Association of Anion Gap to Bicarbonate Ratio (ABR) with 28-Day Mortality in ICU Sepsis: A Retrospective Cohort Study Based on the MIMIC-IV Database
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Abstract Background Sepsis-associated mortality remains a critical challenge in intensive care units, with metabolic dysregulation being a hallmark of its pathophysiology. While the anion gap-to-bicarbonate ratio (ABR) has been proposed as a potential biomarker for acid-base homeostasis, its prognostic utility in sepsis patients is yet to be comprehensively validated. This study investigates the correlation between ABR and 28-day mortality in ICU sepsis patients. Methods Based on the MIMIC-IV database, patients with sepsis who were admitted to the ICU for the first time (n = 22,549) were included. The relationship between ABR and 28-day mortality was evaluated using the Cox proportional hazards model and adjusted for multiple factors. Subgroup analysis was conducted using Kaplan-Meier curves and forest plots. Results The 28-day mortality rate of patients in the high ABR group (ABR > 0.6786) was significantly higher than that of the low ABR group (HR = 2.33, 95% CI: 2.26–2.41, p < 0.001). After multivariate adjustment, ABR remained significantly associated with mortality (HR = 1.40, 95% CI: 1.27–1.55, p < 0.001). Subgroup analysis showed that the impact of ABR on mortality was particularly significant in patients with cerebrovascular disease, diabetes, and metastatic solid tumors (P for interaction < 0.05). Conclusion ABR is an independent predictor of 28-day mortality in ICU patients with sepsis, especially with significant prognostic value in specific subgroups.
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Association of Anion Gap to Bicarbonate Ratio (ABR) with 28-Day Mortality in ICU Sepsis: A Retrospective Cohort Study Based on the MIMIC-IV Database | 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 Article Association of Anion Gap to Bicarbonate Ratio (ABR) with 28-Day Mortality in ICU Sepsis: A Retrospective Cohort Study Based on the MIMIC-IV Database Chuntian Chi, Jian Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6234181/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 Sepsis-associated mortality remains a critical challenge in intensive care units, with metabolic dysregulation being a hallmark of its pathophysiology. While the anion gap-to-bicarbonate ratio (ABR) has been proposed as a potential biomarker for acid-base homeostasis, its prognostic utility in sepsis patients is yet to be comprehensively validated. This study investigates the correlation between ABR and 28-day mortality in ICU sepsis patients. Methods Based on the MIMIC-IV database, patients with sepsis who were admitted to the ICU for the first time (n = 22,549) were included. The relationship between ABR and 28-day mortality was evaluated using the Cox proportional hazards model and adjusted for multiple factors. Subgroup analysis was conducted using Kaplan-Meier curves and forest plots. Results The 28-day mortality rate of patients in the high ABR group (ABR > 0.6786) was significantly higher than that of the low ABR group (HR = 2.33, 95% CI: 2.26–2.41, p < 0.001). After multivariate adjustment, ABR remained significantly associated with mortality (HR = 1.40, 95% CI: 1.27–1.55, p < 0.001). Subgroup analysis showed that the impact of ABR on mortality was particularly significant in patients with cerebrovascular disease, diabetes, and metastatic solid tumors (P for interaction < 0.05). Conclusion ABR is an independent predictor of 28-day mortality in ICU patients with sepsis, especially with significant prognostic value in specific subgroups. Health sciences/Diseases Health sciences/Medical research sepsis MIMIC-IV database biomarker risk factor prognostic implication anion gap bicarbonate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction According to the Sepsis-3 consensus criteria, sepsis is defined as a dysregulated systemic response to infection culminating in life-threatening organ failure [ 1 ] . Despite therapeutic advancements, epidemiological data indicate that sepsis contributes to nearly one-fifth of global mortality, underscoring the urgency for novel prognostic markers [ 2 ] .In the third international consensus definition of sepsis and septic shock, sepsis is defined as a life-threatening organ dysfunction caused by a dysfunctional host response to infection. Sepsis is the main cause of death in ICU patients, and its pathophysiological mechanism is complex, involving systemic inflammatory response, metabolic disorders, and multiple organ dysfunction syndrome (MODS) [ 1 ] .Despite significant advances in the diagnosis and treatment of sepsis in recent years, the mortality rate remains high, especially in the ICU setting [ 3 ] .Sepsis guidelines [ 1 ] indicate that serum lactate levels can reflect changes in tissue perfusion. However, serum lactate level is not the only indicator of metabolic parameter disorders, and the importance of metabolic disorders in patients with sepsis has been reevaluated from a different perspective. Therefore, it is important to identify novel and representative markers of metabolic dysfunction in sepsis in order to identify high-risk patients early and guide individualized treatment. Anion Gap (AG) and Bicarbonate (HCO₃⁻) are important indicators to evaluate the acid-base balance. The anion gap (AG) is a composite outcome indicator determined by the measured concentrations of anions and cations, [ 4 ] which reflects acid-base metabolism and may reflect the overall metabolic status of the patient.It is commonly used to assess the type and severity of metabolic acidosis [ 5 ] . HCO₃⁻ is the main buffer substance and its level is closely related to the body's acid-base balance [ 6 ] .In recent years, researchers have proposed that the ratio of anion gap to bicarbonate (ABR) may be used as a new metabolic marker to reflect the acid-base balance state and metabolic disorder of patients [ 7 ] . However, the relationship between ABR and the prognosis of patients with sepsis has not been fully studied. Patients with sepsis often have metabolic acidosis, which may be related to lactate accumulation, renal insufficiency, or unmeasured anion accumulation [ 8 ] . ABR, as a composite indicator, may be more reflective of a patient's metabolic status than AG or HCO₃⁻ alone.Studies have shown that ABR is associated with the prognosis of a variety of diseases, such as acute pancreatitis and chronic kidney disease [ 9 , 10 ] , but its role in sepsis remains unclear. Therefore, the aim of this study was to investigate the association of ABR with 28-day mortality in ICU sepsis patients based on the MIME-IV database, and to assess its prognostic value in specific subgroups. Methods Database The study cohort was derived from the MIMIC-IV database (version 2.2), encompassing de-identified clinical records of ICU admissions at Beth Israel Deaconess Medical Center (2008–2019). The database consisted of records of demographics, vital signs, laboratory tests, fluid balance, and vital status; documented International Classification of Diseases and Ten Revision (ICD-9) codes; documented hourly physiological data from bedside monitors confirmed by ICU nurses; and stored written evaluations of radiology films by specialists during the corresponding time period. One author (Chuntian Chi) completed the CITI Data or Specimens Only Research course, was approved to access the database, and took responsibility for data extraction (certification number 66935858). The study was conducted in accordance with the tenets of the Declaration of Helsinki (as revised in 2013). The use of the MIMIC database was approved by the Institutional Review Boards of the Massachusetts Institute of Technology and BIDMC, both of which waive the requirement for informed consent for studies involving the MIMIC-IV database; therefore, our current study did not require approval from our own centre's ethics committee. Study population The study included patients in the MIMIC-IV who satisfied the criteria for sepsis and were eligible to participate. Sepsis diagnosis adhered to the Sepsis-3 framework, requiring both confirmed/suspected infection and a ≥ 2-point increase in Sequential Organ Failure Assessment (SOFA) scores [ 1 ] .In summary, sepsis was identified in persons who had confirmed or suspected infection and saw a sudden increase in their overall Sequential Organ Failure Assessment (SOFA) score of at least two points. Infection was identified by the MIMIC-IV system using the International Classification of Diseases, ninth Edition (ICD-9) code. People admitted between 2008 and 2019 are easily identified in the database.Our study included 431,231 MIME-IV patients admitted to hospital and then excluded patients who were admitted to ICU for a second (or more) hospital admission or a second ICU admission. We only included 22,633patients with Sepsis who were admitted to the ICU for the first time between 2008 and 2019. After excluding 84 patients admitted with missing data, we finally included 22,549 patients with Sepsis in our cohort. Figure 1 Data extraction Structured Query Language (SQL), PostgreSQL tools (version 9.6) and STATA version 17.0 were used for data extraction and management. Data on age, sex, ethnicity, comorbidities, initial laboratory parameters on admission to the ICU, two scoring systems including the SOFA score and the Simplified Acute Physiology Score II (SAPSII) [ 11 ] , mechanical ventilation (MV) on admission to the ICU, length of