The Effects of serum glucose level on the association of between serum lactate level and acute kidney injury among critical patient with acute ischemic stroke

The Effects of serum glucose level on the association of between serum lactate level and acute kidney injury among critical patient with acute ischemic stroke | 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 The Effects of serum glucose level on the association of between serum lactate level and acute kidney injury among critical patient with acute ischemic stroke Chunli Yu, Weiguo Yao, Kun Liu, Dingzhong Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4454722/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Serum lactate level has been confirmed to be an independent risk factor for the occurrence of acute kidney injury (AKI) in many diseases. However, the correlation between serum lactate level and AKI in critical patients with acute ischemic stroke (AIS) has not been unclear. Moreover, limited studies have examined the mediating effect of serum glucose on the association between Serum lactate and AKI. Methods: We identified 1,435 AIS patients from the Medical Information Mart for Intensive Care (MIMIC-III) database and divided them into AKI or No-AKI groups. We used a propensity score matching (PSM) method to reduce confounding. Linear regression, logistic regression, and restricted cubic splines (RCS) were used to evaluate relationships between blood lactate levels and serum glucose, serum lactate, as well as AKI. Finally, the mediating role of serum glucose on the relationship between serum lactate and AKI was investigated utilizing the mediation analysis. Results: In the present study, a total of 634 critical patients with AIS aged ≥18 years were included after propensity score matching (1:1). we use RCS plot to reveal a linear association of between serum lactate levels and AKI and between serum glucose levels and serum lactate levels (all P for nonlinear <0.001). After full adjustment for potential confounders (Model 3), serum glucose was positively correlated to serum lactate level (β=0.004, 95% CI: 0.003-0.006, P -value <0.001). High lactate level increased the risk of AKI (OR, 2.216; 95% CI, 1.559-3.271; P -value <0.001). Serum glucose explained 14.9% of the association between serum lactate and AKI among critical patients with AIS ( P -value <0.001), 16.4% among patients with AIS and DM ( P -value =0.24), and 19.5% among patients with AIS and without DM ( P -value <0.001). Conclusion: Serum lactate acid was independently associated with increased risk-adjusted AKI in critical patients with AIS. The increase in serum glucose may have mediated this effect, especially in patients without DM. Health sciences/Nephrology Health sciences/Neurology serum glucose serum lactate acute kidney injury acute ischemic stroke Figures Figure 1 Figure 2 Introduction Ischemic stroke (IS), as a significant global public health concern ( 1 ), is a cerebrovascular disease characterized by the blockage or interruption of blood supply to the brain, leading to ischemia and hypoxia in brain tissue. Acute Kidney Injury (AKI) refers to the sudden impairment of renal function within a short period, characterized by reduced urine output and elevated levels of serum creatinine and blood urea nitrogen. There is a certain interrelationship between IS and AKI, possibly due to shared risk factors and physiological mechanisms. IS and AKI are both associated with hypoperfusion ( 2 , 3 ). In the case of a stroke, insufficient blood supply to the brain can lead to damage in cerebral tissues. Similarly, the kidneys are prone to injury when subjected to low perfusion states. Therefore, the occurrence of an IS and the vulnerability of the kidneys during decreased perfusion may be interconnected. Most importantly, AKI may lead to disturbances in water and electrolyte balance, difficulties in urination, and systemic inflammatory responses, thereby further exacerbating the overall condition of the patients ( 4 ). Therefore, reducing the occurrence of AKI is particularly crucial for patients with acute ischemic stroke (AIS). Lactic acid is an organic acid typically produced through lactic acid fermentation or during aerobic metabolism ( 5 ). For patients with IS, ischemia can lead to hypoxia in brain tissue, affecting energy metabolism. In hypoxic conditions, cells may produce lactic acid through the lactate fermentation pathway to generate a limited amount of energy ( 6 ). Additionally, damage to brain tissue can result in the release of intracellular lactic acid due to cell membrane rupture ( 7 ). Ultimately, this contributes to an increase in the body's serum lactate levels. Therefore, elevated serum lactate levels may reflect the extent of damage to brain tissue, and monitoring serum lactate levels can aid in assessing the severity of IS and potential complications. Extremely elevated serum lactate levels may lead to lactic acidosis, a severe metabolic disorder, and irreversible cell damage caused by acidosis may pose a direct threat to the patient's life ( 8 , 9 ). Furthermore, part of lactate acid is metabolized by the kidneys, and kidney damage may lead to further accumulation of lactate acid ( 10 ). This may lead to poor prognosis in patients. Currently, there are no studies on the relationship between serum lactate level and AKI in patients with AIS. Therefore, in this study, we analyzed retrospectively the relationship between serum lactate level and AKI in patients with AIS through the Medical Information Mart for Intensive Care (MIMIC) III database, and also analyzed whether serum glucose levels have a mediating role in the process of lactic acid-induced AKI. Materials and methods Data Source Participants for this study were sourced from the MIMIC-III database, which received approval for construction from the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). The data utilized in this study, derived from this publicly accessible critical care database, was meticulously extracted by Dingzhong Tang, an author who successfully completed an online training course and passed the exam (ID: 13183421). All methods were performed in accordance with the relevant guidelines and regulations. Study Participants In this retrospective study, a total of 2884 critical patients with AIS were initially considered from the MIMIC-III database. AIS is diagnosed based on ICD 9 codes. Exclusions were made based on specific criteria: 472 participants were excluded due to non-first-time admission or ICU admission. Out of the remaining 2412 participants, 977 were further excluded due to the absence of baseline serum lactate and baseline serum glucose. All participants were confirmed to be above 18 years old. Ultimately, the study included 1,435 participants, comprising 1118 with acute kidney injury (AKI) and 317 without AKI (No-AKI). Endpoint Event and Exposure Variable Acute Kidney Injury (AKI) served as the primary endpoint event, diagnosed based on the criteria established by the kidney disease: Improving Global Outcomes (KDIGO). Diagnosis relied on changes in serum creatinine and urine output, with AKI defined as any of the following: Serum Creatinine Criteria: ( 1 ) A rise of ≥ 0.3 mg/dL (26.5 µmol/L) in serum creatinine within 48 hours. ( 2 ) Serum creatinine surpassing 1.5 times the patient's baseline level. Urine Output Criteria: Urine output persistently less than 0.5 mL/kg body weight per hour for over 6 hours. The severity of AKI is classified into three grades based on the multiple of the increase in serum creatinine relative to the baseline level and the degree of hourly urine output reduction: AKI Grade 1: Serum creatinine rises by 1.5–1.9 times the baseline level. Or, urine output is less than 0.5 mL/kg body weight per hour, lasting 6–12 hours. AKI Grade 2: Serum creatinine rises by 2.0-2.9 times the baseline level. Or, urine output is less than 0.5 mL/kg body weight per hour, lasting 12–24 hours. AKI Grade 3: Serum creatinine rises more than 3.0 times the baseline level. Or, serum creatinine reaches or exceeds 4.0 mg/dL (353.6 µmol/L). Or, urine output is less than 0.3 mL/kg body weight per hour, persisting for 24 hours. Or, there is a requirement for dialysis treatment. The exposure variable was serum lactate level and the mediator is serum glucose. they are all defined as the first measurement within 24 hours after admission to the ICU. Covariates Relevant demographic variables included age, gender, ethnicity, admission type, and marital status. Vital signs encompassed heart rate, respiration rate, systolic and diastolic blood pressure, pulse oxygen saturation (SpO2), partial pressure of oxygen (PaO2), and partial pressure of carbon dioxide (PaCO2). Laboratory tests comprised white blood cell (WBC) count, hemoglobin (Hb), serum creatinine (Scr), sodium, potassium, activated partial thromboplastin time (APTT), prothrombin time (PT), international normalized ratio (INR), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Recorded complications included hyperlipidemia, hypertension, acute myocardial infarction (AMI), and diabetes mellitus (DM). Additionally, data on the use of thrombolytic agents, Sequential Organ Failure Assessment (SOFA) score, and Simplified Acute Physiologic Score (SAPS) II were also extracted. Statistical Analysis All statistical analyses in this study were conducted using Stata (version 15.0) and R Studio (version 4.1.3), with a significance level set at P -value < 0.05. To address potential bias from missing data, multiple imputations were performed using predicted mean matching (PMM) before analyzing the results. PMM utilizes observational data to establish a linear model, filling in missing values. Imputations with ten times were chosen to enhance data robustness. Variables with missing values exceeding 10% were removed, as recommended in the literature to ensure imputation stability ( 11 ). To address potential confounding of the association between serum lactate level and AKI, we used propensity score matching to match patients with AKI to patients without AKI ( 12 ). Baseline characteristics before propensity score matching were outlined in Table S1 . Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables as percentages. T-test and Wilcoxon test compared two groups, while the Kruskal-Walli’s test compared three or more groups for continuous variables. The X 2 test was used for categorical comparisons. Restricted cubic splines (RCS) plots were constructed to evaluate the dose-response relationships between serum glucose, blood lactate, as well as AKI. Three knots were strategically positioned at the 25th, 50th, and 75th quartiles. Univariate and multivariate logistic regression analysis was performed to characterize the association between serum lactate level and AKI among critical patients with AIS, as well as to evaluate the association between various interquartile a of serum lactate level and AKI. In addition, Univariate and multivariate linear regression analysis was performed to characterize the association between serum lactate level and serum glucose among critical patients with AIS, as well as to evaluate the association between various interquartile a of serum glucose and serum lactate level. Model 1 adjusted for age, sex, ethnicity, admission type, married. Model 2 adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C. Model 3 adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C, hyperlipidemia, hypertension, AMI, DM, thrombolytic agent, SAPS II score, SOFA score. Meanwhile, to address collinearity among variables, a stepwise backward regression analysis was performed on the comprehensive model. This method eliminates multicollinearity and selects the optimal regression equation ( Table S2-S3 ). Finally, subgroup analysis was conducted to assess the relationship between blood lactate levels and blood glucose, blood lactate, and AKI, stratified by age, gender, presence of hypertension, hyperlipidemia, AMI, DM, and the use of thrombolytic agents ( Table S4 ). Mediation analysis was conducted to explore whether serum glucose mediated the association between serum lactate levels and AKI in ICU patients with AIS. Causal mediation analysis distinguished total effects (TE), direct effects (DE) on AKI, and indirect effects (IE) through serum glucose ( 13 ). Statistical analyses were performed using Stata 15 Version ( https://www.stata.com/ ) and R Studio (Version 4.1.3, https://www.Rproject.org ). Significance was determined at a P -value < 0.05 for all statistical analyses. Results The baseline characteristics of critical patients with AIS based on propensity score matching (1:1) were shown in Table 1 . A total of 634 critical patients with AIS aged ≥ 18 years were included in the the present study, including 354 (55.8%) males and 280 (44.2%) females, with a mean (SD) age of 69.92 (13.41) years. Compared with the No-AKI critical patients, the AKI critical patients were more likely to be older (mean [SD], 71.55 [12.15] vs 68.29 [14.39]; P -value = 0.002), have higher levels of heart rate (mean [SD], 87.57 [14.49] vs 85.14 [13.86]; P -value = 0.031), PaCO2 (mean [SD], 42.80 [8.22] vs 39.53 [6.25]; P -value < 0.001), serum lactate level (Median [IQR], 2.2 [1.5, 3.6] vs 1.6 [1.1, 2.2]; P -value < 0.001), WBC count (mean [SD], 13.29 [5.72] vs 11.54 [4.39]; P -value < 0.001), glucose (mean [SD], 172.48 [57.50] vs 138.35 [46.91]; P -value < 0.001), Scr (Median [IQR], 1.30 [0.90, 2.10] vs 0.90 [0.70, 1.20]; P -value < 0.001), potassium (mean [SD], 4.35 [0.79] vs 4.09 [0.65]; P -value < 0.001), APTT (Median [IQR], 36.00 [29.70, 48.40] vs 29.50 [25.70, 36.40]; P -value < 0.001), PT (mean [SD], 16.08 [2.45] vs 14.16 [2.16]; P -value < 0.001), INR (mean [SD], 1.50 [0.32] vs 1.27 [0.28]; P -value < 0.001), Saps II score (Median [IQR], 48.00 [41.00, 59.00] vs 32.00 [25.00, 42.00]; P -value < 0.001), SOFA score (Median [IQR], 8.00 [5.00, 10.00] vs 3.00 [1.00, 5.00]; P -value < 0.001), have lower levels of respiration rate (mean [SD], 17.07 [4.59] vs 18.23 [4.35]; P -value = 0.001), Hb (mean [SD], 9.99 [2.25] vs 11.30 [2.03]; P -value = 0.001), sodium (mean [SD], 137.84 [4.38] vs 138.58 [3.99]; P -value = 0.027), TC (Median [IQR], 145.70 [128.00, 160.70] vs 154.12 [141.00, 172.30]; P -value < 0.001), TG (Median [IQR], 127.00 [93.60, 159.00] vs 135.80 [100.40, 172.00]; P -value < 0.001), and HDL-C (Median [IQR], 37.80 [32.00, 44.00] vs 43.70 [37.00, 52.40]; P -value < 0.001), and have combination of AMI (33.1% vs 16.4%; P -value < 0.001), hypertension (48.6% vs 64.7%; P -value < 0.001), DM (56.5% vs 32.5%; P -value < 0.001), and thrombolytic agent (5% vs 11%; P -value = 0.009). There were no significant differences in sex, race, admission type, marital status, SBP, DBP, PaO2, SpO2, LDL-C, and hyperlipidemia between the two groups. In addition, the baseline characteristics of critical patients with AIS before propensity score matching are shown in Table S1 . Table 1 Baseline characteristic of all participants based on propensity score matching. Overall No-AKI AKI P-value N = 634 N = 317 N = 317 Age, year 69.92 (13.41) 68.29 (14.39) 71.55 (12.15) 0.002 Gender (Female), n (%) 280 (44.2) 151 (47.6) 129 (40.7) 0.093 Ethnicity, n (%) 0.087 White 488 (77.0) 236 (74.4) 252 (79.5) Black 37 (5.8) 23 (7.3) 14 (4.4) Hispanic 21 (3.3) 14 (4.4) 7 (2.2) Asian 9 (1.4) 7 (2.2) 2 (0.6) Other 79 (12.5) 37 (11.7) 42 (13.2) Admission type, n (%) 0.209 Elective 83 (13.1) 49 (15.5) 34 (10.7) Emergency 533 (84.1) 259 (81.7) 274 (86.4) Urgent 18 (2.8) 9 (2.8) 9 (2.8) Married, n (%) 324 (51.1) 163 (51.4) 161 (50.8) 0.937 Vital signs, mean (SD) or Median (IQR) Heart rate (bpm) 86.36 (14.22) 85.14 (13.86) 87.57 (14.49) 0.031 Respiration rate (bpm) 17.65 (4.51) 18.23 (4.35) 17.07 (4.59) 0.001 Systolic blood pressure (mmHg) 131.77 (24.59) 130.55 (22.39) 132.98 (26.59) 0.215 Diastolic blood pressure (mmHg) 67.89 (14.44) 67.85 (12.89) 67.94 (15.86) 0.934 PaO2 (mmHg) 198.95 (137.30, 283.60) 200.40 (144.80, 271.70) 198.50 (129.90, 304.00) 0.97 PaCO2 (mmHg) 41.17 (7.48) 39.53 (6.25) 42.80 (8.22) < 0.001 SpO2 (%) 98.03 (2.37) 97.93 (2.23) 98.13 (2.49) 0.303 Laboratory tests, mean (SD) or Median (IQR) Lactate acid (mmol/L) 1.8 (1.3, 2.8) 1.6 (1.1, 2.2) 2.2 (1.5, 3.6) < 0.001 WBC count (×10 9 ) 12.42 (5.17) 11.54 (4.39) 13.29 (5.72) < 0.001 Hb (g/L) 10.65 (2.24) 11.30 (2.03) 9.99 (2.25) < 0.001 Glucose, mg/dL 155.41 (55.14) 138.35 (46.91) 172.48 (57.50) < 0.001 Scr (mg/dL) 1.10 (0.80, 1.58) 0.90 (0.70, 1.20) 1.30 (0.90, 2.10) < 0.001 Sodium (mmol/L) 138.21 (4.20) 138.58 (3.99) 137.84 (4.38) 0.027 Potassium (mmol/L) 4.22 (0.73) 4.09 (0.65) 4.35 (0.79) < 0.001 APTT (seconds) 32.15 (27.00, 41.40) 29.50 (25.70, 36.40) 36.00 (29.70, 48.40) < 0.001 PT (seconds) 15.12 (2.50) 14.16 (2.16) 16.08 (2.45) < 0.001 INR 1.39 (0.32) 1.27 (0.28) 1.50 (0.32) < 0.001 TC (mg/dL) 149.50 (135.00, 166.00) 154.12 (141.00, 172.30) 145.70 (128.00, 160.70) < 0.001 TG (mg/dL) 130.20 (96.70, 167.12) 135.80 (100.40, 172.00) 127.00 (93.60, 159.00) 0.040 HDL-C (mg/dL) 40.10 (34.00, 48.00) 43.70 (37.00, 52.40) 37.80 (32.00, 44.00) < 0.001 LDL-C (mg/dL) 80.80 (68.00, 93.98) 80.80 (67.00, 94.20) 80.80 (68.80, 93.50) 0.860 Complications, n (%) Hyperlipidemia 293 (46.2) 152 (47.9) 141 (44.5) 0.426 Hypertension 359 (56.6) 205 (64.7) 154 (48.6) < 0.001 AMI 157 (24.8) 52 (16.4) 105 (33.1) < 0.001 DM 282 (44.5) 103 (32.5) 179 (56.5) < 0.001 Thrombolytic agent, n (%) 51 (8.0) 35 (11.0) 16 (5.0) 0.009 Saps II score (IQR) 41.50 (31.00, 52.00) 32.00 (25.00, 42.00) 48.00 (41.00, 59.00) < 0.001 SOFA score (IQR) 5.00 (3.00, 8.00) 3.00 (1.00, 5.00) 8.00 (5.00, 10.00) < 0.001 Abbreviation: SpO2, pulse oxygen saturation; PaO2, partial pressure of oxygen; PaCO2, partial pressure of carbon dioxide; WBC, white blood cell; Hb, hemoglobin; Scr, serum creatinine; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AMI, acute myocardial infarction; DM, diabetes mellitus; SAPS, simplified acute physiology score; SOFA, sequential organ failure assessment; AKI, acute kidney injury; SD, standard deviation; IQR; interquartile range. As shown in Fig. 1 , we performed an RCS plot to assess the association between AKI and serum lactate level, as well as the association between serum lactate level and serum glucose, and revealed a linear association of between serum lactate levels and AKI and between serum glucose levels and serum lactate levels (all P for nonlinear < 0.001). Table 2 displays the association between AKI and serum lactate level and the association between serum lactate level and serum glucose in models 1, 2, and 3, respectively. After full adjustment for potential confounders (Model 3), serum glucose was positively correlated to serum lactate level (β = 0.004, 95% CI: 0.003–0.006, P -value < 0.001). High lactate level increased the risk of AKI (OR, 2.216; 95% CI, 1.559–3.271; P -value < 0.001). The above relationship was also found in stepwise logistic regression analysis ( Table S2-S3 ). Similarly, the association between AKI and serum lactate level and the association between serum lactate level and serum glucose was also present in subgroups age, sex, hyperlipidemia, hypertension, AMI, DM, and thrombolytic agent ( Table S4) . After converting serum glucose and serum lactate from a continuous variable to a categorical variable (quartiles), The corresponding β and 95% confidence interval (CI) of both the second, third, and forth quartile of serum glucose for serum lactate level were 0.083 (95% CI, -0.153-0.319), 0.259 (95% CI, 0.014–0.503) and 0.531 (95% CI, 0.272–0.790) compared with the first quartile. The corresponding odds ratio (OR) and 95% CI of both the second, third, and forth quartile of serum lactate for incidence of AKI were 1.709 (95% CI, 0.577–5.161), 5.663 (95% CI, 1.770-19.815) and 12.172 (95% CI, 3.857–42.485) compared with the first quartile (all P for trend < 0.001) (Table 2 ). Table 2 The relationship of between AKI and serum lactate level and between serum lactate level and serum glucose level. Model 1 Model 2 Model 3 Beta/OR (95% CI) P -value Beta/OR (95% CI) P -value Beta/OR (95% CI) P -value Lactate acid-glucose Glucose 0.005(0.003, 0.006) < 0.001 0.004(0.002,0.005) < 0.001 0.004(0.003, 0.006) < 0.001 Interquartile of glucose Q1 Ref. Ref. Ref. Q2 0.138(-0.114,0.390) 0.284 0.073(-0.167,0.312) 0.552 0.083(-0.153,0.319) 0.492 Q3 0.459(0.205,0.713) < 0.001 0.256(0.011,0.502) 0.040 0.259(0.014,0.503) 0.038 Q4 0.613(0.360,0.867) < 0.001 0.475(0.223,0.726) < 0.001 0.531(0.272,0.790) < 0.001 P for trend < 0.001 < 0.001 < 0.001 AKI-lactate acid lactate acid 1.724(1.467,2.040) < 0.001 1.858(1.466,2.387) < 0.001 2.216(1.559,3.271) < 0.001 Interquartile of lactate acid Q1 Ref. Ref. Ref. Q2 1.867(1.150,3.043) 0.012 2.016(0.998,4.115) 0.052 1.709(0.577,5.161) 0.335 Q3 1.961(1.231,3.141) 0.005 2.576(1.282,5.276) 0.009 5.663(1.770,19.815) 0.005 Q4 4.705(2.873,7.822) < 0.001 6.533(3.099,14.228) < 0.001 12.172(3.857,42.485) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Interquartile of glucose: Q1: 48–113 mg/dl; Q2: 113–140 mg/dl; Q3: 140–188 mg/dl; Q4: 188–267 mg/dl. Interquartile of lactate acid: Q1: 0.5–1.3 mmol/L; Q2: 1.3–1.8 mmol/L; Q3: 1.8–2.8 mmol/L; Q4: 2.8–4.7 mmol/L. Model 1 adjusted for age, sex, ethnicity, admission type, married. Model 2 adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C. Model 3 adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C, hyperlipidemia, hypertension, AMI, DM, thrombolytic agent, SAPS II score, SOFA score. Abbreviation: SpO2, pulse oxygen saturation; PaO2, partial pressure of oxygen; PaCO2, partial pressure of carbon dioxide; WBC, white blood cell; Hb, hemoglobin; Scr, serum creatinine; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AMI, acute myocardial infarction; DM, diabetes mellitus; SAPS, simplified acute physiology score; SOFA, sequential organ failure assessment; AKI, acute kidney injury. More importantly, we performed the mediation analysis of serum glucose for association between serum lactate level and AKI. After adjusting for the confounding factors, we found that serum glucose was involved in the process of serum lactate level -related AKI to some extent. serum glucose explains 14.9% of the association between serum lactate and AKI among critical patients with AIS ( P -value < 0.001) (Fig. 2 A), serum glucose explains 16.4% of the association between serum lactate and AKI among critical patients with AIS and with DM ( P -value = 0.24) (Fig. 2 B), and serum glucose explains 19.5% of the association between serum lactate and AKI among critical patients with AIS and without DM ( P -value < 0.001) (Fig. 2 C). However, no statistical significance among critical patients with AIS and with DM was observed (Fig. 2 and Figure S1 -S3 ). Therefore, the above results suggest that serum lactate level have a linear association with AKI, and this relationship is mediated by serum glucose some to extent. Discussion The high disability and death of AIS make it an important healthcare problem worldwide. Therefore, as the second most disabling and lethal disease in the world ( 14 ), the prognosis of patient with acute ischemia stroke require special attention. Due