Association Between Anion Gap and Mortality in Critically Ill Patients with Atrial Fibrillation: A Propensity Score-Matched Study

Association Between Anion Gap and Mortality in Critically Ill Patients with Atrial Fibrillation: A Propensity Score-Matched 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 Between Anion Gap and Mortality in Critically Ill Patients with Atrial Fibrillation: A Propensity Score-Matched Study Rong Ding, Rui Su, Jia Liu, Shaolin Cai, Hui Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7534396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Serum anion gap (AG) is associated with mortality in critical illnesses, yet its prognostic significance specifically in intensive care unit (ICU) patients with atrial fibrillation (AF) remains unclear. This study aimed to investigate the associations between AG and mortality in this high-risk population. Methods We identified critically ill patients with AF from the MIMIC-IV database and stratified them by AG tertiles. Outcomes included 28-day and 365-day mortality. Multivariable Cox regression, propensity score matching (PSM), and restricted cubic splines were employed to examine the association between AG and mortality. Survival differences were evaluated using Kaplan-Meier analysis. Subgroup analyses assessed the consistency of associations, and ROC analysis quantified the incremental predictive value of AG. Results Among 14,635 eligible patients, elevated AG was significantly associated with increased mortality both before and after propensity score matching. In fully adjusted models, each 1-unit increase in AG was associated with a 5% higher risk of 28-day mortality (HR 1.05, 95% CI 1.04–1.05) and a 4% increased risk of 365-day mortality (HR 1.04, 95% CI 1.03–1.05). Patients in the highest AG tertile had substantially increased mortality risk compared to the lowest tertile (28-day HR 2.19, 95% CI 1.90–2.52; 365-day HR 1.98, 95% CI 1.74–2.25). Consistent dose-response relationships were observed across all analytical methods. Subgroup analyses presented in forest plots demonstrated the robustness of this association across various clinical strata. Additionally, AG significantly improved the predictive accuracy of conventional illness severity scores. Conclusions Elevated AG is independently associated with increased 28- and 365-day mortality in critically ill patients with AF. AG provides significant incremental prognostic value to established risk assessment tools such as SOFA and OASIS scores, and may serve as a readily available biomarker for improving risk stratification in this population. Anion gap Atrial fibrillation Mortality Intensive care unit Prognostic biomarker MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Atrial fibrillation (AF), the most prevalent cardiac arrhythmia in clinical practice[ 1 , 2 ], represents a major global cardiovascular burden with a 33% surge in cases over the past two decades[ 3 , 4 ]. This increase is associated with population aging, metabolic derangements involving insulin resistance, and improved survival of cardiovascular diseases[ 5 ]. The escalating epidemic now affects approximately 60 million individuals worldwide[ 6 ], conferring significantly increased risks of stroke, heart failure, myocardial infarction, dementia, chronic kidney disease, and mortality[ 7 ]. In critically ill patients, both new-onset and recurrent AF independently increase in-hospital mortality[ 8 ]. This mortality risk is further compounded by management challenges stemming from complex pathophysiology, heterogeneous patient populations, and the absence of ICU-specific risk stratification tools[ 9 ]. Acute metabolic derangements such as metabolic acidosis frequently manifest in this population. As a key biomarker of these derangements, serum anion gap (AG), calculated as the difference between unmeasured anions and cations in blood, serves as a fundamental metric for assessing acid-base status[ 10 ]. In critical care settings, elevated AG demonstrates significant prognostic value. Substantial evidence links this elevation to increased mortality in sepsis, acute cerebrovascular events, cardiovascular emergencies, and respiratory failure[ 11 – 15 ]. Furthermore, in surgical ICU populations, elevated AG correlates with postoperative delirium, acute kidney injury, and prolonged hospitalization [ 16 – 18 ]. As an inexpensive and accessible biomarker, AG objectively reflects underlying metabolic derangements including lactic acidosis and renal impairment-associated anion accumulation[ 19 , 20 ]. These derangements may adversely impact disease prognosis through potential mechanisms such as amplified inflammatory responses and insulin resistance[ 21 ]. While the association between AG and mortality is established in diverse critical illnesses, its prognostic significance specifically for critically ill patients with AF remains incompletely characterized. This study investigates the relationship between AG levels and mortality outcomes among ICU patients with AF using MIMIC-IV data. Materials and Methods Data source We conducted a retrospective analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1), an open-access database comprising de-identified ICU records from Beth Israel Deaconess Medical Center (Boston, MA, USA) spanning 2008–2019. Ethical approval, including a consent waiver, was granted by the MIT and BIDMC Institutional Review Boards. Author Rong Ding completed the required NIH online training in human research protection (Certification No. 64760223) to access the database. The study reporting adheres to the STROBE guidelines and the principles of the Declaration of Helsinki[ 22 ]. Study population This study included patients with AF who were admitted to the ICU for the first time. The diagnosis of AF was confirmed using the International Classification of Diseases (ICD)-9/10 codes (Supplementary Table 1). The exclusion criteria were as follows: (1) multiple hospital admissions, (2) death or discharge within the first 24 hours of ICU admission, and (3) absence of serum AG data on the first day of ICU admission. Demographic and laboratory variables Structured query language was used to gather data on patient demographics (age, sex, height, weight, and race); medical history (hypertension, diabetes, congestive heart failure, chronic pulmonary disease, cerebrovascular disease, renal disease, malignant cancer, and sepsis); initial laboratory parameters (white blood cell count, platelet count, hemoglobin, hematocrit, sodium, potassium, calcium, chloride, blood glucose, AG, international normalized ratio, bicarbonate, lactate, blood urea nitrogen and creatinine); medications (aspirin, clopidogrel, beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, amiodarone, and statins); treatments (vasoactive drugs and mechanical ventilation); illness severity scores (SAPS II, OASIS and SOFA); vital signs (heart rate, mean arterial pressure, and respiratory rate); and survival time. All vital signs, laboratory results, and scoring systems were collected within the first 24 hours of ICU admission, while treatments were assessed within 72 hours. Body Mass Index (BMI) was calculated as weight (kg) divided by the square height (m²). Missing values (< 15%) were imputed using Bayesian Ridge regression. Research variable and outcomes The primary exposure in this study was AG, a serum electrolyte-derived value calculated using the formula: AG = sodium - (chloride + bicarbonate). The primary endpoint was 28-day mortality, and the secondary endpoints were 365-day mortality. Deaths were recorded as events that occurred within a specific time frame of ICU admission. Statistical analysis In this study, critically ill patients with AF were categorized into three groups based on tertiles of AG levels: T1 (AG < 13), T2 (13 ≤ AG < 17), and T3 (AG ≥ 17). The baseline characteristics of each group were described. Normally distributed continuous variables are reported as mean ± SD; non-normally distributed continuous variables as median (IQR); categorical variables as n (%). Group comparisons used ANOVA or Kruskal-Wallis test for continuous variables (selected based on distribution normality), and chi-square or Fisher’s test for categorical variables, as appropriate. Cox multivariate regression analysis was employed to evaluate the association between AG and clinical outcomes, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Covariates were selected based on either ≥ 10% alteration in AG's HR or established clinical/epidemiological relevance. The final comprehensive model adjusted for: age, sex, BMI, diabetes, congestive heart failure, chronic pulmonary disease, renal disease, malignant cancer, sepsis, heart rate, mean arterial pressure, white blood cell count, hemoglobin, platelets, calcium, vasoactive agents, mechanical ventilation, beta-blockers, amiodarone, ACEI/ARB, SOFA score, and SAPS II. AG was analyzed both continuously and as tertile-based categories, with trend P-values calculated for categorical analyses. To further mitigate confounding, 1:1 nearest neighbor propensity score matching (PSM) with a caliper of 0.2 was implemented. Covariate balance was assessed using standardized mean differences (SMD). Cox proportional hazards regression was then applied to the matched cohort to examine the association between AG and mortality. Additionally, doubly robust estimation was performed by integrating the propensity score model with multivariate Cox regression[ 23 ], enhancing the robustness of our effect estimates for AG-mortality associations. Survival differences across AG tertiles were evaluated using Kaplan-Meier analysis with log-rank tests. The relationship between AG levels and mortality at 28 and 365 days was modeled with restricted cubic splines. Subgroup analyses with interaction testing assessed the consistency of the AG-mortality association across key clinical strata, with results presented in forest plots. All analyses were conducted before and after propensity score matching. We evaluated the incremental prognostic value of AG beyond conventional ICU scoring systems by comparing the area under the receiver operating characteristic (ROC) curve for models with and without AG. All analysis was performed using R 4.2.2 ( http://www.Rproject.org ; The R Foundation, Vienna, Austria) and the Free Statistics software (version 2.2; Beijing FreeClinical Medical Technology Co., Ltd, Beijing, China). Statistical significance was indicated by P < 0.05. Results Baseline characteristics of study subjects The final study cohort comprised 14,635 critically ill patients with AF stratified by AG tertiles (Fig. 1 ). Significant intergroup differences (p < 0.05) were observed across all baseline characteristics except BMI and hypertension prior to PSM, with escalating AG levels correlating with progressively adverse clinical profiles. Following 1:1 matching, the final cohort included 4,143 patients (1,381 per group) as shown in Fig. 2 . With the exception of AG-associated parameters (bicarbonate, BUN, chloride, creatinine, sodium, potassium, lactate), only hemoglobin levels, sepsis prevalence, SOFA scores, and mechanical ventilation requirements demonstrated significant intergroup differences (all p < 0.05) after matching. Full baseline characteristics before and after PSM are presented in Table 1 . Table 1 Baseline Characteristics of Critically Ill Patients with Atrial Fibrillation Before and After Propensity Score Matching. Covariate Original cohort Matched cohort T1 T2 T3 P T1 T2 T3 P n 3635 5884 5116 1381 1381 1381 Age, years 73.0 ± 10.7 75.2 ± 11.6 75.5 ± 12.0 < 0.001 74.3 ± 10.9 74.8 ± 11.5 75.3 ± 11.7 0.081 Sex (male), n (%) 2358 (64.9) 3448 (58.6) 2975 (58.2) < 0.001 846 (61.3) 825 (59.7) 798 (57.8) 0.175 BMI, kg/m² 29.3 ± 6.9 29.1 ± 7.4 29.2 ± 8.4 0.290 29.3 ± 7.1 29.0 ± 7.0 28.9 ± 8.8 0.491 Heart rate, beats/min 81.6 ± 13.2 83.8 ± 15.9 87.9 ± 17.7 < 0.001 83.8 ± 15.3 84.1 ± 15.6 83.0 ± 15.9 0.154 MAP, mmHg 76.3 ± 8.9 78.0 ± 10.5 77.2 ± 10.9 < 0.001 77.7 ± 10.0 76.8 ± 9.4 77.7 ± 10.8 0.045 Hemoglobin, g/dL 9.5 ± 1.9 10.1 ± 2.2 10.1 ± 2.4 < 0.001 9.9 ± 2.0 9.8 ± 2.0 10.0 ± 2.3 0.027 Platelet, 10 9 /L 155.0 ± 75.3 180.4 ± 92.7 187.3 ± 100.4 < 0.001 173.4 ± 87.2 175.3 ± 83.4 180.5 ± 87.4 0.083 WBC, 10 9 /L 13.3 (9.6, 17.5) 12.4 (9.2, 16.9) 13.9 (9.9, 19.2) < 0.001 14.0 ± 9.7 14.1 ± 7.5 13.9 ± 7.5 0.798 AG, mmol/L 10.6 ± 1.4 14.5 ± 1.1 20.4 ± 4.1 < 0.001 12.7 ± 1.9 19.2 ± 3.8 < 0.001 Bicarbonate, mmol/L 23.7 ± 3.8 22.7 ± 3.8 19.3 ± 4.7 < 0.001 24.3 ± 4.3 22.7 ± 3.6 20.4 ± 4.1 < 0.001 BUN, mmol/L 18.0 (14.0, 23.0) 22.0 (16.0, 31.0) 35.0 (22.0, 57.0) < 0.001 20.0 (15.0, 27.0) 22.0 (16.0, 32.0) 26.0 (18.0, 39.0) < 0.001 Calcium, mg/dL 8.1 ± 0.7 8.3 ± 0.8 8.1 ± 0.9 < 0.001 8.2 ± 0.7 8.2 ± 0.7 8.3 ± 0.8 0.191 Chloride, mmol/L 104.6 ± 4.9 102.4 ± 5.6 99.5 ± 6.6 < 0.001 103.9 ± 5.4 102.6 ± 5.6 100.0 ± 5.8 < 0.001 Creatinine, mg/ dL 0.9 (0.7, 1.1) 1.1 (0.8, 1.4) 1.6 (1.1, 2.7) < 0.001 0.9 (0.7, 1.2) 1.1 (0.8, 1.4) 1.2 (0.9, 1.7) < 0.001 Sodium, mmol/L 137.4 ± 3.8 137.1 ± 4.8 135.9 ± 5.7 < 0.001 137.3 ± 4.3 137.2 ± 4.8 136.3 ± 5.2 < 