Association between Serum Magnesium Levels and Adverse Prognosis in Patients with Acute Pancreatitis Complicated by Acute Kidney Injury: A Retrospective Study Based on the MIMIC-IV Database

Association between Serum Magnesium Levels and Adverse Prognosis in Patients with Acute Pancreatitis Complicated by Acute Kidney Injury: A Retrospective Study Based on the MIMIC-IV Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between Serum Magnesium Levels and Adverse Prognosis in Patients with Acute Pancreatitis Complicated by Acute Kidney Injury: A Retrospective Study Based on the MIMIC-IV Database Wei Wang, Jiaming Wang, TONGPING SHEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8277115/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective Acute pancreatitis (AP) concurrent with acute kidney injury (AKI) remarkably elevates the risk of adverse outcomes in affected individuals. Abnormal serum magnesium concentrations have been linked to AKI development across diverse patient populations; however, the prognostic significance of serum magnesium levels at multiple time points (60 days, 90 days, 180 days, and 365 days) remains inadequately explored in AP patients with AKI admitted to the intensive care unit (ICU). This study aimed to assess the dynamic prognostic value of serum magnesium at the aforementioned key time points, clarify its clinical utility for risk stratification in this specific cohort, and investigate prognostic disparities among patients stratified by gender, as well as the presence or absence of diabetes mellitus, congestive heart failure, and pre-existing kidney disease. Methods Study data were extracted from the MIMIC-IV database, which was made publicly available in October 2024. Adult patients (≥18 years) diagnosed with AP, who had an ICU length of stay (LOS) exceeding 24 hours and complete mortality data, were enrolled. Exclusion criteria included missing serum magnesium measurements, ICU LOS < 24 hours, incomplete clinical records, and aberrant survival data. Finally, 492 data samples meeting the inclusion criteria were enrolled in the present study. Serum magnesium levels were stratified into three grades using X-tile software, with stratification thresholds determined based on 60-day survival outcomes. Clinical data were retrieved using SQL and PostgreSQL. Intergroup comparisons were performed using statistical methods including the Wilcoxon rank-sum test, chi-square test, and t-test. Survival analyses were conducted to evaluate the association between serum magnesium levels and prognosis. Univariate Cox regression models were used to initially assess the relationship, and multivariate Cox regression models were constructed to adjust for confounding factors based on key patient characteristics. Results Among the 492 enrolled patients, males accounted for 53.25%. No statistically significant differences were noted in gender distribution or age across the three groups stratified by serum magnesium levels (P > 0.05). The hypermagnesemia group had the longest median ICU length of stay (LOS) (145 hours, interquartile range [IQR]: 62–274 hours), with intergroup differences approaching statistical significance (H = 5.112, P = 0.078). The incidence rates of sepsis and hypertension increased significantly with elevated serum magnesium levels (sepsis: χ² = 11.496, P = 0.003; hypertension: χ² = 6.065, P = 0.048). Additionally, the utilization rate of continuous renal replacement therapy (CRRT) in the hypermagnesemia group (20.75%) was significantly higher than that in the hypomagnesemia group (9.15%) and normomagnesemia group (11.48%) (χ² = 6.302, P = 0.043). In the hypermagnesemia group, serum creatinine, potassium, sodium, and chloride levels were significantly elevated, while serum calcium levels were markedly decreased (all P < 0.05). Disease severity scores, including the Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS II), and Logistic Organ Dysfunction System (LODS) score, were significantly higher in the hypermagnesemia group compared to the other two groups (all P < 0.05). Regarding prognostic outcomes, the hypermagnesemia group had the shortest median survival times at 60, 180, and 365 days, with statistically significant intergroup differences (H-values: 6.75, 6.033, 9.235; all P < 0.049). Its 365-day mortality rate (37.74%) was more than twice that of the hypomagnesemia group (18.61%). Kaplan-Meier analysis revealed that the hypermagnesemia group had significantly lower survival rates at all time points compared to the hypomagnesemia group (log-rank test, P < 0.05). Multivariate Cox regression analysis indicated that the risk of