 1.5 2.6 ± 1.9 0.001 1.183 (0.918–1.525) 0.168 0.194 Aspartate aminotransferase (IU/L) 75.8 ± 221.8 159.8 ± 204.1 0.120 Alanine aminotransferase (IU/L) 35.7 ± 57.7 80.0 ± 139.8 0.006 1.004 (0.999–1.008) 0.004 0.122 Amylase (IU/L) 79.8 ± 77.0 93.0 ± 138.7 0.438 Glucose (mg/dL) 190.6 ± 119.7 170.7 ± 99.5 0.383 Troponin-T (ng/mL) 0.07 ± 0.3 0.09 ± 0.1 0.746 PT (INR) 1.2 ± 0.5 3.0 ± 2.2 < 0.001 1.485 (0.566–3.893) 0.395 0.422 PTT (sec) 26.3 ± 6.5 53.4 ± 30.7 < 0.001 1.070 (1.022–1.120) 0.068 0.004 BUN/Albumin ratio 11.3 ± 10.1 26.4 ± 20.5 < 0.001 1.051 (1.016–1.086) 0.049 0.004 Lactate/Albumin ratio 11.4 ± 14.7 56.6 ± 56.3 < 0.001 1.023 (1.004–1.042) 0.023 0.016 a Data are mean (standard deviation) or number (%) † Data in parentheses are 95% confidence intervals, conducted on variables with a p value of < 0.05 on univariate analysis OR odds ratio, B regression coefficient Boldface type indicates statistical significance (p < 0.05) Predictive performance of L/A ratio compared to AIMS65 score and B/A ratio The cut-off value, AUROC, sensitivity, and specificity for predicting ICU admission and in-hospital mortality of the three scoring tools are shown in Table 3 . For predicting ICU admission, the cut-off value was 7.6 for the L/A ratio, 7.7 for the B/A ratio, and 1 for the AIMS65 score. The AUROC was fair at predicting ICU admission in the order of L/A ratio, B/A ratio, and AIMS65 (0.788 (0.737–0.840), 0.695 (0.636–0.754), and 0.586 (0.524–0.647), respectively) (Fig. 2 ). Table 3 Cut-off value, AUROC, sensitivity and specificity for predict prognosis Cut-off value AUROC (95% CI) Sensitivity, % (95% CI) Specificity, % (95% CI) P-value Lactate/Albumin ratio For predict ICU admission 7.6 0.788 (0.737–0.840) 70.80 (62.4–78.3) 78.70 (71.7–84.6) < 0.001 For predict in-hospital mortality 16.4 0.807 (0.708–0.906) 70.00 (50.6–85.3) 85.14 (80.4–89.1) < 0.001 BUN/Albumin ratio For predict ICU admission 7.7 0.695 (0.636–0.754) 74.45 (66.3–81.5) 57.99 (50.2–65.5) < 0.001 For predict in-hospital mortality 16.1 0.799 (0.724–0.875) 73.33 (54.1–87.7) 79.71 (74.5–84.3) < 0.001 AIMS65 For predict ICU admission 1 0.586 (0.524–0.647) 46.72 (38.1–55.4) 69.82 (62.3–76.6) 0.006 For predict in-hospital mortality 1 0.683 (0.594–0.773) 66.67 (47.2–82.7) 65.58 (59.6–71.2) 0.001 AUROC: Area under the ROC curve, ICU: Intensive care unit For predicting in-hospital mortality, the cut-off value was 16.4 for the L/A ratio, 16.1 for the B/A ratio, and 1 for the AIMS65 score. The prediction ability was fair for in-hospital mortality in the order of L/A ratio, B/A ratio, and AIMS65 (0.807 (0.708–0.906), 0.799 (0.724–0.875), and 0.683 (0.594–0.773), respectively) (Fig. 3 ). Discussion To our knowledge, this is the first study to evaluate the association between L/A ratio and GIB patient prognosis. In this study, the L/A ratio was found to be a significant factor in predicting the prognosis of GIB patients who visited the ED. To demonstrate the performance of the L/A ratio, it was compared with the AIMS65 score, which was previously widely used, and a new concept, the B/A ratio, for predicting the prognosis of GIB patients. As a result, the L/A ratio was found to be superior to the other prediction tools in predicting ICU admission and in-hospital mortality. The L/A ratio has been recognized as a predictor of ICU mortality associated with sepsis and severe heart failure, and this is consistent with the present findings [ 17 – 19 ]. Since GIB is associated with high mortality rates in the ED, early identification of vulnerable patients and the application of proper interventions are essential. Previously, scoring tools such as GBS and RS have been developed to predict clinical outcomes, including mortality, need for transfusion, and other specific interventions [ 7 ]. However, the actual clinical adjustment and utilization of these scores is challenging due to their complexity [ 20 ]. GBS is difficult to calculate, and RS requires endoscopic findings. Therefore, a simpler scoring system, the AIMS65 score, was developed. This score includes four objective factors: 1.5 international normalized ratio, systolic blood pressure < 90 mm Hg, ≥ 65 years of age, and one subjective factor: altered mental status [ 21 ]. Although this can be calculated faster than other scores, a potential problem involves the physician’s subjective judgment of factors such as the patient’s mental status. Additionally, AIMS65 scores are focused on upper GIB, making its predictive ability poor relative to mortality in lower GIB [ 8 , 22 , 23 ]. Similarly, the B/A ratio can be a simple and useful predictor, but this ratio is also related to upper GIB and some studies have used this ratio as a tool for estimating bleeding sources [ 24 , 25 ]. It is difficult to accurately identify the bleeding source in the ED setting. For example, in a randomized trial among GIB patients who were predicted to have upper lower GIB, 15% of the patients were found to have upper GIB sources [ 26 ]. Therefore, we believe that indicators that simultaneously reflect the bleeding regardless of its source, tissue hypoperfusion (lactate), and the chronic state (albumin) would help determine the prognoses of GIB patients. Logistic regression analysis was performed to evaluate independent factors for predicting prognoses. As a result, it was found that the L/A ratio was a significant predictor of both ICU admission and in-hospital mortality. In contrast, the B/A ratio was a potential predictor only for in-hospital mortality. In the multiple logistic regression analysis performed, which included demographic data, initial vital signs, and laboratory tests, only the L/A ratio was found to be a predictor of both ICU admission and in-hospital mortality. Some variables that comprise the