0.001 Potassium, mmol/L 4.5 ± 0.7 4.5 ± 0.7 4.8 ± 1.0 < 0.001 4.5 ± 0.9 4.5 ± 0.7 4.6 ± 0.8 0.033 Lactate, mmol/L 2.2 (1.6, 2.9) 2.0 (1.4, 2.9) 2.4 (1.6, 3.9) < 0.001 2.0 (1.5, 2.8) 2.0 (1.4, 2.9) 2.2 (1.5, 3.3) < 0.001 INR 1.4 (1.3, 1.6) 1.4 (1.2, 1.7) 1.5 (1.2, 2.1) < 0.001 1.4 (1.2, 1.6) 1.4 (1.2, 1.7) 1.4 (1.2, 1.8) 0.100 Hypertension, n (%) 2774 (76.3) 4503 (76.5) 3996 (78.1) 0.073 1050 (76.0) 1035 (74.9) 1054 (76.3) 0.673 Diabetes, n (%) 967 (26.6) 1778 (30.2) 1975 (38.6) < 0.001 423 (30.6) 405 (29.3) 400 (29) 0.602 Congestive Heart Failure, n (%) 1227 (33.8) 2593 (44.1) 2788 (54.5) < 0.001 608 (44.0) 616 (44.6) 598 (43.3) 0.787 Cerebrovascular disease, n (%) 582 (16.0) 1258 (21.4) 947 (18.5) < 0.001 263 (19.0) 269 (19.5) 296 (21.4) 0.247 Chronic pulmonary disease, n (%) 957 (26.3) 1703 (28.9) 1419 (27.7) 0.021 389 (28.2) 377 (27.3) 414 (30.0) 0.282 Renal disease, n (%) 536 (14.7) 1404 (23.9) 2004 (39.2) < 0.001 302 (21.9) 315 (22.8) 311 (22.5) 0.831 Malignant cancer, n (%) 356 (9.8) 736 (12.5) 668 (13.1) < 0.001 173 (12.5) 161 (11.7) 189 (13.7) 0.274 sepsis, n (%) 1669 (45.9) 3019 (51.3) 3255 (63.6) < 0.001 694 (50.3) 750 (54.3) 678 (49.1) 0.016 SAPSII, scores 37.2 ± 11.0 38.6 ± 11.6 45.4 ± 14.3 < 0.001 39.1 ± 12.1 38.1 ± 10.5 38.4 ± 10.8 0.068 OASIS, scores 32.0 ± 7.9 33.4 ± 8.7 36.6 ± 9.7 < 0.001 33.0 ± 8.3 33.1 ± 8.3 33.5 ± 8.5 0.214 SOFA, scores 4.7 ± 2.5 4.5 ± 2.8 6.0 ± 3.7 < 0.001 4.8 ± 2.8 4.5 ± 2.4 4.4 ± 2.8 0.002 Statins, n (%) 378 (10.4) 463 (7.9) 320 (6.3) < 0.001 131 (9.5) 112 (8.1) 100 (7.2) 0.097 Amiodarone, n (%) 1307 (36.0) 1365 (23.2) 1120 (21.9) < 0.001 328 (23.8) 319 (23.1) 298 (21.6) 0.377 Beta-Blockers, n (%) 2836 (78.0) 4109 (69.8) 3086 (60.3) < 0.001 952 (68.9) 993 (71.9) 971 (70.3) 0.232 ACEI/ARB, n (%) 2813 (77.4) 4038 (68.6) 2978 (58.2) < 0.001 932 (67.5) 966 (69.9) 949 (68.7) 0.378 Vasoactive agent, n (%) 1616 (44.5) 2149 (36.5) 2037 (39.8) < 0.001 524 (37.9) 537 (38.9) 470 (34.0) 0.020 Mechanical ventilation, n (%) 1863 (51.3) 2177 (37.0) 1842 (36.0) < 0.001 552 (40.0) 511 (37.0) 513 (37.1) 0.194 Mortality of 28-day, n (%) 257 (7.1) 858 (14.6) 1438 (28.1) < 0.001 157 (11.4) 219 (15.9) 239 (17.3) < 0.001 Mortality of 365-day, n (%) 647 (17.8) 1723 (29.3) 2395 (46.8) < 0.001 362 (26.2) 430 (31.1) 466 (33.7) < 0.001 Length of ICU stay, days 2.2 (1.3, 4.1) 2.7 (1.6, 4.9) 3.2 (1.9, 6.2) < 0.001 2.4 (1.4, 4.9) 2.6 (1.6, 4.8) 2.9 (1.8, 5.0) 0.001 Length of hospital stay, days 7.9 (5.7, 12.6) 8.3 (5.6, 13.8) 9.3 (5.6, 15.9) < 0.001 8.8 (6.0, 14.8) 8.1 (5.6, 13.2) 8.3 (5.1, 14.6) 0.002 AG, anion gap; BMI, body mass index; MAP, mean arterial pressure; SpO₂, percutaneous arterial oxygen saturation; WBC, white blood cell count; BUN, blood urea nitrogen; INR, international normalized ratio; SAPS II, Simplified Acute Physiology Score II; OASIS, Oxford Acute Severity of Illness Score; SOFA, Sequential Organ Failure Assessment; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ICU, intensive care unit Multivariable Cox Regression Multivariable Cox proportional hazards regression was used to assess the association between AG and mortality in critically ill patients with AF. When AG was analyzed as a continuous variable, it demonstrated significant associations with 28-day mortality in both unadjusted (HR 1.10, 95% CI 1.09–1.10; P < 0.001) and fully adjusted models (HR 1.05, 95% CI 1.04–1.05; P < 0.001). For categorical analysis by tertiles, the highest AG tertile (T3) showed substantially elevated 28-day mortality risk versus the lowest tertile (T1), yielding unadjusted HR 4.56 (95% CI 3.99–5.21; P < 0.001) and fully adjusted HR 2.19 (95% CI 1.90–2.52; P < 0.001). Analyses for 365-day mortality consistently mirrored this trend (Table 2 ). Table 2 Multivariate cox regression analyses for 28-day and 365-day mortality. Variable Model 1 Model 2 Model 3 HR (95%CI) P -value HR (95%CI) P -value HR (95%CI) P -value 28-day mortality AG continuous 1.10 (1.09 ~ 1.10) < 0.001 1.10 (1.09 ~ 1.10) < 0.001 1.05 (1.04 ~ 1.05) < 0.001 AG tertiles T1 1(Ref) 1(Ref) 1(Ref) T2 2.15 (1.87 ~ 2.48) < 0.001 2.00 (1.74 ~ 2.30) < 0.001 1.61 (1.39 ~ 1.85) < 0.001 T3 4.56 (3.99 ~ 5.21) < 0.001 4.20 (3.68 ~ 4.80) < 0.001 2.19 (1.90 ~ 2.52) < 0.001 Trend test < 0.001 < 0.001 < 0.001 365-day mortality AG continuous 1.09 (1.08 ~ 1.09) < 0.001 1.09 (1.08 ~ 1.09) < 0.001 1.04 (1.03 ~ 1.05) < 0.001 AG tertiles T1 1(Ref) 1(Ref) 1(Ref) T2 1.78 (1.63 ~ 1.95) < 0.001 1.65 (1.51 ~ 1.81) < 0.001 1.32 (1.21 ~ 1.45) < 0.001 T3 3.32 (3.05 ~ 3.62) < 0.001 3.06 (2.81 ~ 3.34) < 0.001 1.72 (1.56 ~ 1.89) < 0.001 Trend test < 0.001 < 0.001 < 0.001 Model 1: No adjusted. Model 2: Adjusted for age, sex, BMI. Model 3: Adjusted for age, sex, BMI, diabetes, congestive heart failure, chronic pulmonary disease, renal disease, malignant cancer, sepsis, heart rate, mean arterial pressure, white blood cell, hemoglobin, platelets, calcium, vasoactive agent, mechanical ventilation, beta-blockers, amiodarone, ACEI/ARB, SOFA, and SAPSII. AG: T1 (AG < 13), T2 (13 ≤ AG < 17, T3 (AG ≥ 17). HR, hazard ratio; CI, confidential interval; BMI, body mass index; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; SOFA, Sequential Organ Failure Assessment; SAPS II, Simplified Acute Physiology Score II Propensity Score Matching and Doubly Robust Analysis Consistent with the multivariable regression analysis, PSM demonstrated significant associations between AG and mortality. Specifically, each 1-unit AG increase was associated with 5% higher 28-day mortality risk (HR 1.05, 95% CI 1.03–1.07), and T3 patients showed 60% increased mortality versus T1 (HR 1.60, 1.31–1.96). Doubly robust estimation produced comparable risk estimates for continuous AG (28-day HR 1.04, 1.03–1.06) and T3 comparisons (HR 1.75, 1.42–2.15). Qualitatively consistent hazard ratios were observed across multivariable, PSM, and doubly robust models for all endpoints (all P < 0.001) (Table 3 ). Table 3 Primary outcome analysis with different models. Method Variable 28-Day Mortality 365-Day Mortality HR (95%CI) P value HR (95%CI) P value Multivariate Continuous 1.05 (1.04 ~ 1.05) < 0.001 1.04 (1.03 ~ 1.05) < 0.001 T1 Reference 1(Ref) T2 1.61 (1.39 ~ 1.85) < 0.001 1.32 (1.21 ~ 1.45) < 0.001 T3 2.19 (1.90 ~ 2.52) < 0.001 1.72 (1.56 ~ 1.89) < 0.001 Matched Continuous 1.05 (1.03 ~ 1.07) < 0.001 1.04 (1.03 ~ 1.06) < 0.001 T1 Reference 1(Ref) T2 1.44 (1.18 ~ 1.77) < 0.001 1.24 (1.08 ~ 1.43) 0.002 T3 1.60 (1.31 ~ 1.96) < 0.001 1.38 (1.20 ~ 1.58) < 0.001 Doubly robust with all covariates Continuous 1.04 (1.03 ~ 1.06) < 0.001 1.03 (1.02 ~ 1.05) < 0.001 T1 Reference 1(Ref) T2 1.57 (1.27 ~ 1.93) < 0.001 1.32 (1.14 ~ 1.52) < 0.001 T3 1.75 (1.42 ~ 2.15) < 0.001 1.38 (1.20 ~ 1.58) < 0.001 AG: T1 (AG < 13), T2 (13 ≤ AG < 17, T3 (AG ≥ 17). HR, hazard ratio; CI, confidential interval Kaplan-Meier Survival Analysis Figure 3 presents Kaplan-Meier survival curves for 28-day and 365-day mortality, categorized by AG tertiles, both before and after propensity score matching. Notably, significant differences in survival were observed across all tertiles (log-rank P < 0.0001), with T3 consistently exhibiting the lowest survival probabilities at both time points. The survival disparity between T1, T2, and T3 remained substantial even after matching, indicating a persistent ordinal relationship in survival outcomes before and after adjustment. Restricted Cubic Spline Analysis Figure 4 displays restricted cubic spline curves of AG-mortality relationships. Multivariable-adjusted analyses (Panels A-B) showed nonlinear positive associations between AG levels and 28-day/365-day mortality risk. After PSM (Panels C-D), these relationships transitioned to linear positive trajectories for both timepoints. Throughout the clinically relevant AG range, higher levels consistently corresponded to elevated mortality risk. Subgroup analysis Subgroup analyses evaluated the robustness of the association between AG levels and 28-day and 365-day mortality across various clinical factors, including age, sex, congestive heart failure (CHF), chronic pulmonary disease (CPD), diabetes, and renal disease (Fig. 5 ). Consistent positive associations were observed across all subgroups in both pre- and post-matching analyses. Significant interactions ( P for interaction < 0.05) were noted in subgroups such as CHF, CPD, and diabetes, among others, yet the effect sizes between groups remained similar, with no substantial change in the direction or consistency of the overall association. ROC curve analysis The addition of AG significantly improved the predictive performance of both SOFA and OASIS scores for 28-day and 365-day mortality, as evidenced by increased AUC values (all p < 0.001, Fig. 6 ). Discussion This retrospective cohort study of critically ill patients with AF shows an independent association between elevated AG levels and increased mortality. Using the MIMIC-IV database, we found that both continuous and tertile-based AG values are significantly associated with 28- and 365-day mortality. This association was consistently observed across various analytical approaches, including multivariable Cox regression, propensity score matching, and doubly robust estimation. A clear dose-response relationship was evident in Kaplan-Meier, restricted cubic spline, and subgroup analyses. Moreover, incorporating AG significantly enhanced the prognostic accuracy of conventional ICU illness severity scores. AF is a growing global health concern[ 6 ]. Its incidence continues to rise, contributing significantly to disability and healthcare burdens worldwide[ 24 ]. In critically ill patients, AF is often not an isolated event, but rather coexists with conditions such as sepsis, diabetes, hepatic and renal dysfunction, and respiratory impairment[ 25 , 26 ]. These comorbidities collectively disrupt the body's ability to maintain pH and homeostasis[ 27 ]. This pathophysiological interplay worsens organ function, leading to increased mortality and prolonged ICU stays. Serum AG is calculated from routine electrolyte measurements and reflects the balance of unmeasured anions[ 28 ]. Elevated AG levels typically indicate metabolic acidosis caused by the accumulation of organic acids due to either impaired excretion or overproduction[ 29 ]. This elevation is often associated with increased levels of lactic acid[ 30 ], as well as elevations in β-hydroxybutyrate, acetoacetate, phosphate, sulfate, and other organic anions[ 31 ]. Moreover, the decline in glomerular filtration rate and renal tubular damage associated with kidney disease can collectively impair acid elimination, leading to a rise in the AG[ 32 ]. Although some controversy exists, with one meta-analysis not recommending its isolated use for mortality risk assessment[ 33 ], the majority of studies have established a significant association between elevated AG and adverse outcomes in critically ill patients. Li et al. demonstrated that an initial serum AG > 16 mmol/L after ICU admission was associated with increased mortality in this population[ 34 ]. Furthermore, a recent study developed a prognostic model for in-hospital mortality in patients with acute myocardial infarction and AF, in which AG was identified as one of seven key predictive variables[ 35 ]. Several pathophysiological mechanisms may explain this association. First, elevated AG commonly stems from lactate accumulation, which itself typically arises from systemic hypoperfusion and tissue hypoxia[ 36 , 37 ]. These conditions can exacerbate myocardial ischemia and electrical instability, thereby perpetuating AF and compromising cardiac output[ 38 – 40 ]. Second, elevated AG is closely associated with systemic inflammation and oxidative stress[ 41 ]. Acidosis promotes the production of interleukin-1β (IL-1β) and activates the complement system, thereby amplifying inflammatory responses and inducing myocardial remodeling[ 42 , 43 ]. Previous studies have confirmed that increased serum AG levels are correlated with elevated inflammatory biomarkers, including C-reactive protein and white blood cell count[ 44 ]. Third, disrupted acid-base balance impairs electrolyte homeostasis, contributing to the maintenance and progression of AF through electrical remodeling[ 45 ]. This process involves changes in the ion channel protein expression, changes in the ion channel protein expression, and the development of structural fibrosis[ 46 – 48 ]. Fourth, elevated serum AG may also influence insulin resistance (IR)[ 49 ]. Metabolic acidosis has been demonstrated to reduce insulin receptor binding efficiency and impair insulin signaling sensitivity, thereby aggravating IR[ 49 ]. Even in non-diabetic patients, the TyG index, a marker of insulin resistance, maintains an independent association with increased mortality in critically ill AF patients[ 50 ]. This pathological state accelerates disordered glucose metabolism and promotes lipolysis, increasing endogenous acid load and establishing a vicious cycle between metabolic acidosis and IR[ 51 ]. The strong association between elevated AG and mortality in critically ill AF patients underscores its clinical relevance. As