hospital and ICU stay, and date of death of the patients were extracted directly or calculated. Comorbidities identified by ICD-9 code included atrial fibrillation (AFIB), coronary artery disease (CAD), congestive heart failure (CHF), diabetes, malignancy, chronic kidney disease, liver disease, stroke, and laboratory parameters included haemoglobin, platelet count, white blood cell count, percentage of lymphocytes and neutrophils, neutrophil-to-lymphocyte ratio (NLR), PH, partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), bicarbonate, partial thromboplastin time (PTT), prothrombin time (PT), glucose, urea nitrogen, creatinine, lactate, creatine kinase, creatine kinase isoenzyme (CK-MB), creatine kinase, alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP) and anion gap. Statistical analysis Baseline measurements for a normal distribution are expressed as mean ± standard deviation (x¯±s), while data for a non-normal distribution are expressed as median (quartile). Counting data are expressed as frequencies and percentages (%). Missing data were imputed by multiple interpolation. For analysis of baseline characteristics, statistical differences between the two groups for continuous variables were analysed using 1-way analysis of variance or the Kruskal-Wallis H test, and for categorical variables using the chi-squared test. Multivariate Cox regression analyses were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for death in different ABR groups. To control for confounding, factors with a p-value of less than 0.05 in the univariate analysis were included in the multivariate model analysis.Finally, heart_rate_mean,dbp_mean,mbp_mean,resp_rate_mean,spo2_mean,hematocrit,hemoglobin,aniongap,bicarbonate,calcium, sodium,potassium,glucose_mean,wbc,platelets,bun,creat,pt,myocardial_infarct,congestive_heart_failure,peripheral_vascular_ disease,dementia,cerebrovascular_disease,chronic_pulmonary_disease,rheumatic_disease,peptic_ulcer_disease,mild_liver_ disease,diabetes,paraplegia + renal_disease,malignant_cancer,metastatic_solid_tumor,aids,sapsii,oasis,sofa_score were included in the adjusted model. Gender and age are usually required adjustment covariates. Significance of survival was analysed by K-M curve and log-rank test. Univariate and multivariate COX regression models were used to investigate the association between ABR and 28-day mortality in patients with sepsis in ICU. To further analyse the effects of comorbidities and major treatments on study outcomes, subgroup analyses were performed for comorbidities (including cerebrovascular disease, severe liver disease) and sex to assess the stability of study results. All statistical analyses were performed using the R language ( https://www.r-project.org , The R Foundation) and Free Statistics software. All P values reported are 2-tailed, and P values < 0.05 were considered statistically significant. Result The baseline characteristics and laboratory parameters of the study population are shown in Table 1 22,549 patients with a mean age of 66.5 ± 16.4 years were included, of which 9525 (42.2%) participants were female. Results showed that 3,446 patients (15.28%) died during the 28-day follow-up period.Among them, the death group was older than the survivors SOFA, sapsii and OASIS scores were more serious. In addition, the levels of white blood cells, blood glucose, creatinine, BUN and ABR were higher in the death group, and the burden of complications was heavier. More details are provided in Table 1 . Table 1 Baseline characteristics between survivors and non-survivors Independent Variables Total (n = 22549) Survivors (n = 19103) Death (n = 3446) p-value Demographics Gender, n (%) < 0.001 Female 9525 (42.2) 7973 (41.7) 1552 (45) Male 13024 (57.8) 11130 (58.3) 1894 (55) Age,Years 66.5 ± 16.4 65.8 ± 16.5 70.3 ± 15.6 < 0.001 Vital signs HR (bpm) 86.8 ± 16.0 86.0 ± 15.4 91.4 ± 18.2 < 0.001 DBP (mmHg) 61.5 ± 10.3 61.7 ± 10.2 60.3 ± 11.1 < 0.001 MBP (mmHg) 76.7 ± 10.2 77.0 ± 10.0 74.6 ± 11.1 < 0.001 RR (bpm) 19.6 ± 4.1 19.3 ± 3.8 21.7 ± 4.6 < 0.001 SpO 2 (%) 96.9 ± 2.4 97.1 ± 1.9 95.9 ± 4.2 < 0.001 Glucose, mg/dL 131.3 (114.0, 159.5) 130.3 (114.0, 155.4) 141.1 (114.0, 183.3) < 0.001 WBC (10 9 /L) 13.8 (9.9, 18.7) 13.6 (9.8, 18.3) 15.4 (10.5, 21.4) < 0.001 Platelet (10 9 /L) 200.0 (145.0, 271.0) 200.0 (148.0, 268.0) 199.0 (128.0, 285.0) 0.007 Bun, mg/dL 22.0 (15.0, 37.0) 21.0 (15.0, 34.0) 33.0 (21.0, 53.0) < 0.001 Creatinine (mg/dL) 1.2 (0.9, 2.0) 1.1 (0.8, 1.7) 1.9 (1.1, 3.2) < 0.001 pt,s 14.9 (13.1, 17.9) 14.7 (13.0, 17.2) 16.7 (13.7, 24.4) < 0.001 Hematocrit (%) 34.9 ± 6.2 34.9 ± 6.1 34.9 ± 7.0 0.802 Hemoglobin (g/dL) 11.5 ± 2.1 11.5 ± 2.1 11.3 ± 2.3 < 0.001 Aniongap,mmol/L 16.7 ± 5.3 16.1 ± 4.8 20.1 ± 6.8 < 0.001 Bicarbonate ,mmol/L 24.1 ± 4.4 24.4 ± 4.1 22.6 ± 5.4 < 0.001 Calcium ,mmol/L 8.5 ± 0.9 8.5 ± 0.9 8.6 ± 1.2 < 0.001 Sodium ,mmol/L 140.0 ± 5.2 139.9 ± 4.9 140.5 ± 6.9 < 0.001 Potassium ,mmol/L 4.6 ± 0.9 4.6 ± 0.8 4.9 ± 1.0 < 0.001 ABR 0.7 (0.5, 0.8) 0.6 (0.5, 0.8) 0.8 (0.6, 1.1) < 0.001 Comorbidities Myocardial infarct, < 0.001 n (%) 18769 (83.2) 16039 (84) 2730 (79.2) 1 3780 (16.8) 3064 (16) 716 (20.8) Congestive heart failure, n (%) < 0.001 0 16209 (71.9) 13929 (72.9) 2280 (66.2) 1 6340 (28.1) 5174 (27.1) 1166 (33.8) Peripheral vascular disease, n (%) 0.001 0 19873 (88.1) 16892 (88.4) 2981 (86.5) 1 2676 (11.9) 2211 (11.6) 465 (13.5) Dementia, n (%) 0.001 0 21523 (95.4) 18270 (95.6) 3253 (94.4) 1 1026 ( 4.6) 833 (4.4) 193 (5.6) Cerebrovascular disease, n (%) < 0.001 0 19392 (86.0) 16630 (87.1) 2762 (80.2) 1 3157 (14.0) 2473 (12.9) 684 (19.8) Chronic pulmonary disease, n (%) 0.002 0 16751 (74.3) 14263 (74.7) 2488 (72.2) 1 5798 (25.7) 4840 (25.3) 958 (27.8) Rheumatic disease, n (%) 0.13 0 21739 (96.4) 18432 (96.5) 3307 (96) 1 810 ( 3.6) 671 (3.5) 139 (4) Peptic ulcer disease, n (%) 0.185 0 21870 (97.0) 18540 (97.1) 3330 (96.6) 1 679 ( 3.0) 563 (2.9) 116 (3.4) Mild liver disease, n (%) < 0.001 0 19301 (85.6) 16685 (87.3) 2616 (75.9) 1 3248 (14.4) 2418 (12.7) 830 (24.1) Diabetes, n (%) 0.185 0 17213 (76.3) 14552 (76.2) 2661 (77.2) 1 5336 (23.7) 4551 (23.8) 785 (22.8) Paraplegia, n (%) < 0.001 0 21491 (95.3) 18268 (95.6) 3223 (93.5) 1 1058 ( 4.7) 835 (4.4) 223 (6.5) Renal disease, n (%) < 0.001 0 17765 (78.8) 15208 (79.6) 2557 (74.2) 1 4784 (21.2) 3895 (20.4) 889 (25.8) Malignant cancer, n (%) < 0.001 0 19463 (86.3) 16748 (87.7) 2715 (78.8) 1 3086 (13.7) 2355 (12.3) 731 (21.2) Severe liver disease, n (%) < 0.001 0 21149 (93.8) 18135 (94.9) 3014 (87.5) 1 1400 ( 6.2) 968 (5.1) 432 (12.5) Aids, n (%) 0.372 0 22399 (99.3) 18972 (99.3) 3427 (99.4) 1 150 ( 0.7) 131 (0.7) 19 (0.6) Severity of illness Sapsii 39.6 ± 14.5 37.3 ± 12.9 52.2 ± 16.2 < 0.001 Oasis 34.7 ± 9.3 33.3 ± 8.7 42.3 ± 9.1 < 0.001 Sofa score 3.0 (2.0, 4.0) 3.0 (2.0, 4.0) 4.0 (2.0, 6.0) < 0.001 SOFA Sequential Organ Failure Assessment, OASIS Oxford Acute Disease Severity Score, Sapsii Simplified Acute Physiology Score II; SD standard deviation, WBC white blood cells, ABR anion gap bicarbonate ratio, HR heart rate, RR respiratory rate, MBP mean arterial blood pressure Primary outcome:ABR and 28-day mortality In this study, we constructed four models for COX regression analysis of the independent effect of early ABR on 28-day mortality. The effect size (HR) and 95% CI are listed in Table 2 . In unadjusted models, model-based effect sizes could be explained as the association between ABR and 28-day mortality.For example, an effect size of 2.33 for 28-day mortality implies a 2.33-fold increase in 28-day mortality for every increase in ABR (HR 2.33,95% CI 2.26–2.41, P < .001).In adjusted models I, II and III, the 28-day mortality increased by 2.31-fold (HR 2.31,95% CI 2.24–2.39, P < .001) and 1.36-fold (HR 1.36,95% CI 1.24–1.49, respectively) with each increase in ABR. P < .001) and 1.4 times (HR 1.4,95% CI 1.27–1.55, P < .001).Adjust I Adjust age and gender, Adjust II Adjust I plus heart_rate_mean,dbp_mean,mbp_mean,resp_rate_mean,spo2_mean,hematocrit,hemoglobin,aniongap,bicarbonate,calcium,sodium,pot Assium glucose_mean, WBC, platelets, bun, creat, pt.Adjustment III Adjustment II Add myocardial_infarct, congestive_heart_failure, peripheral_vascular_disease, dementia, cerebrovascular_disease, chronic_pulmon ary_disease,rheumatic_disease,peptic_ulcer_disease,mild_liver_disease,diabetes,paraplegia + renal_disease,malignant_cancer Metastatic_solid_tumor, AIDS, sapsii, oasis, sofa_score.More details are provided in Table 2 . Table 2 Multivariate COX regression analysis of the association between anion gap levels and 28-day mortality in patients with sepsis. Non-adjusted Adjust I Adjust II Adjust III Exposure HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value ABR 2.33 (2.26 ~ 2.41) < 0.001 2.31 (2.24 ~ 2.39) < 0.001 1.36 (1.24 ~ 1.49) < 0.001 1.4 (1.27 ~ 1.55) < 0.001 ABR was positively correlated with 28-day mortality We analyzed the relationship between the ABR and 28-day mortality, which showed a positive trend ( Fig. 2 ). The multivariate Cox regression model and smoothed curve fitting showed that the positive association between ABR level and 28-day mortality was almost non-linear after adjustment for aheart_rate_mean,dbp_mean,mbp_mean,resp_rate_mean,spo2_mean,hematocrit,haemoglobin,aniongap,bicarbonate,calcium, sodium,potassium,glucose_mean,wbc,platelets,thrombocytes,creat,pt,myocardial_infarction,congestive_heart_failure, peripheral_vascular_disease,dementia,cerebrovascular_disease,chronic_pulmonary_disease,rheumatic_disease,peptic_ulcer_ disease,mild_liver_disease,diabetes,paraplegia + renal_disease,malignant_cancer,metastatic_solid_tumour, AIDS,sapsii,oasis,sofa_score. The multivariate Cox regression model and smoothed curve fitting revealed that ABR had a U-shaped relationship with 28-day mortality, and ABR inflection point was was0.6786 (P for nonlinearity = 0.022) (see Fig. 2 ). We fitted 2 different slopes with the segmented multivariate Cox regression models, and found that the P value of the likelihood ratio test was 0.001 (see Table 3 ). Thus, we used 2 segmented models to fit the association between ABR and 28-day mortality. The effect value was 1.46 (HR = 0.1.46; 95% CI: 0.79 ~ 2.68 P = 0.228) for ABR < 0.6786; however, when ABR was ≥ 0.6786, the effect value was 2.12 (HR = 2.12; 95% CI: 2.03 ~ 2.2, P < 0.001) (see Table 3 ). Table 3 The non-linear relationship between ABR and 30-day mortality Threshold of driving pressure HR 95% CI P value < 0.6786 1.46 (0.79 ~ 2.68) 0.228 ≥ 0.6786 2.12 (2.03 ~ 2.2) p < 0.001 Likelihood ratio test 0.001 According to smoothed curve fitting revealed that ABR inflection point was0.6786, patients were divided into low ABR group (ABR < 0.6786, n = 12233) and high ABR group (ABR ≥ 0.6786, n = 10316). As shown in Fig. 3 . The survival curve showed that the prognosis of the high LAR group was significantly worse than that of the low LAR group (p < 0.001). ROC curve analysis We plotted ROC curves for the two indicators of ABR, SOFA for predicting 28-day mortality in ICU patients with sepsis. The detailed information in Fig. 4 . The AUC of ABR (0.565; 95% CI 0.547–0.584) was comparable with SOFA (0.502; 95% CI 0.483–0.520) . Hierarchical analysis Figure 5 provides the results of a hierarchical analysis of different subgroups. These include Myocardial Infarct, Cerebrovascular Disease, Peptic Ulcer Disease, Liver Disease, Diabetes, Paraplegia, Metastatic Solid In the subgroup of Tumor comorbidized patients, the P value was < 0.05 in COX analysis after adjusting all covariables.The results showed that in all subgroups except for those with Myocardial Infarct or Paraplegia, high ABR levels were significantly associated with a significantly increased risk of death within 28 days. There was no significant interaction between ABR and most subgroups (interaction P > 0.05). However, an interaction was observed between the Cerebrovascular Disease and Diabetes subgroups (interaction P < 0.05). Discussion This study, based on the MIMIC-IV database, conducted the first large-scale investigation of the relationship between ABR and 28-day mortality in ICU patients with sepsis. The results showed that ABR was an independent predictor of 28-day mortality, and its relationship with mortality was non-linear, with an inflection point of 0.6786. The 28-day mortality of patients in the high ABR group was significantly higher than that of patients in the low ABR group. Even after adjustment for demographics, vital signs, laboratory indicators, and comorbidities, ABR remained significantly associated with mortality (HR = 1.40, 95% CI: 1.27–1.55, p < 0.001). In addition, in this study, we found that the predictive value of ABR for 28-day mortality in patients with sepsis revealed by ROC curve was superior to SOFA.But, this does not completely rule out that this result was influenced by FOFA itself. In addition, We also add the ROC of ABR and bicarbonate for analysis, and found that ABR and 28-day mortality were statistically better than bicarbonate. The findings could help add valuable clinical insight.however,This finding suggests that ABR may influence the prognosis of sepsis patients by reflecting their metabolic derangements and acid-base status. Metabolic acidosis is usually associated with the severity of illness in patients with sepsis. Lactate, serum total carbon dioxide, serum anion gap (AG) and bicarbonate are indicators of metabolic acidosis. Serum total carbon dioxide and lactate are closely related to the prognosis of sepsis [ 12 , 13 ] . Prior studies have shown poor correlation between lactate and other serum biomarkers, but these studies used traditional cut-off values for serum anion gap and bicarbonate, rather than defining useful biomarker thresholds based on disease-specific data [ 14 , 15 ] . Another important point is that these tests are still risk stratification tools, not diagnostic tools. In fact, the utility of lactic acid measurement is to predict mortality, and related studies have shown that elevated anion gaps can also predict mortality. Patients with elevated anion gaps are three times more likely to die than lactate. [ 16 ] Our assessment of the correlation between ABR and the prognosis of sepsis is different from that of lactate and serum total carbon dioxide. It is a different perspective on the risk of metabolic disturbances in sepsis and also the significance of this study. ABR is also one of the indicators that is easy to obtain in clinical practice and has practical clinical guidance. As a comprehensive indicator, ABR may reflect the metabolic status of patients better than AG or HCO₃ alone, and a high ABR usually indicates metabolic acidosis, which may be related to lactate accumulation, renal insufficiency, or accumulation of unmeasured anions [ 8 ] . Patients with sepsis often have lactic acidosis, which is caused by increased lactate production due to tissue hypoxia and increased glycolysis [ 17 ] .In addition, renal insufficiency in patients with sepsis may lead to decreased excretion of acidic metabolites, further exacerbating acidosis [ 18 ] . Therefore, ABR may serve as an important prognostic marker for sepsis patients by reflecting these pathological physiological changes. This study found that the influence of ABR on mortality was particularly significant in specific subgroups. For instance, in patients with cerebrovascular diseases, a higher ABR was associated with a stronger correlation with mortality (HR = 2.56, 95% CI: 1.57–4.16, P for interaction = 0.001). This might be related to the fact that patients with cerebrovascular diseases often have metabolic disorders and compromised immune function [ 19 ] .Furthermore, in diabetic patients, ABR also significantly affects mortality (HR = 1.63, 95% CI: 1.42–1.88, P for interaction = 0.002), which may be related to the fact that diabetic patients often have chronic metabolic disorders and microvascular lesions [ 20 ] . In patients with metastatic solid tumors, ABR also had a significant impact on mortality (HR = 2.20, 95% CI: 1.37–3.53, P for interaction = 0.127). This might be related to the fact that patients with tumors often have metabolic disorders and compromised immune function [ 21 ] .Furthermore, in patients with chronic lung diseases, ABR also significantly affects mortality (HR = 1.11, 95% CI: 1.03–1.19, P for interaction = 0.007), which may be related to the fact that patients with chronic lung diseases often have chronic hypoxia and metabolic disorders [ 22 ] . The results of this study are consistent with previous studies in this area. For example, a study of acute pancreatitis found that ABR was significantly associated with patient mortality [ 9 ] . Another study on chronic kidney disease also showed that ABR was an important indicator for predicting the prognosis of patients [ 10 ] , but these studies mainly focused on individual diseases, while this research is the first to verify the prognostic value of ABR in patients with sepsis and to perform a deep stratified analysis. Limitations of the Study Although this study provides new evidence for ABR in the prognosis of sepsis, there are still some limitations. Firstly, as it is a retrospective study, there may be selection bias and the influence of confounding factors.Secondly, some variables in the MIMIC-IV database may be missing. Although we have dealt with this in various ways (such as by excluding variables with a lot of missing data), some data were missing, resulting in incomplete sample inclusion; for example, in 84 patients, aniongap or bicarbonate tests were not performed within 24 hours after ICU admission There are still variables that could affect the ABR and unmeasured confounders (such as APACHE II data were unavailable) that could still affect the accuracy of the results. Finally, this study did not consider the impact of therapeutic interventions on ABR and prognosis. Future research should further explore the application value of ABR in guiding individualized treatment. Conclusion ABR is an independent predictor of 28-day mortality in ICU patients with sepsis, and it has significant prognostic value especially in specific subgroups. Future studies should further explore the clinical application value of ABR in the management of sepsis. Declarations Acknowledgments We are grateful for the excellent work of the MIMIC team (MIT Computational Physiology Laboratory), who continuously collect bedside data and provide a database for every critical care researcher. Footnote Reporting Checklist: The authors have completed the STROBE reporting checklist. Conflicts of Interest: Not applicable. Ethical Statement: The use of the MIMIC database was approved by the Institutional Review Board of Massachusetts Institute of Technology and BIDMC, both of which waive the need for informed consent for studies related to the MIMIC-IV database; thus, our current study did not need an approval from the ethics committee of our own center. Funding: This study was financially supported by Yiwu Science and Technology Plan Project (24-3-104). The funders had no role in the design of the study, the collection, analysis and interpretation of the data, or preparation of the manuscript. Author Contribution the first author wrote the main manuscript textCorresponding author completed the experimental design and data statistics References Singer, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315 , 801–810 (2016). Rudd, K. E. et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet 395 , 200–211 (2020). Fleischmann, C. et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. *American J. Respiratory Crit. Care Medicine* . 