Association of elevated albumin-corrected anion gap with all-cause mortality risk in atrial fibrillation: a retrospective study

Association of elevated albumin-corrected anion gap with all-cause mortality risk in atrial fibrillation: a retrospective study | 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 Association of elevated albumin-corrected anion gap with all-cause mortality risk in atrial fibrillation: a retrospective study Jia Xu, Zhen Wang, Yun Wang, Xinran Chen, Lan Ma, Xiaochen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5329034/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background Compared to the conventional anion gap, albumin-corrected anion gap (ACAG) offers a more precise measure of acid-base imbalance in patients than, providing superior prognostic insight. However, the prognostic relevance of ACAG in individuals of atrial fibrillation (AF) remains insufficiently explored. This research seeks to evaluate the correlation between ACAG levels and mortality risk in individuals with AF. Methods We identified individuals diagnosed with AF from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Participants were categorized into quartiles in accordance with their ACAG levels. The outcomes included 30 days and 365 days all-cause mortality. Cumulative survival across the quartiles was assessed using Kaplan–Meier survival curves. We applied Cox regression and restricted cubic spline regression analyses to evaluate the correlation between ACAG levels and prognosis. Subgroup analyses and interaction assessments were applied to confirm the robustness of the findings. Results A total of 2920 AF patients (54.93% male) were incorporated into the analysis. The 30 and 365-day mortality were 22.91% and 39.21%, respectively. Kaplan–Meier survival curves demonstrated that elevated ACAG levels were significantly linked to increased mortality (log-rank P < 0.001). In multivariate Cox proportional hazards analyses, increased ACAG independently predicted mortality at 30 days (adjusted hazard ratio [aHR], 1.04; 95% CI, 1.02–1.05; P < 0.01) and 365 days (aHR, 1.03; 95% CI, 1.02–1.05; P < 0.01) after adjusting for potential confounders. A positive relationship between rising ACAG levels and mortality risk, as showed by restricted cubic spline analysis. Subgroup analyses revealed no significant interactions (all interaction P -values > 0.05). Conclusions In individuals with AF, higher ACAG levels are related to a greater mortality risk at 30 and 365 days. These results show the potential value of ACAG as a prognostic indicator for patient stratification. Incorporating ACAG into clinical decision-making could support improved therapeutic strategies and enhance patient outcomes. Atrial fibrillation Albumin-corrected anion gap Intensive care unit Mortality retrospective analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Atrial fibrillation (AF) constitutes the most prevalent cardiovascular disease, with a rising incidence worldwide[ 1 ]. It affects approximately 1–3% of the general population, with higher rates observed in older individuals, males, and individuals with comorbidities[ 2 – 6 ]. In 2019, global epidemiological data indicated approximately 59.7 million cases of AF, including 4.72 million new diagnoses that year[ 7 ]. AF is linked to a range severe serious complications, making it a key element in the global burden of cardiovascular disease[ 8 – 11 ]. Despite advancements in available therapies, managing AF remains challenging due to the complexity of its pathophysiology and the variability in patient responses to treatment. Identifying reliable prognostic markers is essential for preventing complications and improving outcomes in these patients. The anion gap (AG), developed in the mid-20th century, is commonly employed to evaluate acid-base disturbances in critical patients. Beyond its role in metabolic assessments, AG has been associated with disease severity and clinical outcomes[ 12 – 15 ]. AG reflects the concentration of unmeasured anions, including serum albumin, lactate, and acetoacetate. However, low serum albumin levels, frequently observed in critically ill individuals, can produce falsely low AG values, limiting its prognostic accuracy. To overcome this limitation, the albumin-corrected anion gap (ACAG) was introduced. ACAG provides a more accurate assessment of unmeasured anions by adjusting for fluctuations in serum albumin levels[ 16 ]. ACAG has been linked not only to disease risk[ 17 ] but also to clinical outcomes in a variety of conditions[ 18 – 22 ], including sepsis, acute pancreatitis, acute myocardial infarction(AMI), heart failure, and acute kidney injury (AKI),. However, its prognostic significance in AF patients remains unexplored. Given the role of metabolic imbalances in cardiovascular disease, ACAG could also serve as an important marker for adverse outcomes in AF. Investigating the correlation between ACAG levels and mortality among individuals with AF is essential for guiding clinical decision-making. This study assesses the correlation between ACAG and all-cause mortality at 30 and 365 days in AF patients by utilizing the Medical Information Mart for Intensive Care (MIMIC)-IV database. Our findings aim to offer meaningful insights for risk stratification and inform treatment strategies. By demonstrating the prognostic utility of ACAG, we hope to offer evidence supporting its incorporation into clinical practice to enhance patient outcomes. Methods Data source This study utilized information from version 2.2 of the MIMIC-IV database, a publicly available, large-scale, and de-identified dataset that offers extensive clinical data from intensive care units (ICU) patients in Beth Israel Deaconess Medical Center in Boston, Massachusetts. This dataset provides detailed recodes, including demographic information, nursing notes, laboratory findings, medications usage, and mortality data. The first author, Jia Xu, fulfilled all eligibility requirements for accessing the database (Certification ID: 64822128) and took charge of data extraction. Since the dataset contains anonymized patient information without identifiable health data, informed consent was not required. Participant selection We identified 59,865 admissions with the diagnosis of AF. From these recodes, 20,797 ICU patients were selected for further analysis. Exclusion criteria were: (1) younger than 18; (2) multiple hospital admissions; (3) multiple ICU admissions; (4) comorbidities such as end-stage renal disease, cirrhosis, or cancer; (5) diagnosis of AIDS; (6) ICU stays shorter than 24 hours; and (7) insufficient data on AG and albumin levels. Following these criteria, a cohort of 2,920 patients was incorporate into the final analysis, with participants divided into quartiles according to their ACAG levels for further comparison (Fig. 1 ). Data collection Navicat Premium (version 15) was used to extract data. To minimize the impact of treatment interventions, data were gathered from the initial 24 hours following ICU admission. Collected demographic data included age, sex, and race. Documented comorbidities were myocardial infarction (MI), congestive heart failure (CHF), hypertension, diabetes, obesity, peripheral vascular disease (PVD), cerebrovascular disease (CVD), chronic obstructive pulmonary disease (COPD), rheumatic disease, paraplegia, renal disease, and liver disease. Laboratory parameters included white blood cell (WBC), platelets, hematocrit, hemoglobin, red cell distribution width (RDW), anion gap (AG), albumin, blood urea nitrogen (BUN), calcium, chloride, creatinine, glucose, sodium, potassium, international normalized ratio (INR), prothrombin time (PT), and partial thromboplastin time (PTT). Vital signs were heart rate and blood pressure parameters comprised systolic, diastolic, and mean blood pressures (SBP, DBP, and MBP, respectively). The assessment of severity utilized the Oxford Acute Severity of Illness Score (OASIS), the Sequential Organ Failure Assessment (SOFA), and the Simplified Acute Physiology Score II (SAPS II), were also recorded. Medications tracked included aspirin, clopidogrel, beta-blockers, amiodarone, dabigatran, statins, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (ACEI/ARB), heparin, and warfarin. Length of stay (LOS) was recorded for both the hospital and ICU, with clinical outcomes including mortality rates at hospital, 30 days, and 365 days. We excluded variables with more than 20% missing data to minimize bias, and used multiple imputation, performed through a random forest technique to predict missing entries[ 23 , 24 ]. Clinical outcomes The primary follow-up period commenced at the time of hospital admission. The main endpoint is 365-day all-cause mortality, and the additional endpoint is 30-day all-cause mortality. Calculation of ACAG AG and albumin were directly retrieved. The following equation was used to calculate the ACAG: ACAG (mmol/l) = AG (mmol/l) + [4.4 -Albumin (g/dl)] *2.5[ 16 ]. Statistical analysis The distribution of continuous variables was assessed with the Kolmogorov-Smirnov test. In this study, none of the parameters conformed to a normal distribution. Therefore, they were summarized with the median and interquartile range (IQR) and analyzed through the Mann-Whitney U test for comparison. Kruskal–Wallis test. Categorical variables were expressed as percentages and analyzed through chi-square test for comparison. Kaplan–Meier survival curves were used to assess cumulative survival across the quartiles based on ACAG. Univariable Cox regression analysis was applied to determine the factors associated with the mortality risk. Multivariate Cox regression models served to confirm the relationship between ACAG and outcomes, with adjustments made in various models. Confounding factors included those with a p -value < 0.05 in univariable Cox regression analysis, along with clinically relevant and prognostically significant variables. Model 1 conducted without any adjustments. Model 2 accounted for age, sex, and race. Model 3 incorporated additional variables. such as heart rate, SBP, DBP, MBP, MI, CHF, PVD, CVD, obesity, paraplegia, renal disease, liver disease, statin, beta-blockers, ACEI/ARB, heparin, warfarin. model 4 further incorporated hematocrit, hemoglobin, WBC, RDW, BUN, creatinine, sodium, potassium, glucose, INR, PT, PTT, SOFA, OASIS and SAPSII. Both continuous and categorical ACAG variables were analyzed, using the lowest quartile as the reference group. Trend p -value was computed across quartiles. Restricted cubic spline (RCS) regression with four knots was applied to assess non-linear relationships between baseline ACAG and mortality outcomes. Stratified analyses explored the prognostic value of ACAG by sex, age (< 65 or ≥ 65 years), race, diabetes, and hypertension status. Interaction effects were evaluated through likelihood ratio tests. Statistical significance was defined as a two-tailed P -value < 0.05. All analyses were conducted using R (version 4.4.1) and SPSS (version 25.0). Results This study analyzed 2,920 patients diagnosed with AF, with a median age of 78 years (IQR: 69–86) (Table 1 ). Among them, 1,604 (54.93%) were male. The median ACAG level was 19.25 mmol/L (IQR: 16.75–22.50). Hospital mortality observed in 18.15% of patients, while the 30 days and 365 days mortality rates stood at 22.91% and 39.21%, respectively. Table 1 Characteristics and outcomes of participants categorized by ACAG a Variables Overall (N = 2920) Quartile 1 (N = 680) Quartile 2 (N = 741) Quartile 3 (N = 757) Quartile 4 (n = 742) P-value ACAG, mmol/L 19.25 (16.75, 22.50) 15.25 (14.00,16.00) 18.00 (17.25,18.50) 20.50 (19.75,21.50) 25.25 (23.56,28.25) < 0.01 Demographic variables Age (years) 78.00 (69.00, 86.00) 77.00 (68.00,85.00) 80.00 (69.00,86.00) 78.00 (71.00,86.00) 77.00 (67.00,85.00) < 0.01 Sex, male, n(%) 1604 (54.93) 388 (57.06) 380 (51.28) 405 (53.50) 431 (58.09) 0.03 Race,White, n(%) 2046 (70.07) 475 (69.85) 544 (73.41) 523 (69.09) 504 (67.92) 0.11 Comorbidities, n (%) MI 747 (25.58) 144 (21.18) 166 (22.40) 203 (26.82) 234 (31.54) < 0.01 CHF 1500 (51.37) 277 (40.74) 367 (49.53) 428 (56.54) 428 (57.68) < 0.01 Diabetes 969 (33.18) 170 (25.00) 219 (29.55) 262 (34.61) 318 (42.86) < 0.01 Hypertension 1154 (39.52) 278 (40.88) 333 (44.94) 296 (39.10) 247 (33.29) < 0.01 Obesity 341 (11.68) 61 (8.97) 80 (10.80) 88 (11.62) 112 (15.09) < .01 PVD 446 (15.27) 94 (13.82) 124 (16.73) 101 (13.34) 127 (17.12) 0.09 CVD 725 (24.83) 219 (32.21) 210 (28.34) 171 (22.59) 125 (16.85) < 0.01 COPD 866 (29.66) 201 (29.56) 201 (27.13) 249 (32.89) 215 (28.98) 0.10 Rheumatic Disease 138 (4.73) 27 (3.97) 32 (4.32) 37 (4.89) 42 (5.66) 0.45 Paraplegia 298 (10.21) 103 (15.15) 87 (11.74) 67 (8.85) 41 (5.53) < 0.01 Renal Disease 805 (27.57) 100 (14.71) 182 (24.56) 247 (32.63) 276 (37.20) < 0.01 Liver Disease 178 (6.10) 19 (2.79) 35 (4.72) 36 (4.76) 88 (11.86) < 0.01 Laboratory data Hematocrit, % 35.80 (31.60, 40.50) 36.90 (32.30,41.00) 35.30 (31.30,40.10) 35.50 (31.40,40.20) 36.05 (31.50,40.80) < 0.01 Hemoglobin, g/dL 11.70 (10.20, 13.30) 12.10 (10.60,13.50) 11.60 (10.20,13.10) 11.60 (10.10,13.20) 11.70 (10.03,13.30) < 0.01 Platelets, K/uL 211.00 (161.00, 279.00) 201.00 (158.00,251.25) 212.00 (167.00,270.00) 213.00 (163.00,284.00) 220.00 (157.25,306.00) < 0.01 WBC, K/uL 12.90 (9.30, 18.00) 10.30 (8.00,14.10) 12.40 (9.10,16.80) 13.20 (9.50,18.30) 16.25 (11.30,22.17) < 0.01 RDW, % 14.60 (13.70, 16.00) 14.10 (13.40,15.30) 14.40 (13.70,15.70) 14.80 (13.70,16.30) 15.00 (14.00,16.67) < 0.01 Albumin, g/dL 3.40 (2.90, 3.80) 3.70 (3.30,4.00) 3.40 (3.00,3.80) 3.40 (2.90,3.70) 3.20 (2.70,3.60) < 0.01 AG, mmol/L 17.00 (14.00, 20.00) 13.00 (12.00,14.00) 15.00 (14.00,16.00) 18.00 (17.00,19.00) 22.00 (20.25,25.00) < 0.01 BUN, mg/dL 27.00 (19.00, 45.00) 21.00 (16.00,28.00) 24.00 (17.00,34.00) 29.00 (21.00,48.00) 46.00 (29.00,69.00) < 0.01 Calcium, mg/dL 8.60 (8.20, 9.10) 8.80 (8.30,9.20) 8.60 (8.20,9.10) 8.60 (8.20,9.10) 8.50 (8.00,9.10) < 0.01 Chloride, mEq/L 105.00 (102.00, 109.00) 106.00 (102.00,109.00) 106.00 (102.00,109.00) 105.00 (101.00,110.00) 105.00 (101.00,110.00) 0.67 Creatinine, mg/dL 1.30 (0.90, 1.90) 1.00 (0.80,1.20) 1.10 (0.80,1.50) 1.30 (1.00,1.90) 2.10 (1.40,3.10) < 0.01 Glucose, g/dL 152.00 (121.00, 208.00) 133.00 (110.00,165.00) 146.00 (119.00,188.00) 159.00 (126.00,212.00) 189.00 (140.00,269.00) < 0.01 Sodium, mEq/L 140.00 (138.00, 143.00) 141.00 (138.00,143.00) 140.00 (138.00,143.00) 140.00 (137.00,143.00) 140.00 (137.00,144.00) 0.19 Potassium, mEq/L 4.50 (4.10, 5.00) 4.30 (4.00,4.70) 4.40 (4.10,4.80) 4.40 (4.10,5.00) 4.90 (4.40,5.60) < 0.01 INR 1.40 (1.20, 2.10) 1.30 (1.20,1.70) 1.40 (1.20,2.00) 1.40 (1.20,2.00) 1.60 (1.30,2.77) < 0.01 PT, s 15.70 (13.30, 22.40) 14.55 (12.90,18.20) 15.80 (13.20,21.60) 15.50 (13.30,21.30) 17.85 (14.30,29.80) < 0.01 PTT, s 35.40 (29.60, 54.52) 33.00 (28.80,42.50) 34.50 (29.00,49.50) 35.50 (29.30,57.20) 41.25 (31.63,70.28) < 0.01 Vital signs HR, beats/min 85.00 (73.00, 98.00) 79.00 (70.00,91.00) 83.00 (72.00,96.00) 87.00 (75.00,99.00) 92.00 (78.00,105.00) < 0.01 SBP, mmHg 115.00 (106.00, 128.00) 121.00 (110.00,134.00) 119.00 (108.00,131.00) 114.00 (105.00,128.00) 110.00 (102.00,118.00) < 0.01 DBP, mmHg 62.00 (55.00, 69.00) 64.00 (56.00,72.00) 63.00 (55.00,70.00) 62.00 (55.00,69.00) 60.00 (53.25,67.00) < 0.01 MBP, mmHg 77.00 (71.00, 85.00) 80.00 (73.00,88.00) 78.00 (71.00,85.00) 76.00 (70.00,84.00) 74.00 (68.00,81.00) < 0.01 Clinical severity SOFA 5.00 (3.00, 8.00) 3.00 (2.00,5.00) 4.00 (2.00,6.00) 5.00 (3.00,8.00) 7.00 (5.00,11.00) < 0.01 OASIS 34.00 (29.00, 40.00) 31.00 (26.00,37.00) 34.00 (28.00,39.00) 34.00 (29.00,39.00) 38.00 (32.00,46.00) < 0.01 SAPS II 39.00 (32.00, 49.00) 34.00 (28.00,40.00) 37.00 (31.00,45.00) 40.00 (33.00,48.00) 49.00 (40.00,60.00) < 0.01 Medication, n(%) Aspirin 1246 (42.67) 301 (44.26) 303 (40.89) 337 (44.52) 305 (41.11) 0.33 Clopidogrel 218 (7.47) 45 (6.62) 47 (6.34) 63 (8.32) 63 (8.49) 0.26 Statin 1220 (41.78) 321 (47.21) 333 (44.94) 315 (41.61) 251 (33.83) < 0.01 Beta-Blockers 1896 (64.93) 461 (67.79) 484 (65.32) 505 (66.71) 446 (60.11) 0.01 ACEI/ARB 286 (9.79) 73 (10.74) 89 (12.01) 70 (9.25) 54 (7.28) 0.02 Amiodarone 720 (24.66) 140 (20.59) 163 (22.00) 172 (22.72) 245 (33.02) < 0.01 Dabigatran 26 (0.89) 5 (0.74) 6 (0.81) 10 (1.32) 5 (0.67) 0.53 Heparin 2263 (77.50) 473 (69.56) 561 (75.71) 597 (78.86) 632 (85.18) < 0.01 Warfarin 568 (19.45) 128 (18.82) 163 (22.00) 139 (18.36) 138 (18.60) 0.25 LOS, days Los in Hospital 9.00 (5.00, 14.00) 7.00 (5.00,12.00) 9.00 (6.00,14.00) 9.00 (5.00,15.00) 10.00 (6.00,17.00) < 0.01 Los in Icu 3.00 (2.00, 6.00) 3.00 (2.00,5.00) 3.00 (2.00,6.00) 3.00 (2.00,6.00) 4.00 (2.00,8.00) < 0.01 Events, n (%) hospital mortality 530 (18.15) 72 (10.59) 84 (11.34) 137 (18.10) 237 (31.94) < 0.01 30-day mortality 669 (22.91) 99 (14.56) 124 (16.73) 175 (23.12) 271 (36.52) < 0.01 365-day mortality 1145 (39.21) 192 (28.24) 239 (32.25) 314 (41.48) 400 (53.91) < 0.01 a ACAG: Q1 (9.00-16.75), Q2 (16.75–19.25), Q3 (19.25–22.50), Q4 (22.50-51.25) Abbreviation: ACAG, albumin-corrected anion gap; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; COPD, chronic pulmonary disease; WBC, white blood cell; RDW, red cell distribution width; AG, anion gap; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time; PTT, partial thromboplastin time; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker; LOS, length of stay. Baseline characteristics of participants Individuals were divided into four quartiles: Quartile (Q) 1: 9.00-16.75mmol/L; Q2: 16.75–19.25 mmol/L; Q3:19.25–22.50 mmol/L; Q4: 22.50-51.25mmol/L(Table 1 ). The median ACAG values for these quartiles were 15.25 mmol/L (IQR: 14.00–16.00), 18.00 mmol/L (IQR: 17.25–18.50), 20.50 mmol/L (IQR: 19.75–21.50), and 25.25 mmol/L (IQR: 23.56–28.25), respectively. Higher quartile patients exhibited increased levels of platelets, WBC, RDW, AG, BUN, creatinine, glucose, potassium, INR, PT, PTT, while their albumin levels were lower. With rising ACAG levels, conditions like MI, CHF, diabetes, obesity, renal disease, and liver disease became more common, whereas CVD and paraplegia were less frequently reported. Higher ACAG levels also correlated with elevated severity scores and more frequent use of amiodarone and heparin (all P < 0.05). Mortality rates rose across quartiles: hospital mortality ranged from 10.59% in Q1 to 31.94% in Q4 (P < 0.01), 30-day mortality from 14.56% in Q1 to 36.52% in Q4 (P < 0.01), and 365-day mortality from 28.24% in Q1 to 53.91% in Q4 (P < 0.01). Baseline differences between 365-day survivors and non-survivors are shown in Table 2 . Non-survivors tended to be of greater age, presented with higher admission severity scores, and displayed a greater prevalence of MI, CHF, diabetes, obesity, PVD, CVD, paraplegia, renal disease, and liver disease. Non-survivors also exhibited elevated levels of platelets, WBC, RDW, AG, BUN, creatinine, glucose, sodium, potassium, INR, PT, PTT, and heart rate, but lower hematocrit, hemoglobin, albumin, SBP, DBP, and MBP. Statin, beta-blocker, ACEI/ARB, heparin, and warfarin use was less frequent among non-survivors. Non-survivors had notably higher ACAG levels than survivors. (20.50 vs. 18.50, P < 0.01). Table 2 Baseline characteristics between survivors and non-survivors at 365 days Variables Total (N = 2920) survivors (N = 1775) non-survivors (N = 1145) P-value ACAG, mmol/L 19.25 (16.75, 22.50) 18.50 (16.25, 21.25) 20.50 (17.75, 24.25) < 0.01 Demographic variables Age (years) 78.00 (69.00, 86.00) 75.00 (66.00, 83.00) 82.00 (74.00, 88.00) < 0.01 Sex, male, n (%) 1604 (54.93) 1012 (57.01) 592 (51.70) < 0.01 Race, White, n (%) 2046 (70.07) 1246 (70.20) 800 (69.87) 0.85 Comorbidities, n(%) MI 747 (25.58) 415 (23.38) 332 (29.00) < 0.01 CHF 1500 (51.37) 851 (47.94) 649 (56.68) < 0.01 Diabetes 969 (33.18) 562 (31.66) 407 (35.55) 0.03 Hypertension 1154 (39.52) 716 (40.34) 438 (38.25) 0.26 Obesity 341 (11.68) 240 (13.52) 101 (8.82) < 0.01 PVD 446 (15.27) 242 (13.63) 204 (17.82) < 0.01 CVD 725 (24.83) 398 (22.42) 327 (28.56) < 0.01 COPD 866 (29.66) 518 (29.18) 348 (30.39) 0.48 Rheumatic Disease 138 (4.73) 78 (4.39) 60 (5.24) 0.29 Paraplegia 298 (10.21) 162 (9.13) 136 (11.88) 0.02 Renal Disease 805 (27.57) 417 (23.49) 388 (33.89) < 0.01 Liver Disease 178 (6.10) 84 (4.73) 94 (8.21) < 0.01 Laboratory data Hematocrit, % 35.80 (31.60, 40.50) 36.20 (32.00, 40.80) 35.30 (30.90, 40.00) < 0.01 Hemoglobin, g/dL 11.70 (10.20, 13.30) 11.90 (10.40, 13.50) 11.40 (9.90, 12.80) < 0.01 Platelets, K/uL 211.00 (161.00, 279.00) 210.00 (162.00, 273.00) 213.00 (159.00, 288.00) 0.42 WBC, K/uL 12.90 (9.30, 18.00) 12.40 (9.10, 17.10) 13.70 (9.70, 19.20) < 0.01 RDW, % 14.60 (13.70, 16.00) 14.30 (13.50, 15.50) 15.10 (14.00, 16.70) < 0.01 Albumin, g/dL 3.40 (2.90, 3.80) 3.50 (3.00, 3.90) 3.30 (2.80, 3.70) < 0.01 AG, mmol/L 17.00 (14.00, 20.00) 16.00 (14.00, 19.00) 18.00 (15.00, 21.00) < 0.01 BUN, mg/dL 27.00 (19.00, 45.00) 24.00 (17.00, 37.50) 34.00 (23.00, 56.00) < 0.01 Calcium, mg/dL 8.60 (8.20, 9.10) 8.70 (8.20, 9.10) 8.60 (8.10, 9.10) 0.34 Chloride, mEq/L 105.00 (102.00, 109.00) 106.00 (102.00, 109.00) 105.00 (101.00, 110.00) 0.72 Creatinine, mg/dL 1.30 (0.90, 1.90) 1.20 (0.90, 1.70) 1.50 (1.00, 2.30) < 0.01 Glucose, g/dL 152.00 (121.00, 208.00) 147.00 (118.00, 194.00) 165.00 (126.00, 226.00) < 0.01 Sodium, mEq/L 140.00 (138.00, 143.00) 140.00 (138.00, 143.00) 141.00 (138.00, 144.00) < 0.01 Potassium, mEq/L 4.50 (4.10, 5.00) 4.40 (4.10, 4.90) 4.60 (4.20, 5.30) < 0.01 INR 1.40 (1.20, 2.10) 1.40 (1.20, 1.90) 1.50 (1.20, 2.40) < 0.01 PT, s 15.70 (13.30, 22.40) 15.30 (13.10, 20.30) 16.60 (13.70, 25.90) < 0.01 PTT, s 35.40 (29.60, 54.52) 34.60 (29.50, 53.10) 37.00 (29.80, 57.20) 0.02 Vital signs HR, beats/min 85.00 (73.00, 98.00) 84.00 (72.50, 98.00) 86.00 (74.00, 99.00) 0.05 SBP, mmHg 115.00 (106.00, 128.00) 117.00 (107.00, 129.00) 113.00 (104.00, 126.00) < 0.01 DBP, mmHg 62.00 (55.00, 69.00) 63.00 (56.00, 71.00) 60.00 (54.00, 68.00) < 0.01 MBP, mmHg 77.00 (71.00, 85.00) 78.00 (72.00, 86.00) 75.00 (69.00, 83.00) < 0.01 Clinical severity SOFA 5.00 (3.00, 8.00) 4.00 (2.00, 7.00) 6.00 (4.00, 9.00) < 0.01 OASIS 34.00 (29.00, 40.00) 33.00 (27.00, 38.00) 37.00 (32.00, 44.00) < 0.01 SAPS II 39.00 (32.00, 49.00) 36.00 (30.00, 44.00) 45.00 (37.00, 54.00) < 0.01 Medication, n (%) Aspirin 1246 (42.67) 759 (42.76) 487 (42.53) 0.90 Clopidogrel 218 (7.47) 121 (6.82) 97 (8.47) 0.10 Statin 1220 (41.78) 783 (44.11) 437 (38.17) < 0.01 Beta-Blockers 1896 (64.93) 1191 (67.10) 705 (61.57) < 0.01 ACEI/ARB 286 (9.79) 194 (10.93) 92 (8.03) 0.01 Amiodarone 720 (24.66) 428 (24.11) 292 (25.50) 0.40 Dabigatran 26 (0.89) 20 (1.13) 6 (0.52) 0.09 Heparin 2263 (77.50) 1351 (76.11) 912 (79.65) 0.03 Warfarin 568 (19.45) 389 (21.92) 179 (15.63) < .01 Abbreviation: ACAG, albumin-corrected anion gap; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; COPD, chronic pulmonary disease; WBC, white blood cell; RDW, red cell distribution width; AG, anion gap; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time; PTT, partial thromboplastin time; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker Primary outcomes Kaplan-Meier survival curves (Fig. 2 ) demonstrate that higher ACAG levels corresponded with increased risks of mortality at both 30 days and 365 days. Univariate Cox regression analysis (Table 3 ) was performed using covariates with statistically significant differences (P < 0.05) identified in Table 2 . Unadjusted analyses revealed a significant association between ACAG and 365-day all-cause mortality (HR: 1.07, 95% CI: 1.06–1.08; P < 0.01). Multivariate Cox regression (Table 4 ) confirmed the relationship between ACAG and 365-day mortality across all models: model 1: HR: 1.07 (95%CI: 1.06–1.08, P<0.01), model 2: HR, 1.07, (95%CI:1.06–1.08, P<0.01), model 3: HR:1.06 (95%CI 1.05–1.07, P<0.01) and model 4: HR: 1.03 ( 95%CI: 1.02–1.05, P<0.01). When ACAG was analyzed as an ordinal parameter, patients in the highest quartile exhibited a markedly increased risk of 365-day mortality compared to those in the lowest quartile: model 1: HR 2.45 (95% CI 2.06–2.91; P<0.01), model 2: HR 2.21 (95% CI 1.85–2.66; P<0.01), model 3: HR 1.93 (95% CI 1.60–2.33; P<0.01) and model 4: HR 1.31 (95% CI 1.05–1.61; P = 0.01). A similar trend of increased mortality risk with higher ACAG levels was observed for 30-day mortality (Table 4 ). Table 3 Univariate COX analysis of risk factors correlated with 365-day all-cause mortality Variables HR (95%CI) P-value ACAG 1.07 (1.06 ~ 1.08) < 0.01 Age (years) 1.04 (1.03 ~ 1.04) < 0.01 Sex, male, n (%) 0.84 (0.74 ~ 0.94) < 0.01 MI, n (%) 1.26 (1.11 ~ 1.43) < 0.01 CHF, n (%) 1.28 (1.14 ~ 1.43) < 0.01 Diabetes, n (%) 1.13 (1.01 ~ 1.28) 0.05 Obesity, n (%) 0.69 (0.57 ~ 0.85) < 0.01 PVD, n (%) 1.24 (1.06 ~ 1.44) < 0.01 CVD, n (%) 1.33 (1.17 ~ 1.51) < 0.01 Paraplegia, n (%) 1.30 (1.09 ~ 1.56) < 0.01 Renal Disease, n (%) 1.45 (1.28 ~ 1.64) < 0.01 Liver Disease, n (%) 1.58 (1.28 ~ 1.96) < 0.01 Hematocrit, % 0.99 (0.98 ~ 0.99) 0.02 Hemoglobin, g/dL 0.94 (0.91 ~ 0.96) < 0.01 WBC, K/uL 1.01 (1,.01 ~ 1.02) < 0.01 RDW, % 1.12 (1.10 ~ 1.15) < 0.01 Albumin, g/dL 0.68 (0.62 ~ 0.75) < 0.01 AG, mmol/L 1.06 (1.05 ~ 1.07) < 0.01 BUN, mg/dL 1.01 (1.01 ~ 1.01) < 0.01 Creatinine, mg/dL 1.11 (1.08 ~ 1.15) < 0.01 Glucose, g/dL 1.01 (1.01 ~ 1.01) < 0.01 Sodium, mEq/L 1.03 (1.01 ~ 1.04) < 0.01 Potassium, mEq/L 1.24 (1.17 ~ 1.30) < 0.01 INR 1.07 (1.05 ~ 1.10) < 0.01 PT, s 1.01 (1.01 ~ 1.01) < 0.01 PTT, s 1.01 (1.01 ~ 1.01) 0.02 SBP, mmHg 0.99 (0.99 ~ 0.99) < 0.01 DBP, mmHg 0.98 (0.98 ~ 0.99) < 0.01 MBP, mmHg 0.98 (0.98 ~ 0.99) < 0.01 SOFA 1.11 (1.09 ~ 1.12) < 0.01 OASIS 1.06 (1.05 ~ 1.06) < 0.01 SAPS II 1.04 (1.04 ~ 1.04) < 0.01 Statin 0.80 (0.71 ~ 0.90) < 0.01 Beta-Blockers 0.79 (0.70 ~ 0.89) < 0.01 ACEI/ARB 0.73 (0.59 ~ 0.90) < 0.01 Heparin 1.18 (1.02 ~ 1.36) 0.02 Warfarin 0.68 (0.58 ~ 0.80) < 0.01 Abbreviation: HR, Hazard Ratio; CI, Confidence Interval; ACAG, albumin-corrected anion gap; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; WBC, white blood cell; RDW, red cell distribution width; AG, anion gap; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time ; PTT, partial thromboplastin time; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker. Table 4 Cox proportional hazard ratios (HR) for all-cause mortality Variables Model 1 Model 2 Model 3 Model 4 HR (95%CI) P-value HR (95%CI) P-value HR (95%CI) P-value HR (95%CI) P-value 365-day mortality Continues variable per unit 1.07 (1.06 ~ 1.08) < 0.01 1.07 (1.06 ~ 1.08) < 0.01 1.06 (1.05 ~ 1.07) < 0.01 1.03 (1.02 ~ 1.05) < 0.01 Quartile Q1 (N = 680) Ref. Ref. Ref. Ref. Q2 (N = 741) 1.18 (0.98 ~ 1.43) 0.09 1.08 (0.89 ~ 1.31) 0.43 1.02 (0.84 ~ 1.24) 0.81 0.96 (0.79 ~ 1.16) 0.67 Q3 (N = 757) 1.62 (1.35 ~ 1.93) < 0.01 1.46 (1.22 ~ 1.76) < 0.01 1.35 (1.12 ~ 1.62) < 0.01 1.15 (0.95 ~ 1.40) 0.14 Q4 (N = 742) 2.45 (2.06 ~ 2.91) < 0.01 2.21 (1.85 ~ 2.66) < .01 1.93 (1.60 ~ 2.33) < 0.01 1.31 (1.05 ~ 1.61) 0.01 P for trend < 0.01 < 0.01 < 0.01 < 0.01 30-day mortality Continues variable per unit 1.08 (1.07 ~ 1.10) < 0.01 1.08 (1.07 ~ 1.09) < 0.01 1.07 (1.06 ~ 1.09) < 0.01 1.04 (1.02 ~ 1.05) < 0.01 Quartile Q1 (N = 680) Ref. Ref. Ref. Ref Q2 (N = 741) 1.16 (0.89 ~ 1.51) 0.26 1.10 (0.84 ~ 1.43) 0.50 1.03 (0.79 ~ 1.35) 0.81 0.94 (0.71 ~ 1.23) 0.63 Q3 (N = 757) 1.66 (1.30 ~ 2.13) < 0.01 1.54 (1.20 ~ 1.98) < 0.01 1.41 (1.09 ~ 1.82) 0.01 1.16 (0.90 ~ 1.50) 0.26 Q4 (N = 742) 2.91 (2.31 ~ 3.67) < 0.01 2.62 (2.06 ~ 3.35) < 0.01 2.26 (1.76 ~ 2.91) < 0.01 1.43 (1.08 ~ 1.89) 0.01 P for trend < 0.01 < 0.01 < 0.01 < 0.01 Model 1: unadjusted. Model 2: adjusted for age, sex, race. Model 3: adjusted for Model2 + SBP, DBP, MBP, MI, CHD, PVD, CVD, obesity, paraplegia, renal disease, liver disease, statin, beta-blockers, ACEI/ARB, heparin, warfarin. Model 4: Model 3 + hematocrit, hemoglobin, WBC, RDW, BUN, creatinine, glucose, sodium, potassium, INR, PT, and PTT, SOFA, OASIS, SAPSII. Abbreviation: HR, Hazard Ratio; CI, Confidence Interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker; WBC, white blood cell; RDW, red cell distribution width; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time ; PTT, partial thromboplastin time, SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score. RCS analyses shown in Fig. 3 indicated a linear link between increased ACAG and mortality outcomes at 30 days and 365 days, with no significant non-linearity detected (P for non-linearity = 0.729 and 0.503, respectively) after adjusting for relevant confounders. Subgroup analyses We employed subgroup analyses to investigate whether ACAG remained a significant predictor of mortality across various demographic and clinical groups (Fig. 4 ). Higher ACAG levels were consistently associated with 365-day mortality across all subgroups, including by sex, age (< 65 and ≥ 65 years), race, diabetes, and hypertension status ( P < 0.05). Similarly, ACAG significantly predicted 30-day mortality across subgroups, including males and females, individuals ≥ 65 years, and those with or without diabetes or hypertension, as well as among non-White patients ( P < 0.05). Interaction terms between ACAG and subgroup factors failed to achieve statistical significance. Discussion This is the first investigation to assess the connection between ACAG and mortality outcomes in the context of AF, offering new insights into the prognostic value of ACAG. Our analysis showed a clear, linear relationship between higher ACAG levels and increased risks of all-cause mortality at both 1 month and 1 year, even after controlling for multiple confounders. The robustness of these results across various statistical approaches highlights their dependability. Subgroup analyses further confirmed that ACAG remains a significant prognostic marker across diverse clinical and demographic groups, suggesting it could serve as a universal risk indicator in AF patients. As a readily available biomarker, ACAG demonstrates potential as a clinical decision-support tool, complementing traditional risk assessments. The traditional AG is frequently employed to evaluate acid-base disturbances. It is defined by the gap between measured serum cations and anions. Elevated AG is often seen in cases of lactic acidosis, diabetic ketoacidosis, and renal failure, and it correlates with worse outcomes in critical patients [ 25 , 26 ]. However, AG is influenced by serum albumin levels, with each 1 g/L reduction in albumin lowering AG by approximately 2.3–2.5 mmol/L[ 27 ]. Hypoalbuminemia is prevalent among critically ill patients[ 28 ], including AF patients, with approximately 54% of our cohort exhibiting reduced albumin levels. Consequently, relying on AG alone may result in false negatives, impairing clinical judgment and risk stratification. ACAG, which adjusts AG for serum albumin, improves the sensitivity of metabolic acidosis diagnosis and offers better prognostic accuracy. It provides a more reliable marker of disease severity and outcomes in ICU patients. Prior research has highlighted ACAG’s utility in predicting mortality across various conditions. Hu et al.