an inexpensive and readily available marker, AG enhances risk stratification in this population. It may serve as an early indicator, prompting clinicians to identify and address underlying causes of metabolic acidosis, including occult hypoperfusion, evolving sepsis, or deteriorating renal function. Incorporating AG into established ICU prognostic scores could improve their accuracy and support more personalized treatment approaches. Future studies should examine whether AG-guided management strategies, such optimizing tissue perfusion or correcting acid-base disturbances, improve outcomes in these patients. There are several limitations to our study. First, the use of a single-center database may limit the generalizability of our findings to other populations. Second, the retrospective design is inherently prone to residual confounding due to unmeasured or unknown factors, despite adjustment for numerous covariates. Third, we only used the initial AG measurement after ICU admission and did not capture its dynamic changes, which may better reflect clinical progression and patient outcomes. Therefore, future prospective studies are essential to validate our findings and further investigate this issue. Conclusions Our study demonstrates that elevated AG is significantly associated with increased mortality in critically ill patients with AF. These findings support its utility as an accessible and prognostic biomarker for risk stratification in this high-risk population. Abbreviations AG Anion gap ICU Intensive Care Unit AF Atrial fibrillation MIMIC-IV Medical Information Mart for Intensive Care IV STROBE Strengthening the Reporting of Observational Studies in Epidemiology PSM Propensity score matching SAPS II Simplified Acute Physiology Score II OASIS Oxford Acute Severity of Illness Score SOFA Sequential Organ Failure Assessment BMI Body Mass Index ACEI Angiotensin-Converting Enzyme Inhibitor ARB Angiotensin II Receptor Blocker HR Hazard ratio CI Confidence interval SD Standard Deviation IQR Interquartile Range SMD Standardized mean difference ROC Receiver operating characteristic AUC Area Under the Curve BUN Blood Urea Nitrogen CHF Congestive heart failure CPD Chronic pulmonary disease IR Insulin resistance Declarations Ethics approval and consent to participate Approval for using the MIMIC-IV database was obtained from the IRBs of both MIT and BIDMC. The MIMIC database's existing ethical approval applies to the data in this study, eliminating the requirement for additional ethical approval or informed consent. Consent for publication Not applicable. Data Sharing Statement Data were sourced from the MIMIC-IV database ( https://mimic.physionet.org/ ). The corresponding author will provide the datasets used and analyzed during the current work upon reasonable request. Competing interests The authors declare that they have no competing interests. Clinical trial number Not applicable. Funding This work was supported by The Hospital Fund Project of the Second Hospital of Shanxi Medical University (NO. 202404-10). Authors' contributions The study was designed by RD and HZ. RD performed data collection, analysis, and wrote the initial manuscript. RS, JL, and SC contributed to data analysis and figure preparation. HZ participated in writing and editing the manuscript. All authors reviewed and approved the final version of the manuscript. Acknowledgments The authors thank the Laboratory for Computational Physiology at MIT (LCP-MIT) for providing access to the MIMIC-IV database. We acknowledge Jie Liu (People’s Liberation Army General Hospital), Qilin Yang (The Second Affiliated Hospital of Guangzhou Medical University), and Haibo Li (Fujian Maternity and Child Health Hospital) for their contributions to study design and statistical analysis. References Ko D, Chung MK, Evans PT, Benjamin EJ, Helm RH (2025) Atrial fibrillation: a review. JAMA 333:329 Brundel BJJM, Ai X, Hills MT, Kuipers MF, Lip GYH, De Groot NMS (2022) Atrial fibrillation. Nat Rev Dis Primers 8:21 Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C et al (2021) Corrigendum to: 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J 42:4194–4194 Lippi G, Sanchis-Gomar F, Cervellin G (2021) Global epidemiology of atrial fibrillation: an increasing epidemic and public health challenge. Int J Stroke 16:217–221 Joglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM et al (2024) 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation. J Am Coll Cardiol 83:109–279 Elliott AD, Middeldorp ME, Van Gelder IC, Albert CM, Sanders P (2023) Epidemiology and modifiable risk factors for atrial fibrillation. Nat Rev Cardiol 20:404–417 Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B et al (2017) 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Rev Esp Cardiol (Engl Ed) 70:50 Shaver CM, Chen W, Janz DR, May AK, Darbar D, Bernard GR et al (2015) Atrial fibrillation is an independent predictor of mortality in critically ill patients*. Crit Care Med 43:2104–2111 Sibley S, Bedford J, Wetterslev M, Johnston B, Garside T, Kanji S et al (2025) Atrial fibrillation in critical illness: state of the art. Intensive Care Med 51:904–916 Kraut JA, Madias NE (2007) Serum anion gap: its uses and limitations in clinical medicine. Clin J Am Soc Nephrol 2:162–174 Lou Z, Zeng F, Huang W, Xiao L, Zou K, Zhou H (2024) Association between the anion-gap and 28-day mortality in critically ill adult patients with sepsis: a retrospective cohort study. Med (Baltim) 103:e39029 Jhou H-J, Chen P-H, Yang L-Y, Chang S-H, Lee C-H (2021) Plasma anion gap and risk of In-hospital mortality in patients with acute ischemic stroke: analysis from the MIMIC-IV database. J Pers Med 11:1004 Li M, Li C, Wang J, Yuan Q (2025) The association between anion gap and prognosis in patients myocardial infarction with congestive heart failure: a retrospective analysis of the MIMIC-IV database. Int J Emerg Med 18:33 Chen J, Dai C, Yang Y, Wang Y, Zeng R, Li B et al (2022) The association between anion gap and in-hospital mortality of post-cardiac arrest patients: a retrospective study. Sci Rep 12:7405 Qu J, Tang X, Cheng Y, Xiong W, Zhao Y (2025) Association between albumin corrected anion gap and 28-day all‐cause mortality in patients with acute respiratory failure in ICU: a retrospective study based on the MIMIC‐IV database. Clin Respir J 19:e70100 Wang J, Zhong H, Chen L, Ding H-C, Lu Z-J, Wang B-S et al (2025) Association between anion gap and postoperative delirium in patients undergoing open heart surgery. Front Cardiovasc Med 12:1592161 Pan Q, Mu Z, Li Y, Gu C, Liu T, Wang B et al (2023) The association between serum anion gap and acute kidney injury after coronary artery bypass grafting in patients with acute coronary syndrome. BMC Cardiovasc Disord 23:542 Sun T, Cai C, Shen H, Yang J, Guo Q, Zhang J et al (2020) Anion gap was associated with inhospital mortality and adverse clinical outcomes of coronary care unit patients. Piccione G, editor. Biomed Res Int. ;2020:4598462 Kamel KS, Oh MS, Halperin ML (2020) L-lactic acidosis: pathophysiology, classification, and causes; emphasis on biochemical and metabolic basis. Kidney Int 97:75–88 Zanza C, Facelli V, Romenskaya T, Bottinelli M, Caputo G, Piccioni A et al (2022) Lactic acidosis related to pharmacotherapy and human diseases. Pharmaceuticals 15:1496 Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL (2017) Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis 11:215–225 Cuschieri S (2019) The STROBE guidelines. Saudi J Anaesth 13:31 McCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF (2013) A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med 32:3388–3414 Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ et al (2014) Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation 129:837–847 Kuipers S, Klouwenberg PMK, Cremer OL (2014) Incidence, risk factors and outcomes of new-onset atrial fibrillation in patients with sepsis: a systematic review. Crit Care 18:688 Oltman CG, Kim TP, Lee JWY, Lupu JD, Zhu R, Moussa ID (2024) Prevalence, management, and comorbidities of adults with atrial fibrillation in the United States, 2019 to 2023. JACC: Adv. ;3:101330 Achanti A, Szerlip HM (2023) Acid-base disorders in the critically ill patient. Clin J Am Soc Nephrol 18:102–112 Fenves AZ, Emmett M (2021) Approach to patients with high anion gap metabolic acidosis: core curriculum 2021. Am J Kidney Dis 78:590–600 Kraut JA, Madias NE (2010) Metabolic acidosis: pathophysiology, diagnosis and management. Nat Rev Nephrol 6:274–285 Kraut JA, Nagami GT (2013) The serum anion gap in the evaluation of acid-base disorders: what are its limitations and can its effectiveness Be improved? Clin J Am Soc Nephrol 8:2018–2024 Huang Y, Ao T, Zhen P, Hu M (2024) Association between serum anion gap and 28-day mortality in critically ill patients with infective endocarditis: a retrospective cohort study from MIMIC IV database. BMC Cardiovasc Disord 24:585 Abramowitz MK, Hostetter TH, Melamed ML (2012) The serum anion gap is altered in early kidney disease and associates with mortality. Kidney Int 82:701–709 Glasmacher SA, Stones W (2015) Anion gap as a prognostic tool for risk stratification in critically ill patients – a systematic review and meta-analysis. BMC Anesthesiol 16:68 Li R, Jin X, Ren J, Deng G, Li J, Gao Y et al (2022) Relationship of admission serum anion gap and prognosis of critically ill patients: a large multicenter cohort study. Zeng X, editor. Dis Markers. ;2022:1–10 Tan W, Duan R, Zeng C, Yang Z, Dai L, Xu T et al (2025) A nomogram for predicting In-hospital mortality in critically ill patients with myocardial infarction and atrial fibrillation. Nurs Crit Care 30:e70116 Huang Y, Yin Z, Han W (2025) Anion gap associated with 28-days all-cause mortality in acute cholangitis patients admitted to the intensive care unit in MIMIC-IV database: a retrospective cohort study. Front Med 12:1591096 Vichot AA, Rastegar A (2014) Use of anion gap in the evaluation of a patient with metabolic acidosis. Am J Kidney Dis 64:653–657 Allen DG, Orchard CH (1987) Myocardial contractile function during ischemia and hypoxia. Circ Res 60:153–168 Orchard CH, Kentish JC (1990) Effects of changes of pH on the contractile function of cardiac muscle. Am J Physiol Cell Physiol 258:C967–C981 Andersen LW, Holmberg MJ, Doherty M, Khabbaz K, Lerner A, Berg KM et al (2015) Postoperative lactate levels and hospital length of stay after cardiac surgery. J Cardiothorac Vasc Anesth 29:1454–1460 Balan AI, Halațiu VB, Scridon A (2024) Oxidative stress, inflammation, and mitochondrial dysfunction: a link between obesity and atrial fibrillation. Antioxidants 13:117 Erra Díaz F, Dantas E, Geffner J (2018) Unravelling the interplay between extracellular acidosis and immune cells. Mediators Inflamm 2018:1–11 Nath KA, Hostetter MK, Hostetter TH (1985) Pathophysiology of chronic tubulo-interstitial disease in rats. Interactions of dietary acid load, ammonia, and complement component C3. J Clin Invest 76:667–675 Farwell WR, Taylor EN (2010) Serum anion gap, bicarbonate and biomarkers of inflammation in healthy individuals in a national survey. Can Med Assoc J 182:137–141 Rafaqat S, Rafaqat S, Khurshid H, Rafaqat S (2022) Electrolyte’s imbalance role in atrial fibrillation: pharmacological management. Int J Arrhythmia 23:15 Deo M, Weinberg SH, Boyle PM (2017) Calcium dynamics and cardiac arrhythmia. Clin Med Insights: Cardiol 11:117954681773952 Jiang Y-Y, Hou H-T, Yang Q, Liu X-C, He G-W (2017) Chloride channels are involved in the development of atrial fibrillation – a transcriptomic and proteomic study. Sci Rep 7:10215 Ravens U, Cerbai E (2008) Role of potassium currents in cardiac arrhythmias. Europace 10:1133–1137 Han Y-N, Xiong C-Y, Wang Y-X, Yuan J-L, Li L, Xiao Z-X (2025) Association between serum anion gap and all-cause mortality in critically ill patients with diabetic kidney disease: analysis of the MIMIC-IV database. D’Addio F, editor. PLOS One. ;20:e0329269 Ding R, Cheng E, Wei M, Pan L, Ye L, Han Y et al (2025) Association between triglyceride–glucose index and mortality in critically ill patients with atrial fibrillation: a retrospective cohort study. Cardiovasc Diabetol 24:138 Kaimori J-Y, Sakaguchi Y, Kajimoto S, Asahina Y, Oka T, Hattori K et al (2022) Diagnosing metabolic acidosis in chronic kidney disease: importance of blood pH and serum anion gap. Kidney Res Clin Pract 41:288–297 Supplementary Files COIallauthorsform.