AIMS65 score were found to be significant in the univariate analysis predicting ICU admission and in-hospital mortality, but each variable was not a significant factor in the multivariate logistic regression analysis. As a result of the AUROC curve analysis, it was found that the L/A ratio is superior in predicting ICU admission and in-hospital mortality compared to the AIMS65 score. Additionally, compared to the B/A ratio, the L/A ratio was better at predicting ICU admission; however, both tools showed excellent prediction performances without a significant difference in predicting in-hospital mortality. Limitations This study has some limitations. First, this study utilized a retrospective design and was performed at a single academic tertiary hospital. Therefore, it may be difficult to represent all GIB patients and the proportion of severe patients may be relatively greater. Therefore, the occurrence of a selection bias is possible. Second, the relatively small sample size of in-hospital mortality cases compared to survivors may be insufficient to identify all factors. A multi-center study utilizing a prospective design involving more emergency centers may be necessary in the future. Finally, the variables used in this study did not include treatment methods that can affect prognoses, such as endoscopy, blood transfusion, and embolization. Conclusion The L/A ratio, consisting of serum lactate and albumin levels, demonstrated superior performance compared with other tools (B/A ratio and the AIMS65 score) in predicting ICU admissions and in-hospital mortality among GIB patients. The L/A ratio was not only a more useful tool for predicting prognosis but was also easier to use than the AIMS65. Thus, by predicting severity in GIB patients, the L/A ratio may help identify optimal treatments. Declarations Conflict of interest The authors have no conflicts of interest to declare that are relevant to the content of this article. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article. Availability of data and materials The data sets used in this study are available from the corresponding author on reasonable request. Human rights There were no human rights conflicts to declare and the study was in accordance with Declaration of Helsinki. 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Cite Share Download PDF Status: Published Journal Publication published 25 Jul, 2024 Read the published version in Internal and Emergency Medicine → Version 1 posted Reviewers agreed at journal 10 Mar, 2024 Reviewers invited by journal 10 Mar, 2024 Editor assigned by journal 07 Mar, 2024 First submitted to journal 06 Mar, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4013025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277903142,"identity":"5250ec31-0c58-4919-9f72-323ac4ae5fa8","order_by":0,"name":"SungJin Bae","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACxgYgYVPPD2InFBCvJS1BEkQlGBBv1eEEgwMgmhgtBucPt27m+ZOWZ3x+deKHBwYM8vxiBwhoOXCw7TYPj02x2Y23myWADjOcOTuBgJaDjUAtEmmM226c3QDSkmBwm5CWw4xALUBy84yzm38Qp+UYSEvC4cQN/L3biLNF8gxj2805B9KMJW7wbrNIMJAg7Be+88ef3Xjzx0aOv//s5ps/Kmzk+aUJaEEACbBKCWKVgwD/AVJUj4JRMApGwUgCAC7+STjO31ieAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2871-3323","institution":"Chung-Ang University Gwangmyeong Hospital","correspondingAuthor":true,"prefix":"","firstName":"SungJin","middleName":"","lastName":"Bae","suffix":""},{"id":277903143,"identity":"b30d33c6-cc13-4545-951b-d44b8c00fd2c","order_by":1,"name":"Myeong Namgung","email":"","orcid":"","institution":"Chung-Ang University Gwangmyeong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Myeong","middleName":"","lastName":"Namgung","suffix":""},{"id":277903144,"identity":"57e33da2-a323-4eca-9e4e-86e5f9c03009","order_by":2,"name":"Kwang Yul Jung","email":"","orcid":"","institution":"Chung-Ang University Gwangmyeong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kwang","middleName":"Yul","lastName":"Jung","suffix":""},{"id":277903145,"identity":"21baf3e2-c632-4e72-9035-0b5a595db5fb","order_by":3,"name":"Dong Hoon Lee","email":"","orcid":"","institution":"Chung-Ang University Gwangmyeong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Hoon","lastName":"Lee","suffix":""},{"id":277903146,"identity":"a758afd7-4ae8-4f8e-ae2d-a2d648a0919e","order_by":4,"name":"Yoon Hee Choi","email":"","orcid":"","institution":"Ewha Womens University Mokdong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yoon","middleName":"Hee","lastName":"Choi","suffix":""},{"id":277903147,"identity":"1d919b6f-34a3-46a1-9a61-93acc303d945","order_by":5,"name":"Yunhyung Choi","email":"","orcid":"","institution":"Chung-Ang University Gwangmyeong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunhyung","middleName":"","lastName":"Choi","suffix":""},{"id":277903148,"identity":"f64a7e97-1d0a-416a-8874-dc7d4860e1ca","order_by":6,"name":"Ho Sub Chung","email":"","orcid":"https://orcid.org/0000-0001-8141-3867","institution":"Chung-Ang University Gwangmyeong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ho","middleName":"Sub","lastName":"Chung","suffix":""}],"badges":[],"createdAt":"2024-03-04 16:37:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4013025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4013025/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11739-024-03723-3","type":"published","date":"2024-07-26T00:27:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52620321,"identity":"bce09bc6-158f-4430-b204-1515fd85f7b9","added_by":"auto","created_at":"2024-03-13 16:45:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":327164,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy patient flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4013025/v1/2f5acea68d1c183f630a507a.png"},{"id":52620322,"identity":"55e2168c-70ce-4529-8994-76fcf5e5a96d","added_by":"auto","created_at":"2024-03-13 16:45:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the AUROCs curvefor predicting ICU admission.