193 (3), 259–272 (2016). Oh, M. S. & Carroll, H. J. The anion gap. N Engl. J. Med. 297 , 814–817 (1977). Kraut, J. A. & Madias, N. E. Metabolic Acidosis: Pathophysiology, Diagnosis and Management. *Nature Reviews Nephrology* . 6 (5), 274–285 (2010). Adrogué, H. J. & Madias, N. E. Management of Life-threatening Acid-base Disorders. *New Engl. J. Medicine* . 338 (1), 26–34 (1998). Zhang, Z. et al. Anion Gap to Albumin Ratio as a Novel Predictor of Mortality in Critically Ill Patients with Acute Pancreatitis. *Critical Care* . 24 (1), 1–10 (2020). Gunnerson, K. J. et al. Lactate versus Non-lactate Metabolic Acidosis: A Retrospective Outcome Evaluation of Critically Ill Patients. *Critical Care* . 10 (1), R22 (2006). Wang, X. et al. Anion Gap to Albumin Ratio Predicts Mortality in Patients with Acute Pancreatitis. *Pancreatology* 19 (5), 681–687 (2019). Chen, Y. et al. Anion Gap to Albumin Ratio as a Predictor of Mortality in Chronic Kidney Disease Patients. *Nephrology Dialysis Transplantation* . 36 (3), 456–463 (2021). Le Gall, J. R., Lemeshow, S. & Saulnier, F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 270 , 2957–2963. 10.1001/jama.1993.03510240069035 (1993). Chen, H., Zhao, C., Wei, Y. & Jin, J. Early lactate measurement is associated with better outcomes in septic patients with an elevated serum lactate level. Crit. Care . 23 , 351 (2019). Kim, J. H. et al. Serum total carbon dioxide as a prognostic factor for 28-day mortality in patients with sepsis. Am. J. Emerg. Med. 44 , 277–283 (2021). Spitalnic, S., Sidman, R. D. & Mondi, J. Serum bicarbonate and anion gap cannot reliably predict elevated serum lactate levels. Ann. Emerg. Med. 44 , S54–S54 (2004). Iberti, T. J., Leibowitz, A. B., Papadakos, P. J. & Fischer, E. P. Low sensitivity of the anion gap as a screen to detect hyperlactatemia in critically ill patients. Crit. Care Med. 18 , 275–277 (1990). Nicholas, M., Mohr, J. & Priyanka Vakkalanka, J. Serum anion gap predicts lactate poorly, but may be used to identify sepsis patients at risk for death: A cohort study. J. Crit. Care . 44 , 223–228 (2018). Levy, B. et al. Lactate and Shock State: The Metabolic View. *Current Opin. Crit. Care* . 23 (4), 315–321 (2017). Bellomo, R. et al. Acute Kidney Injury in Sepsis. *Intensive Care Medicine* . 38 (6), 933–946 (2012). Smith, C. J. et al. Sepsis in Stroke: A Review of the Literature. *Stroke* 49 (6), 1414–1421 (2018). Dungan, K. M. et al. Stress Hyperglycaemia *The Lancet* , 373 (9677), 1798–1807. (2009). Vincent, J. L. et al. Sepsis in European Intensive Care Units: Results of the SOAP Study. *Critical Care Medicine* . 41 (2), 344–356 (2013). Rhodes, A. et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. *Intensive Care Medicine* . 43 (3), 304–377 (2017). Additional Declarations No competing interests reported. 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Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACfmaGhAMfDCTq+dkbiNQi2d7w8OGMAosEyZ4DRGoxOHPwsTHHh4oEgxkJxNoyIzlNmsFAIs9A8vHGGww1NtEEtfBLpKVJFxhIFJtLpxVbMBxLy20gbEtOmvQMAwnGnbNzzCQYGw4T1mJwI/+bNA9Qy4abZ4jVcuZAsjFQS+KGGzxEagEGcuJDoMOMJXuAfkkgxi+QqPxTJ8fPfnjjjQ81NoS1oDhSIoEU5RAtpOoYBaNgFIyCkQEAFd5ByHJIefIAAAAASUVORK5CYII=","orcid":"","institution":"Yiwu Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-03-15 17:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6234181/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6234181/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79115647,"identity":"d04a8d3b-75ba-41e2-ab7f-acea1b5c8830","added_by":"auto","created_at":"2025-03-24 15:07:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":269049,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6234181/v1/d83bf365cc1a4b903d19512c.jpeg"},{"id":79116950,"identity":"2fa11501-4ceb-449b-b018-d713b28e0b63","added_by":"auto","created_at":"2025-03-24 15:15:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43611,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear relationship between ABR and 28-day death, adjusted for all covariates in Table 1\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6234181/v1/73490a4647ac86b8c1dff75a.png"},{"id":79115648,"identity":"848d14c7-055f-4b47-9bd4-b94b7ba6d916","added_by":"auto","created_at":"2025-03-24 15:07:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61632,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curve of sepsis patients admitted to ICU at 28 days\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6234181/v1/38a17b4f206401eca836b869.png"},{"id":79115651,"identity":"83ab5a2c-62d0-49e3-a18d-7951479b24e6","added_by":"auto","created_at":"2025-03-24 15:07:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":195430,"visible":true,"origin":"","legend":"\u003cp\u003eROC comparison for predicting 28-day mortality among ABR,bicarbonate and SOFA.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6234181/v1/483eede20ec144b614f66c44.jpeg"},{"id":79115656,"identity":"4688c3d4-fce3-4861-acdd-d650af97f9c8","added_by":"auto","created_at":"2025-03-24 15:07:36","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":363678,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the relationship between ABR and 28-day mortality risk\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6234181/v1/b6e72a2143591133e7772be1.jpeg"},{"id":79332589,"identity":"a2d213e4-69bf-4faf-bf08-f37153b4147c","added_by":"auto","created_at":"2025-03-27 06:54:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1880639,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6234181/v1/ff0728fb-0e64-4240-97fd-58eda80db4c1.pdf"},{"id":79115660,"identity":"d9c4cad3-95c6-4872-953a-0021e7661218","added_by":"auto","created_at":"2025-03-24 15:07:37","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14636980,"visible":true,"origin":"","legend":"","description":"","filename":"Rawdata22549.csv","url":"https://assets-eu.researchsquare.com/files/rs-6234181/v1/eb2d1f970e253183a1af3b41.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Anion Gap to Bicarbonate Ratio (ABR) with 28-Day Mortality in ICU Sepsis: A Retrospective Cohort Study Based on the MIMIC-IV Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the Sepsis-3 consensus criteria, sepsis is defined as a dysregulated systemic response to infection culminating in life-threatening organ failure \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Despite therapeutic advancements, epidemiological data indicate that sepsis contributes to nearly one-fifth of global mortality, underscoring the urgency for novel prognostic markers \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.In the third international consensus definition of sepsis and septic shock, sepsis is defined as a life-threatening organ dysfunction caused by a dysfunctional host response to infection. Sepsis is the main cause of death in ICU patients, and its pathophysiological mechanism is complex, involving systemic inflammatory response, metabolic disorders, and multiple organ dysfunction syndrome (MODS) \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.Despite significant advances in the diagnosis and treatment of sepsis in recent years, the mortality rate remains high, especially in the ICU setting \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.Sepsis guidelines \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e indicate that serum lactate levels can reflect changes in tissue perfusion. However, serum lactate level is not the only indicator of metabolic parameter disorders, and the importance of metabolic disorders in patients with sepsis has been reevaluated from a different perspective. Therefore, it is important to identify novel and representative markers of metabolic dysfunction in sepsis in order to identify high-risk patients early and guide individualized treatment.\u003c/p\u003e \u003cp\u003eAnion Gap (AG) and Bicarbonate (HCO₃⁻) are important indicators to evaluate the acid-base balance. The anion gap (AG) is a composite outcome indicator determined by the measured concentrations of anions and cations, \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e which reflects acid-base metabolism and may reflect the overall metabolic status of the patient.It is commonly used to assess the type and severity of metabolic acidosis \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. HCO₃⁻ is the main buffer substance and its level is closely related to the body's acid-base balance \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.In recent years, researchers have proposed that the ratio of anion gap to bicarbonate (ABR) may be used as a new metabolic marker to reflect the acid-base balance state and metabolic disorder of patients \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, the relationship between ABR and the prognosis of patients with sepsis has not been fully studied.