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7534396","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524037487,"identity":"e556143c-8cae-4e77-b503-6fedcb37c2da","order_by":0,"name":"Rong Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDCCw1CaH0ozNhCtRbKBaC0HoLTBAWK18B3nPfy6ouKO3ebzZ0w38zDYyG44wPzsAT4tkof50izPnHmWvO1GWtptHoY04w0H2MwN8GkxOMxjZtjYdjjZ7AbzMaCWw4kbDvCwSRClxbj/YBtQy3+itBg/BGqxM2BIBtlygLAWSaAtjA1nDidIAP1yc45BsvHMw2xmeLXwnT9j/LGh4rA9f/8ZsxtvKuxk+443P8OrBQjAzkhsgLgTiJkJqAcp+QAk7AmrGwWjYBSMghELADh7TgBexHU5AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-9265-8330","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Ding","suffix":""},{"id":524037488,"identity":"cac6c084-b038-467f-b28c-1d59e8b491b6","order_by":1,"name":"Rui Su","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Su","suffix":""},{"id":524037489,"identity":"9c72c5d8-94c0-48d0-b848-7a545431dccd","order_by":2,"name":"Jia Liu","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":524037490,"identity":"77501380-a27b-444b-acc5-28ea03abfe71","order_by":3,"name":"Shaolin Cai","email":"","orcid":"","institution":"Jinan University First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaolin","middleName":"","lastName":"Cai","suffix":""},{"id":524037491,"identity":"81aa1831-d43f-43b8-8b25-2dbf6e485be3","order_by":4,"name":"Hui Zhao","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-09-04 09:22:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7534396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7534396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93726855,"identity":"23939093-7c76-4793-a75c-89cedc6b6b70","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8752,"visible":true,"origin":"","legend":"","description":"","filename":"iaemIAEMD2501391.xml","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/57029181b5fffd10b3f5b0d1.xml"},{"id":93726851,"identity":"1fec5032-bf17-473f-aed9-2450410b3f55","added_by":"auto","created_at":"2025-10-17 02:05:19","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":972,"visible":true,"origin":"","legend":"","description":"","filename":"IAEMD250139133941.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/5fdce8344283bba2a30832bd.xml"},{"id":93726862,"identity":"4a6a176f-2970-416b-8769-1db7bd810071","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":909,"visible":true,"origin":"","legend":"","description":"","filename":"IAEMD2501391Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/438a35cba62396d05203ad91.xml"},{"id":93726868,"identity":"26d3a07a-5cc8-47cd-b7a8-e8fef9208e90","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157410,"visible":true,"origin":"","legend":"","description":"","filename":"IAEMD25013910enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/c52de76d9fe8f06d76918b97.xml"},{"id":93726841,"identity":"dc4c9650-bd12-429e-bc09-cde21029ab75","added_by":"auto","created_at":"2025-10-17 02:05:19","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":261428,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/e4245bde0fb0637303959566.jpeg"},{"id":93726852,"identity":"9b4c6ea9-351a-407d-85a2-fdcf94c2ebcb","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":574842,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/d7d53bbb0a1ef0ac25187018.jpeg"},{"id":93726866,"identity":"f6e77036-aae3-4ab2-8aef-0eaa56cdda4c","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":330046,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/7939454f72fd03b0e4ced8df.jpeg"},{"id":93726850,"identity":"6b2645a4-11a8-4682-ac47-71e33adf2893","added_by":"auto","created_at":"2025-10-17 02:05:19","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":239430,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/729eefa97cb15b71429ddc21.jpeg"},{"id":93726849,"identity":"481b16a0-5f18-4f9d-af76-c29ce2a0fe2d","added_by":"auto","created_at":"2025-10-17 02:05:19","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":272968,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/c202ab7d99fbf0b77e2d24a5.jpeg"},{"id":93726858,"identity":"06cbabed-a467-4655-9b76-3f7e739ba8d9","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":352282,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/a17fe418ca680bcfbf2bbcc4.jpeg"},{"id":93726848,"identity":"11c789fa-e928-4cb3-bf81-8f23c836c20a","added_by":"auto","created_at":"2025-10-17 02:05:19","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44947,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/29724c6e5aa70c7eb6759ebb.png"},{"id":93726865,"identity":"5c053adb-dc49-486d-8c36-0a3b547c1f4c","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80854,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/366745c04dcd7e515cb7c0b1.png"},{"id":93726854,"identity":"cd36590a-5238-4e27-b251-be7f931e5425","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54675,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/69f0f04b92a231b785065636.png"},{"id":93730618,"identity":"efee4f99-5c59-4d8c-8326-82d348ee30e2","added_by":"auto","created_at":"2025-10-17 02:21:20","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45949,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/1b11d0cafbb2150ebe82c279.png"},{"id":93728887,"identity":"8ee5c270-f7e8-420c-97ac-c2e6aca3b532","added_by":"auto","created_at":"2025-10-17 02:13:20","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59410,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/52d38add0cd7da2ab93c0a3f.png"},{"id":93730620,"identity":"307d907f-36f4-4f83-bb80-5c52357f8441","added_by":"auto","created_at":"2025-10-17 02:21:20","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70615,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/ad633c225f1ab494307837f5.png"},{"id":93732276,"identity":"524efc74-154c-4a27-8ac3-0b731ea7e3b8","added_by":"auto","created_at":"2025-10-17 02:29:20","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156304,"visible":true,"origin":"","legend":"","description":"","filename":"IAEMD25013910structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/ad250f103e7ff6544eb91a41.xml"},{"id":93726864,"identity":"11baa2f4-b1e4-4436-8454-7938c708b836","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163713,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/4527f60c1add8ffb89e7606b.html"},{"id":93726863,"identity":"a207e799-71b0-46dd-ab63-fce0bcd9449a","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":376549,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the Study Cohort. AF, Atrial Fibrillation; ICU, Intensive Care Unit; MIMIC-IV, Medical Information Mart for Intensive Care IV; AG, Anion Gap\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/6275727f4565211bc0ed9d97.png"},{"id":93728886,"identity":"ce2ba30e-8c77-4021-9db2-5af2dcd11fe7","added_by":"auto","created_at":"2025-10-17 02:13:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":490129,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized Mean Differences (SMD) of Covariates Between Original and Matched Cohorts.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/35f4353bb6e20c69a26a3fc6.png"},{"id":93726847,"identity":"ee6cad50-aa79-447d-be19-1766cdc7701f","added_by":"auto","created_at":"2025-10-17 02:05:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":637434,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for 28-day and 365-day mortality: multivariable-adjusted analysis (A, B) and propensity score-matched analysis (C, D). AG: T1 (AG\u0026lt;13), T2 (13 ≤ AG\u0026lt;17, T3 (AG ≥ 17).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/b38e517610d9de973150e7ee.png"},{"id":93728890,"identity":"caabdb27-f29d-4281-bf04-602653700db4","added_by":"auto","created_at":"2025-10-17 02:13:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":411228,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curves for the association between AG and mortality: multivariable-adjusted analysis for 28-day (A) and 365-day (B) mortality, and propensity score-matched analysis for 28-day (C) and 365-day (D) mortality.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/9f21fdf994d3b7b9a41b9b50.png"},{"id":93726845,"identity":"11a7389f-675e-4201-964c-327f0a9c60d5","added_by":"auto","created_at":"2025-10-17 02:05:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":592971,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between AG and 28-day and 365-day mortality. (A) Multivariable-adjusted model. (B) Propensity score-matched model.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/7eb8e0d5d1a7b749d2344bfc.png"},{"id":93726867,"identity":"346c20df-778d-41d0-acd5-7a2937d46002","added_by":"auto","created_at":"2025-10-17 02:05:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":555739,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of the incremental value of anion gap (AG) to the OASIS and SOFA scores for predicting mortality. (A, C): 28-day mortality; (B, D): 365-day mortality.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/af44bd285f921006308d01d5.png"},{"id":100363724,"identity":"3d3d7db7-30bb-4137-9827-331bbfeabda6","added_by":"auto","created_at":"2026-01-16 07:51:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4286289,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/e7127dc4-e51c-41a2-958c-a255270d6910.pdf"},{"id":93728893,"identity":"7fb6851e-23d5-4e0e-8589-cd708de9d177","added_by":"auto","created_at":"2025-10-17 02:13:21","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":38748318,"visible":true,"origin":"","legend":"","description":"","filename":"COIallauthorsform.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7534396/v1/6fc394cb7110998639d1b4e8.pdf"}],"financialInterests":"","formattedTitle":"Association Between Anion Gap and Mortality in Critically Ill Patients with Atrial Fibrillation: A Propensity Score-Matched Study","fulltext":[{"header":"Background","content":"\u003cp\u003eAtrial fibrillation (AF), the most prevalent cardiac arrhythmia in clinical practice[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], represents a major global cardiovascular burden with a 33% surge in cases over the past two decades[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This increase is associated with population aging, metabolic derangements involving insulin resistance, and improved survival of cardiovascular diseases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The escalating epidemic now affects approximately 60\u0026nbsp;million individuals worldwide[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], conferring significantly increased risks of stroke, heart failure, myocardial infarction, dementia, chronic kidney disease, and mortality[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn critically ill patients, both new-onset and recurrent AF independently increase in-hospital mortality[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This mortality risk is further compounded by management challenges stemming from complex pathophysiology, heterogeneous patient populations, and the absence of ICU-specific risk stratification tools[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Acute metabolic derangements such as metabolic acidosis frequently manifest in this population. As a key biomarker of these derangements, serum anion gap (AG), calculated as the difference between unmeasured anions and cations in blood, serves as a fundamental metric for assessing acid-base status[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn critical care settings, elevated AG demonstrates significant prognostic value. Substantial evidence links this elevation to increased mortality in sepsis, acute cerebrovascular events, cardiovascular emergencies, and respiratory failure[\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, in surgical ICU populations, elevated AG correlates with postoperative delirium, acute kidney injury, and prolonged hospitalization [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As an inexpensive and accessible biomarker, AG objectively reflects underlying metabolic derangements including lactic acidosis and renal impairment-associated anion accumulation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These derangements may adversely impact disease prognosis through potential mechanisms such as amplified inflammatory responses and insulin resistance[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile the association between AG and mortality is established in diverse critical illnesses, its prognostic significance specifically for critically ill patients with AF remains incompletely characterized. This study investigates the relationship between AG levels and mortality outcomes among ICU patients with AF using MIMIC-IV data.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective analysis using the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1), an open-access database comprising de-identified ICU records from Beth Israel Deaconess Medical Center (Boston, MA, USA) spanning 2008\u0026ndash;2019. Ethical approval, including a consent waiver, was granted by the MIT and BIDMC Institutional Review Boards. Author Rong Ding completed the required NIH online training in human research protection (Certification No. 64760223) to access the database. The study reporting adheres to the STROBE guidelines and the principles of the Declaration of Helsinki[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThis study included patients with AF who were admitted to the ICU for the first time. The diagnosis of AF was confirmed using the International Classification of Diseases (ICD)-9/10 codes (Supplementary Table\u0026nbsp;1). The exclusion criteria were as follows: (1) multiple hospital admissions, (2) death or discharge within the first 24 hours of ICU admission, and (3) absence of serum AG data on the first day of ICU admission.