death gradually increased with rising serum magnesium levels, and hypermagnesemia was associated with a 54% higher risk of 365-day mortality (HR = 1.54, 95% CI: 0.54–4.43). Restricted cubic spline (RCS) analysis demonstrated a significant increase in mortality risk when serum magnesium levels exceeded 1.9 mg/dL. Subgroup analysis confirmed that the association between serum magnesium levels and prognosis was consistent across different subgroups. Furthermore, during the 365-day follow-up, the hypermagnesemia-related mortality risk was significantly elevated in obese patients and those with sepsis (P 2.3 mg/dL) serves as an independent prognostic biomarker in this patient population. The stratified thresholds for serum magnesium (1.9 mg/dL and 2.3 mg/dL) identified in this study can be used as practical biological markers for risk stratification in AP patients with AKI. Routine serum magnesium monitoring is recommended for the early management of these patients, which may contribute to improved risk assessment and lay the groundwork for subsequent mechanistic research and intervention strategy development. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background AP is a prevalent digestive disorder globally, often necessitating inpatient care and placing a heavy healthcare burden on medical systems [ 1 ]. Well-documented risk factors, including alcohol intake, obesity, advanced age, and cholelithiasis, have contributed to a rising incidence of AP, with an annual growth rate of 3% [ 2 ]. Clinically, AP is categorized into mild, moderate, and severe subtypes, and it frequently presents with concurrent comorbidities. Among these complications, AKI stands out as one of the most critical, as it notably heightens the likelihood of adverse outcomes in AP patients [ 3 ]. AKI is defined as a clinical syndrome characterized by an abrupt deterioration in renal function, primarily manifested by a decrease in glomerular filtration rate (GFR). As AKI progresses, nitrogenous metabolic wastes accumulate systemically, accompanied by disruptions in fluid balance, electrolyte homeostasis, and acid-base equilibrium—all of which may eventually trigger multiple systemic complications [ 4 ]. The development of AKI in the setting of AP is closely linked to systemic inflammatory responses, fluid derangements, and insufficient renal perfusion, particularly in the context of severe AP [ 5 ]. Magnesium, as an essential cation in the human body, participates in numerous physiological and biochemical processes and plays a crucial role [ 6 ]. Its functions include enzyme activation, maintenance of nucleic acid stability, and protein synthesis. Meanwhile, it can regulate neurological and cardiac functions, support mitochondrial function, and maintain cytoskeletal integrity [ 7 , 8 ]. In addition, magnesium can catalyze more than 300 intracellular reactions, including neurotransmitter release, energy production, and intracellular calcium regulation [ 9 , 10 , 11 ]. Serum magnesium abnormalities not only impair multiple physiological processes but also exacerbate disease progression, particularly renal dysfunction. Moreover, recent clinical evidence has confirmed an association between aberrant serum magnesium levels and the risk of AKI across diverse patient cohorts, such as critically ill or post-surgical individuals [ 12 , 13 , 14 ]. Nevertheless, the prognostic value of serum magnesium concentrations with respect to AKI-related outcomes in intensive care unit (ICU)-admitted AP patients remain largely unelucidated. Prior investigations have primarily explored the link between serum magnesium and AKI occurrence, yet most were confined to single endpoint analyses. To fill this knowledge void, this study pioneers the assessment of serum magnesium’s dynamic prognostic significance at four key follow-up time points. From a comprehensive perspective, it delineates the clinical utility of serum magnesium for risk stratification in AP patients complicated by AKI. Additionally, we further performed subgroup analyses to dissect prognostic disparities among patients stratified by gender, as well as the presence or absence of diabetes mellitus, congestive heart failure, and pre-existing kidney disease. 