to the redistribution of systemic blood flow after the occurrence of AIS, AIS patients are prone to AKI ( 15 ). AKI is a common complication. Studies have demonstrated that the mortality rate among AIS patients with AKI is three times higher than that among those without AKI ( 16 ). In addition, for intensive care patients, occurrence of AKI will increase their mortality ( 17 ). Therefore, it is particularly important to screen for AKI-related risk factors among critical patient with AIS. As a common serum marker, serum lactate level has been confirmed to be an independent risk factor for the occurrence of AKI in many diseases ( 18 – 20 ). However, the correlation between serum lactate level and AKI in patients with AIS has not been unclear. This study used MIMIC III data to analyze the relationship between blood lactate levels and AKI in critical patients with AIS, and the incidence of AKI in critical patients with AIS was 77.9%. To address potential confounding of the association between serum lactate level and AKI, we used propensity score matching to match patients with AKI to patients without AKI. Finally, we found linear the relationship of between blood lactate levels and AKI, which means that the incidence of acute kidney injury increases with the increase in lactate levels among critical AIS patients, and the risk of AKI increased by 121.6% for every 1 mmol/L increase in serum lactate acid value. (OR: 2.216, 95% CI: 1.559–3.271, P -value < 0.001). This may be because AIS patients with underlying renal insufficiency have a reduced ability of the kidneys to clear inflammatory mediators. In addition, serum lactate level can assess the hemodynamic status of critical patients, and elevated lactate levels indicate poor tissue oxygenation, increased anaerobic metabolism, and insufficient organ perfusion. Importantly, lactic acidosis itself may lead to impaired kidney function. Therefore, the incidence of AKI will increase with increasing lactate levels. Subgroup analysis in the present study, stratified by age, gender, presence of hypertension, hyperlipidemia, AMI, DM, and the use of thrombolytic agents, further confirmed stability of association between blood lactate levels and AKI. Meanwhile, we also found that high blood glucose levels are related to increase of lactate levels (β = 0.004, 95% CI: 0.003–0.006, P -value < 0.001). Lactate and glucose production are strongly linked through both glycolysis and gluconeogenesis. The stress response serves as a common factor contributing to both hyperlactatemia and hyperglycemia in critically ill patients( 21 – 23 ). As part of the metabolic response to critical illness, Grealish et al. found that hyperglycemia and hyperlactatemia were independent risk factors for ICU admission/hospital death ( 24 ). However, they were unable to further investigate whether blood glucose is involved in the association between lactate and ICU admission/hospital death. In the present study, we found that high blood glucose levels strengthen the relationship between high lactate levels and high incidence of AKI (serum glucose explains 14.9% of the association between serum lactate and AKI among critical patients with AIS). Disturbed lactate and glucose levels are common in critically ill patients ( 25 , 26 ). In critically ill patients, Freire et al. found that measurements of abnormal combined lactate and glucose could serve as an early indicator of renal dysfunction, and there was a significant interaction observed between lactate and glucose levels on reflecting renal dysfunction ( P -value ≤ 0.001) ( 27 ). This is consistent with our findings. However, we found that blood glucose levels were not involved in the relationship between lactate levels and the incidence of AKI among critical patients with AIS and DM. Previous research found that insulin injections could lower blood sugar levels and improve outcomes( 28 ), and lead to the resolution of the lactic acidosis ( 29 ). Therefore, the effects of serum glucose level on the association between lactate and the occurrence of AKI was reduced. Limitation Firstly, the single-center retrospective design introduces selection bias that warrants consideration. Furthermore, the study participants are exclusively from the United States. Consequently, the findings may not be readily applicable to other centers, and the generalizability of the results awaits validation in diverse populations. Finally, the study solely focused on serum glucose and lactate levels at admission as observational indices, not evaluating the impact of dynamic changes in these levels over time on AKI. Conclusion In conclusion, Serum lactate acid was independently associated with increased risk-adjusted AKI in critical patients with AIS. The increase in serum glucose may have mediated this effect, especially in patients without DM. Abbreviations SpO2 pulse oxygen saturation PaO2 partial pressure of oxygen PaCO2 partial pressure of carbon dioxide WBC white blood cell Hb hemoglobin Scr serum creatinine PT prothrombin time APTT activated partial thromboplastin time INR international normalized ratio TC total cholesterol TG triglycerides HDL-C high-density lipoprotein cholesterol LDL-C low-density lipoprotein cholesterol AMI acute myocardial infarction DM diabetes mellitus SAPS simplified acute physiology score SOFA sequential organ failure assessment AKI acute kidney injury. Declarations Conflicts of Interest All patients have no competing interests. Funding This work was supported by Shanghai Jinshan District Medical and Health Science and Technology Innovation Funding Project (grant number: 2022-WS-02). Ethics Statement Data of the present study was from the MIMIC-III database. The MIMIC III database was approved to build by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Data for the present study was extracted by one author, Dingzhong Tang, who has completed the online training course and passed the exam (ID: 13183421). In addition, the Ethics Committee of Jinshan Branch of Shanghai Sixth People's Hospital approved the conduct of this study (ID: jszxyy202334). the review committee waived the requirement for written informed consent because of the retrospective nature of the study. Prior to analysis, confidential patient information was deleted from the entire data set prior to analysis. Data Availability Statement The data of the present study come from https://mimic.mit.edu/. The data supporting the findings of the present paper could be provided by contacting the author ( [email protected] ), without reservation. Author contributions Chunli Yu and Dingzhong Tang contributed to hypothesis development and manuscript preparation. Weiguo Yao, Kun Liu, Chunli Yu, and Dingzhong Tang contributed to the study design. Chunli Yu and Dingzhong Tang undertook data analyses and drafted and revised the manuscript. All authors approved the final draft of the manuscript for publication. References Feigin VL, Roth GA, Naghavi M, Parmar P, Krishnamurthi R, Chugh S, et al. Global burden of stroke and risk factors in 188 countries, during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet Neurology. 2016;15(9):913–24. Yeh TH, Tu KC, Wang HY, Chen JY. From Acute to Chronic: Unraveling the Pathophysiological Mechanisms of the Progression from Acute Kidney Injury to Acute Kidney Disease to Chronic Kidney Disease. International journal of molecular sciences. 2024;25(3). Tsui B, Chen IE, Nour M, Kihira S, Tavakkol E, Polson J, et al. Perfusion Collateral Index versus Hypoperfusion Intensity Ratio in Assessment of Collaterals in Patients with Acute Ischemic Stroke. AJNR American journal of neuroradiology. 2023;44(11):1249–55. Chawla LS, Kimmel PL. Acute kidney injury and chronic kidney disease: an integrated clinical syndrome. Kidney international. 2012;82(5):516–24. Mark PB, Stevens KK, Jardine AG. Electrolytes: Acid–base balance. In: Caballero B, editor. Encyclopedia of Human Nutrition (Fourth Edition). Oxford: Academic Press; 2013. p. 104 – 12. Dirnagl U, Iadecola C, Moskowitz MA. Pathobiology of ischaemic stroke: an integrated view. Trends in neurosciences. 1999;22(9):391–7. Dienel GA. Lactate Shuttling and Lactate use as Fuel after Traumatic Brain Injury: Metabolic Considerations. Journal of Cerebral Blood Flow & Metabolism. 2014;34(11):1736–48. Siesjö BK. Lactic acidosis in the brain: occurrence, triggering mechanisms and pathophysiological importance. Ciba Foundation symposium. 1982;87:77–100. Kraut JA, Madias NE. Lactic acidosis. The New England journal of medicine. 2014;371(24):2309–19. Bellomo R. Bench-to-bedside review: lactate and the kidney. Critical care (London, England). 2002;6(4):322-6. Bennett DA. How can I deal with missing data in my study? Australian and New Zealand journal of public health. 2001;25(5):464–9. Rosenbaum PR, Rubin DBJB. The central role of the propensity score in observational studies for causal effects. 1983;70(1):41–55. Vanderweele TJ, Vansteelandt S. Odds ratios for mediation analysis for a dichotomous outcome. American journal of epidemiology. 2010;172(12):1339–48. Saini V, Guada L, Yavagal DR. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions. Neurology. 2021;97(20 Suppl 2):S6-s16. Wang J, Zhang J, Ye Y, Xu Q, Li Y, Feng S, et al. Peripheral Organ Injury After Stroke. Frontiers in immunology. 2022;13:901209. Khatri M, Himmelfarb J, Adams D, Becker K, Longstreth W, Tirschwell DLJJoS, et al. Acute kidney injury is associated with increased hospital mortality after stroke. 2014;23(1):25–30. García AF, Manzano-Nunez R, Bayona JG, Naranjo MP, Villa DN, Moreno M, et al. Acute kidney injury in severely injured patients admitted to the intensive care unit. Military Medical Research. 2020;7(1):47. Choi S, You J, Kim YJ, Lee HC, Park HP, Park CK, et al. High Intraoperative Serum Lactate Level is Associated with Acute Kidney Injury after Brain Tumor Resection. Journal of neurosurgical anesthesiology. 2024. Zhou X, He Y, Hu L, Zhu Q, Lin Q, Hong X, et al. Lactate level and lactate clearance for acute kidney injury prediction among patients admitted with ST-segment elevation myocardial infarction: A retrospective cohort study. Frontiers in cardiovascular medicine. 2022;9:930202. Kahyaoglu M, Karaduman A, Geçmen Ç, Candan Ö, Güner A, Cakmak EO, et al. Serum lactate level may predict the development of acute kidney injury in acute decompensated heart failure. Turk Kardiyoloji Dernegi arsivi: Turk Kardiyoloji Derneginin yayin organidir. 2020;48(7):683–9. Kaukonen K-M, Bailey M, Egi M, Orford N, Glassford NJ, Marik PE, et al. Stress hyperlactatemia modifies the relationship between stress hyperglycemia and outcome: a retrospective observational study. 2014;42(6):1379–85. Marik PE, Bellomo RJCc. Stress hyperglycemia: an essential survival response! 2013;17:1–7. Garcia-Alvarez M, Marik P, Bellomo RJTlD, endocrinology. Stress hyperlactataemia: present understanding and controversy. 2014;2(4):339–47. Grealish M, Chiew AL, Varndell W, Depczynski B. The relationship between admission glucose and lactate with critical illness amongst adult patients presenting to the emergency department. Acta diabetologica. 2021;58(10):1343–9. Bakker J, Nijsten MW, Jansen TCJAoic. Clinical use of lactate monitoring in critically ill patients. 2013;3:1–8. Mizock BAJBP, Endocrinology RC, Metabolism. Alterations in fuel metabolism in critical illness: hyperglycaemia. 2001;15(4):533 – 51. Freire Jorge P, Wieringa N, de Felice E, van der Horst ICC, Oude Lansink A, Nijsten MW. The association of early combined lactate and glucose levels with subsequent renal and liver dysfunction and hospital mortality in critically ill patients. Critical care (London, England). 