\u003c/p\u003e\n\u003ch3\u003eDemographic and laboratory variables\u003c/h3\u003e\n\u003cp\u003eStructured query language was used to gather data on patient demographics (age, sex, height, weight, and race); medical history (hypertension, diabetes, congestive heart failure, chronic pulmonary disease, cerebrovascular disease, renal disease, malignant cancer, and sepsis); initial laboratory parameters (white blood cell count, platelet count, hemoglobin, hematocrit, sodium, potassium, calcium, chloride, blood glucose, AG, international normalized ratio, bicarbonate, lactate, blood urea nitrogen and creatinine); medications (aspirin, clopidogrel, beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, amiodarone, and statins); treatments (vasoactive drugs and mechanical ventilation); illness severity scores (SAPS II, OASIS and SOFA); vital signs (heart rate, mean arterial pressure, and respiratory rate); and survival time. All vital signs, laboratory results, and scoring systems were collected within the first 24 hours of ICU admission, while treatments were assessed within 72 hours. Body Mass Index (BMI) was calculated as weight (kg) divided by the square height (m\u0026sup2;). Missing values (\u0026lt;\u0026thinsp;15%) were imputed using Bayesian Ridge regression.\u003c/p\u003e\n\u003ch3\u003eResearch variable and outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary exposure in this study was AG, a serum electrolyte-derived value calculated using the formula: AG\u0026thinsp;=\u0026thinsp;sodium - (chloride\u0026thinsp;+\u0026thinsp;bicarbonate).\u003c/p\u003e\u003cp\u003eThe primary endpoint was 28-day mortality, and the secondary endpoints were 365-day mortality. Deaths were recorded as events that occurred within a specific time frame of ICU admission.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eIn this study, critically ill patients with AF were categorized into three groups based on tertiles of AG levels: T1 (AG\u0026thinsp;\u0026lt;\u0026thinsp;13), T2 (13\u0026thinsp;\u0026le;\u0026thinsp;AG\u0026thinsp;\u0026lt;\u0026thinsp;17), and T3 (AG\u0026thinsp;\u0026ge;\u0026thinsp;17). The baseline characteristics of each group were described. Normally distributed continuous variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; non-normally distributed continuous variables as median (IQR); categorical variables as n (%). Group comparisons used ANOVA or Kruskal-Wallis test for continuous variables (selected based on distribution normality), and chi-square or Fisher\u0026rsquo;s test for categorical variables, as appropriate.\u003c/p\u003e\u003cp\u003eCox multivariate regression analysis was employed to evaluate the association between AG and clinical outcomes, with results expressed as hazard ratios (HRs) and 95% confidence intervals (CIs). Covariates were selected based on either \u0026ge;\u0026thinsp;10% alteration in AG's HR or established clinical/epidemiological relevance. The final comprehensive model adjusted for: age, sex, BMI, diabetes, congestive heart failure, chronic pulmonary disease, renal disease, malignant cancer, sepsis, heart rate, mean arterial pressure, white blood cell count, hemoglobin, platelets, calcium, vasoactive agents, mechanical ventilation, beta-blockers, amiodarone, ACEI/ARB, SOFA score, and SAPS II. AG was analyzed both continuously and as tertile-based categories, with trend P-values calculated for categorical analyses.\u003c/p\u003e\u003cp\u003eTo further mitigate confounding, 1:1 nearest neighbor propensity score matching (PSM) with a caliper of 0.2 was implemented. Covariate balance was assessed using standardized mean differences (SMD). Cox proportional hazards regression was then applied to the matched cohort to examine the association between AG and mortality. Additionally, doubly robust estimation was performed by integrating the propensity score model with multivariate Cox regression[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], enhancing the robustness of our effect estimates for AG-mortality associations.\u003c/p\u003e\u003cp\u003eSurvival differences across AG tertiles were evaluated using Kaplan-Meier analysis with log-rank tests. The relationship between AG levels and mortality at 28 and 365 days was modeled with restricted cubic splines. Subgroup analyses with interaction testing assessed the consistency of the AG-mortality association across key clinical strata, with results presented in forest plots. All analyses were conducted before and after propensity score matching.\u003c/p\u003e\u003cp\u003eWe evaluated the incremental prognostic value of AG beyond conventional ICU scoring systems by comparing the area under the receiver operating characteristic (ROC) curve for models with and without AG.\u003c/p\u003e\u003cp\u003eAll analysis was performed using R 4.2.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; The R Foundation, Vienna, Austria) and the Free Statistics software (version 2.2; Beijing FreeClinical Medical Technology Co., Ltd, Beijing, China). Statistical significance was indicated by P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of study subjects\u003c/h2\u003e\u003cp\u003eThe final study cohort comprised 14,635 critically ill patients with AF stratified by AG tertiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Significant intergroup differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed across all baseline characteristics except BMI and hypertension prior to PSM, with escalating AG levels correlating with progressively adverse clinical profiles. Following 1:1 matching, the final cohort included 4,143 patients (1,381 per group) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. With the exception of AG-associated parameters (bicarbonate, BUN, chloride, creatinine, sodium, potassium, lactate), only hemoglobin levels, sepsis prevalence, SOFA scores, and mechanical ventilation requirements demonstrated significant intergroup differences (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after matching. Full baseline characteristics before and after PSM are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of Critically Ill Patients with Atrial Fibrillation Before and After Propensity Score Matching.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCovariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eOriginal cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eMatched cohort\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\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\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e75.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.081\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\u003e2358 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3448 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2975 (58.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e846 (61.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e825 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e798 (57.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate, beats/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e83.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e76.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e77.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.045\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\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155.0\u0026thinsp;\u0026plusmn;\u0026thinsp;75.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180.4\u0026thinsp;\u0026plusmn;\u0026thinsp;92.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e187.3\u0026thinsp;\u0026plusmn;\u0026thinsp;100.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e173.4\u0026thinsp;\u0026plusmn;\u0026thinsp;87.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175.3\u0026thinsp;\u0026plusmn;\u0026thinsp;83.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e180.5\u0026thinsp;\u0026plusmn;\u0026thinsp;87.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.3 (9.6, 17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.4 (9.2, 16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.9 (9.9, 19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.798\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\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicarbonate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.0 (14.0, 23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.0 (16.0, 31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.0 (22.0, 57.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.0 (15.0, 27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.0 (16.0, 32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26.0 (18.0, 39.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChloride, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e103.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e102.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine, mg/ dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9 (0.7, 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1 (0.8, 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6 (1.1, 2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9 (0.7, 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.1 (0.8, 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.2 (0.9, 1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e137.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e137.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e136.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2 (1.6, 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0 (1.4, 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4 (1.6, 3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.0 (1.5, 2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.0 (1.4, 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.2 (1.5, 3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4 (1.3, 1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4 (1.2, 1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5 (1.2, 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.4 (1.2, 1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.4 (1.2, 1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.4 (1.2, 1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2774 (76.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4503 (76.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3996 (78.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1050 (76.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1035 (74.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1054 (76.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.673\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\u003e967 (26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1778 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1975 (38.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e423 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e405 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e400 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive Heart Failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1227 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2593 (44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2788 (54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e608 (44.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e616 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e598 (43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e582 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1258 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e947 (18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e263 (19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e269 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e296 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic pulmonary disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e957 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1703 (28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1419 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e389 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e377 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e414 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.282\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\u003e536 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1404 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2004 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e302 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e315 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e311 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant cancer, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e356 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e736 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e668 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e173 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e161 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e189 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esepsis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1669 (45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3019 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3255 (63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e694 (50.