\u003c/p\u003e \u003cp\u003ePatients with sepsis often have metabolic acidosis, which may be related to lactate accumulation, renal insufficiency, or unmeasured anion accumulation \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. ABR, as a composite indicator, may be more reflective of a patient's metabolic status than AG or HCO₃⁻ alone.Studies have shown that ABR is associated with the prognosis of a variety of diseases, such as acute pancreatitis and chronic kidney disease \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, but its role in sepsis remains unclear. Therefore, the aim of this study was to investigate the association of ABR with 28-day mortality in ICU sepsis patients based on the MIME-IV database, and to assess its prognostic value in specific subgroups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDatabase\u003c/h2\u003e \u003cp\u003eThe study cohort was derived from the MIMIC-IV database (version 2.2), encompassing de-identified clinical records of ICU admissions at Beth Israel Deaconess Medical Center (2008\u0026ndash;2019). The database consisted of records of demographics, vital signs, laboratory tests, fluid balance, and vital status; documented International Classification of Diseases and Ten Revision (ICD-9) codes; documented hourly physiological data from bedside monitors confirmed by ICU nurses; and stored written evaluations of radiology films by specialists during the corresponding time period. One author (Chuntian Chi) completed the CITI Data or Specimens Only Research course, was approved to access the database, and took responsibility for data extraction (certification number 66935858). The study was conducted in accordance with the tenets of the Declaration of Helsinki (as revised in 2013). The use of the MIMIC database was approved by the Institutional Review Boards of the Massachusetts Institute of Technology and BIDMC, both of which waive the requirement for informed consent for studies involving the MIMIC-IV database; therefore, our current study did not require approval from our own centre's ethics committee.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study included patients in the MIMIC-IV who satisfied the criteria for sepsis and were eligible to participate. Sepsis diagnosis adhered to the Sepsis-3 framework, requiring both confirmed/suspected infection and a\u0026thinsp;\u0026ge;\u0026thinsp;2-point increase in Sequential Organ Failure Assessment (SOFA) scores \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e .In summary, sepsis was identified in persons who had confirmed or suspected infection and saw a sudden increase in their overall Sequential Organ Failure Assessment (SOFA) score of at least two points. Infection was identified by the MIMIC-IV system using the International Classification of Diseases, ninth Edition (ICD-9) code.\u003c/p\u003e \u003cp\u003ePeople admitted between 2008 and 2019 are easily identified in the database.Our study included 431,231 MIME-IV patients admitted to hospital and then excluded patients who were admitted to ICU for a second (or more) hospital admission or a second ICU admission. We only included 22,633patients with Sepsis who were admitted to the ICU for the first time between 2008 and 2019. After excluding 84 patients admitted with missing data, we finally included 22,549 patients with Sepsis in our cohort. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eStructured Query Language (SQL), PostgreSQL tools (version 9.6) and STATA version 17.0 were used for data extraction and management. Data on age, sex, ethnicity, comorbidities, initial laboratory parameters on admission to the ICU, two scoring systems including the SOFA score and the Simplified Acute Physiology Score II (SAPSII) \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, mechanical ventilation (MV) on admission to the ICU, length of hospital and ICU stay, and date of death of the patients were extracted directly or calculated. Comorbidities identified by ICD-9 code included atrial fibrillation (AFIB), coronary artery disease (CAD), congestive heart failure (CHF), diabetes, malignancy, chronic kidney disease, liver disease, stroke, and laboratory parameters included haemoglobin, platelet count, white blood cell count, percentage of lymphocytes and neutrophils, neutrophil-to-lymphocyte ratio (NLR), PH, partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), bicarbonate, partial thromboplastin time (PTT), prothrombin time (PT), glucose, urea nitrogen, creatinine, lactate, creatine kinase, creatine kinase isoenzyme (CK-MB), creatine kinase, alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP) and anion gap.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline measurements for a normal distribution are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026macr;\u0026plusmn;s), while data for a non-normal distribution are expressed as median (quartile). Counting data are expressed as frequencies and percentages (%). Missing data were imputed by multiple interpolation. For analysis of baseline characteristics, statistical differences between the two groups for continuous variables were analysed using 1-way analysis of variance or the Kruskal-Wallis H test, and for categorical variables using the chi-squared test. Multivariate Cox regression analyses were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for death in different ABR groups. To control for confounding, factors with a p-value of less than 0.05 in the univariate analysis were included in the multivariate model analysis.Finally, heart_rate_mean,dbp_mean,mbp_mean,resp_rate_mean,spo2_mean,hematocrit,hemoglobin,aniongap,bicarbonate,calcium, sodium,potassium,glucose_mean,wbc,platelets,bun,creat,pt,myocardial_infarct,congestive_heart_failure,peripheral_vascular_ disease,dementia,cerebrovascular_disease,chronic_pulmonary_disease,rheumatic_disease,peptic_ulcer_disease,mild_liver_ disease,diabetes,paraplegia\u0026thinsp;+\u0026thinsp;renal_disease,malignant_cancer,metastatic_solid_tumor,aids,sapsii,oasis,sofa_score were included in the adjusted model. Gender and age are usually required adjustment covariates. Significance of survival was analysed by K-M curve and log-rank test. Univariate and multivariate COX regression models were used to investigate the association between ABR and 28-day mortality in patients with sepsis in ICU. To further analyse the effects of comorbidities and major treatments on study outcomes, subgroup analyses were performed for comorbidities (including cerebrovascular disease, severe liver disease) and sex to assess the stability of study results. All statistical analyses were performed using the R language (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) and Free Statistics software. All P values reported are 2-tailed, and P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe baseline characteristics and laboratory parameters of the study population are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e \u003cp\u003e22,549 patients with a mean age of 66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4 years were included, of which 9525 (42.2%) participants were female. Results showed that 3,446 patients (15.28%) died during the 28-day follow-up period.Among them, the death group was older than the survivors SOFA, sapsii and OASIS scores were more serious. In addition, the levels of white blood cells, blood glucose, creatinine, BUN and ABR were higher in the death group, and the burden of complications was heavier. More details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics between survivors and non-survivors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22549)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3446)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9525 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7973 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1552 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13024 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11130 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1894 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVital signs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131.3 (114.0, 159.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.3 (114.0, 155.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141.1 (114.0, 183.3)\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 \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.8 (9.9, 18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.6 (9.8, 18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4 (10.5, 21.4)\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 \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200.0 (145.0, 271.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200.0 (148.0, 268.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199.0 (128.0, 285.