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4013025/v1/5a0649a8441d4675c61b7a99.png"},{"id":52620320,"identity":"7b8cd652-3ddd-43b8-92c1-4f26e2262b32","added_by":"auto","created_at":"2024-03-13 16:45:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the AUROCs curvefor predicting in-hospital mortality.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4013025/v1/11cdb7d81bb8f9b0ac71fa30.png"},{"id":61197034,"identity":"8732b21f-b91c-4edb-a790-50a300733e02","added_by":"auto","created_at":"2024-07-27 00:27:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4013025/v1/d8612a53-f2a4-47e8-84de-d4e284c46b2d.pdf"}],"financialInterests":"","formattedTitle":"Lactate to albumin ratio as a prognosis predictor in gastrointestinal bleeding in the emergency department","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastrointestinal bleeding (GIB) is the fifth most common cause of emergency department (ED) visits in the category of gastrointestinal problems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in treatment strategy, including endoscopic and radiologic technology, GIB is still associated with morbidity and mortality, resulting in economic burdens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The overall mortality rate of GIB varies from 5\u0026ndash;10% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, the medication and follow-up alone range from treating minor bleeding to severe bleeding that requires an urgent procedure. Therefore, early risk stratification in the ED based on clinical parameters helps to establish a patient's treatment plan and identify and reduce the risk of adverse outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several tools for predicting the prognosis of GIB patients. These include the Glasgow-Blatchford score (GBS), Rockall score (RS), and the AIMS65 score [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, they are difficult to apply to all GIB patients in an ED because they have been developed specifically for upper GIB and are complicated [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, a simpler indicator that can predict the prognosis of patients is needed. According to a recent study, the ratio of blood urea nitrogen (BUN) and albumin can be used as an equivalent predictive tool for elderly GIB patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, BUN is also known as a more useful predictor of upper GIB than lower GIB since its level increases when patients ingest a lot of protein or blood [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, BUN is also affected by dehydration, and it is difficult to determine whether it was caused by acute GIB in patients with existing kidney disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In contrast, lactate is known as a useful prognostic marker that can predict tissue hypoxia and hypoperfusion. According to earlier studies, the lactate level helped screen severe patients and predict mortality associated with GIB [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, several conditions such as sepsis, septic shock, hypovolemia, hepatic or renal dysfunctions, or even certain medications can increase lactate levels [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, since the blood concentration of lactic acid is affected by both the production rate and the removal rate, it is difficult to estimate the patient's prognosis by simply measuring it once or twice [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, predicting severity using only lactate levels may be inadequate and inaccurate.\u003c/p\u003e \u003cp\u003eSome studies have recently suggested that lactate/albumin (L/A) ratios help predict the prognosis of severely ill patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Albumin is known as a negative acute phase protein, and its levels are affected by nutritional status and chronic inflammatory conditions, both additional parameters for mortality and prognosis. Serum albumin levels are also affected by several conditions including liver cirrhosis, malnutrition, and inflammations which may also cause GIB.\u003c/p\u003e \u003cp\u003eWe hypothesized that the lactate and albumin levels had a reverse correlation in GIB patients and that the L/A ratio would help predict the prognoses of GIB patients. Therefore, this study aims to investigate the usefulness of the L/A ratio as a predictor of mortality and intensive care unit (ICU) admission among GIB patients.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis single-center, retrospective study was conducted among GIB patients who visited an academic tertiary university hospital from 1 January to 31 December 2019. Utilizing electronic medical records, we collected clinical data by selecting GIB patients who visited the ED complaining of GIB symptoms, including hematemesis, melena, and hematochezia. Patients over the age of 18 years whose diagnoses were confirmed by a certified emergency physician or gastroenterologist were included in the study analysis. Patients who visited the ED for non-medical purposes or trauma or had incomplete EMR data due to being discharged against medical advice or transferred to another hospital were excluded from the study. The study was approved by the hospital's institutional review board, and the requirement for written informed consent was waived.