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e750 (54.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e678 (49.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPSII, scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOASIS, scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA, scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e378 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e463 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e320 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e131 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e112 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmiodarone, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1307 (36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1365 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1120 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e328 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e319 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e298 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta-Blockers, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2836 (78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4109 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3086 (60.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e952 (68.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e993 (71.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e971 (70.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACEI/ARB, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2813 (77.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4038 (68.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2978 (58.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e932 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e966 (69.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e949 (68.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVasoactive agent, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1616 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2149 (36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2037 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e524 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e537 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e470 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1863 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2177 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1842 (36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e552 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e511 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e513 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality of 28-day, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e257 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e858 (14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1438 (28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e157 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e219 (15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e239 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality of 365-day, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e647 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1723 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2395 (46.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e362 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e430 (31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e466 (33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of ICU stay, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2 (1.3, 4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7 (1.6, 4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2 (1.9, 6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4 (1.4, 4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.6 (1.6, 4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.9 (1.8, 5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of hospital stay, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.9 (5.7, 12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.3 (5.6, 13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.3 (5.6, 15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.8 (6.0, 14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.1 (5.6, 13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.3 (5.1, 14.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAG, anion gap; BMI, body mass index; MAP, mean arterial pressure; SpO₂, percutaneous arterial oxygen saturation; WBC, white blood cell count; BUN, blood urea nitrogen; INR, international normalized ratio; SAPS II, Simplified Acute Physiology Score II; OASIS, Oxford Acute Severity of Illness Score; SOFA, Sequential Organ Failure Assessment; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ICU, intensive care unit\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariable Cox Regression\u003c/h3\u003e\n\u003cp\u003eMultivariable Cox proportional hazards regression was used to assess the association between AG and mortality in critically ill patients with AF. When AG was analyzed as a continuous variable, it demonstrated significant associations with 28-day mortality in both unadjusted (HR 1.10, 95% CI 1.09\u0026ndash;1.10; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and fully adjusted models (HR 1.05, 95% CI 1.04\u0026ndash;1.05; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For categorical analysis by tertiles, the highest AG tertile (T3) showed substantially elevated 28-day mortality risk versus the lowest tertile (T1), yielding unadjusted HR 4.56 (95% CI 3.99\u0026ndash;5.21; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and fully adjusted HR 2.19 (95% CI 1.90\u0026ndash;2.52; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Analyses for 365-day mortality consistently mirrored this trend (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate cox regression analyses for 28-day and 365-day mortality.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e28-day mortality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAG continuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10 (1.09\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10 (1.09\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.05 (1.04\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAG tertiles\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(Ref)\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\u003e1(Ref)\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\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.15 (1.87\u0026thinsp;~\u0026thinsp;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.00 (1.74\u0026thinsp;~\u0026thinsp;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.61 (1.39\u0026thinsp;~\u0026thinsp;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.56 (3.99\u0026thinsp;~\u0026thinsp;5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.20 (3.68\u0026thinsp;~\u0026thinsp;4.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.19 (1.90\u0026thinsp;~\u0026thinsp;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e365-day mortality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAG continuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09 (1.08\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09 (1.08\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.04 (1.03\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAG tertiles\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(Ref)\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\u003e1(Ref)\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\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.78 (1.63\u0026thinsp;~\u0026thinsp;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.65 (1.51\u0026thinsp;~\u0026thinsp;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.32 (1.21\u0026thinsp;~\u0026thinsp;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.32 (3.05\u0026thinsp;~\u0026thinsp;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.06 (2.81\u0026thinsp;~\u0026thinsp;3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.72 (1.56\u0026thinsp;~\u0026thinsp;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 1: No adjusted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 2: Adjusted for age, sex, BMI.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 3: Adjusted for age, sex, BMI, diabetes, congestive heart failure, chronic pulmonary disease, renal disease, malignant cancer, sepsis, heart rate, mean arterial pressure, white blood cell, hemoglobin, platelets, calcium, vasoactive agent, mechanical ventilation, beta-blockers, amiodarone, ACEI/ARB, SOFA, and SAPSII.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAG: T1 (AG\u0026thinsp;\u0026lt;\u0026thinsp;13), T2 (13\u0026thinsp;\u0026le;\u0026thinsp;AG\u0026thinsp;\u0026lt;\u0026thinsp;17, T3 (AG\u0026thinsp;\u0026ge;\u0026thinsp;17). HR, hazard ratio; CI, confidential interval; BMI, body mass index; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; SOFA, Sequential Organ Failure Assessment; SAPS II, Simplified Acute Physiology Score II\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\u003ePropensity Score Matching and Doubly Robust Analysis\u003c/h2\u003e\u003cp\u003eConsistent with the multivariable regression analysis, PSM demonstrated significant associations between AG and mortality. Specifically, each 1-unit AG increase was associated with 5% higher 28-day mortality risk (HR 1.05, 95% CI 1.03\u0026ndash;1.07), and T3 patients showed 60% increased mortality versus T1 (HR 1.60, 1.31\u0026ndash;1.96). Doubly robust estimation produced comparable risk estimates for continuous AG (28-day HR 1.04, 1.03\u0026ndash;1.06) and T3 comparisons (HR 1.75, 1.42\u0026ndash;2.15). Qualitatively consistent hazard ratios were observed across multivariable, PSM, and doubly robust models for all endpoints (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimary outcome analysis with different models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e28-Day Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e365-Day Mortality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05 (1.04\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04 (1.03\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\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\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.61 (1.39\u0026thinsp;~\u0026thinsp;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32 (1.21\u0026thinsp;~\u0026thinsp;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.19 (1.90\u0026thinsp;~\u0026thinsp;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.72 (1.56\u0026thinsp;~\u0026thinsp;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05 (1.03\u0026thinsp;~\u0026thinsp;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04 (1.03\u0026thinsp;~\u0026thinsp;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\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\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.44 (1.18\u0026thinsp;~\u0026thinsp;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.24 (1.08\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60 (1.31\u0026thinsp;~\u0026thinsp;1.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38 (1.20\u0026thinsp;~\u0026thinsp;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDoubly robust with all covariates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04 (1.03\u0026thinsp;~\u0026thinsp;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03 (1.02\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\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\u003e1(Ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.57 (1.27\u0026thinsp;~\u0026thinsp;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32 (1.14\u0026thinsp;~\u0026thinsp;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.75 (1.42\u0026thinsp;~\u0026thinsp;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38 (1.20\u0026thinsp;~\u0026thinsp;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAG: T1 (AG\u0026thinsp;\u0026lt;\u0026thinsp;13), T2 (13\u0026thinsp;\u0026le;\u0026thinsp;AG\u0026thinsp;\u0026lt;\u0026thinsp;17, T3 (AG\u0026thinsp;\u0026ge;\u0026thinsp;17). HR, hazard ratio; CI, confidential interval\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eKaplan-Meier Survival Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents Kaplan-Meier survival curves for 28-day and 365-day mortality, categorized by AG tertiles, both before and after propensity score matching. Notably, significant differences in survival were observed across all tertiles (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with T3 consistently exhibiting the lowest survival probabilities at both time points. The survival disparity between T1, T2, and T3 remained substantial even after matching, indicating a persistent ordinal relationship in survival outcomes before and after adjustment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRestricted Cubic Spline Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays restricted cubic spline curves of AG-mortality relationships. Multivariable-adjusted analyses (Panels A-B) showed nonlinear positive associations between AG levels and 28-day/365-day mortality risk. After PSM (Panels C-D), these relationships transitioned to linear positive trajectories for both timepoints. Throughout the clinically relevant AG range, higher levels consistently corresponded to elevated mortality risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eSubgroup analyses evaluated the robustness of the association between AG levels and 28-day and 365-day mortality across various clinical factors, including age, sex, congestive heart failure (CHF), chronic pulmonary disease (CPD), diabetes, and renal disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Consistent positive associations were observed across all subgroups in both pre- and post-matching analyses. Significant interactions (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were noted in subgroups such as CHF, CPD, and diabetes, among others, yet the effect sizes between groups remained similar, with no substantial change in the direction or consistency of the overall association.