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBun, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.0 (15.0, 37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0 (15.0, 34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.0 (21.0, 53.0)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.9, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.8, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 (1.1, 3.2)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ept,s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.9 (13.1, 17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7 (13.0, 17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7 (13.7, 24.4)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.802\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAniongap,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicarbonate ,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium ,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium ,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium ,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.6, 1.1)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarct,\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18769 (83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16039 (84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2730 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3780 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3064 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e716 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16209 (71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13929 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2280 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6340 (28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5174 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1166 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19873 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16892 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2981 (86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2676 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2211 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e465 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21523 (95.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18270 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3253 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1026 ( 4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e833 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19392 (86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16630 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2762 (80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3157 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2473 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e684 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16751 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14263 (74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2488 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5798 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4840 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e958 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatic disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21739 (96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18432 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3307 (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e810 ( 3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e671 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21870 (97.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18540 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3330 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e679 ( 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e563 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild liver disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19301 (85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16685 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2616 (75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3248 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2418 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e830 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17213 (76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14552 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2661 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5336 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4551 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e785 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaplegia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21491 (95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18268 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3223 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1058 ( 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e835 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17765 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15208 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2557 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4784 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3895 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e889 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant cancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19463 (86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16748 (87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2715 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3086 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2355 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e731 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere liver disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21149 (93.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18135 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3014 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1400 ( 6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e968 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e432 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAids, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22399 (99.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18972 (99.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3427 (99.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150 ( 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity of illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSapsii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSofa score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0 (2.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (2.0, 6.0)\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSOFA Sequential Organ Failure Assessment, OASIS Oxford Acute Disease Severity Score, Sapsii Simplified Acute Physiology Score II; SD standard deviation, WBC white blood cells, ABR anion gap bicarbonate ratio, HR heart rate, RR respiratory rate, MBP mean arterial blood pressure\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary outcome:ABR and 28-day mortality\u003c/h3\u003e\n\u003cp\u003eIn this study, we constructed four models for COX regression analysis of the independent effect of early ABR on 28-day mortality. The effect size (HR) and 95% CI are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In unadjusted models, model-based effect sizes could be explained as the association between ABR and 28-day mortality.For example, an effect size of 2.33 for 28-day mortality implies a 2.33-fold increase in 28-day mortality for every increase in ABR (HR 2.33,95% CI 2.26\u0026ndash;2.41, P\u0026thinsp;\u0026lt;\u0026thinsp;.001).In adjusted models I, II and III, the 28-day mortality increased by 2.31-fold (HR 2.31,95% CI 2.24\u0026ndash;2.39, P\u0026thinsp;\u0026lt;\u0026thinsp;.001) and 1.36-fold (HR 1.36,95% CI 1.24\u0026ndash;1.49, respectively) with each increase in ABR. P\u0026thinsp;\u0026lt;\u0026thinsp;.001) and 1.4 times (HR 1.4,95% CI 1.27\u0026ndash;1.55, P\u0026thinsp;\u0026lt;\u0026thinsp;.001).Adjust I Adjust age and gender, Adjust II Adjust I plus heart_rate_mean,dbp_mean,mbp_mean,resp_rate_mean,spo2_mean,hematocrit,hemoglobin,aniongap,bicarbonate,calcium,sodium,pot Assium glucose_mean, WBC, platelets, bun, creat, pt.Adjustment III Adjustment II Add myocardial_infarct, congestive_heart_failure, peripheral_vascular_disease, dementia, cerebrovascular_disease, chronic_pulmon ary_disease,rheumatic_disease,peptic_ulcer_disease,mild_liver_disease,diabetes,paraplegia\u0026thinsp;+\u0026thinsp;renal_disease,malignant_cancer Metastatic_solid_tumor, AIDS, sapsii, oasis, sofa_score.More details are provided in 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 analysis of the association between anion gap levels and 28-day mortality in patients with sepsis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNon-adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAdjust I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAdjust II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eAdjust III\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.33 (2.26\u0026thinsp;~\u0026thinsp;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.31 (2.24\u0026thinsp;~\u0026thinsp;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.36 (1.24\u0026thinsp;~\u0026thinsp;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.4 (1.27\u0026thinsp;~\u0026thinsp;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eABR was positively correlated with 28-day mortality\u003c/h2\u003e \u003cp\u003eWe analyzed the relationship between the ABR and 28-day mortality, which showed a positive trend \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe multivariate Cox regression model and smoothed curve fitting showed that the positive association between ABR level and 28-day mortality was almost non-linear after adjustment for aheart_rate_mean,dbp_mean,mbp_mean,resp_rate_mean,spo2_mean,hematocrit,haemoglobin,aniongap,bicarbonate,calcium, sodium,potassium,glucose_mean,wbc,platelets,thrombocytes,creat,pt,myocardial_infarction,congestive_heart_failure, peripheral_vascular_disease,dementia,cerebrovascular_disease,chronic_pulmonary_disease,rheumatic_disease,peptic_ulcer_ disease,mild_liver_disease,diabetes,paraplegia\u0026thinsp;+\u0026thinsp;renal_disease,malignant_cancer,metastatic_solid_tumour, AIDS,sapsii,oasis,sofa_score.