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and outcome measurement\u003c/h2\u003e \u003cp\u003eCollected data included demographics such as age, sex, and initial vital signs, including systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), respiratory rate (RR), body temperature (BT), and mental status. Additionally, laboratory tests including BUN, albumin, and lactic acid, which were initially performed after the initial ED visit, were collected for evaluation as independent factors for prognosis prediction. Data on clinical outcomes including the final disposition of patients (discharge, general ward (GW) admission, ICU admission) and in-hospital mortality was collected. Patients who were admitted to the ICU were defined as the ICU group and patients who were discharged from the ED or admitted to the GW were defined as the non-ICU group. Additionally, patients who died in the ED or after admission were defined as the mortality group, and those who did not die were defined as the non-mortality group. The primary outcome was ICU admission, and the secondary outcome was in-hospital mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables are expressed as counts and percentages, and continuous variables are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. For continuous variables, the independent \u003cem\u003et\u003c/em\u003e-test was used for normally distributed data, and the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test was used for skewed data. The Pearson chi-square test or the Fisher exact test was used for nominal variables, and particularly, the Fisher exact test was applied when more than 20% of cells had expected frequencies less than five. Continuous variables are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or medians (interquartile range), and categorical variables are expressed as counts and percentages. Logistic regression was used to investigate the clinical factors associated with ICU admission and mortality. Differences of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 were considered statistically significant. All statistical analyses were performed using SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA). The analysis of the AUROC utilized the DeLong method using MedCalc Statistical Software version 19 (MedCalc Software bvba, Ostend, Belgium).\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 306 patients were enrolled in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age was 63.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5 years, and among the 306 patients, 67.9% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;208) were male. Of all the patients, 44.8% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;137) were admitted to the ICU, and in-hospital mortality was 9.8% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30). The average BUN/albumin ratio (B/A ratio) was 12.77\u0026thinsp;\u0026plusmn;\u0026thinsp;12.34, and the average L/A ratio was 15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;26.0.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical factors associated with ICU admission of GI bleeding patients\u003c/h2\u003e \u003cp\u003eThe ICU group had significantly lower SBP, DBP, and BT compared to the non-ICU group. The ICU group had a significantly higher proportion of altered mental status. Significant intergroup differences were observed in white blood cell, hemoglobin, hematocrit, platelet, AST, ALT, glucose, PT, and PTT values. The B/A ratio and L/A ratio were significantly higher in the ICU group. Multivariate logistic regression analysis using variables that were statistically significant in univariate regression analysis showed that DBP, AST, glucose, and L/A ratios were significant independent variables in predicting ICU admission (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of ICU admission predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enon-ICU group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICU group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;169\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;137\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex ; Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic Blood Pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.0\u0026thinsp;\u0026plusmn;\u0026thinsp;32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.012 (0.997\u0026ndash;1.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic Blood Pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.036 (1.021\u0026ndash;1.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.6\u0026thinsp;\u0026plusmn;\u0026thinsp;27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate (breath/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody temperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAltered mental status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.569 (0.140\u0026ndash;2.309)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory test\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.980 (0.939\u0026ndash;1.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.074 (0.978\u0026ndash;1.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.923 (0.722\u0026ndash;1.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232.0\u0026thinsp;\u0026plusmn;\u0026thinsp;157.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.8\u0026thinsp;\u0026plusmn;\u0026thinsp;99.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002 (1.000\u0026ndash;1.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.8\u0026thinsp;\u0026plusmn;\u0026thinsp;71.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.3\u0026thinsp;\u0026plusmn;\u0026thinsp;316.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997 (0.995\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;56.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.2\u0026thinsp;\u0026plusmn;\u0026thinsp;84.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.004 (0.996\u0026ndash;1.