2017;21(1):218. Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, et al. Intensive insulin therapy in critically ill patients. 2001;345(19):1359–67. Darwish R, Chen E, Minear S, Sheffield C. Resolution of insulin resistance, lactic acidosis, and decrease in mechanical support requirements in patients post orthotopic heart transplant with the use of long-acting insulin glargine. Journal of cardiothoracic surgery. 2024;19(1):99. Additional Declarations No competing interests reported. Supplementary Files S.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 20 Aug, 2024 Editor invited by journal 30 May, 2024 Submission checks completed at journal 29 May, 2024 First submitted to journal 21 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4454722","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312879845,"identity":"c9bd59ef-f6e0-4494-9f82-e6c1b2c2116b","order_by":0,"name":"Chunli Yu","email":"","orcid":"","institution":"Jinshan Branch of Shanghai Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunli","middleName":"","lastName":"Yu","suffix":""},{"id":312879846,"identity":"55b9bc5d-4c80-4834-8897-d4eb909a9dba","order_by":1,"name":"Weiguo Yao","email":"","orcid":"","institution":"Jinshan Branch of Shanghai Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Yao","suffix":""},{"id":312879847,"identity":"6dfaba17-e90e-4e1f-8459-6ff2b451e0d4","order_by":2,"name":"Kun Liu","email":"","orcid":"","institution":"Jinshan Branch of Shanghai Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Liu","suffix":""},{"id":312879848,"identity":"fe870e29-1d6e-42c5-86b6-fd6d03588ddb","order_by":3,"name":"Dingzhong Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACNvb+jw8SKmrk2NibD4IYhLXw8RwwNnhw5pgxP8+xZBCDsBY5iQQzyYdtzIkzZ+SoST5sYSbCYTwHEiQS29gSN9zIYatIbGBj4G/vTiDgl4YDBgnnZIw3nHl77EbiDhkGiTNnNxCw5WBDQkIZm+yG43lpNxLPsDEYSOQS0CKRzHAggY2ZccOBHLOCxDZmYrSkMTYktDErzuzIMWMgTgvPGWaGBGggSwAZPAT9It/ew/7zBzQqP4IY/O29+LVgAB7SlI+CUTAKRsEowAoA6fZPVlNuDwoAAAAASUVORK5CYII=","orcid":"","institution":"Jinshan Branch of Shanghai Sixth People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dingzhong","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-05-21 12:12:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4454722/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4454722/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58246381,"identity":"66cb8187-d3f9-4846-aa1b-3297f3a99096","added_by":"auto","created_at":"2024-06-13 02:27:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23331,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline plots of the association between AKI and serum lactate level, and the association between serum lactate level and serum glucose.\u003c/p\u003e\n\u003cp\u003eAge, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C, hyperlipidemia, hypertension, AMI, DM, thrombolytic agent, SAPS II score, and SOFA score were adjusted.\u003c/p\u003e\n\u003cp\u003eAbbreviation: SpO2, pulse oxygen saturation; PaO2, partial pressure of oxygen; PaCO2, partial pressure of carbon dioxide; WBC, white blood cell; Hb, hemoglobin; Scr, serum creatinine; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AMI, acute myocardial infarction; DM, diabetes mellitus; SAPS, simplified acute physiology score; SOFA, sequential organ failure assessment; AKI, acute kidney injury.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4454722/v1/e02e087385617cce7c7a1a80.png"},{"id":58246382,"identity":"d331bbd6-3a4a-4190-b032-99e225931b90","added_by":"auto","created_at":"2024-06-13 02:27:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15477,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of serum glucose on the interaction between serum lactate level and AKI. Age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C, hyperlipidemia, hypertension, AMI, DM, thrombolytic agent, SAPS II score, and SOFA score were adjusted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Mediation models of serum glucose, serum lactate level, and AKI among ICU patients with acute ischemic stroke: direct effect (TE= 0.059; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) of serum lactate level (exposure) toward AKI (outcome), and serum glucose medication proportion is 14.9%; indirect effect (IE= 0.009; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) of serum lactate level (exposure) toward serum glucose (mediator) and effect obesity (DE= 0.051; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), from serum glucose (mediator) toward AKI (outcome). \u003cstrong\u003eB:\u003c/strong\u003e Mediation models of serum glucose, serum lactate level, and AKI among ICU patients with acute ischemic stroke and without DM: direct effect (TE= 0.029; \u003cem\u003eP\u003c/em\u003e=0.24) of serum lactate level (exposure) toward AKI (outcome), and serum glucose medication proportion is 16.4%; indirect effect (IE= 0.006; \u003cem\u003eP\u003c/em\u003e=0.04) of serum lactate level (exposure) toward serum glucose (mediator) and effect obesity (DE= 0.023; \u003cem\u003eP\u003c/em\u003e=0.36), from serum glucose (mediator) toward AKI (outcome). \u003cstrong\u003eC:\u003c/strong\u003e Mediation models of serum glucose, serum lactate level, and AKI among ICU patients with acute ischemic stroke and with DM: direct effect (TE= 0.070; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) of serum lactate level (exposure) toward AKI (outcome), and serum glucose medication proportion is 19.5%; indirect effect (IE= 0.014; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) of serum lactate level (exposure) toward serum glucose (mediator) and effect obesity (DE= 0.056; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), from serum glucose (mediator) toward AKI (outcome).\u003c/p\u003e\n\u003cp\u003eAbbreviation: SpO2, pulse oxygen saturation; PaO2, partial pressure of oxygen; PaCO2, partial pressure of carbon dioxide; WBC, white blood cell; Hb, hemoglobin; Scr, serum creatinine; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AMI, acute myocardial infarction; DM, diabetes mellitus; SAPS, simplified acute physiology score; SOFA, sequential organ failure assessment; AKI, acute kidney injure.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4454722/v1/72b9bf176cead47796afee1f.png"},{"id":58246384,"identity":"906e421b-408e-4f97-af1f-b67956c6c392","added_by":"auto","created_at":"2024-06-13 02:27:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":713791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4454722/v1/e3039f35-89b5-41b3-b845-7cf1b22726ce.pdf"},{"id":58246383,"identity":"aadcf470-8160-4d3e-8c8f-6a0e31fcdcb9","added_by":"auto","created_at":"2024-06-13 02:27:11","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":144319,"visible":true,"origin":"","legend":"","description":"","filename":"S.docx","url":"https://assets-eu.researchsquare.com/files/rs-4454722/v1/3b96033912c5fa0351086f90.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effects of serum glucose level on the association of between serum lactate level and acute kidney injury among critical patient with acute ischemic stroke","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke (IS), as a significant global public health concern (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), is a cerebrovascular disease characterized by the blockage or interruption of blood supply to the brain, leading to ischemia and hypoxia in brain tissue. Acute Kidney Injury (AKI) refers to the sudden impairment of renal function within a short period, characterized by reduced urine output and elevated levels of serum creatinine and blood urea nitrogen. There is a certain interrelationship between IS and AKI, possibly due to shared risk factors and physiological mechanisms. IS and AKI are both associated with hypoperfusion (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In the case of a stroke, insufficient blood supply to the brain can lead to damage in cerebral tissues. Similarly, the kidneys are prone to injury when subjected to low perfusion states. Therefore, the occurrence of an IS and the vulnerability of the kidneys during decreased perfusion may be interconnected. Most importantly, AKI may lead to disturbances in water and electrolyte balance, difficulties in urination, and systemic inflammatory responses, thereby further exacerbating the overall condition of the patients (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, reducing the occurrence of AKI is particularly crucial for patients with acute ischemic stroke (AIS).\u003c/p\u003e \u003cp\u003eLactic acid is an organic acid typically produced through lactic acid fermentation or during aerobic metabolism (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For patients with IS, ischemia can lead to hypoxia in brain tissue, affecting energy metabolism. In hypoxic conditions, cells may produce lactic acid through the lactate fermentation pathway to generate a limited amount of energy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, damage to brain tissue can result in the release of intracellular lactic acid due to cell membrane rupture (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Ultimately, this contributes to an increase in the body's serum lactate levels. Therefore, elevated serum lactate levels may reflect the extent of damage to brain tissue, and monitoring serum lactate levels can aid in assessing the severity of IS and potential complications. Extremely elevated serum lactate levels may lead to lactic acidosis, a severe metabolic disorder, and irreversible cell damage caused by acidosis may pose a direct threat to the patient's life (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Furthermore, part of lactate acid is metabolized by the kidneys, and kidney damage may lead to further accumulation of lactate acid (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This may lead to poor prognosis in patients.\u003c/p\u003e \u003cp\u003eCurrently, there are no studies on the relationship between serum lactate level and AKI in patients with AIS. Therefore, in this study, we analyzed retrospectively the relationship between serum lactate level and AKI in patients with AIS through the Medical Information Mart for Intensive Care (MIMIC) III database, and also analyzed whether serum glucose levels have a mediating role in the process of lactic acid-induced AKI.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003e Participants for this study were sourced from the MIMIC-III database, which received approval for construction from the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). The data utilized in this study, derived from this publicly accessible critical care database, was meticulously extracted by Dingzhong Tang, an author who successfully completed an online training course and passed the exam (ID: 13183421). All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eIn this retrospective study, a total of 2884 critical patients with AIS were initially considered from the MIMIC-III database. AIS is diagnosed based on ICD 9 codes. Exclusions were made based on specific criteria: 472 participants were excluded due to non-first-time admission or ICU admission. Out of the remaining 2412 participants, 977 were further excluded due to the absence of baseline serum lactate and baseline serum glucose. All participants were confirmed to be above 18 years old. Ultimately, the study included 1,435 participants, comprising 1118 with acute kidney injury (AKI) and 317 without AKI (No-AKI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEndpoint Event and Exposure Variable\u003c/h2\u003e \u003cp\u003eAcute Kidney Injury (AKI) served as the primary endpoint event, diagnosed based on the criteria established by the kidney disease: Improving Global Outcomes (KDIGO). Diagnosis relied on changes in serum creatinine and urine output, with AKI defined as any of the following: Serum Creatinine Criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) A rise of \u0026ge;\u0026thinsp;0.3 mg/dL (26.5 \u0026micro;mol/L) in serum creatinine within 48 hours. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Serum creatinine surpassing 1.5 times the patient's baseline level. Urine Output Criteria: Urine output persistently less than 0.5 mL/kg body weight per hour for over 6 hours. The severity of AKI is classified into three grades based on the multiple of the increase in serum creatinine relative to the baseline level and the degree of hourly urine output reduction: AKI Grade 1: Serum creatinine rises by 1.5\u0026ndash;1.9 times the baseline level. Or, urine output is less than 0.5 mL/kg body weight per hour, lasting 6\u0026ndash;12 hours. AKI Grade 2: Serum creatinine rises by 2.0-2.9 times the baseline level. Or, urine output is less than 0.5 mL/kg body weight per hour, lasting 12\u0026ndash;24 hours. AKI Grade 3: Serum creatinine rises more than 3.0 times the baseline level. Or, serum creatinine reaches or exceeds 4.0 mg/dL (353.6 \u0026micro;mol/L). Or, urine output is less than 0.3 mL/kg body weight per hour, persisting for 24 hours. Or, there is a requirement for dialysis treatment. The exposure variable was serum lactate level and the mediator is serum glucose. they are all defined as the first measurement within 24 hours after admission to the ICU.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eRelevant demographic variables included age, gender, ethnicity, admission type, and marital status. Vital signs encompassed heart rate, respiration rate, systolic and diastolic blood pressure, pulse oxygen saturation (SpO2), partial pressure of oxygen (PaO2), and partial pressure of carbon dioxide (PaCO2). Laboratory tests comprised white blood cell (WBC) count, hemoglobin (Hb), serum creatinine (Scr), sodium, potassium, activated partial thromboplastin time (APTT), prothrombin time (PT), international normalized ratio (INR), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Recorded complications included hyperlipidemia, hypertension, acute myocardial infarction (AMI), and diabetes mellitus (DM). Additionally, data on the use of thrombolytic agents, Sequential Organ Failure Assessment (SOFA) score, and Simplified Acute Physiologic Score (SAPS) II were also extracted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses in this study were conducted using Stata (version 15.0) and R Studio (version 4.1.3), with a significance level set at \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To address potential bias from missing data, multiple imputations were performed using predicted mean matching (PMM) before analyzing the results. PMM utilizes observational data to establish a linear model, filling in missing values. Imputations with ten times were chosen to enhance data robustness. Variables with missing values exceeding 10% were removed, as recommended in the literature to ensure imputation stability (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). To address potential confounding of the association between serum lactate level and AKI, we used propensity score matching to match patients with AKI to patients without AKI (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Baseline characteristics before propensity score matching were outlined in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as percentages. T-test and Wilcoxon test compared two groups, while the Kruskal-Walli\u0026rsquo;s test compared three or more groups for continuous variables. The \u003cem\u003eX\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test was used for categorical comparisons.\u003c/p\u003e \u003cp\u003eRestricted cubic splines (RCS) plots were constructed to evaluate the dose-response relationships between serum glucose, blood lactate, as well as AKI. Three knots were strategically positioned at the 25th, 50th, and 75th quartiles. Univariate and multivariate logistic regression analysis was performed to characterize the association between serum lactate level and AKI among critical patients with AIS, as well as to evaluate the association between various interquartile a of serum lactate level and AKI. In addition, Univariate and multivariate linear regression analysis was performed to characterize the association between serum lactate level and serum glucose among critical patients with AIS, as well as to evaluate the association between various interquartile a of serum glucose and serum lactate level. Model 1 adjusted for age, sex, ethnicity, admission type, married. Model 2 adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C. Model 3 adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C, hyperlipidemia, hypertension, AMI, DM, thrombolytic agent, SAPS II score, SOFA score. Meanwhile, to address collinearity among variables, a stepwise backward regression analysis was performed on the comprehensive model. This method eliminates multicollinearity and selects the optimal regression equation (\u003cb\u003eTable S2-S3\u003c/b\u003e). Finally, subgroup analysis was conducted to assess the relationship between blood lactate levels and blood glucose, blood lactate, and AKI, stratified by age, gender, presence of hypertension, hyperlipidemia, AMI, DM, and the use of thrombolytic agents (\u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eMediation analysis was conducted to explore whether serum glucose mediated the association between serum lactate levels and AKI in ICU patients with AIS. Causal mediation analysis distinguished total effects (TE), direct effects (DE) on AKI, and indirect effects (IE) through serum glucose (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Statistical analyses were performed using Stata 15 Version (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stata.com/\u003c/span\u003e\u003cspan address=\"https://www.stata.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and R Studio (Version 4.1.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.Rproject.org\u003c/span\u003e\u003cspan address=\"https://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Significance was determined at a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all statistical analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics of critical patients with AIS based on propensity score matching (1:1) were shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 634 critical patients with AIS aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years were included in the the present study, including 354 (55.8%) males and 280 (44.2%) females, with a mean (SD) age of 69.92 (13.41) years. Compared with the No-AKI critical patients, the AKI critical patients were more likely to be older (mean [SD], 71.55 [12.15] vs 68.29 [14.39]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.002), have higher levels of heart rate (mean [SD], 87.57 [14.49] vs 85.14 [13.86]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.031), PaCO2 (mean [SD], 42.80 [8.22] vs 39.53 [6.25]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum lactate level (Median [IQR], 2.2 [1.5, 3.6] vs 1.6 [1.1, 2.2]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), WBC count (mean [SD], 13.29 [5.72] vs 11.54 [4.39]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), glucose (mean [SD], 172.48 [57.50] vs 138.35 [46.91]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Scr (Median [IQR], 1.30 [0.90, 2.10] vs 0.90 [0.70, 1.20]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), potassium (mean [SD], 4.35 [0.79] vs 4.09 [0.65]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), APTT (Median [IQR], 36.00 [29.70, 48.40] vs 29.50 [25.70, 36.40]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PT (mean [SD], 16.08 [2.45] vs 14.16 [2.16]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), INR (mean [SD], 1.50 [0.32] vs 1.27 [0.28]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Saps II score (Median [IQR], 48.00 [41.00, 59.00] vs 32.00 [25.00, 42.00]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SOFA score (Median [IQR], 8.00 [5.00, 10.00] vs 3.00 [1.00, 5.00]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), have lower levels of respiration rate (mean [SD], 17.07 [4.59] vs 18.23 [4.35]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.001), Hb (mean [SD], 9.99 [2.25] vs 11.30 [2.03]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.001), sodium (mean [SD], 137.84 [4.38] vs 138.58 [3.99]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.027), TC (Median [IQR], 145.70 [128.00, 160.70] vs 154.12 [141.00, 172.30]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TG (Median [IQR], 127.00 [93.60, 159.00] vs 135.80 [100.40, 172.00]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and HDL-C (Median [IQR], 37.80 [32.00, 44.00] vs 43.70 [37.00, 52.40]; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and have combination of AMI (33.1% vs 16.4%; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypertension (48.6% vs 64.7%; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), DM (56.5% vs 32.5%; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and thrombolytic agent (5% vs 11%; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.009). There were no significant differences in sex, race, admission type, marital status, SBP, DBP, PaO2, SpO2, LDL-C, and hyperlipidemia between the two groups. In addition, the baseline characteristics of critical patients with AIS before propensity score matching are shown in \u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristic of all participants based on propensity score matching.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo-AKI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAKI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;634\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;317\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;317\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e69.92 (13.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68.29 (14.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.55 (12.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender (Female), n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e280 (44.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e151 (47.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (40.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEthnicity, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e488 (77.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e236 (74.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e252 (79.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlack\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23 (7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21 (3.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14 (4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9 (1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7 (2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37 (11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 (13.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAdmission type, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.209\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElective\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83 (13.