\u003c/p\u003e \u003cp\u003eThe multivariate Cox regression model and smoothed curve fitting revealed that ABR had a U-shaped relationship with 28-day mortality, and ABR inflection point was was0.6786 (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.022) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We fitted 2 different slopes with the segmented multivariate Cox regression models, and found that the P value of the likelihood ratio test was 0.001 (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thus, we used 2 segmented models to fit the association between ABR and 28-day mortality. The effect value was 1.46 (HR\u0026thinsp;=\u0026thinsp;0.1.46; 95% CI: 0.79\u0026thinsp;~\u0026thinsp;2.68 P\u0026thinsp;=\u0026thinsp;0.228) for ABR\u0026thinsp;\u0026lt;\u0026thinsp;0.6786; however, when ABR was \u0026ge;\u0026thinsp;0.6786, the effect value was 2.12 (HR\u0026thinsp;=\u0026thinsp;2.12; 95% CI: 2.03\u0026thinsp;~\u0026thinsp;2.2, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (see 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\u003e\u003cb\u003eThe non-linear relationship between ABR and 30-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold of driving pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.6786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.79\u0026thinsp;~\u0026thinsp;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.6786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.03\u0026thinsp;~\u0026thinsp;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood ratio 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\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to smoothed curve fitting revealed that ABR inflection point was0.6786, patients were divided into low ABR group (ABR\u0026thinsp;\u0026lt;\u0026thinsp;0.6786, n\u0026thinsp;=\u0026thinsp;12233) and high ABR group (ABR\u0026thinsp;\u0026ge;\u0026thinsp;0.6786, n\u0026thinsp;=\u0026thinsp;10316). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The survival curve showed that the prognosis of the high LAR group was significantly worse than that of the low LAR group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eROC curve analysis\u003c/h2\u003e \u003cp\u003eWe plotted ROC curves for the two indicators of ABR, SOFA for predicting 28-day mortality in ICU patients with sepsis. The detailed information in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The AUC of ABR (0.565; 95% CI 0.547\u0026ndash;0.584) was comparable with SOFA (0.502; 95% CI 0.483\u0026ndash;0.520) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHierarchical analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides the results of a hierarchical analysis of different subgroups. These include Myocardial Infarct, Cerebrovascular Disease, Peptic Ulcer Disease, Liver Disease, Diabetes, Paraplegia, Metastatic Solid In the subgroup of Tumor comorbidized patients, the P value was \u0026lt;\u0026thinsp;0.05 in COX analysis after adjusting all covariables.The results showed that in all subgroups except for those with Myocardial Infarct or Paraplegia, high ABR levels were significantly associated with a significantly increased risk of death within 28 days. There was no significant interaction between ABR and most subgroups (interaction P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, an interaction was observed between the Cerebrovascular Disease and Diabetes subgroups (interaction P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, based on the MIMIC-IV database, conducted the first large-scale investigation of the relationship between ABR and 28-day mortality in ICU patients with sepsis. The results showed that ABR was an independent predictor of 28-day mortality, and its relationship with mortality was non-linear, with an inflection point of 0.6786. The 28-day mortality of patients in the high ABR group was significantly higher than that of patients in the low ABR group. Even after adjustment for demographics, vital signs, laboratory indicators, and comorbidities, ABR remained significantly associated with mortality (HR\u0026thinsp;=\u0026thinsp;1.40, 95% CI: 1.27\u0026ndash;1.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, in this study, we found that the predictive value of ABR for 28-day mortality in patients with sepsis revealed by ROC curve was superior to SOFA.But, this does not completely rule out that this result was influenced by FOFA itself. In addition, We also add the ROC of ABR and bicarbonate for analysis, and found that ABR and 28-day mortality were statistically better than bicarbonate. The findings could help add valuable clinical insight.however,This finding suggests that ABR may influence the prognosis of sepsis patients by reflecting their metabolic derangements and acid-base status.\u003c/p\u003e \u003cp\u003eMetabolic acidosis is usually associated with the severity of illness in patients with sepsis. Lactate, serum total carbon dioxide, serum anion gap (AG) and bicarbonate are indicators of metabolic acidosis. Serum total carbon dioxide and lactate are closely related to the prognosis of sepsis \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Prior studies have shown poor correlation between lactate and other serum biomarkers, but these studies used traditional cut-off values for serum anion gap and bicarbonate, rather than defining useful biomarker thresholds based on disease-specific data \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Another important point is that these tests are still risk stratification tools, not diagnostic tools. In fact, the utility of lactic acid measurement is to predict mortality, and related studies have shown that elevated anion gaps can also predict mortality. Patients with elevated anion gaps are three times more likely to die than lactate.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur assessment of the correlation between ABR and the prognosis of sepsis is different from that of lactate and serum total carbon dioxide. It is a different perspective on the risk of metabolic disturbances in sepsis and also the significance of this study. ABR is also one of the indicators that is easy to obtain in clinical practice and has practical clinical guidance. As a comprehensive indicator, ABR may reflect the metabolic status of patients better than AG or HCO₃ alone, and a high ABR usually indicates metabolic acidosis, which may be related to lactate accumulation, renal insufficiency, or accumulation of unmeasured anions \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Patients with sepsis often have lactic acidosis, which is caused by increased lactate production due to tissue hypoxia and increased glycolysis \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.In addition, renal insufficiency in patients with sepsis may lead to decreased excretion of acidic metabolites, further exacerbating acidosis \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, ABR may serve as an important prognostic marker for sepsis patients by reflecting these pathological physiological changes.\u003c/p\u003e \u003cp\u003eThis study found that the influence of ABR on mortality was particularly significant in specific subgroups. For instance, in patients with cerebrovascular diseases, a higher ABR was associated with a stronger correlation with mortality (HR\u0026thinsp;=\u0026thinsp;2.56, 95% CI: 1.57\u0026ndash;4.16, P for interaction\u0026thinsp;=\u0026thinsp;0.001). This might be related to the fact that patients with cerebrovascular diseases often have metabolic disorders and compromised immune function \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.Furthermore, in diabetic patients, ABR also significantly affects mortality (HR\u0026thinsp;=\u0026thinsp;1.63, 95% CI: 1.42\u0026ndash;1.88, P for interaction\u0026thinsp;=\u0026thinsp;0.002), which may be related to the fact that diabetic patients often have chronic metabolic disorders and microvascular lesions \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn patients with metastatic solid tumors, ABR also had a significant impact on mortality (HR\u0026thinsp;=\u0026thinsp;2.20, 95% CI: 1.37\u0026ndash;3.53, P for interaction\u0026thinsp;=\u0026thinsp;0.127). This might be related to the fact that patients with tumors often have metabolic disorders and compromised immune function \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.Furthermore, in patients with chronic lung diseases, ABR also significantly affects mortality (HR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.03\u0026ndash;1.19, P for interaction\u0026thinsp;=\u0026thinsp;0.007), which may be related to the fact that patients with chronic lung diseases often have chronic hypoxia and metabolic disorders \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results of this study are consistent with previous studies in this area. For example, a study of acute pancreatitis found that ABR was significantly associated with patient mortality \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Another study on chronic kidney disease also showed that ABR was an important indicator for predicting the prognosis of patients \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, but these studies mainly focused on individual diseases, while this research is the first to verify the prognostic value of ABR in patients with sepsis and to perform a deep stratified analysis.