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmylase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.8\u0026thinsp;\u0026plusmn;\u0026thinsp;108.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166.9\u0026thinsp;\u0026plusmn;\u0026thinsp;101.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215.6\u0026thinsp;\u0026plusmn;\u0026thinsp;131.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.997 (0.994\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin-T (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (INR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.843 (0.484\u0026ndash;1.469)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.006 (0.976\u0026ndash;1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Albumin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.991 (0.962\u0026ndash;1.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate/Albumin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.965 (0.947\u0026ndash;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e Data are mean (standard deviation) or number (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026dagger; Data in parentheses are 95% confidence intervals, conducted on variables with a p value of \u0026lt;\u0026thinsp;0.05 on univariate analysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOR odds ratio, B regression coefficient\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBoldface type indicates statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical factors associated with in-hospital mortality of GIB patients\u003c/h2\u003e \u003cp\u003eThe mortality group had significantly lower SBP, DBP, and BT compared to the non-mortality group. The mortality group also had a significantly higher portion of altered mental status. In laboratory tests, significant differences between the two groups were observed in hemoglobin, hematocrit, platelet, C-reactive protein, creatinine, ALT, PT, and PTT values. The B/A ratio and L/A ratio were significantly higher in the mortality group. The multivariate logistic regression analysis demonstrated that PTT, B/A ratio, and L/A ratio were significant independent variables for predicting mortality (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eLogistic regression analysis of mortality predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Mortality group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;276\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex ; Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic Blood Pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.996 (0.969\u0026ndash;1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic Blood Pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.7\u0026thinsp;\u0026plusmn;\u0026thinsp;33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.003 (0.977\u0026ndash;1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.2\u0026thinsp;\u0026plusmn;\u0026thinsp;34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate (breath/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody temperature (\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.064 (0.892\u0026ndash;1.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAltered mental status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.152(0.355\u0026ndash;13.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory test\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.613 (0.685\u0026ndash;9.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.678 (0.418\u0026ndash;1.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216.5\u0026thinsp;\u0026plusmn;\u0026thinsp;140.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.9\u0026thinsp;\u0026plusmn;\u0026thinsp;70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999 (0.992\u0026ndash;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.005 (0.924\u0026ndash;1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.183 (0.918\u0026ndash;1.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;221.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.8\u0026thinsp;\u0026plusmn;\u0026thinsp;204.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.7\u0026thinsp;\u0026plusmn;\u0026thinsp;57.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0\u0026thinsp;\u0026plusmn;\u0026thinsp;139.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.004 (0.999\u0026ndash;1.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmylase (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.8\u0026thinsp;\u0026plusmn;\u0026thinsp;77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.0\u0026thinsp;\u0026plusmn;\u0026thinsp;138.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.6\u0026thinsp;\u0026plusmn;\u0026thinsp;119.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.7\u0026thinsp;\u0026plusmn;\u0026thinsp;99.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin-T (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (INR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.485 (0.566\u0026ndash;3.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.070 (1.022\u0026ndash;1.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN/Albumin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.051 (1.016\u0026ndash;1.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate/Albumin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.023 (1.004\u0026ndash;1.