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eROC curve analysis\u003c/h2\u003e\u003cp\u003eThe addition of AG significantly improved the predictive performance of both SOFA and OASIS scores for 28-day and 365-day mortality, as evidenced by increased AUC values (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study of critically ill patients with AF shows an independent association between elevated AG levels and increased mortality. Using the MIMIC-IV database, we found that both continuous and tertile-based AG values are significantly associated with 28- and 365-day mortality. This association was consistently observed across various analytical approaches, including multivariable Cox regression, propensity score matching, and doubly robust estimation. A clear dose-response relationship was evident in Kaplan-Meier, restricted cubic spline, and subgroup analyses. Moreover, incorporating AG significantly enhanced the prognostic accuracy of conventional ICU illness severity scores.\u003c/p\u003e\u003cp\u003eAF is a growing global health concern[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Its incidence continues to rise, contributing significantly to disability and healthcare burdens worldwide[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In critically ill patients, AF is often not an isolated event, but rather coexists with conditions such as sepsis, diabetes, hepatic and renal dysfunction, and respiratory impairment[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These comorbidities collectively disrupt the body's ability to maintain pH and homeostasis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This pathophysiological interplay worsens organ function, leading to increased mortality and prolonged ICU stays.\u003c/p\u003e\u003cp\u003eSerum AG is calculated from routine electrolyte measurements and reflects the balance of unmeasured anions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Elevated AG levels typically indicate metabolic acidosis caused by the accumulation of organic acids due to either impaired excretion or overproduction[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This elevation is often associated with increased levels of lactic acid[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], as well as elevations in β-hydroxybutyrate, acetoacetate, phosphate, sulfate, and other organic anions[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, the decline in glomerular filtration rate and renal tubular damage associated with kidney disease can collectively impair acid elimination, leading to a rise in the AG[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although some controversy exists, with one meta-analysis not recommending its isolated use for mortality risk assessment[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], the majority of studies have established a significant association between elevated AG and adverse outcomes in critically ill patients. Li et al. demonstrated that an initial serum AG\u0026thinsp;\u0026gt;\u0026thinsp;16 mmol/L after ICU admission was associated with increased mortality in this population[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, a recent study developed a prognostic model for in-hospital mortality in patients with acute myocardial infarction and AF, in which AG was identified as one of seven key predictive variables[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral pathophysiological mechanisms may explain this association. First, elevated AG commonly stems from lactate accumulation, which itself typically arises from systemic hypoperfusion and tissue hypoxia[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These conditions can exacerbate myocardial ischemia and electrical instability, thereby perpetuating AF and compromising cardiac output[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Second, elevated AG is closely associated with systemic inflammation and oxidative stress[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Acidosis promotes the production of interleukin-1β (IL-1β) and activates the complement system, thereby amplifying inflammatory responses and inducing myocardial remodeling[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Previous studies have confirmed that increased serum AG levels are correlated with elevated inflammatory biomarkers, including C-reactive protein and white blood cell count[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Third, disrupted acid-base balance impairs electrolyte homeostasis, contributing to the maintenance and progression of AF through electrical remodeling[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This process involves changes in the ion channel protein expression, changes in the ion channel protein expression, and the development of structural fibrosis[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Fourth, elevated serum AG may also influence insulin resistance (IR)[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Metabolic acidosis has been demonstrated to reduce insulin receptor binding efficiency and impair insulin signaling sensitivity, thereby aggravating IR[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Even in non-diabetic patients, the TyG index, a marker of insulin resistance, maintains an independent association with increased mortality in critically ill AF patients[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This pathological state accelerates disordered glucose metabolism and promotes lipolysis, increasing endogenous acid load and establishing a vicious cycle between metabolic acidosis and IR[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe strong association between elevated AG and mortality in critically ill AF patients underscores its clinical relevance. As an inexpensive and readily available marker, AG enhances risk stratification in this population. It may serve as an early indicator, prompting clinicians to identify and address underlying causes of metabolic acidosis, including occult hypoperfusion, evolving sepsis, or deteriorating renal function. Incorporating AG into established ICU prognostic scores could improve their accuracy and support more personalized treatment approaches. Future studies should examine whether AG-guided management strategies, such optimizing tissue perfusion or correcting acid-base disturbances, improve outcomes in these patients.\u003c/p\u003e\u003cp\u003eThere are several limitations to our study. First, the use of a single-center database may limit the generalizability of our findings to other populations. Second, the retrospective design is inherently prone to residual confounding due to unmeasured or unknown factors, despite adjustment for numerous covariates. Third, we only used the initial AG measurement after ICU admission and did not capture its dynamic changes, which may better reflect clinical progression and patient outcomes. Therefore, future prospective studies are essential to validate our findings and further investigate this issue.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study demonstrates that elevated AG is significantly associated with increased mortality in critically ill patients with AF. These findings support its utility as an accessible and prognostic biomarker for risk stratification in this high-risk population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnion gap\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntensive Care Unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtrial fibrillation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMIMIC-IV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMedical Information Mart for Intensive Care IV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePropensity score matching\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAPS II\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSimplified Acute Physiology Score II\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOASIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACEI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAngiotensin-Converting Enzyme Inhibitor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAngiotensin II Receptor Blocker\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile Range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandardized mean difference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBlood Urea Nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCongestive heart failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInsulin resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eApproval for using the MIMIC-IV database was obtained from the IRBs of both MIT and BIDMC. The MIMIC database's existing ethical approval applies to the data in this study, eliminating the requirement for additional ethical approval or informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eData Sharing Statement\u003c/h2\u003e\u003cp\u003eData were sourced from the MIMIC-IV database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mimic.physionet.org/\u003c/span\u003e\u003cspan address=\"https://mimic.physionet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The corresponding author will provide the datasets used and analyzed during the current work upon reasonable request.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by The Hospital Fund Project of the Second Hospital of Shanxi Medical University (NO. 202404-10).\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\u003cp\u003eThe study was designed by RD and HZ. RD performed data collection, analysis, and wrote the initial manuscript. RS, JL, and SC contributed to data analysis and figure preparation. HZ participated in writing and editing the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors thank the Laboratory for Computational Physiology at MIT (LCP-MIT) for providing access to the MIMIC-IV database. We acknowledge Jie Liu (People\u0026rsquo;s Liberation Army General Hospital), Qilin Yang (The Second Affiliated Hospital of Guangzhou Medical University), and Haibo Li (Fujian Maternity and Child Health Hospital) for their contributions to study design and statistical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKo D, Chung MK, Evans PT, Benjamin EJ, Helm RH (2025) Atrial fibrillation: a review. JAMA 333:329\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrundel BJJM, Ai X, Hills MT, Kuipers MF, Lip GYH, De Groot NMS (2022) Atrial fibrillation. Nat Rev Dis Primers 8:21\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomstr\u0026ouml;m-Lundqvist C et al (2021) Corrigendum to: 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J 42:4194\u0026ndash;4194\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLippi G, Sanchis-Gomar F, Cervellin G (2021) Global epidemiology of atrial fibrillation: an increasing epidemic and public health challenge. Int J Stroke 16:217\u0026ndash;221\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoglar JA, Chung MK, Armbruster AL, Benjamin EJ, Chyou JY, Cronin EM et al (2024) 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation. J Am Coll Cardiol 83:109\u0026ndash;279\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElliott AD, Middeldorp ME, Van Gelder IC, Albert CM, Sanders P (2023) Epidemiology and modifiable risk factors for atrial fibrillation. Nat Rev Cardiol 20:404\u0026ndash;417\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B et al (2017) 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Rev Esp Cardiol (Engl Ed) 70:50\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShaver CM, Chen W, Janz DR, May AK, Darbar D, Bernard GR et al (2015) Atrial