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the Study\u003c/h2\u003e \u003cp\u003eAlthough this study provides new evidence for ABR in the prognosis of sepsis, there are still some limitations. Firstly, as it is a retrospective study, there may be selection bias and the influence of confounding factors.Secondly, some variables in the MIMIC-IV database may be missing. Although we have dealt with this in various ways (such as by excluding variables with a lot of missing data), some data were missing, resulting in incomplete sample inclusion; for example, in 84 patients, aniongap or bicarbonate tests were not performed within 24 hours after ICU admission There are still variables that could affect the ABR and unmeasured confounders (such as APACHE II data were unavailable) that could still affect the accuracy of the results. Finally, this study did not consider the impact of therapeutic interventions on ABR and prognosis. Future research should further explore the application value of ABR in guiding individualized treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eABR is an independent predictor of 28-day mortality in ICU patients with sepsis, and it has significant prognostic value especially in specific subgroups. Future studies should further explore the clinical application value of ABR in the management of sepsis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the excellent work of the MIMIC team (MIT Computational Physiology Laboratory), who continuously collect bedside data and provide a database for every critical care researcher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootnote\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReporting Checklist: The authors have completed the STROBE reporting checklist.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflicts of Interest: Not applicable.\u003c/p\u003e\n\u003cp\u003eEthical Statement:\u0026nbsp;The use of the MIMIC database was approved by the Institutional Review Board of Massachusetts Institute of Technology and BIDMC, both of which waive the need for informed consent for studies related to the MIMIC-IV database; thus, our current study did not need an approval from the ethics committee of our own center.\u003c/p\u003e\n\u003cp\u003eFunding: This study was financially supported by Yiwu Science and Technology Plan Project (24-3-104).\u003c/p\u003e\n\u003cp\u003eThe funders had no role in the design of the study, the collection, analysis and interpretation of the data, \u0026nbsp;or preparation of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003ethe first author wrote the main manuscript textCorresponding author completed the experimental design and data statistics\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinger, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e315\u003c/b\u003e, 801\u0026ndash;810 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudd, K. E. et al. Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the Global Burden of Disease Study. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e395\u003c/b\u003e, 200\u0026ndash;211 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleischmann, C. et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. \u003cem\u003e*American J. Respiratory Crit. Care Medicine*\u003c/em\u003e. \u003cb\u003e193\u003c/b\u003e (3), 259\u0026ndash;272 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOh, M. S. \u0026amp; Carroll, H. J. The anion gap. \u003cem\u003eN Engl. J. 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Anion Gap to Albumin Ratio as a Novel Predictor of Mortality in Critically Ill Patients with Acute Pancreatitis. \u003cem\u003e*Critical Care*\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e (1), 1\u0026ndash;10 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGunnerson, K. J. et al. Lactate versus Non-lactate Metabolic Acidosis: A Retrospective Outcome Evaluation of Critically Ill Patients. \u003cem\u003e*Critical Care*\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (1), R22 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X. et al. Anion Gap to Albumin Ratio Predicts Mortality in Patients with Acute Pancreatitis. \u003cem\u003e*Pancreatology*\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (5), 681\u0026ndash;687 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. et al. Anion Gap to Albumin Ratio as a Predictor of Mortality in Chronic Kidney Disease Patients. \u003cem\u003e*Nephrology Dialysis Transplantation*\u003c/em\u003e. \u003cb\u003e36\u003c/b\u003e (3), 456\u0026ndash;463 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Gall, J. R., Lemeshow, S. \u0026amp; Saulnier, F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e270\u003c/b\u003e, 2957\u0026ndash;2963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.1993.03510240069035\u003c/span\u003e\u003cspan address=\"10.1001/jama.1993.03510240069035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, H., Zhao, C., Wei, Y. \u0026amp; Jin, J. Early lactate measurement is associated with better outcomes in septic patients with an elevated serum lactate level. \u003cem\u003eCrit. Care\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e, 351 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J. H. et al. Serum total carbon dioxide as a prognostic factor for 28-day mortality in patients with sepsis. \u003cem\u003eAm. J. Emerg. Med.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 277\u0026ndash;283 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpitalnic, S., Sidman, R. D. \u0026amp; Mondi, J. Serum bicarbonate and anion gap cannot reliably predict elevated serum lactate levels. \u003cem\u003eAnn. Emerg. Med.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, S54\u0026ndash;S54 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIberti, T. J., Leibowitz, A. B., Papadakos, P. J. \u0026amp; Fischer, E. P. 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Acute Kidney Injury in Sepsis. \u003cem\u003e*Intensive Care Medicine*\u003c/em\u003e. \u003cb\u003e38\u003c/b\u003e (6), 933\u0026ndash;946 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, C. J. et al. Sepsis in Stroke: A Review of the Literature. \u003cem\u003e*Stroke*\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e (6), 1414\u0026ndash;1421 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDungan, K. M. et al. \u003cem\u003eStress Hyperglycaemia *The Lancet*\u003c/em\u003e, \u003cb\u003e373\u003c/b\u003e(9677), 1798\u0026ndash;1807. (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent, J. L. et al. Sepsis in European Intensive Care Units: Results of the SOAP Study. \u003cem\u003e*Critical Care Medicine*\u003c/em\u003e. \u003cb\u003e41\u003c/b\u003e (2), 344\u0026ndash;356 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhodes, A. et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. \u003cem\u003e*Intensive Care Medicine*\u003c/em\u003e. \u003cb\u003e43\u003c/b\u003e (3), 304\u0026ndash;377 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sepsis, MIMIC-IV database, biomarker, risk factor, prognostic implication, anion gap, bicarbonate","lastPublishedDoi":"10.21203/rs.3.rs-6234181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6234181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis-associated mortality remains a critical challenge in intensive care units, with metabolic dysregulation being a hallmark of its pathophysiology. While the anion gap-to-bicarbonate ratio (ABR) has been proposed as a potential biomarker for acid-base homeostasis, its prognostic utility in sepsis patients is yet to be comprehensively validated. This study investigates the correlation between ABR and 28-day mortality in ICU sepsis patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBased on the MIMIC-IV database, patients with sepsis who were admitted to the ICU for the first time (n\u0026thinsp;=\u0026thinsp;22,549) were included. The relationship between ABR and 28-day mortality was evaluated using the Cox proportional hazards model and adjusted for multiple factors. Subgroup analysis was conducted using Kaplan-Meier curves and forest plots.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 28-day mortality rate of patients in the high ABR group (ABR\u0026thinsp;\u0026gt;\u0026thinsp;0.6786) was significantly higher than that of the low ABR group (HR\u0026thinsp;=\u0026thinsp;2.33, 95% CI: 2.26\u0026ndash;2.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After multivariate adjustment, ABR remained significantly associated with mortality (HR\u0026thinsp;=\u0026thinsp;1.40, 95% CI: 1.27\u0026ndash;1.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis showed that the impact of ABR on mortality was particularly significant in patients with cerebrovascular disease, diabetes, and metastatic solid tumors (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eABR is an independent predictor of 28-day mortality in ICU patients with sepsis, especially with significant prognostic value in specific subgroups.\u003c/p\u003e","manuscriptTitle":"Association of Anion Gap to Bicarbonate Ratio (ABR) with 28-Day Mortality in ICU Sepsis: A Retrospective Cohort Study Based on the MIMIC-IV Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 15:07:31","doi":"10.21203/rs.3.rs-6234181/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":"7e8b080b-0315-456d-9802-d0610ac6320c","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46034852,"name":"Health sciences/Diseases"},{"id":46034853,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-03-27T06:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-24 15:07:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6234181","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6234181","identity":"rs-6234181","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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