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e Data are mean (standard deviation) or number (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026dagger; Data in parentheses are 95% confidence intervals, conducted on variables with a p value of \u0026lt;\u0026thinsp;0.05 on univariate analysis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOR odds ratio, B regression coefficient\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBoldface type indicates statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePredictive performance of L/A ratio compared to AIMS65 score and B/A ratio\u003c/h2\u003e \u003cp\u003eThe cut-off value, AUROC, sensitivity, and specificity for predicting ICU admission and in-hospital mortality of the three scoring tools are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. For predicting ICU admission, the cut-off value was 7.6 for the L/A ratio, 7.7 for the B/A ratio, and 1 for the AIMS65 score. The AUROC was fair at predicting ICU admission in the order of L/A ratio, B/A ratio, and AIMS65 (0.788 (0.737\u0026ndash;0.840), 0.695 (0.636\u0026ndash;0.754), and 0.586 (0.524\u0026ndash;0.647), respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\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\u003eCut-off value, AUROC, sensitivity and specificity for predict prognosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUROC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity, % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity, % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLactate/Albumin ratio\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor predict ICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788 (0.737\u0026ndash;0.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.80 (62.4\u0026ndash;78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.70 (71.7\u0026ndash;84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor predict in-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.807 (0.708\u0026ndash;0.906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.00 (50.6\u0026ndash;85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.14 (80.4\u0026ndash;89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBUN/Albumin ratio\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor predict ICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.695 (0.636\u0026ndash;0.754)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.45 (66.3\u0026ndash;81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57.99 (50.2\u0026ndash;65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor predict in-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.799 (0.724\u0026ndash;0.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.33 (54.1\u0026ndash;87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.71 (74.5\u0026ndash;84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAIMS65\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor predict ICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.586 (0.524\u0026ndash;0.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.72 (38.1\u0026ndash;55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.82 (62.3\u0026ndash;76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFor predict in-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.683 (0.594\u0026ndash;0.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.67 (47.2\u0026ndash;82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.58 (59.6\u0026ndash;71.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAUROC: Area under the ROC curve, ICU: Intensive care unit\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor predicting in-hospital mortality, the cut-off value was 16.4 for the L/A ratio, 16.1 for the B/A ratio, and 1 for the AIMS65 score. The prediction ability was fair for in-hospital mortality in the order of L/A ratio, B/A ratio, and AIMS65 (0.807 (0.708\u0026ndash;0.906), 0.799 (0.724\u0026ndash;0.875), and 0.683 (0.594\u0026ndash;0.773), respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to evaluate the association between L/A ratio and GIB patient prognosis. In this study, the L/A ratio was found to be a significant factor in predicting the prognosis of GIB patients who visited the ED. To demonstrate the performance of the L/A ratio, it was compared with the AIMS65 score, which was previously widely used, and a new concept, the B/A ratio, for predicting the prognosis of GIB patients. As a result, the L/A ratio was found to be superior to the other prediction tools in predicting ICU admission and in-hospital mortality. The L/A ratio has been recognized as a predictor of ICU mortality associated with sepsis and severe heart failure, and this is consistent with the present findings [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince GIB is associated with high mortality rates in the ED, early identification of vulnerable patients and the application of proper interventions are essential. Previously, scoring tools such as GBS and RS have been developed to predict clinical outcomes, including mortality, need for transfusion, and other specific interventions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the actual clinical adjustment and utilization of these scores is challenging due to their complexity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. GBS is difficult to calculate, and RS requires endoscopic findings. Therefore, a simpler scoring system, the AIMS65 score, was developed. This score includes four objective factors: \u0026lt; 3.0 g/dL albumin, \u0026gt; 1.5 international normalized ratio, systolic blood pressure\u0026thinsp;\u0026lt;\u0026thinsp;90 mm Hg, \u0026ge; 65 years of age, and one subjective factor: altered mental status [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although this can be calculated faster than other scores, a potential problem involves the physician\u0026rsquo;s subjective judgment of factors such as the patient\u0026rsquo;s mental status. Additionally, AIMS65 scores are focused on upper GIB, making its predictive ability poor relative to mortality in lower GIB [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, the B/A ratio can be a simple and useful predictor, but this ratio is also related to upper GIB and some studies have used this ratio as a tool for estimating bleeding sources [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is difficult to accurately identify the bleeding source in the ED setting. For example, in a randomized trial among GIB patients who were predicted to have upper lower GIB, 15% of the patients were found to have upper GIB sources [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, we believe that indicators that simultaneously reflect the bleeding regardless of its source, tissue hypoperfusion (lactate), and the chronic state (albumin) would help determine the prognoses of GIB patients.