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49 (15.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34 (10.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmergency\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e533 (84.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e259 (81.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e274 (86.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUrgent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e324 (51.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e163 (51.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161 (50.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.937\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eVital signs, mean (SD) or Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeart rate (bpm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86.36 (14.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85.14 (13.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.57 (14.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRespiration rate (bpm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.65 (4.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.23 (4.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.07 (4.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e131.77 (24.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e130.55 (22.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e132.98 (26.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.215\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.89 (14.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.85 (12.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.94 (15.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.934\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePaO2 (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e198.95 (137.30, 283.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e200.40 (144.80, 271.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198.50 (129.90, 304.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePaCO2 (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.17 (7.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.53 (6.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.80 (8.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpO2 (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98.03 (2.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.93 (2.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.13 (2.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.303\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory tests, mean (SD) or Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLactate acid (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.8 (1.3, 2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.6 (1.1, 2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2 (1.5, 3.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC count (\u0026times;10\u003csup\u003e9\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.42 (5.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.54 (4.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.29 (5.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHb (g/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.65 (2.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.30 (2.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.99 (2.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlucose, mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e155.41 (55.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e138.35 (46.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e172.48 (57.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScr (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.10 (0.80, 1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90 (0.70, 1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30 (0.90, 2.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSodium (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e138.21 (4.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e138.58 (3.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e137.84 (4.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePotassium (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.22 (0.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.09 (0.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.35 (0.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTT (seconds)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.15 (27.00, 41.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.50 (25.70, 36.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.00 (29.70, 48.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePT (seconds)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.12 (2.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.16 (2.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.08 (2.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eINR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.39 (0.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.27 (0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.50 (0.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTC (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e149.50 (135.00, 166.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e154.12 (141.00, 172.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e145.70 (128.00, 160.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTG (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e130.20 (96.70, 167.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e135.80 (100.40, 172.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127.00 (93.60, 159.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.10 (34.00, 48.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.70 (37.00, 52.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.80 (32.00, 44.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.80 (68.00, 93.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.80 (67.00, 94.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.80 (68.80, 93.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.860\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComplications, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHyperlipidemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e293 (46.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e152 (47.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141 (44.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.426\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e359 (56.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e205 (64.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e154 (48.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e157 (24.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52 (16.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e105 (33.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e282 (44.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e103 (32.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e179 (56.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThrombolytic agent, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51 (8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (5.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSaps II score (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.50 (31.00, 52.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.00 (25.00, 42.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.00 (41.00, 59.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSOFA score (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.00 (3.00, 8.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.00 (1.00, 5.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.00 (5.00, 10.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eAbbreviation: SpO2, pulse oxygen saturation; PaO2, partial pressure of oxygen; PaCO2, partial pressure of carbon dioxide; WBC, white blood cell; Hb, hemoglobin; Scr, serum creatinine; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AMI, acute myocardial infarction; DM, diabetes mellitus; SAPS, simplified acute physiology score; SOFA, sequential organ failure assessment; AKI, acute kidney injury; SD, standard deviation; IQR; interquartile range.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, we performed an RCS plot to assess the association between AKI and serum lactate level, as well as the association between serum lactate level and serum glucose, and revealed a linear association of between serum lactate levels and AKI and between serum glucose levels and serum lactate levels (all \u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the association between AKI and serum lactate level and the association between serum lactate level and serum glucose in models 1, 2, and 3, respectively. After full adjustment for potential confounders (Model 3), serum glucose was positively correlated to serum lactate level (\u0026beta;\u0026thinsp;=\u0026thinsp;0.004, 95% CI: 0.003\u0026ndash;0.006, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High lactate level increased the risk of AKI (OR, 2.216; 95% CI, 1.559\u0026ndash;3.271; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The above relationship was also found in stepwise logistic regression analysis (\u003cstrong\u003eTable S2-S3\u003c/strong\u003e). Similarly, the association between AKI and serum lactate level and the association between serum lactate level and serum glucose was also present in subgroups age, sex, hyperlipidemia, hypertension, AMI, DM, and thrombolytic agent (\u003cstrong\u003eTable S4)\u003c/strong\u003e. After converting serum glucose and serum lactate from a continuous variable to a categorical variable (quartiles), The corresponding \u0026beta; and 95% confidence interval (CI) of both the second, third, and forth quartile of serum glucose for serum lactate level were 0.083 (95% CI, -0.153-0.319), 0.259 (95% CI, 0.014\u0026ndash;0.503) and 0.531 (95% CI, 0.272\u0026ndash;0.790) compared with the first quartile. The corresponding odds ratio (OR) and 95% CI of both the second, third, and forth quartile of serum lactate for incidence of AKI were 1.709 (95% CI, 0.577\u0026ndash;5.161), 5.663 (95% CI, 1.770-19.815) and 12.172 (95% CI, 3.857\u0026ndash;42.485) compared with the first quartile (all \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe relationship of between AKI and serum lactate level and between serum lactate level and serum glucose level.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeta/OR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeta/OR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeta/OR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLactate acid-glucose\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGlucose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005(0.003, 0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004(0.002,0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004(0.003, 0.