fibrillation is an independent predictor of mortality in critically ill patients*. Crit Care Med 43:2104\u0026ndash;2111\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSibley S, Bedford J, Wetterslev M, Johnston B, Garside T, Kanji S et al (2025) Atrial fibrillation in critical illness: state of the art. Intensive Care Med 51:904\u0026ndash;916\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKraut JA, Madias NE (2007) Serum anion gap: its uses and limitations in clinical medicine. Clin J Am Soc Nephrol 2:162\u0026ndash;174\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLou Z, Zeng F, Huang W, Xiao L, Zou K, Zhou H (2024) Association between the anion-gap and 28-day mortality in critically ill adult patients with sepsis: a retrospective cohort study. Med (Baltim) 103:e39029\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJhou H-J, Chen P-H, Yang L-Y, Chang S-H, Lee C-H (2021) Plasma anion gap and risk of In-hospital mortality in patients with acute ischemic stroke: analysis from the MIMIC-IV database. J Pers Med 11:1004\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Li C, Wang J, Yuan Q (2025) The association between anion gap and prognosis in patients myocardial infarction with congestive heart failure: a retrospective analysis of the MIMIC-IV database. Int J Emerg Med 18:33\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J, Dai C, Yang Y, Wang Y, Zeng R, Li B et al (2022) The association between anion gap and in-hospital mortality of post-cardiac arrest patients: a retrospective study. Sci Rep 12:7405\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQu J, Tang X, Cheng Y, Xiong W, Zhao Y (2025) Association between albumin corrected anion gap and 28-day all‐cause mortality in patients with acute respiratory failure in ICU: a retrospective study based on the MIMIC‐IV database. Clin Respir J 19:e70100\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Zhong H, Chen L, Ding H-C, Lu Z-J, Wang B-S et al (2025) Association between anion gap and postoperative delirium in patients undergoing open heart surgery. Front Cardiovasc Med 12:1592161\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan Q, Mu Z, Li Y, Gu C, Liu T, Wang B et al (2023) The association between serum anion gap and acute kidney injury after coronary artery bypass grafting in patients with acute coronary syndrome. BMC Cardiovasc Disord 23:542\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun T, Cai C, Shen H, Yang J, Guo Q, Zhang J et al (2020) Anion gap was associated with inhospital mortality and adverse clinical outcomes of coronary care unit patients. Piccione G, editor. Biomed Res Int. ;2020:4598462\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamel KS, Oh MS, Halperin ML (2020) L-lactic acidosis: pathophysiology, classification, and causes; emphasis on biochemical and metabolic basis. Kidney Int 97:75\u0026ndash;88\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZanza C, Facelli V, Romenskaya T, Bottinelli M, Caputo G, Piccioni A et al (2022) Lactic acidosis related to pharmacotherapy and human diseases. Pharmaceuticals 15:1496\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRochlani Y, Pothineni NV, Kovelamudi S, Mehta JL (2017) Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis 11:215\u0026ndash;225\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuschieri S (2019) The STROBE guidelines. Saudi J Anaesth 13:31\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF (2013) A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med 32:3388\u0026ndash;3414\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ et al (2014) Worldwide epidemiology of atrial fibrillation: a global burden of disease 2010 study. Circulation 129:837\u0026ndash;847\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuipers S, Klouwenberg PMK, Cremer OL (2014) Incidence, risk factors and outcomes of new-onset atrial fibrillation in patients with sepsis: a systematic review. Crit Care 18:688\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOltman CG, Kim TP, Lee JWY, Lupu JD, Zhu R, Moussa ID (2024) Prevalence, management, and comorbidities of adults with atrial fibrillation in the United States, 2019 to 2023. JACC: Adv. ;3:101330\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAchanti A, Szerlip HM (2023) Acid-base disorders in the critically ill patient. Clin J Am Soc Nephrol 18:102\u0026ndash;112\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFenves AZ, Emmett M (2021) Approach to patients with high anion gap metabolic acidosis: core curriculum 2021. Am J Kidney Dis 78:590\u0026ndash;600\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKraut JA, Madias NE (2010) Metabolic acidosis: pathophysiology, diagnosis and management. Nat Rev Nephrol 6:274\u0026ndash;285\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKraut JA, Nagami GT (2013) The serum anion gap in the evaluation of acid-base disorders: what are its limitations and can its effectiveness Be improved? Clin J Am Soc Nephrol 8:2018\u0026ndash;2024\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Ao T, Zhen P, Hu M (2024) Association between serum anion gap and 28-day mortality in critically ill patients with infective endocarditis: a retrospective cohort study from MIMIC IV database. BMC Cardiovasc Disord 24:585\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbramowitz MK, Hostetter TH, Melamed ML (2012) The serum anion gap is altered in early kidney disease and associates with mortality. Kidney Int 82:701\u0026ndash;709\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlasmacher SA, Stones W (2015) Anion gap as a prognostic tool for risk stratification in critically ill patients \u0026ndash; a systematic review and meta-analysis. BMC Anesthesiol 16:68\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi R, Jin X, Ren J, Deng G, Li J, Gao Y et al (2022) Relationship of admission serum anion gap and prognosis of critically ill patients: a large multicenter cohort study. Zeng X, editor. Dis Markers. ;2022:1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan W, Duan R, Zeng C, Yang Z, Dai L, Xu T et al (2025) A nomogram for predicting In-hospital mortality in critically ill patients with myocardial infarction and atrial fibrillation. Nurs Crit Care 30:e70116\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Y, Yin Z, Han W (2025) Anion gap associated with 28-days all-cause mortality in acute cholangitis patients admitted to the intensive care unit in MIMIC-IV database: a retrospective cohort study. Front Med 12:1591096\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVichot AA, Rastegar A (2014) Use of anion gap in the evaluation of a patient with metabolic acidosis. Am J Kidney Dis 64:653\u0026ndash;657\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen DG, Orchard CH (1987) Myocardial contractile function during ischemia and hypoxia. Circ Res 60:153\u0026ndash;168\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrchard CH, Kentish JC (1990) Effects of changes of pH on the contractile function of cardiac muscle. Am J Physiol Cell Physiol 258:C967\u0026ndash;C981\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndersen LW, Holmberg MJ, Doherty M, Khabbaz K, Lerner A, Berg KM et al (2015) Postoperative lactate levels and hospital length of stay after cardiac surgery. J Cardiothorac Vasc Anesth 29:1454\u0026ndash;1460\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalan AI, Halațiu VB, Scridon A (2024) Oxidative stress, inflammation, and mitochondrial dysfunction: a link between obesity and atrial fibrillation. Antioxidants 13:117\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErra D\u0026iacute;az F, Dantas E, Geffner J (2018) Unravelling the interplay between extracellular acidosis and immune cells. Mediators Inflamm 2018:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNath KA, Hostetter MK, Hostetter TH (1985) Pathophysiology of chronic tubulo-interstitial disease in rats. Interactions of dietary acid load, ammonia, and complement component C3. J Clin Invest 76:667\u0026ndash;675\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarwell WR, Taylor EN (2010) Serum anion gap, bicarbonate and biomarkers of inflammation in healthy individuals in a national survey. Can Med Assoc J 182:137\u0026ndash;141\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRafaqat S, Rafaqat S, Khurshid H, Rafaqat S (2022) Electrolyte\u0026rsquo;s imbalance role in atrial fibrillation: pharmacological management. Int J Arrhythmia 23:15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeo M, Weinberg SH, Boyle PM (2017) Calcium dynamics and cardiac arrhythmia. Clin Med Insights: Cardiol 11:117954681773952\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang Y-Y, Hou H-T, Yang Q, Liu X-C, He G-W (2017) Chloride channels are involved in the development of atrial fibrillation \u0026ndash; a transcriptomic and proteomic study. Sci Rep 7:10215\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRavens U, Cerbai E (2008) Role of potassium currents in cardiac arrhythmias. Europace 10:1133\u0026ndash;1137\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan Y-N, Xiong C-Y, Wang Y-X, Yuan J-L, Li L, Xiao Z-X (2025) Association between serum anion gap and all-cause mortality in critically ill patients with diabetic kidney disease: analysis of the MIMIC-IV database. D\u0026rsquo;Addio F, editor. PLOS One. ;20:e0329269\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing R, Cheng E, Wei M, Pan L, Ye L, Han Y et al (2025) Association between triglyceride\u0026ndash;glucose index and mortality in critically ill patients with atrial fibrillation: a retrospective cohort study. Cardiovasc Diabetol 24:138\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaimori J-Y, Sakaguchi Y, Kajimoto S, Asahina Y, Oka T, Hattori K et al (2022) Diagnosing metabolic acidosis in chronic kidney disease: importance of blood pH and serum anion gap. Kidney Res Clin Pract 41:288\u0026ndash;297\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anion gap, Atrial fibrillation, Mortality, Intensive care unit, Prognostic biomarker, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-7534396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7534396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSerum anion gap (AG) is associated with mortality in critical illnesses, yet its prognostic significance specifically in intensive care unit (ICU) patients with atrial fibrillation (AF) remains unclear. This study aimed to investigate the associations between AG and mortality in this high-risk population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe identified critically ill patients with AF from the MIMIC-IV database and stratified them by AG tertiles. Outcomes included 28-day and 365-day mortality. Multivariable Cox regression, propensity score matching (PSM), and restricted cubic splines were employed to examine the association between AG and mortality. Survival differences were evaluated using Kaplan-Meier analysis. Subgroup analyses assessed the consistency of associations, and ROC analysis quantified the incremental predictive value of AG.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 14,635 eligible patients, elevated AG was significantly associated with increased mortality both before and after propensity score matching. In fully adjusted models, each 1-unit increase in AG was associated with a 5% higher risk of 28-day mortality (HR 1.05, 95% CI 1.04\u0026ndash;1.05) and a 4% increased risk of 365-day mortality (HR 1.04, 95% CI 1.03\u0026ndash;1.05). Patients in the highest AG tertile had substantially increased mortality risk compared to the lowest tertile (28-day HR 2.19, 95% CI 1.90\u0026ndash;2.52; 365-day HR 1.98, 95% CI 1.74\u0026ndash;2.25). Consistent dose-response relationships were observed across all analytical methods. Subgroup analyses presented in forest plots demonstrated the robustness of this association across various clinical strata. Additionally, AG significantly improved the predictive accuracy of conventional illness severity scores.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eElevated AG is independently associated with increased 28- and 365-day mortality in critically ill patients with AF. AG provides significant incremental prognostic value to established risk assessment tools such as SOFA and OASIS scores, and may serve as a readily available biomarker for improving risk stratification in this population.\u003c/p\u003e","manuscriptTitle":"Association Between Anion Gap and Mortality in Critically Ill Patients with Atrial Fibrillation: A Propensity Score-Matched Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:05:14","doi":"10.21203/rs.3.rs-7534396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"214770ba-c228-4eaf-9756-f3338e42f4e0","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T14:15:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 02:05:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7534396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7534396","identity":"rs-7534396","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}