\u003c/p\u003e \u003cp\u003eLogistic regression analysis was performed to evaluate independent factors for predicting prognoses. As a result, it was found that the L/A ratio was a significant predictor of both ICU admission and in-hospital mortality. In contrast, the B/A ratio was a potential predictor only for in-hospital mortality. In the multiple logistic regression analysis performed, which included demographic data, initial vital signs, and laboratory tests, only the L/A ratio was found to be a predictor of both ICU admission and in-hospital mortality. Some variables that comprise the AIMS65 score were found to be significant in the univariate analysis predicting ICU admission and in-hospital mortality, but each variable was not a significant factor in the multivariate logistic regression analysis. As a result of the AUROC curve analysis, it was found that the L/A ratio is superior in predicting ICU admission and in-hospital mortality compared to the AIMS65 score. Additionally, compared to the B/A ratio, the L/A ratio was better at predicting ICU admission; however, both tools showed excellent prediction performances without a significant difference in predicting in-hospital mortality.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First, this study utilized a retrospective design and was performed at a single academic tertiary hospital. Therefore, it may be difficult to represent all GIB patients and the proportion of severe patients may be relatively greater. Therefore, the occurrence of a selection bias is possible. Second, the relatively small sample size of in-hospital mortality cases compared to survivors may be insufficient to identify all factors. A multi-center study utilizing a prospective design involving more emergency centers may be necessary in the future. Finally, the variables used in this study did not include treatment methods that can affect prognoses, such as endoscopy, blood transfusion, and embolization.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe L/A ratio, consisting of serum lactate and albumin levels, demonstrated superior performance compared with other tools (B/A ratio and the AIMS65 score) in predicting ICU admissions and in-hospital mortality among GIB patients. The L/A ratio was not only a more useful tool for predicting prognosis but was also easier to use than the AIMS65. Thus, by predicting severity in GIB patients, the L/A ratio may help identify optimal treatments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman rights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no human rights conflicts to declare and the study was in accordance with Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePeery AF, Crockett SD, Murphy CC, Lund JL, Dellon ES, Williams JL et al (2019). Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2018. \u003cem\u003eGastroenterology, 156\u003c/em\u003e(1), 254-272.e211, https://doi.org/10.1053/j.gastro.2018.08.063.\u003c/li\u003e\n \u003cli\u003eBeejay U, Wolfe MM (2000). Acute gastrointestinal bleeding in the intensive care unit. The gastroenterologist\u0026apos;s perspective. \u003cem\u003eGastroenterol Clin North Am, 29\u003c/em\u003e(2), 309-336, https://doi.org/10.1016/s0889-8553(05)70118-7.\u003c/li\u003e\n \u003cli\u003eGupta R, Nageshwar Reddy D (2013). Upper GI bleeding - has mortality changed with advancements in therapy? \u003cem\u003eTrop Gastroenterol, 34\u003c/em\u003e(1), 5-6, https://doi.org/10.7869/tg.2012.83.\u003c/li\u003e\n \u003cli\u003eKumar R, Mills AM (2011). 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Lactate Level Predicts Mortality in Patients with Upper Gastrointestinal Bleeding. \u003cem\u003eGastroenterol Res Pract, 2019\u003c/em\u003e, 5048078, https://doi.org/10.1155/2019/5048078.\u003c/li\u003e\n \u003cli\u003eBerger M, Divilov V, Teressa G (2019). Lactic Acid Is an Independent Predictor of Mortality and Improves the Predictive Value of Existing Risk Scores in Patients Presenting With Acute Gastrointestinal Bleeding. \u003cem\u003eGastroenterology Res, 12\u003c/em\u003e(1), 1-7, https://doi.org/10.14740/gr1085w.\u003c/li\u003e\n \u003cli\u003eAndersen LW, Mackenhauer J, Roberts JC, Berg KM, Cocchi MN, Donnino MW (2013). Etiology and therapeutic approach to elevated lactate levels. \u003cem\u003eMayo Clin Proc, 88\u003c/em\u003e(10), 1127-1140, https://doi.org/10.1016/j.mayocp.2013.06.012.\u003c/li\u003e\n \u003cli\u003eLevy B (2006). Lactate and shock state: the metabolic view. \u003cem\u003eCurr Opin Crit Care, 12\u003c/em\u003e(4), 315-321, https://doi.org/10.1097/01.ccx.0000235208.77450.15.\u003c/li\u003e\n \u003cli\u003eGharipour A, Razavi R, Gharipour M, Mukasa D (2020). Lactate/albumin ratio: An early prognostic marker in critically ill patients. \u003cem\u003eAm J Emerg Med, 38\u003c/em\u003e(10), 2088-2095, https://doi.org/10.1016/j.ajem.2020.06.067.