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterquartile of glucose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.138(-0.114,0.390)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.073(-0.167,0.312)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.552\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.083(-0.153,0.319)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.492\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.459(0.205,0.713)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.256(0.011,0.502)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.259(0.014,0.503)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.613(0.360,0.867)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.475(0.223,0.726)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.531(0.272,0.790)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAKI-lactate acid\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003elactate acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.724(1.467,2.040)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.858(1.466,2.387)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.216(1.559,3.271)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInterquartile of lactate acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.867(1.150,3.043)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.016(0.998,4.115)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.709(0.577,5.161)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.335\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.961(1.231,3.141)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.576(1.282,5.276)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.663(1.770,19.815)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.705(2.873,7.822)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.533(3.099,14.228)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.172(3.857,42.485)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eInterquartile of glucose: Q1: 48\u0026ndash;113 mg/dl; Q2: 113\u0026ndash;140 mg/dl; Q3: 140\u0026ndash;188 mg/dl; Q4: 188\u0026ndash;267 mg/dl.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eInterquartile of lactate acid: Q1: 0.5\u0026ndash;1.3 mmol/L; Q2: 1.3\u0026ndash;1.8 mmol/L; Q3: 1.8\u0026ndash;2.8 mmol/L; Q4: 2.8\u0026ndash;4.7 mmol/L.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e adjusted for age, sex, ethnicity, admission type, married.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eModel 2 adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e adjusted for age, sex, ethnicity, admission type, married, HR, RR, PaO2, PaCO2, SpO2, Systolic blood pressure, diastolic blood pressure, WBC, Hb, SCr, sodium, potassium, APTT, PT, INR, TC, TG, HDL-C, LDL-C, hyperlipidemia, hypertension, AMI, DM, thrombolytic agent, SAPS II score, SOFA score.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eAbbreviation: SpO2, pulse oxygen saturation; PaO2, partial pressure of oxygen; PaCO2, partial pressure of carbon dioxide; WBC, white blood cell; Hb, hemoglobin; Scr, serum creatinine; PT, prothrombin time; APTT, activated partial thromboplastin time; INR, international normalized ratio; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; AMI, acute myocardial infarction; DM, diabetes mellitus; SAPS, simplified acute physiology score; SOFA, sequential organ failure assessment; AKI, acute kidney injury.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMore importantly, we performed the mediation analysis of serum glucose for association between serum lactate level and AKI. After adjusting for the confounding factors, we found that serum glucose was involved in the process of serum lactate level -related AKI to some extent. serum glucose explains 14.9% of the association between serum lactate and AKI among critical patients with AIS (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA), serum glucose explains 16.4% of the association between serum lactate and AKI among critical patients with AIS and with DM (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.24) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB), and serum glucose explains 19.5% of the association between serum lactate and AKI among critical patients with AIS and without DM (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). However, no statistical significance among critical patients with AIS and with DM was observed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cstrong\u003eand Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3\u003c/strong\u003e). Therefore, the above results suggest that serum lactate level have a linear association with AKI, and this relationship is mediated by serum glucose some to extent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe high disability and death of AIS make it an important healthcare problem worldwide. Therefore, as the second most disabling and lethal disease in the world (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), the prognosis of patient with acute ischemia stroke require special attention. Due to the redistribution of systemic blood flow after the occurrence of AIS, AIS patients are prone to AKI (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). AKI is a common complication. Studies have demonstrated that the mortality rate among AIS patients with AKI is three times higher than that among those without AKI (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In addition, for intensive care patients, occurrence of AKI will increase their mortality (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Therefore, it is particularly important to screen for AKI-related risk factors among critical patient with AIS.\u003c/p\u003e \u003cp\u003eAs a common serum marker, serum lactate level has been confirmed to be an independent risk factor for the occurrence of AKI in many diseases (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, the correlation between serum lactate level and AKI in patients with AIS has not been unclear. This study used MIMIC III data to analyze the relationship between blood lactate levels and AKI in critical patients with AIS, and the incidence of AKI in critical patients with AIS was 77.9%. To address potential confounding of the association between serum lactate level and AKI, we used propensity score matching to match patients with AKI to patients without AKI. Finally, we found linear the relationship of between blood lactate levels and AKI, which means that the incidence of acute kidney injury increases with the increase in lactate levels among critical AIS patients, and the risk of AKI increased by 121.6% for every 1 mmol/L increase in serum lactate acid value. (OR: 2.216, 95% CI: 1.559–3.271, \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.001). This may be because AIS patients with underlying renal insufficiency have a reduced ability of the kidneys to clear inflammatory mediators. In addition, serum lactate level can assess the hemodynamic status of critical patients, and elevated lactate levels indicate poor tissue oxygenation, increased anaerobic metabolism, and insufficient organ perfusion. Importantly, lactic acidosis itself may lead to impaired kidney function. Therefore, the incidence of AKI will increase with increasing lactate levels. Subgroup analysis in the present study, stratified by age, gender, presence of hypertension, hyperlipidemia, AMI, DM, and the use of thrombolytic agents, further confirmed stability of association between blood lactate levels and AKI.\u003c/p\u003e \u003cp\u003eMeanwhile, we also found that high blood glucose levels are related to increase of lactate levels (β = 0.004, 95% CI: 0.003–0.006, \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.001). Lactate and glucose production are strongly linked through both glycolysis and gluconeogenesis. The stress response serves as a common factor contributing to both hyperlactatemia and hyperglycemia in critically ill patients(\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). As part of the metabolic response to critical illness, Grealish et al. found that hyperglycemia and hyperlactatemia were independent risk factors for ICU admission/hospital death (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, they were unable to further investigate whether blood glucose is involved in the association between lactate and ICU admission/hospital death. In the present study, we found that high blood glucose levels strengthen the relationship between high lactate levels and high incidence of AKI (serum glucose explains 14.9% of the association between serum lactate and AKI among critical patients with AIS). Disturbed lactate and glucose levels are common in critically ill patients (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In critically ill patients, Freire et al. found that measurements of abnormal combined lactate and glucose could serve as an early indicator of renal dysfunction, and there was a significant interaction observed between lactate and glucose levels on reflecting renal dysfunction (\u003cem\u003eP\u003c/em\u003e-value ≤ 0.001) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This is consistent with our findings. However, we found that blood glucose levels were not involved in the relationship between lactate levels and the incidence of AKI among critical patients with AIS and DM. Previous research found that insulin injections could lower blood sugar levels and improve outcomes(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and lead to the resolution of the lactic acidosis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Therefore, the effects of serum glucose level on the association between lactate and the occurrence of AKI was reduced.\u003c/p\u003e "},{"header":"Limitation","content":"\u003cp\u003eFirstly, the single-center retrospective design introduces selection bias that warrants consideration. Furthermore, the study participants are exclusively from the United States. Consequently, the findings may not be readily applicable to other centers, and the generalizability of the results awaits validation in diverse populations. Finally, the study solely focused on serum glucose and lactate levels at admission as observational indices, not evaluating the impact of dynamic changes in these levels over time on AKI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, Serum lactate acid was independently associated with increased risk-adjusted AKI in critical patients with AIS. The increase in serum glucose may have mediated this effect, especially in patients without DM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSpO2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epulse oxygen saturation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePaO2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epartial pressure of oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePaCO2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epartial pressure of carbon dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eScr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eserum creatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprothrombin time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eactivated partial thromboplastin time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einternational normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esimplified acute physiology score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esequential organ failure assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute kidney injury.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shanghai Jinshan District Medical and Health Science and Technology Innovation Funding Project (grant number: 2022-WS-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData of the present study was from the MIMIC-III database. The MIMIC III database was approved to build by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Data for the present study was extracted by one author, Dingzhong Tang, who has completed the online training course and passed the exam (ID:\u0026nbsp;13183421). In addition, the Ethics Committee of Jinshan Branch of Shanghai Sixth People\u0026apos;s Hospital approved the conduct of this study (ID:\u0026nbsp;jszxyy202334).