[ 18 ] reported that ACAG offers more reliable forecast of in-hospital mortality than either albumin or AG in patients with sepsis. Similarly, Li et al. [ 20 ]showed that elevated ACAG levels were linked to increase in-hospital mortality in individuals with acute pancreatitis, even after controlling for confounding variables. In AMI, elevated ACAG levels outperformed AG in predictive value for 30 days mortality[ 29 ], and Sheng H et al.[ 22 ] further identified increased ACAG as an important marker for forecasting long-term mortality in severe AMI patients. Other studies have also linked higher ACAG levels with increased ICU mortality among individuals with AKI receiving continuous renal replacement therapy[ 30 ] and demonstrated its prognostic value for 30 days and one year mortality in severe AKI patients[ 19 ]. Consistent with these findings, our study evaluated the correlation between ACAG and mortality in individuals with AF, revealing that increased ACAG independently predicts both 30 days and one year mortality. These findings highlight the potential value of ACAG in pinpointing at-risk AF patients and facilitating early therapeutic actions. Although the precise mechanisms linking elevated ACAG to poor outcomes in AF patients are not fully understood, several plausible pathways exist. However, several plausible pathways may contribute to this relationship. Systemic inflammation[ 31 ] and oxidative stress[ 32 ], common in AF, are often exacerbated by metabolic acidosis[ 33 ]. Elevated ACAG levels reflect more severe acidosis, contributing to adverse outcomes. Additionally, ACAG could serve as an indirect marker of systemic inflammation, with higher levels indicating a more pronounced inflammatory response, impairing recovery. Electrolyte imbalances, reflected in elevated ACAG, may also play a role, given their association with AF risk and adverse outcomes[ 34 ]. Furthermore, critically ill AF patients often experience reduced cardiac output, impairing systemic perfusion and leading to tissue hypoxia. The resulting accumulation of lactic acid is reflected in elevated ACAG, which may signal the presence of critical illness, multi-organ dysfunction, and increased mortality risk. This study’s primary strength lies in its identification of ACAG as an important predictor of mortality risk at one month and one year in AF patients. As far as we are aware, no prior research has documented this association in this patient population. The robustness of our findings across various statistical models, combined with the use of a large dataset, enhances the reliability of our conclusions. However, several limitations should be acknowledged. First, while our sample size was large, the cohort was derived from a single database (MIMIC-IV), which may limit the suitability of our findings for different patient groups or clinical settings. Second, despite the use of multiple statistical analyses, residual confounding cannot be entirely excluded, as certain variables—such as the timing of AF, use of advanced cardiac therapies, and specific causes of death—were not available in the database. Third, the retrospective framework of the research restricts our capacity to draw causal conclusions. Lastly, we only assessed ACAG during the initial 24 hours of ICU admission and unable to track variations during the hospital stay. Conclusions This research highlights the significance of ACAG as a valuable prognostic indicator for predicting both one month and one year mortality in individuals with AF. As an independent risk factor, ACAG can offer clinicians a valuable tool for pinpointing high-risk individuals and initiating timely interventions designed to enhance clinical outcomes. Future prospective investigations are essential to validate these results in diverse populations and further investigate the mechanisms driving the relationship between elevated ACAG and poor prognosis. Evaluating dynamic changes in ACAG during hospitalization may also enhance its role in clinical decision-making. Abbreviations AF Atrial fibrillation AG Anion gap ACAG Albumin-corrected anion gap AMI Acute myocardial infarct AKI Acute kidney injury MIMIC-IV Medical Information Mart for Intensive Care IV ICU Intensive care unit IQR Interquartile range HR Hazard ratios CI Confidence intervals CHF Congestive heart failure PVD Peripheral vascular disease CVD Cerebrovascular disease COPD Chronic pulmonary disease WBC white blood cell RDW Red cell distribution width; BUN Blood urea nitrogen INR International normalized ratio PT Prothrombin time PTT Partial thromboplastin time SBP Systolic blood pressure; DBP, Diastolic blood pressure; MBP Mean blood pressure SOFA Sequential organ failure assessment OASIS Oxford Acute Severity of Illness Score SAPS II Simplified acute physiology score ACEI/ARB Angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker LOS Length of stay RCS Restricted cubic spline regression Declarations Ethics approval and consent to participate The study gathered information from MIMIC-IV. As the database contains de-identified patient information, privacy is safeguarded, and no further ethical approval or consent from patients was needed. Consent for publication Not applicable. Availability of data and materials Anyone who meet the data use agreement requirement are eligible to use the MIMIC-IV database. Clinical trial number Not applicable. Competing interests No conflicts of interest are declared by the authors. Funding This investigation received funding from the Anhui Provincial Health Research Project. (NO. AHWJ2022b020). Authors’ contributions Jia Xu and Zhen Wang conceptualized and designed the study. Jia Xu carried out the data extraction. Jia Xu, Yun Wang, and Xinran Chen analysis data and manuscript drafting. Lan Ma and Xiaochen Wang contributed to manuscript revision. Each author has made meaningful intellectual contributions and endorsed the final manuscript prepared for submission. 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Sheng H, Lu J, Zhong L, Hu B, Sun X, Dong H. The correlation between albumin‐corrected anion gap and prognosis in patients with acute myocardial infarction. ESC Heart Fail. 2024;11:826–36. Austin PC, White IR, Lee DS, Van Buuren S. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can J Cardiol. 2021;37:1322–31. Gravesteijn BY, Sewalt CA, Venema E, Nieboer D, Steyerberg EW, the CENTER-TBI Collaborators, et al. Missing Data in Prediction Research: A Five-Step Approach for Multiple Imputation, Illustrated in the CENTER-TBI Study. J Neurotrauma. 2021;38:1842–57. Zhang T, Wang J, Li X. Association Between Anion Gap and Mortality in Critically Ill Patients with Cardiogenic Shock. Int J Gen Med. 2021;Volume 14:4765–73. Mohr NM, Vakkalanka JP, Faine BA, Skow B, Harland KK, Dick-Perez R, et al. Serum anion gap predicts lactate poorly, but may be used to identify sepsis patients at risk for death: A cohort study. J Crit Care. 2018;44:223–8. Integration of acid–base and electrolyte disorders. N Engl J Med. 2015;372:389–92. Nicholson JP, Wolmarans MR, Park GR. The role of albumin in critical illness. Br J Anaesth. 2000;85:599–610. Jian L, Zhang Z, Zhou Q, Duan X, Xu H, Ge L. Association between albumin corrected anion gap and 30-day all-cause mortality of critically ill patients with acute myocardial infarction: a retrospective analysis based on the MIMIC-IV database. BMC Cardiovasc Disord. 2023;23:211. Zhong L, Xie B, Ji X-W, Yang X-H. The association between albumin corrected anion gap and ICU mortality in acute kidney injury patients requiring continuous renal replacement therapy. Intern Emerg Med. 2022;17:2315–22. Yao C, Veleva T, Scott L, Cao S, Li L, Chen G, et al. Enhanced Cardiomyocyte NLRP3 Inflammasome Signaling Promotes Atrial Fibrillation. Circulation. 2018;138:2227–42. Balan AI, Halațiu VB, Scridon A. Oxidative Stress, Inflammation, and Mitochondrial Dysfunction: A Link between Obesity and Atrial Fibrillation. Antioxidants. 2024;13:117. Mohsin M, Zeyad H, Khalid H, Gapizov A, Bibi R, Kamani YG, et al. The Synergistic Relationship Between Atrial Fibrillation and Diabetes Mellitus: Implications for Cardiovascular and Metabolic Health. Cureus. 2023. https://doi.org/10.7759/cureus.45881. Wu Y, Kong X-J, Ji Y-Y, Fan J, Ji C-C, Chen X-M, et al. Serum electrolyte concentrations and risk of atrial fibrillation: an observational and mendelian randomization study. BMC Genomics. 2024;25:280. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 04 Dec, 2024 Reviews received at journal 20 Nov, 2024 Reviews received at journal 20 Nov, 2024 Reviewers agreed at journal 16 Nov, 2024 Reviewers agreed at journal 16 Nov, 2024 Reviewers invited by journal 13 Nov, 2024 Editor invited by journal 05 Nov, 2024 Editor assigned by journal 04 Nov, 2024 Submission checks completed at journal 04 Nov, 2024 First submitted to journal 24 Oct, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5329034","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377695502,"identity":"2123248b-2dbf-4483-b989-fa5f806280b9","order_by":0,"name":"Jia Xu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Xu","suffix":""},{"id":377695504,"identity":"584254ac-2e4a-486d-b742-d873d689ee88","order_by":1,"name":"Zhen Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":377695506,"identity":"255417ba-9378-45b0-ba6a-f9e43c683dd8","order_by":2,"name":"Yun Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Wang","suffix":""},{"id":377695507,"identity":"0113886f-ec6c-4c84-9fa3-e5cebe8ff713","order_by":3,"name":"Xinran Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Chen","suffix":""},{"id":377695508,"identity":"fd486a0e-4d06-4676-a7fb-3ec78a218e7c","order_by":4,"name":"Lan Ma","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Ma","suffix":""},{"id":377695509,"identity":"8f4f3173-72dc-43ab-9891-39420398e62c","order_by":5,"name":"Xiaochen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYBACA+YDBgwMFRJy/MRrYUsAajljYSzZQJIWxraKxA1EazFnY9744Oc8CcYNDMwPH90gRotlG1uxYe82CWZzBjZj4xyiHHa/x0yacZsEm2UDD5s0cVqO8QC1zJHgMThAmpYGCQlStAD90nNMwkCymWi/HAOG2I+auvp+9uaHj4nSggDMpCkfBaNgFIyCUYAPAABWeiqkeMKgwAAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaochen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-10-25 02:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5329034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5329034/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-04518-w","type":"published","date":"2025-01-27T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70386550,"identity":"6e115c6a-8c03-478d-83b3-d284153f8e4c","added_by":"auto","created_at":"2024-12-02 17:21:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90333,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of study sample selection steps. AF,atrial fibrillation; ICU, intensive care unit; AIDS, acquired immune deficiency syndrome.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5329034/v1/fefd85344f68971d29823ee4.png"},{"id":70386518,"identity":"635cd6a9-8c9d-48d2-bc4d-0906a3dfbb15","added_by":"auto","created_at":"2024-12-02 17:21:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103495,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis curves for (A)30-day and (B)365-day all-cause mortality. ACAG quantile: Q1 (9.00-16.75), Q2 (16.75-19.25), Q3 (19.25-22.50), Q4 (22.50-51.25)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5329034/v1/68063ae481073b3346c0bf31.png"},{"id":70386464,"identity":"201fe6a0-1645-4177-a816-76d09c724a87","added_by":"auto","created_at":"2024-12-02 17:20:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84041,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve for (A)30-day and (B)365-day all-cause mortality. ACAG, albumin-corrected anion gap; CI, confidence interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5329034/v1/9e42755c0104fb1aee2738fd.png"},{"id":70386399,"identity":"14565cfa-242d-4b30-9d4f-6a86590141d4","added_by":"auto","created_at":"2024-12-02 17:19:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104608,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of HRs for the mortality in different subgroups. HR, hazard ratios.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5329034/v1/9767e69eca10c7332dbdb35b.png"},{"id":75351692,"identity":"c18ce87c-a87d-45d1-846f-bb48ca0e6a5b","added_by":"auto","created_at":"2025-02-03 16:12:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2004240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5329034/v1/f18ccbca-2e26-4d1c-9d42-51153ef5b54d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of elevated albumin-corrected anion gap with all-cause mortality risk in atrial fibrillation: a retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) constitutes the most prevalent cardiovascular disease, with a rising incidence worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It affects approximately 1\u0026ndash;3% of the general population, with higher rates observed in older individuals, males, and individuals with comorbidities[\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In 2019, global epidemiological data indicated approximately 59.7\u0026nbsp;million cases of AF, including 4.72\u0026nbsp;million new diagnoses that year[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. AF is linked to a range severe serious complications, making it a key element in the global burden of cardiovascular disease[\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite advancements in available therapies, managing AF remains challenging due to the complexity of its pathophysiology and the variability in patient responses to treatment. Identifying reliable prognostic markers is essential for preventing complications and improving outcomes in these patients.