1 Materials and methods 1.1 Data sources Housing high-quality clinical data of critically ill patients treated at Beth Israel Deaconess Medical Center over a 14-year timeframe (2008–2022), this database is openly accessible to qualified researchers. For the current study, all data were derived from the latest version of the database, MIMIC-IV (v3.1), which was officially released in October 2024 [ 15 ]. Author Tongping Shen obtained legal access to the MIMIC database (Record ID: 14348115) after completing the mandatory training specified by the National Institutes of Health (NIH) and passing the Collaborative Institutional Training Initiative (CITI) Program assessment. Given that all data in the MIMIC database were anonymized to protect patient privacy, the requirement for written informed consent was waived. This study was designed and conducted in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational. 1.2 Study participants This study concentrated on patients with AP who were admitted to the ICU for the first time, with study participants identified using the International Classification of Diseases, ICD-10 diagnostic codes, namely K8500, K8502, K851, K8520, and K8590. The inclusion criteria were established as follows: (1) age ≥ 18 years old; (2) ICU length of stay (LOS) exceeding 24 hours; (3) availability of complete follow-up data on mortality outcomes at 60, 90, 180, and 365 days after admission. Exclusion criteria were established as: (1) missing serum magnesium level data; (2) ICU LOS < 24 hours; (3) incomplete clinical records; (4) abnormal survival outcome data. Initially, 4930 AP patients were retrieved from the MIMIC-IV database; however, only 492 patients met the eligibility criteria for final analysis after applying the above-mentioned inclusion and exclusion criteria. Specifically, the reasons for exclusion were: 2936 patients due to non-first-time ICU admission, 285 patients due to ICU LOS < 24 hours, 711 patients with a pre-existing kidney disease history, and 506 patients due to missing albumin or serum magnesium data. Stratified analysis was performed using X-tile software. Based on 60-day survival data, serum magnesium levels were divided into three grades via the software’s built-in optimal cut-off value calculation function, corresponding to three groups in the study cohort: Q1 group ( 2.3 mg/dL), as shown in Fig. 1 . The screening process of participants is illustrated in Fig. 2 . 1.3 Data Collection For this study, baseline characteristics of the study population were retrieved from the MIMIC-IV database using SQL and PostgreSQL. These characteristics covered multiple dimensions, including demographic information, vital signs, laboratory parameters, comorbidities, medical interventions, disease severity scores, and study outcomes, as detailed below: Specifically, demographic characteristics encompassed gender, age, and weight. Vital sign parameters included heart rate, systolic blood pressure (SBP), mean blood pressure (MBP), peripheral capillary oxygen saturation (SpO₂), and other relevant indicators. Laboratory parameters were comprehensive, comprising red blood cell count (RBC), red cell distribution width (RDW), hemoglobin, hematocrit, white blood cell count (WBC), platelet count, albumin, anion gap, bicarbonate, blood urea nitrogen (BUN), serum calcium, chloride, creatinine, sodium, potassium, prothrombin time (PT), international normalized ratio (INR), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), among others. Comorbidities and prior medical history of the study participants were ascertained using the International Classification of Diseases, ICD-10 diagnostic codes, encompassing AKI, atrial fibrillation, heart failure, respiratory failure, and preexisting renal disease. Medical intervention measures involved vasopressin administration, octreotide use, mechanical ventilation, continuous renal replacement therapy (CRRT), and endoscopic retrograde cholangiopancreatography (ERCP). The severity of patients’ conditions was evaluated using the SOFA score and Charlson Comorbidity Index (CCI) immediately after ICU admission. The primary study outcomes were defined as all-cause mortality at four time points following ICU admission: 60-day, 90-day, 180-day, and 365-day mortality. 1.4 Statistical Analysis For data processing and statistical analyses, the following approaches were implemented. Continuous variables were first subjected to normality testing: those following a normal distribution were expressed as mean ± standard deviation, with comparisons between two groups conducted via Student’s t-test, and comparisons among three or more groups carried out using one-way analysis of variance. For continuous variables with a non-normal distribution, descriptive statistics were reported as median and interquartile range (IQR), and intergroup differences were assessed using the Wilcoxon rank-sum test. Categorical variables were described by frequencies and percentages, and intergroup comparisons were conducted using the chi-square test or