\u003c/li\u003e\n \u003cli\u003eCakir E, Turan IO (2021). Lactate/albumin ratio is more effective than lactate or albumin alone in predicting clinical outcomes in intensive care patients with sepsis. \u003cem\u003eScand J Clin Lab Invest, 81\u003c/em\u003e(3), 225-229, https://doi.org/10.1080/00365513.2021.1901306.\u003c/li\u003e\n \u003cli\u003eLu Y, Guo H, Chen X, Zhang Q (2021). Association between lactate/albumin ratio and all-cause mortality in patients with acute respiratory failure: A retrospective analysis. \u003cem\u003ePLoS One, 16\u003c/em\u003e(8), e0255744, https://doi.org/10.1371/journal.pone.0255744.\u003c/li\u003e\n \u003cli\u003eKim MS, Choi J, Shin WC (2019). AIMS65 scoring system is comparable to Glasgow-Blatchford score or Rockall score for prediction of clinical outcomes for non-variceal upper gastrointestinal bleeding. \u003cem\u003eBMC Gastroenterol, 19\u003c/em\u003e(1), 136, https://doi.org/10.1186/s12876-019-1051-8.\u003c/li\u003e\n \u003cli\u003eSaltzman JR, Tabak YP, Hyett BH, Sun X, Travis AC, Johannes RS (2011). A simple risk score accurately predicts in-hospital mortality, length of stay, and cost in acute upper GI bleeding. \u003cem\u003eGastrointest Endosc, 74\u003c/em\u003e(6), 1215-1224, https://doi.org/10.1016/j.gie.2011.06.024.\u003c/li\u003e\n \u003cli\u003eAbougergi MS, Charpentier JP, Bethea E, Rupawala A, Kheder J, Nompleggi D et al (2016). A Prospective, Multicenter Study of the AIMS65 Score Compared With the Glasgow-Blatchford Score in Predicting Upper Gastrointestinal Hemorrhage Outcomes. \u003cem\u003eJ Clin Gastroenterol, 50\u003c/em\u003e(6), 464-469, https://doi.org/10.1097/mcg.0000000000000395.\u003c/li\u003e\n \u003cli\u003eOakland K, Jairath V, Uberoi R, Guy R, Ayaru L, Mortensen N et al (2017). Derivation and validation of a novel risk score for safe discharge after acute lower gastrointestinal bleeding: a modelling study. \u003cem\u003eLancet Gastroenterol Hepatol, 2\u003c/em\u003e(9), 635-643, https://doi.org/10.1016/s2468-1253(17)30150-4.\u003c/li\u003e\n \u003cli\u003eErnst AA, Haynes ML, Nick TG, Weiss SJ (1999). Usefulness of the blood urea nitrogen/creatinine ratio in gastrointestinal bleeding. \u003cem\u003eAm J Emerg Med, 17\u003c/em\u003e(1), 70-72, https://doi.org/10.1016/s0735-6757(99)90021-9.\u003c/li\u003e\n \u003cli\u003eZia Ziabari SM, Rimaz S, Shafaghi A, Shakiba M, Pourkazemi Z, Karimzadeh E et al (2019). Blood Urea Nitrogen to Creatinine ratio in Differentiation of Upper and Lower Gastrointestinal Bleedings; a Diagnostic Accuracy Study. \u003cem\u003eArch Acad Emerg Med, 7\u003c/em\u003e(1), e30.\u003c/li\u003e\n \u003cli\u003eLaine L, Shah A (2010). Randomized trial of urgent vs. elective colonoscopy in patients hospitalized with lower GI bleeding. \u003cem\u003eAm J Gastroenterol, 105\u003c/em\u003e(12), 2636-2641; quiz 2642, https://doi.org/10.1038/ajg.2010.277.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Albumins, Emergency department, Gastrointestinal Hemorrhage, Lactates","lastPublishedDoi":"10.21203/rs.3.rs-4013025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4013025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGastrointestinal bleeding (GIB) is a common cause of emergency department (ED) visits and has a variety of prognoses. This study aimed to verify the prognosis prediction ability of the lactate/albumin ratio (L/A ratio) in GIB patients compared to the AIMS65 score and the blood urea nitrogen/albumin ratio (B/A ratio).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis retrospective study was conducted among patients complaining of GIB symptoms who visited an ED in 2019. Baseline characteristics and laboratory data were obtained to calculate the L/A ratio, B/A ratio, and AIMS65 score. Each score was evaluated as a predictor of ICU admission and in-hospital mortality using the area under the receiver operating characteristic (AUROC) curve.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMultivariate logistic regression revealed that the L/A ratio significantly predicted ICU admission and in-hospital mortality. The AUROC scores for predicting ICU admission were 0.788 for the L/A ratio, 0.695 for the B/A ratio, and 0.586 for the AIMS65 score. For predicting in-hospital mortality, the scores were 0.807 for the L/A ratio, 0.799 for the B/A ratio, and 0.683 for AIMS65.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe L/A ratio, consisting of serum lactate and albumin levels, had superior performance relative to the other tools (B/A and AIMS65) in predicting the prognosis of GIB patients.\u003c/p\u003e","manuscriptTitle":"Lactate to albumin ratio as a prognosis predictor in gastrointestinal bleeding in the emergency department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 16:45:15","doi":"10.21203/rs.3.rs-4013025/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-03-10T15:29:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-10T13:24:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-08T01:19:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2024-03-06T11:50:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"477e9e25-0dc5-4ba4-bc80-882799530082","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-27T00:27:01+00:00","versionOfRecord":{"articleIdentity":"rs-4013025","link":"https://doi.org/10.1007/s11739-024-03723-3","journal":{"identity":"internal-and-emergency-medicine","isVorOnly":false,"title":"Internal and Emergency Medicine"},"publishedOn":"2024-07-26 00:27:01","publishedOnDateReadable":"July 26th, 2024"},"versionCreatedAt":"2024-03-13 16:45:15","video":"","vorDoi":"10.1007/s11739-024-03723-3","vorDoiUrl":"https://doi.org/10.1007/s11739-024-03723-3","workflowStages":[]},"version":"v1","identity":"rs-4013025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4013025","identity":"rs-4013025","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}