\u003c/p\u003e \u003cp\u003eThe anion gap (AG), developed in the mid-20th century, is commonly employed to evaluate acid-base disturbances in critical patients. Beyond its role in metabolic assessments, AG has been associated with disease severity and clinical outcomes[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. AG reflects the concentration of unmeasured anions, including serum albumin, lactate, and acetoacetate. However, low serum albumin levels, frequently observed in critically ill individuals, can produce falsely low AG values, limiting its prognostic accuracy. To overcome this limitation, the albumin-corrected anion gap (ACAG) was introduced. ACAG provides a more accurate assessment of unmeasured anions by adjusting for fluctuations in serum albumin levels[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eACAG has been linked not only to disease risk[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] but also to clinical outcomes in a variety of conditions[\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], including sepsis, acute pancreatitis, acute myocardial infarction(AMI), heart failure, and acute kidney injury (AKI),. However, its prognostic significance in AF patients remains unexplored. Given the role of metabolic imbalances in cardiovascular disease, ACAG could also serve as an important marker for adverse outcomes in AF. Investigating the correlation between ACAG levels and mortality among individuals with AF is essential for guiding clinical decision-making. This study assesses the correlation between ACAG and all-cause mortality at 30 and 365 days in AF patients by utilizing the Medical Information Mart for Intensive Care (MIMIC)-IV database. Our findings aim to offer meaningful insights for risk stratification and inform treatment strategies. By demonstrating the prognostic utility of ACAG, we hope to offer evidence supporting its incorporation into clinical practice to enhance patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThis study utilized information from version 2.2 of the MIMIC-IV database, a publicly available, large-scale, and de-identified dataset that offers extensive clinical data from intensive care units (ICU) patients in Beth Israel Deaconess Medical Center in Boston, Massachusetts. This dataset provides detailed recodes, including demographic information, nursing notes, laboratory findings, medications usage, and mortality data. The first author, Jia Xu, fulfilled all eligibility requirements for accessing the database (Certification ID: 64822128) and took charge of data extraction. Since the dataset contains anonymized patient information without identifiable health data, informed consent was not required.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipant selection\u003c/h3\u003e\n\u003cp\u003eWe identified 59,865 admissions with the diagnosis of AF. From these recodes, 20,797 ICU patients were selected for further analysis. Exclusion criteria were: (1) younger than 18; (2) multiple hospital admissions; (3) multiple ICU admissions; (4) comorbidities such as end-stage renal disease, cirrhosis, or cancer; (5) diagnosis of AIDS; (6) ICU stays shorter than 24 hours; and (7) insufficient data on AG and albumin levels. Following these criteria, a cohort of 2,920 patients was incorporate into the final analysis, with participants divided into quartiles according to their ACAG levels for further comparison (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eNavicat Premium (version 15) was used to extract data. To minimize the impact of treatment interventions, data were gathered from the initial 24 hours following ICU admission. Collected demographic data included age, sex, and race. Documented comorbidities were myocardial infarction (MI), congestive heart failure (CHF), hypertension, diabetes, obesity, peripheral vascular disease (PVD), cerebrovascular disease (CVD), chronic obstructive pulmonary disease (COPD), rheumatic disease, paraplegia, renal disease, and liver disease. Laboratory parameters included white blood cell (WBC), platelets, hematocrit, hemoglobin, red cell distribution width (RDW), anion gap (AG), albumin, blood urea nitrogen (BUN), calcium, chloride, creatinine, glucose, sodium, potassium, international normalized ratio (INR), prothrombin time (PT), and partial thromboplastin time (PTT). Vital signs were heart rate and blood pressure parameters comprised systolic, diastolic, and mean blood pressures (SBP, DBP, and MBP, respectively). The assessment of severity utilized the Oxford Acute Severity of Illness Score (OASIS), the Sequential Organ Failure Assessment (SOFA), and the Simplified Acute Physiology Score II (SAPS II), were also recorded. Medications tracked included aspirin, clopidogrel, beta-blockers, amiodarone, dabigatran, statins, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (ACEI/ARB), heparin, and warfarin. Length of stay (LOS) was recorded for both the hospital and ICU, with clinical outcomes including mortality rates at hospital, 30 days, and 365 days. We excluded variables with more than 20% missing data to minimize bias, and used multiple imputation, performed through a random forest technique to predict missing entries[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eClinical outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary follow-up period commenced at the time of hospital admission. The main endpoint is 365-day all-cause mortality, and the additional endpoint is 30-day all-cause mortality.\u003c/p\u003e\n\u003ch3\u003eCalculation of ACAG\u003c/h3\u003e\n\u003cp\u003eAG and albumin were directly retrieved. The following equation was used to calculate the ACAG: ACAG (mmol/l)\u0026thinsp;=\u0026thinsp;AG (mmol/l) + [4.4 -Albumin (g/dl)] *2.5[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe distribution of continuous variables was assessed with the Kolmogorov-Smirnov test. In this study, none of the parameters conformed to a normal distribution. Therefore, they were summarized with the median and interquartile range (IQR) and analyzed through the Mann-Whitney U test for comparison. Kruskal\u0026ndash;Wallis test. Categorical variables were expressed as percentages and analyzed through chi-square test for comparison. Kaplan\u0026ndash;Meier survival curves were used to assess cumulative survival across the quartiles based on ACAG. Univariable Cox regression analysis was applied to determine the factors associated with the mortality risk. Multivariate Cox regression models served to confirm the relationship between ACAG and outcomes, with adjustments made in various models. Confounding factors included those with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariable Cox regression analysis, along with clinically relevant and prognostically significant variables. Model 1 conducted without any adjustments. Model 2 accounted for age, sex, and race. Model 3 incorporated additional variables. such as heart rate, SBP, DBP, MBP, MI, CHF, PVD, CVD, obesity, paraplegia, renal disease, liver disease, statin, beta-blockers, ACEI/ARB, heparin, warfarin. model 4 further incorporated hematocrit, hemoglobin, WBC, RDW, BUN, creatinine, sodium, potassium, glucose, INR, PT, PTT, SOFA, OASIS and SAPSII. Both continuous and categorical ACAG variables were analyzed, using the lowest quartile as the reference group. Trend \u003cem\u003ep\u003c/em\u003e-value was computed across quartiles. Restricted cubic spline (RCS) regression with four knots was applied to assess non-linear relationships between baseline ACAG and mortality outcomes. Stratified analyses explored the prognostic value of ACAG by sex, age (\u0026lt;\u0026thinsp;65 or \u0026ge;\u0026thinsp;65 years), race, diabetes, and hypertension status. Interaction effects were evaluated through likelihood ratio tests. Statistical significance was defined as a two-tailed \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using R (version 4.4.1) and SPSS (version 25.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study analyzed 2,920 patients diagnosed with AF, with a median age of 78 years (IQR: 69\u0026ndash;86) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, 1,604 (54.93%) were male. The median ACAG level was 19.25 mmol/L (IQR: 16.75\u0026ndash;22.50). Hospital mortality observed in 18.15% of patients, while the 30 days and 365 days mortality rates stood at 22.91% and 39.21%, respectively.\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\u003eCharacteristics and outcomes of participants categorized by ACAG\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2920)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;680)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;741)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;757)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;742)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACAG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.25 (16.75, 22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.25 (14.00,16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.00 (17.25,18.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.50 (19.75,21.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.25 (23.56,28.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic variables\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.00 (69.00, 86.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.00 (68.00,85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.00 (69.00,86.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.00 (71.00,86.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.00 (67.00,85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1604 (54.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e388 (57.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e380 (51.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e405 (53.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e431 (58.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace,White, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2046 (70.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e475 (69.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e544 (73.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e523 (69.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e504 (67.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities, n (%)\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747 (25.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (21.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (22.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e203 (26.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e234 (31.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500 (51.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277 (40.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e367 (49.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e428 (56.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e428 (57.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e969 (33.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219 (29.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e262 (34.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e318 (42.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1154 (39.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (40.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333 (44.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296 (39.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e247 (33.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (8.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (10.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (11.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112 (15.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446 (15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (13.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (16.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101 (13.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127 (17.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e725 (24.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (32.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210 (28.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171 (22.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125 (16.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e866 (29.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (29.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (27.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249 (32.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215 (28.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatic Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (10.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (15.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (11.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (8.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41 (5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e805 (27.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (14.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182 (24.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e247 (32.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e276 (37.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (4.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88 (11.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\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\u003e35.80 (31.60, 40.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.90 (32.30,41.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.30 (31.30,40.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.50 (31.40,40.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.05 (31.50,40.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e11.70 (10.20, 13.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.10 (10.60,13.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.60 (10.20,13.