\u0026nbsp;the review committee waived the requirement for written informed consent because of the retrospective nature of the study. Prior to analysis, confidential patient information was deleted from the entire data set prior to analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of the present study come from https://mimic.mit.edu/. The data supporting the findings of the present paper could be provided by contacting the author (\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e), without reservation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChunli Yu and Dingzhong Tang contributed to hypothesis development and manuscript preparation. Weiguo Yao, Kun Liu, Chunli Yu, and Dingzhong Tang contributed to the study design. Chunli Yu and Dingzhong Tang undertook data analyses and drafted and revised the manuscript. All authors approved the final draft of the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeigin VL, Roth GA, Naghavi M, Parmar P, Krishnamurthi R, Chugh S, et al. Global burden of stroke and risk factors in 188 countries, during 1990\u0026ndash;2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet Neurology. 2016;15(9):913\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeh TH, Tu KC, Wang HY, Chen JY. From Acute to Chronic: Unraveling the Pathophysiological Mechanisms of the Progression from Acute Kidney Injury to Acute Kidney Disease to Chronic Kidney Disease. International journal of molecular sciences. 2024;25(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsui B, Chen IE, Nour M, Kihira S, Tavakkol E, Polson J, et al. Perfusion Collateral Index versus Hypoperfusion Intensity Ratio in Assessment of Collaterals in Patients with Acute Ischemic Stroke. AJNR American journal of neuroradiology. 2023;44(11):1249\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChawla LS, Kimmel PL. Acute kidney injury and chronic kidney disease: an integrated clinical syndrome. Kidney international. 2012;82(5):516\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMark PB, Stevens KK, Jardine AG. Electrolytes: Acid\u0026ndash;base balance. In: Caballero B, editor. Encyclopedia of Human Nutrition (Fourth Edition). Oxford: Academic Press; 2013. p. 104\u0026thinsp;\u0026ndash;\u0026thinsp;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirnagl U, Iadecola C, Moskowitz MA. Pathobiology of ischaemic stroke: an integrated view. Trends in neurosciences. 1999;22(9):391\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDienel GA. Lactate Shuttling and Lactate use as Fuel after Traumatic Brain Injury: Metabolic Considerations. Journal of Cerebral Blood Flow \u0026amp; Metabolism. 2014;34(11):1736\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiesj\u0026ouml; BK. Lactic acidosis in the brain: occurrence, triggering mechanisms and pathophysiological importance. Ciba Foundation symposium. 1982;87:77\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraut JA, Madias NE. Lactic acidosis. The New England journal of medicine. 2014;371(24):2309\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellomo R. Bench-to-bedside review: lactate and the kidney. Critical care (London, England). 2002;6(4):322-6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBennett DA. How can I deal with missing data in my study? Australian and New Zealand journal of public health. 2001;25(5):464\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenbaum PR, Rubin DBJB. The central role of the propensity score in observational studies for causal effects. 1983;70(1):41\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanderweele TJ, Vansteelandt S. Odds ratios for mediation analysis for a dichotomous outcome. American journal of epidemiology. 2010;172(12):1339\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaini V, Guada L, Yavagal DR. Global Epidemiology of Stroke and Access to Acute Ischemic Stroke Interventions. Neurology. 2021;97(20 Suppl 2):S6-s16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Zhang J, Ye Y, Xu Q, Li Y, Feng S, et al. Peripheral Organ Injury After Stroke. Frontiers in immunology. 2022;13:901209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhatri M, Himmelfarb J, Adams D, Becker K, Longstreth W, Tirschwell DLJJoS, et al. Acute kidney injury is associated with increased hospital mortality after stroke. 2014;23(1):25\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a AF, Manzano-Nunez R, Bayona JG, Naranjo MP, Villa DN, Moreno M, et al. Acute kidney injury in severely injured patients admitted to the intensive care unit. Military Medical Research. 2020;7(1):47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi S, You J, Kim YJ, Lee HC, Park HP, Park CK, et al. High Intraoperative Serum Lactate Level is Associated with Acute Kidney Injury after Brain Tumor Resection. Journal of neurosurgical anesthesiology. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, He Y, Hu L, Zhu Q, Lin Q, Hong X, et al. Lactate level and lactate clearance for acute kidney injury prediction among patients admitted with ST-segment elevation myocardial infarction: A retrospective cohort study. Frontiers in cardiovascular medicine. 2022;9:930202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahyaoglu M, Karaduman A, Ge\u0026ccedil;men \u0026Ccedil;, Candan \u0026Ouml;, G\u0026uuml;ner A, Cakmak EO, et al. Serum lactate level may predict the development of acute kidney injury in acute decompensated heart failure. Turk Kardiyoloji Dernegi arsivi: Turk Kardiyoloji Derneginin yayin organidir. 2020;48(7):683\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaukonen K-M, Bailey M, Egi M, Orford N, Glassford NJ, Marik PE, et al. Stress hyperlactatemia modifies the relationship between stress hyperglycemia and outcome: a retrospective observational study. 2014;42(6):1379\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarik PE, Bellomo RJCc. Stress hyperglycemia: an essential survival response! 2013;17:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia-Alvarez M, Marik P, Bellomo RJTlD, endocrinology. Stress hyperlactataemia: present understanding and controversy. 2014;2(4):339\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrealish M, Chiew AL, Varndell W, Depczynski B. The relationship between admission glucose and lactate with critical illness amongst adult patients presenting to the emergency department. Acta diabetologica. 2021;58(10):1343\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakker J, Nijsten MW, Jansen TCJAoic. Clinical use of lactate monitoring in critically ill patients. 2013;3:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMizock BAJBP, Endocrinology RC, Metabolism. Alterations in fuel metabolism in critical illness: hyperglycaemia. 2001;15(4):533\u0026thinsp;\u0026ndash;\u0026thinsp;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreire Jorge P, Wieringa N, de Felice E, van der Horst ICC, Oude Lansink A, Nijsten MW. The association of early combined lactate and glucose levels with subsequent renal and liver dysfunction and hospital mortality in critically ill patients. Critical care (London, England). 2017;21(1):218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, et al. Intensive insulin therapy in critically ill patients. 2001;345(19):1359\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarwish R, Chen E, Minear S, Sheffield C. Resolution of insulin resistance, lactic acidosis, and decrease in mechanical support requirements in patients post orthotopic heart transplant with the use of long-acting insulin glargine. Journal of cardiothoracic surgery. 2024;19(1):99.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"serum glucose, serum lactate, acute kidney injury, acute ischemic stroke","lastPublishedDoi":"10.21203/rs.3.rs-4454722/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4454722/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSerum lactate level has been confirmed to be an independent risk factor for the occurrence of acute kidney injury (AKI) in many diseases. However, the correlation between serum lactate level and AKI in critical patients with acute ischemic stroke (AIS) has not been unclear. Moreover, limited studies have examined the mediating effect of serum glucose on the association between Serum lactate and AKI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe identified 1,435 AIS patients from the Medical Information Mart for Intensive Care (MIMIC-III) database and divided them into AKI or No-AKI groups. We used a propensity score matching (PSM) method to reduce confounding. Linear regression, logistic regression, and restricted cubic splines (RCS) were used to evaluate relationships between blood lactate levels and serum glucose, serum lactate, as well as AKI. Finally, the mediating role of serum glucose on the relationship between serum lactate and AKI was investigated utilizing the mediation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn the present study, a total of 634 critical patients with AIS aged ≥18 years were included after propensity score matching (1:1). we use RCS plot to reveal a linear association of between serum lactate levels and AKI and between serum glucose levels and serum lactate levels (all \u003cem\u003eP\u003c/em\u003e for nonlinear \u0026lt;0.001). After full adjustment for potential confounders (Model 3), serum glucose was positively correlated to serum lactate level (β=0.004, 95% CI: 0.003-0.006, \u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.001). High lactate level increased the risk of AKI (OR, 2.216; 95% CI, 1.559-3.271; \u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.001). Serum glucose explained 14.9% of the association between serum lactate and AKI among critical patients with AIS (\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.001), 16.4% among patients with AIS and DM (\u003cem\u003eP\u003c/em\u003e-value =0.24), and 19.5% among patients with AIS and without DM (\u003cem\u003eP\u003c/em\u003e-value \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Serum lactate acid was independently associated with increased risk-adjusted AKI in critical patients with AIS. The increase in serum glucose may have mediated this effect, especially in patients without DM.\u003c/p\u003e","manuscriptTitle":"The Effects of serum glucose level on the association of between serum lactate level and acute kidney injury among critical patient with acute ischemic stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 02:27:06","doi":"10.21203/rs.3.rs-4454722/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-08-20T07:07:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-30T18:17:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-29T13:18:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-21T12:10:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e7e3482-b80b-4b0f-9f50-fdf718aaa6e1","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":33075654,"name":"Health sciences/Nephrology"},{"id":33075655,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2024-06-13T02:27:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 02:27:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4454722","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4454722","identity":"rs-4454722","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}