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.60 (10.10,13.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.70 (10.03,13.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, K/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211.00 (161.00, 279.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201.00 (158.00,251.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212.00 (167.00,270.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213.00 (163.00,284.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e220.00 (157.25,306.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, K/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.90 (9.30, 18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.30 (8.00,14.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.40 (9.10,16.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.20 (9.50,18.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.25 (11.30,22.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.60 (13.70, 16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.10 (13.40,15.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.40 (13.70,15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.80 (13.70,16.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.00 (14.00,16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.40 (2.90, 3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.70 (3.30,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.40 (3.00,3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.40 (2.90,3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.20 (2.70,3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.00 (14.00, 20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00 (12.00,14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.00 (14.00,16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.00 (17.00,19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.00 (20.25,25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00 (19.00, 45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.00 (16.00,28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.00 (17.00,34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.00 (21.00,48.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.00 (29.00,69.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.60 (8.20, 9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.80 (8.30,9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.60 (8.20,9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.60 (8.20,9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.50 (8.00,9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.00 (102.00, 109.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.00 (102.00,109.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.00 (102.00,109.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.00 (101.00,110.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e105.00 (101.00,110.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.67\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\u003e1.30 (0.90, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.80,1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.80,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30 (1.00,1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.10 (1.40,3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152.00 (121.00, 208.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.00 (110.00,165.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.00 (119.00,188.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159.00 (126.00,212.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189.00 (140.00,269.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.00 (138.00, 143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.00 (138.00,143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.00 (138.00,143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.00 (137.00,143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140.00 (137.00,144.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.50 (4.10, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.30 (4.00,4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.40 (4.10,4.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.40 (4.10,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.90 (4.40,5.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.20, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.20,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 (1.20,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40 (1.20,2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.60 (1.30,2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.70 (13.30, 22.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.55 (12.90,18.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.80 (13.20,21.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.50 (13.30,21.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.85 (14.30,29.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.40 (29.60, 54.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.00 (28.80,42.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.50 (29.00,49.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.50 (29.30,57.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.25 (31.63,70.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR, beats/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.00 (73.00, 98.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.00 (70.00,91.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.00 (72.00,96.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.00 (75.00,99.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.00 (78.00,105.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.00 (106.00, 128.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.00 (110.00,134.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.00 (108.00,131.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114.00 (105.00,128.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.00 (102.00,118.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.00 (55.00, 69.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.00 (56.00,72.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.00 (55.00,70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.00 (55.00,69.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.00 (53.25,67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.00 (71.00, 85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.00 (73.00,88.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.00 (71.00,85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.00 (70.00,84.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.00 (68.00,81.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical severity\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00 (3.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (2.00,6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.00 (3.00,8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.00 (5.00,11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.00 (29.00, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.00 (26.00,37.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.00 (28.00,39.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.00 (29.00,39.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.00 (32.00,46.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00 (32.00, 49.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.00 (28.00,40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.00 (31.00,45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.00 (33.00,48.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.00 (40.00,60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication, n(%)\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1246 (42.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301 (44.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303 (40.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337 (44.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e305 (41.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (6.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (8.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (8.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1220 (41.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e321 (47.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333 (44.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e315 (41.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e251 (33.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1896 (64.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461 (67.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484 (65.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e505 (66.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e446 (60.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (10.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (12.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (9.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54 (7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmiodarone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e720 (24.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (20.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172 (22.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245 (33.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDabigatran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeparin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2263 (77.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473 (69.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e561 (75.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e597 (78.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e632 (85.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarfarin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e568 (19.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128 (18.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (18.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138 (18.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOS, days\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLos in Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.00 (5.00, 14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00 (5.00,12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.00 (6.00,14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.00 (5.00,15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.00 (6.00,17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLos in Icu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 (2.00, 6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00 (2.00,6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00 (2.00,6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00 (2.00,8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEvents, n (%)\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530 (18.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (10.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (11.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137 (18.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e237 (31.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e669 (22.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (14.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (16.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175 (23.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e271 (36.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e365-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1145 (39.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (28.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239 (32.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314 (41.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e400 (53.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eACAG: Q1 (9.00-16.75), Q2 (16.75\u0026ndash;19.25), Q3 (19.25\u0026ndash;22.50), Q4 (22.50-51.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAbbreviation: ACAG, albumin-corrected anion gap; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; COPD, chronic pulmonary disease; WBC, white blood cell; RDW, red cell distribution width; AG, anion gap; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time; PTT, partial thromboplastin time; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker; LOS, length of stay.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eBaseline characteristics of participants\u003c/h3\u003e\n\u003cp\u003eIndividuals were divided into four quartiles: Quartile (Q) 1: 9.00-16.75mmol/L; Q2: 16.75\u0026ndash;19.25 mmol/L; Q3:19.25\u0026ndash;22.50 mmol/L; Q4: 22.50-51.25mmol/L(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median ACAG values for these quartiles were 15.25 mmol/L (IQR: 14.00\u0026ndash;16.00), 18.00 mmol/L (IQR: 17.25\u0026ndash;18.50), 20.50 mmol/L (IQR: 19.75\u0026ndash;21.50), and 25.25 mmol/L (IQR: 23.56\u0026ndash;28.25), respectively. Higher quartile patients exhibited increased levels of platelets, WBC, RDW, AG, BUN, creatinine, glucose, potassium, INR, PT, PTT, while their albumin levels were lower. With rising ACAG levels, conditions like MI, CHF, diabetes, obesity, renal disease, and liver disease became more common, whereas CVD and paraplegia were less frequently reported. Higher ACAG levels also correlated with elevated severity scores and more frequent use of amiodarone and heparin (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Mortality rates rose across quartiles: hospital mortality ranged from 10.59% in Q1 to 31.94% in Q4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), 30-day mortality from 14.56% in Q1 to 36.52% in Q4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and 365-day mortality from 28.24% in Q1 to 53.91% in Q4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eBaseline differences between 365-day survivors and non-survivors are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Non-survivors tended to be of greater age, presented with higher admission severity scores, and displayed a greater prevalence of MI, CHF, diabetes, obesity, PVD, CVD, paraplegia, renal disease, and liver disease. Non-survivors also exhibited elevated levels of platelets, WBC, RDW, AG, BUN, creatinine, glucose, sodium, potassium, INR, PT, PTT, and heart rate, but lower hematocrit, hemoglobin, albumin, SBP, DBP, and MBP. Statin, beta-blocker, ACEI/ARB, heparin, and warfarin use was less frequent among non-survivors. Non-survivors had notably higher ACAG levels than survivors. (20.50 vs. 18.50, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\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\u003eBaseline characteristics between survivors and non-survivors at 365 days\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;2920)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esurvivors\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1775)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enon-survivors\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1145)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACAG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.25 (16.75, 22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.50 (16.25, 21.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.50 (17.75, 24.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.00 (69.00, 86.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.00 (66.00, 83.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.00 (74.00, 88.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1604 (54.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1012 (57.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e592 (51.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, White, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2046 (70.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1246 (70.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e800 (69.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747 (25.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e415 (23.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332 (29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500 (51.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e851 (47.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e649 (56.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e969 (33.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e562 (31.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e407 (35.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1154 (39.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e716 (40.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e438 (38.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240 (13.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (8.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446 (15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242 (13.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204 (17.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e725 (24.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398 (22.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e327 (28.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e866 (29.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e518 (29.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e348 (30.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatic Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (10.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (11.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e805 (27.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e417 (23.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e388 (33.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.80 (31.60, 40.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.20 (32.00, 40.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.30 (30.90, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e11.70 (10.20, 13.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.90 (10.40, 13.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.40 (9.90, 12.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, K/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211.00 (161.00, 279.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.00 (162.00, 273.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213.00 (159.00, 288.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, K/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.90 (9.30, 18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.40 (9.10, 17.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.70 (9.70, 19.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.60 (13.70, 16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.30 (13.50, 15.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.10 (14.00, 16.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.40 (2.90, 3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50 (3.00, 3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.30 (2.80, 3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.00 (14.00, 20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.00 (14.00, 19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.00 (15.00, 21.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00 (19.00, 45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.00 (17.00, 37.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.00 (23.00, 56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.60 (8.20, 9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.70 (8.20, 9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.60 (8.10, 9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.00 (102.00, 109.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.00 (102.00, 109.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.00 (101.00, 110.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\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\u003e1.30 (0.90, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.90, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50 (1.00, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152.00 (121.00, 208.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147.00 (118.00, 194.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165.00 (126.00, 226.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.00 (138.00, 143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.00 (138.00, 143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141.00 (138.00, 144.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.50 (4.10, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.40 (4.10, 4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.60 (4.20, 5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.20, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.20, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50 (1.20, 2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.70 (13.30, 22.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.30 (13.10, 20.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.60 (13.70, 25.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.40 (29.60, 54.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.60 (29.50, 53.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.00 (29.80, 57.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR, beats/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.00 (73.00, 98.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.00 (72.50, 98.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.00 (74.00, 99.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.00 (106.00, 128.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.00 (107.00, 129.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.00 (104.00, 126.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.00 (55.00, 69.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.00 (56.00, 71.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.00 (54.00, 68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.00 (71.00, 85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.00 (72.00, 86.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.00 (69.00, 83.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical severity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00 (3.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 (2.00, 7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00 (4.00, 9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.00 (29.00, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.00 (27.00, 38.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.00 (32.00, 44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00 (32.00, 49.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.00 (30.00, 44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.00 (37.00, 54.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1246 (42.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e759 (42.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e487 (42.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (6.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (8.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1220 (41.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e783 (44.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437 (38.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1896 (64.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1191 (67.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e705 (61.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286 (9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (10.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmiodarone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e720 (24.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428 (24.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292 (25.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDabigatran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeparin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2263 (77.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1351 (76.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e912 (79.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarfarin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e568 (19.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389 (21.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (15.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: ACAG, albumin-corrected anion gap; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; COPD, chronic pulmonary disease; WBC, white blood cell; RDW, red cell distribution width; AG, anion gap; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time; PTT, partial thromboplastin time; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrimary outcomes\u003c/h2\u003e \u003cp\u003eKaplan-Meier survival curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrate that higher ACAG levels corresponded with increased risks of mortality at both 30 days and 365 days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnivariate Cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was performed using covariates with statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Unadjusted analyses revealed a significant association between ACAG and 365-day all-cause mortality (HR: 1.07, 95% CI: 1.06\u0026ndash;1.08; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Multivariate Cox regression (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) confirmed the relationship between ACAG and 365-day mortality across all models: model 1: HR: 1.07 (95%CI: 1.06\u0026ndash;1.08, P\u0026lt;0.01), model 2: HR, 1.07, (95%CI:1.06\u0026ndash;1.08, P\u0026lt;0.01), model 3: HR:1.06 (95%CI 1.05\u0026ndash;1.07, P\u0026lt;0.01) and model 4: HR: 1.03 ( 95%CI: 1.02\u0026ndash;1.05, P\u0026lt;0.01). When ACAG was analyzed as an ordinal parameter, patients in the highest quartile exhibited a markedly increased risk of 365-day mortality compared to those in the lowest quartile: model 1: HR 2.45 (95% CI 2.06\u0026ndash;2.91; P\u0026lt;0.01), model 2: HR 2.21 (95% CI 1.85\u0026ndash;2.66; P\u0026lt;0.01), model 3: HR 1.93 (95% CI 1.60\u0026ndash;2.33; P\u0026lt;0.01) and model 4: HR 1.31 (95% CI 1.05\u0026ndash;1.61; P\u0026thinsp;=\u0026thinsp;0.01). A similar trend of increased mortality risk with higher ACAG levels was observed for 30-day mortality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eUnivariate COX analysis of risk factors correlated with 365-day all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.06\u0026thinsp;~\u0026thinsp;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.03\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.74\u0026thinsp;~\u0026thinsp;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.11\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (1.14\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.01\u0026thinsp;~\u0026thinsp;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.57\u0026thinsp;~\u0026thinsp;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.06\u0026thinsp;~\u0026thinsp;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33 (1.17\u0026thinsp;~\u0026thinsp;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaplegia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (1.09\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45 (1.28\u0026thinsp;~\u0026thinsp;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58 (1.28\u0026thinsp;~\u0026thinsp;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\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\u003e0.94 (0.91\u0026thinsp;~\u0026thinsp;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, K/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1,.01\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.10\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.62\u0026thinsp;~\u0026thinsp;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.05\u0026thinsp;~\u0026thinsp;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e1.11 (1.08\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.01\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.17\u0026thinsp;~\u0026thinsp;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.05\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.09\u0026thinsp;~\u0026thinsp;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.05\u0026thinsp;~\u0026thinsp;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.04\u0026thinsp;~\u0026thinsp;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.71\u0026thinsp;~\u0026thinsp;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.70\u0026thinsp;~\u0026thinsp;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.59\u0026thinsp;~\u0026thinsp;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeparin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.02\u0026thinsp;~\u0026thinsp;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarfarin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.58\u0026thinsp;~\u0026thinsp;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAbbreviation: HR, Hazard Ratio; CI, Confidence Interval; ACAG, albumin-corrected anion gap; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; WBC, white blood cell; RDW, red cell distribution width; AG, anion gap; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time ; PTT, partial thromboplastin time; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazard ratios (HR) for all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e365-day mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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 \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinues variable per unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.06\u0026thinsp;~\u0026thinsp;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 (1.06\u0026thinsp;~\u0026thinsp;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (1.05\u0026thinsp;~\u0026thinsp;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (N\u0026thinsp;=\u0026thinsp;680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (N\u0026thinsp;=\u0026thinsp;741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (0.98\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.89\u0026thinsp;~\u0026thinsp;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02 (0.84\u0026thinsp;~\u0026thinsp;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.96 (0.79\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (N\u0026thinsp;=\u0026thinsp;757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62 (1.35\u0026thinsp;~\u0026thinsp;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46 (1.22\u0026thinsp;~\u0026thinsp;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35 (1.12\u0026thinsp;~\u0026thinsp;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.15 (0.95\u0026thinsp;~\u0026thinsp;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (N\u0026thinsp;=\u0026thinsp;742)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45 (2.06\u0026thinsp;~\u0026thinsp;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.21 (1.85\u0026thinsp;~\u0026thinsp;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.93 (1.60\u0026thinsp;~\u0026thinsp;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.31 (1.05\u0026thinsp;~\u0026thinsp;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30-day mortality\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinues variable per unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.07\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (1.07\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.07 (1.06\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04 (1.02\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (N\u0026thinsp;=\u0026thinsp;680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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 \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (N\u0026thinsp;=\u0026thinsp;741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.89\u0026thinsp;~\u0026thinsp;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10 (0.84\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03 (0.79\u0026thinsp;~\u0026thinsp;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94 (0.71\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (N\u0026thinsp;=\u0026thinsp;757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66 (1.30\u0026thinsp;~\u0026thinsp;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54 (1.20\u0026thinsp;~\u0026thinsp;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41 (1.09\u0026thinsp;~\u0026thinsp;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.16 (0.90\u0026thinsp;~\u0026thinsp;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (N\u0026thinsp;=\u0026thinsp;742)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.91 (2.31\u0026thinsp;~\u0026thinsp;3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.62 (2.06\u0026thinsp;~\u0026thinsp;3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.26 (1.76\u0026thinsp;~\u0026thinsp;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.43 (1.08\u0026thinsp;~\u0026thinsp;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003efor trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 1: unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 2: adjusted for age, sex, race.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 3: adjusted for Model2\u0026thinsp;+\u0026thinsp;SBP, DBP, MBP, MI, CHD, PVD, CVD, obesity, paraplegia, renal disease, liver disease, statin, beta-blockers, ACEI/ARB, heparin, warfarin.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eModel 4: Model 3\u0026thinsp;+\u0026thinsp;hematocrit, hemoglobin, WBC, RDW, BUN, creatinine, glucose, sodium, potassium, INR, PT, and PTT, SOFA, OASIS, SAPSII.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviation: HR, Hazard Ratio; CI, Confidence Interval; SBP, systolic blood pressure; DBP, diastolic blood pressure; MBP, mean blood pressure; MI, myocardial infarct; CHF, congestive heart failure; PVD, peripheral vascular disease; CVD, cerebrovascular disease; ACEI/ARB, angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker; WBC, white blood cell; RDW, red cell distribution width; BUN, blood urea nitrogen; INR, international normalized ratio; PT, prothrombin time ; PTT, partial thromboplastin time, SOFA, sequential organ failure assessment; OASIS, Oxford Acute Severity of Illness Score; SAPS II, simplified acute physiology score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRCS analyses shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicated a linear link between increased ACAG and mortality outcomes at 30 days and 365 days, with no significant non-linearity detected (P for non-linearity\u0026thinsp;=\u0026thinsp;0.729 and 0.503, respectively) after adjusting for relevant confounders.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eWe employed subgroup analyses to investigate whether ACAG remained a significant predictor of mortality across various demographic and clinical groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Higher ACAG levels were consistently associated with 365-day mortality across all subgroups, including by sex, age (\u0026lt;\u0026thinsp;65 and \u0026ge;\u0026thinsp;65 years), race, diabetes, and hypertension status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, ACAG significantly predicted 30-day mortality across subgroups, including males and females, individuals\u0026thinsp;\u0026ge;\u0026thinsp;65 years, and those with or without diabetes or hypertension, as well as among non-White patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interaction terms between ACAG and subgroup factors failed to achieve statistical significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first investigation to assess the connection between ACAG and mortality outcomes in the context of AF, offering new insights into the prognostic value of ACAG. Our analysis showed a clear, linear relationship between higher ACAG levels and increased risks of all-cause mortality at both 1 month and 1 year, even after controlling for multiple confounders. The robustness of these results across various statistical approaches highlights their dependability. Subgroup analyses further confirmed that ACAG remains a significant prognostic marker across diverse clinical and demographic groups, suggesting it could serve as a universal risk indicator in AF patients. As a readily available biomarker, ACAG demonstrates potential as a clinical decision-support tool, complementing traditional risk assessments.\u003c/p\u003e \u003cp\u003eThe traditional AG is frequently employed to evaluate acid-base disturbances. It is defined by the gap between measured serum cations and anions. Elevated AG is often seen in cases of lactic acidosis, diabetic ketoacidosis, and renal failure, and it correlates with worse outcomes in critical patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, AG is influenced by serum albumin levels, with each 1 g/L reduction in albumin lowering AG by approximately 2.3\u0026ndash;2.5 mmol/L[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Hypoalbuminemia is prevalent among critically ill patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], including AF patients, with approximately 54% of our cohort exhibiting reduced albumin levels. Consequently, relying on AG alone may result in false negatives, impairing clinical judgment and risk stratification.\u003c/p\u003e \u003cp\u003eACAG, which adjusts AG for serum albumin, improves the sensitivity of metabolic acidosis diagnosis and offers better prognostic accuracy. It provides a more reliable marker of disease severity and outcomes in ICU patients. Prior research has highlighted ACAG\u0026rsquo;s utility in predicting mortality across various conditions. Hu et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] reported that ACAG offers more reliable forecast of in-hospital mortality than either albumin or AG in patients with sepsis. Similarly, Li et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]showed that elevated ACAG levels were linked to increase in-hospital mortality in individuals with acute pancreatitis, even after controlling for confounding variables. In AMI, elevated ACAG levels outperformed AG in predictive value for 30 days mortality[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and Sheng H et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] further identified increased ACAG as an important marker for forecasting long-term mortality in severe AMI patients. Other studies have also linked higher ACAG levels with increased ICU mortality among individuals with AKI receiving continuous renal replacement therapy[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and demonstrated its prognostic value for 30 days and one year mortality in severe AKI patients[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consistent with these findings, our study evaluated the correlation between ACAG and mortality in individuals with AF, revealing that increased ACAG independently predicts both 30 days and one year mortality. These findings highlight the potential value of ACAG in pinpointing at-risk AF patients and facilitating early therapeutic actions.\u003c/p\u003e \u003cp\u003eAlthough the precise mechanisms linking elevated ACAG to poor outcomes in AF patients are not fully understood, several plausible pathways exist. However, several plausible pathways may contribute to this relationship. Systemic inflammation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and oxidative stress[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], common in AF, are often exacerbated by metabolic acidosis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Elevated ACAG levels reflect more severe acidosis, contributing to adverse outcomes. Additionally, ACAG could serve as an indirect marker of systemic inflammation, with higher levels indicating a more pronounced inflammatory response, impairing recovery. Electrolyte imbalances, reflected in elevated ACAG, may also play a role, given their association with AF risk and adverse outcomes[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, critically ill AF patients often experience reduced cardiac output, impairing systemic perfusion and leading to tissue hypoxia. The resulting accumulation of lactic acid is reflected in elevated ACAG, which may signal the presence of critical illness, multi-organ dysfunction, and increased mortality risk.\u003c/p\u003e \u003cp\u003eThis study\u0026rsquo;s primary strength lies in its identification of ACAG as an important predictor of mortality risk at one month and one year in AF patients. As far as we are aware, no prior research has documented this association in this patient population. The robustness of our findings across various statistical models, combined with the use of a large dataset, enhances the reliability of our conclusions. However, several limitations should be acknowledged. First, while our sample size was large, the cohort was derived from a single database (MIMIC-IV), which may limit the suitability of our findings for different patient groups or clinical settings. Second, despite the use of multiple statistical analyses, residual confounding cannot be entirely excluded, as certain variables\u0026mdash;such as the timing of AF, use of advanced cardiac therapies, and specific causes of death\u0026mdash;were not available in the database. Third, the retrospective framework of the research restricts our capacity to draw causal conclusions. Lastly, we only assessed ACAG during the initial 24 hours of ICU admission and unable to track variations during the hospital stay.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis research highlights the significance of ACAG as a valuable prognostic indicator for predicting both one month and one year mortality in individuals with AF. As an independent risk factor, ACAG can offer clinicians a valuable tool for pinpointing high-risk individuals and initiating timely interventions designed to enhance clinical outcomes. Future prospective investigations are essential to validate these results in diverse populations and further investigate the mechanisms driving the relationship between elevated ACAG and poor prognosis. Evaluating dynamic changes in ACAG during hospitalization may also enhance its role in clinical decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eAG \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eAnion gap\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eACAG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eAlbumin-corrected anion gap\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eAMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eAcute myocardial infarct\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eAKI \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eAcute kidney injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eMIMIC-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eMedical Information Mart for Intensive Care IV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eICU \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eIntensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eIQR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eInterquartile range\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eHazard ratios\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eCI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eConfidence intervals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eCHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003ePVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003ePeripheral vascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eCOPD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003ewhite blood cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eRed cell distribution width;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eBlood urea nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eInternational normalized ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eProthrombin time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003ePTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003ePartial thromboplastin time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eSystolic blood pressure;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eDBP,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eDiastolic blood pressure;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eMBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eMean blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eSequential organ failure assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eOASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eSAPS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eSimplified acute physiology score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eACEI/ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eAngiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eLOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eLength of stay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003eRCS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003eRestricted cubic spline regression\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.677%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study gathered information from MIMIC-IV. As the database contains de-identified patient information, privacy is safeguarded, and no further ethical approval or consent from patients was needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnyone who meet the data use agreement requirement are eligible to use the MIMIC-IV database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest are declared by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis investigation received funding from the Anhui Provincial Health Research Project. (NO. AHWJ2022b020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJia Xu and Zhen Wang conceptualized and designed the study. Jia Xu carried out the data extraction. Jia Xu, Yun Wang, and Xinran Chen analysis data and manuscript drafting. Lan Ma and Xiaochen Wang contributed to manuscript revision. Each author has made meaningful intellectual contributions and endorsed the final manuscript prepared for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our heartfelt appreciation to the team behind the development and upkeep of the MIMIC-IV database for their significant efforts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMurphy A, Banerjee A, Breithardt G, Camm AJ, Commerford P, Freedman B, et al. The World Heart Federation Roadmap for Nonvalvular Atrial Fibrillation. Glob Heart. 2017;12:273.\u003c/li\u003e\n\u003cli\u003eNielsen JC. European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome, in the right population. J Arrhythm. 2020;36:553\u0026ndash;607.\u003c/li\u003e\n\u003cli\u003eKirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J. 2016;37:2893\u0026ndash;962.\u003c/li\u003e\n\u003cli\u003eMareev YuV, Polyakov DS, Vinogradova NG, Fomin IV, Mareev VYu, Belenkov YuN, et al. Epidemiology of atrial fibrillation in a representative sample of the European part of the Russian Federation. Analysis of EPOCH-CHF study. Kardiologiia. 2022;62:12\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eDe Burgos-Lunar C, Del Cura-Gonz\u0026aacute;lez I, C\u0026aacute;rdenas-Valladolid J, G\u0026oacute;mez-Campelo P, Ab\u0026aacute;nades-Herranz JC, L\u0026oacute;pez-de Andr\u0026eacute;s A, et al. Real-world data in primary care: validation of diagnosis of atrial fibrillation in primary care electronic medical records and estimated prevalence among consulting patients\u0026rsquo;. BMC Prim Care. 2023;24:4.\u003c/li\u003e\n\u003cli\u003eLi C, Wang H, Li M, Qiu X, Wang Q, Sun J, et al. Epidemiology of atrial fibrillation and related myocardial ischemia or arrhythmia events in Chinese community population in 2019. Front Cardiovasc Med. 2022;9:821960.\u003c/li\u003e\n\u003cli\u003eMa Q, Zhu J, Zheng P, Zhang J, Xia X, Zhao Y, et al. Global burden of atrial fibrillation/flutter: Trends from 1990 to 2019 and projections until 2044. Heliyon. 2024;10:e24052.\u003c/li\u003e\n\u003cli\u003eJanuary CT, Wann LS, Calkins H, Chen LY, Cigarroa JE, Cleveland JC, et al. 2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society in Collaboration With the Society of Thoracic Surgeons. Circulation. 2019;140.\u003c/li\u003e\n\u003cli\u003eBrachmann J, Sohns C, Andresen D, Siebels J, Sehner S, Boersma L, et al. Atrial Fibrillation Burden and Clinical Outcomes in Heart Failure. JACC Clin Electrophysiol. 2021;7:594\u0026ndash;603.\u003c/li\u003e\n\u003cli\u003eSingh SM, Abdel-Qadir H, Pang A, Fang J, Koh M, Dorian P, et al. Population Trends in All‐Cause Mortality and Cause Specific\u0026ndash;Death With Incident Atrial Fibrillation. J Am Heart Assoc. 2020;9:e016810.\u003c/li\u003e\n\u003cli\u003eFreedman B, Hindricks G, Banerjee A, Baranchuk A, Ching CK, Du X, et al. World heart federation roadmap on atrial fibrillation \u0026ndash; a 2020 update. Glob Heart. 2021;16.\u003c/li\u003e\n\u003cli\u003eChen J, Dai C, Yang Y, Wang Y, Zeng R, Li B, et al. The association between anion gap and in-hospital mortality of post-cardiac arrest patients: A retrospective study. Sci Rep. 2022;12:7405.\u003c/li\u003e\n\u003cli\u003eGong F, Zhou Q, Gui C, Huang S, Qin Z. The Relationship Between the Serum Anion Gap and All-Cause Mortality in Acute Pancreatitis: An Analysis of the MIMIC-III Database. Int J Gen Med. 2021;Volume 14:531\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eCheng B, Li D, Gong Y, Ying B, Wang B. Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database. Dis Markers. 2020;2020:1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eHuang Y, Ao T, Zhen P, Hu M. Association between serum anion gap and 28-day mortality in critically ill patients with infective endocarditis: a retrospective cohort study from MIMIC IV database.\u003c/li\u003e\n\u003cli\u003eHatherill M. Correction of the anion gap for albumin in order to detect occult tissue anions in shock. Arch Dis Child. 2002;87:526\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eZhao X, Han J, Hu J, Qiu Z, Lu L, Xia C, et al. Association between albumin-corrected anion gap level and the risk of acute kidney injury in intensive care unit. Int Urol Nephrol. 2024;56:1117\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eHu T, Zhang Z, Jiang Y. Albumin corrected anion gap for predicting in-hospital mortality among intensive care patients with sepsis: A retrospective propensity score matching analysis. Clin Chim Acta. 2021;521:272\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eGao P, Min J, Zhong L, Shao M. Association between albumin corrected anion gap and all-cause mortality in critically ill patients with acute kidney injury: a retrospective study based on MIMIC-IV database. Ren Fail. 2023;45:2282708.\u003c/li\u003e\n\u003cli\u003eLi P, Shi L, Yan X, Wang L, Wan D, Zhang Z, et al. Albumin Corrected Anion Gap and the Risk of in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study. J Inflamm Res. 2023;Volume 16:2415\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eAydın SŞ, Aksakal E. Relationship Between Albumin-Corrected Anion Gap and Mortality in Hospitalized Heart Failure Patients. Cureus. 2023. https://doi.org/10.7759/cureus.45967.\u003c/li\u003e\n\u003cli\u003eSheng H, Lu J, Zhong L, Hu B, Sun X, Dong H. The correlation between albumin‐corrected anion gap and prognosis in patients with acute myocardial infarction. ESC Heart Fail. 2024;11:826\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eAustin PC, White IR, Lee DS, Van Buuren S. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can J Cardiol. 2021;37:1322\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eGravesteijn BY, Sewalt CA, Venema E, Nieboer D, Steyerberg EW, the CENTER-TBI Collaborators, et al. Missing Data in Prediction Research: A Five-Step Approach for Multiple Imputation, Illustrated in the CENTER-TBI Study. J Neurotrauma. 2021;38:1842\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eZhang T, Wang J, Li X. Association Between Anion Gap and Mortality in Critically Ill Patients with Cardiogenic Shock. Int J Gen Med. 2021;Volume 14:4765\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eMohr NM, Vakkalanka JP, Faine BA, Skow B, Harland KK, Dick-Perez R, et al. Serum anion gap predicts lactate poorly, but may be used to identify sepsis patients at risk for death: A cohort study. J Crit Care. 2018;44:223\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eIntegration of acid\u0026ndash;base and electrolyte disorders. N Engl J Med. 2015;372:389\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eNicholson JP, Wolmarans MR, Park GR. The role of albumin in critical illness. Br J Anaesth. 2000;85:599\u0026ndash;610.\u003c/li\u003e\n\u003cli\u003eJian L, Zhang Z, Zhou Q, Duan X, Xu H, Ge L. Association between albumin corrected anion gap and 30-day all-cause mortality of critically ill patients with acute myocardial infarction: a retrospective analysis based on the MIMIC-IV database. BMC Cardiovasc Disord. 2023;23:211.\u003c/li\u003e\n\u003cli\u003eZhong L, Xie B, Ji X-W, Yang X-H. The association between albumin corrected anion gap and ICU mortality in acute kidney injury patients requiring continuous renal replacement therapy. Intern Emerg Med. 2022;17:2315\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eYao C, Veleva T, Scott L, Cao S, Li L, Chen G, et al. Enhanced Cardiomyocyte NLRP3 Inflammasome Signaling Promotes Atrial Fibrillation. Circulation. 2018;138:2227\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eBalan AI, Halațiu VB, Scridon A. Oxidative Stress, Inflammation, and Mitochondrial Dysfunction: A Link between Obesity and Atrial Fibrillation. Antioxidants. 2024;13:117.\u003c/li\u003e\n\u003cli\u003eMohsin M, Zeyad H, Khalid H, Gapizov A, Bibi R, Kamani YG, et al. The Synergistic Relationship Between Atrial Fibrillation and Diabetes Mellitus: Implications for Cardiovascular and Metabolic Health. Cureus. 2023. https://doi.org/10.7759/cureus.45881.\u003c/li\u003e\n\u003cli\u003eWu Y, Kong X-J, Ji Y-Y, Fan J, Ji C-C, Chen X-M, et al. Serum electrolyte concentrations and risk of atrial fibrillation: an observational and mendelian randomization study. BMC Genomics. 2024;25:280.\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atrial fibrillation, Albumin-corrected anion gap, Intensive care unit, Mortality, retrospective analysis","lastPublishedDoi":"10.21203/rs.3.rs-5329034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5329034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCompared to the conventional anion gap, albumin-corrected anion gap (ACAG) offers a more precise measure of acid-base imbalance in patients than, providing superior prognostic insight. However, the prognostic relevance of ACAG in individuals of atrial fibrillation (AF) remains insufficiently explored. This research seeks to evaluate the correlation between ACAG levels and mortality risk in individuals with AF.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe identified individuals diagnosed with AF from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Participants were categorized into quartiles in accordance with their ACAG levels. The outcomes included 30 days and 365 days all-cause mortality. Cumulative survival across the quartiles was assessed using Kaplan\u0026ndash;Meier survival curves. We applied Cox regression and restricted cubic spline regression analyses to evaluate the correlation between ACAG levels and prognosis. Subgroup analyses and interaction assessments were applied to confirm the robustness of the findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2920 AF patients (54.93% male) were incorporated into the analysis. The 30 and 365-day mortality were 22.91% and 39.21%, respectively. Kaplan\u0026ndash;Meier survival curves demonstrated that elevated ACAG levels were significantly linked to increased mortality (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariate Cox proportional hazards analyses, increased ACAG independently predicted mortality at 30 days (adjusted hazard ratio [aHR], 1.04; 95% CI, 1.02\u0026ndash;1.05; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 365 days (aHR, 1.03; 95% CI, 1.02\u0026ndash;1.05; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) after adjusting for potential confounders. A positive relationship between rising ACAG levels and mortality risk, as showed by restricted cubic spline analysis. Subgroup analyses revealed no significant interactions (all interaction \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn individuals with AF, higher ACAG levels are related to a greater mortality risk at 30 and 365 days. These results show the potential value of ACAG as a prognostic indicator for patient stratification. Incorporating ACAG into clinical decision-making could support improved therapeutic strategies and enhance patient outcomes.\u003c/p\u003e","manuscriptTitle":"Association of elevated albumin-corrected anion gap with all-cause mortality risk in atrial fibrillation: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 16:27:01","doi":"10.21203/rs.3.rs-5329034/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-04T06:04:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T15:23:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T09:17:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327521433631481241910609052949177219262","date":"2024-11-16T20:42:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219630294291508077558246441698806814343","date":"2024-11-16T11:50:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-13T13:23:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-05T07:36:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-04T11:04:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-04T11:02:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-10-25T02:47:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcb99149-79ff-40c8-9f5b-a49aed9cf5e6","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:07:53+00:00","versionOfRecord":{"articleIdentity":"rs-5329034","link":"https://doi.org/10.1186/s12872-025-04518-w","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-01-27 15:57:26","publishedOnDateReadable":"January 27th, 2025"},"versionCreatedAt":"2024-12-02 16:27:01","video":"","vorDoi":"10.1186/s12872-025-04518-w","vorDoiUrl":"https://doi.org/10.1186/s12872-025-04518-w","workflowStages":[]},"version":"v1","identity":"rs-5329034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5329034","identity":"rs-5329034","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}