Fisher’s exact test (applied when expected frequencies were too low). Regarding missing data management, variables with missing rates 20% were excluded from the analysis to ensure data reliability. Serum magnesium levels served as the stratification variable for grouping. Kaplan-Meier (KM) survival curves were used to estimate the cumulative incidence of primary outcomes, and the log-rank test was applied to compare survival differences across serum magnesium strata. Univariate Cox proportional hazards regression models were initially constructed to assess the crude association between serum magnesium levels and mortality at the four aforementioned time points. To explore the potential nonlinear relationship between serum magnesium levels and AKI-related all-cause mortality, a RCS model was established. A piecewise fitting approach was used to characterize the continuous association between serum magnesium levels and outcome variables, and smooth curves were plotted to visually demonstrate the dose-response relationship. Four default knots were set in the RCS model, located at the 5th, 35th, 65th, and 95th percentiles of serum magnesium levels, balancing model fitting accuracy and robustness. Multivariate Cox proportional hazards regression models were further constructed to verify the independent association between serum magnesium levels and all-cause mortality in AP patients with AKI. Two adjusted models were designed: Model 1 (unadjusted model) without confounding factor adjustment; Model 2 (fully adjusted model) adjusting for heart rate, sepsis, octreotide use, CRRT administration, serum calcium, creatinine, potassium levels, systemic inflammatory response syndrome (SIRS), LODS score, and CCI. To avoid multicollinearity, the variance inflation factor (VIF) of each variable was calculated during model construction, and variables with VIF > 5 were excluded. In both models, the lowest serum magnesium stratum (Q1) was set as the reference group. Stratified analyses were performed based on key clinical characteristics, including gender, diabetes status, hypertension status, obesity, sepsis, AKI severity, respiratory failure, CRRT treatment, endoscopic retrograde cholangiopancreatography (ERCP) history, and pre-existing kidney disease, to examine whether the association between serum magnesium levels and outcomes was consistent across different subgroups. Interaction tests were conducted to clarify the moderating effect of specific variables on the relationship between serum magnesium levels and prognostic outcomes. A two-sided P < 0.05 was considered statistically significant. All statistical analyses were implemented using R software (Version 4.3.0). 2 Results 2.1 Baseline characteristics stratified by Serum Magnesium level Table 1 summarizes the baseline characteristics and prognostic outcomes of patients stratified by serum magnesium levels. In terms of demographic features, males constituted 53.25% (262/492) of the total cohort, while females accounted for 46.75% (230/492). The median age of the entire study population was 58 years (IQR: 46–74 years), encompassing middle-aged to elderly individuals. Across the three serum magnesium level groups, no statistically significant differences were observed in terms of gender distribution (χ²=0.72, p = 0.698) or age (H = 4.289, p = 0.117), indicating that the demographic baselines of each group were well-balanced and comparable. Regarding clinical indicators, heart rate was significantly higher in the hypomagnesemia group (99.13 ± 18.27 beats per minute) than in the normomagnesemia group (93.33 ± 16.81 beats per minute) and hypermagnesemia group (94.18 ± 19.62 beats per minute), with statistically significant differences (χ²=5.33, p = 0.005). The hypermagnesemia group had the longest ICU length of stay (median: 145 hours, Q1-Q3: 62–274 hours), and the intergroup difference approached statistical significance (H = 5.112, p = 0.078). No significant differences were observed among the three groups in systolic blood pressure (SBP, p = 0.921), mean arterial pressure (MBP, p = 0.286), peripheral capillary oxygen saturation (SpO₂, p = 0.503), respiratory rate (p = 0.583), blood glucose (p = 0.247), or body weight (p = 0.901) (all p > 0.05). With respect to comorbidities, sepsis incidence rose notably as serum magnesium levels increased: the hypermagnesemia group exhibited an 83.02% sepsis rate, which was markedly higher than the 62.15% in the hypomagnesemia group and 72.95% in the normomagnesemia group, with a statistically significant difference (χ²=11.496, p = 0.003). AKI was observed in 88.68% of patients with hypermagnesemia, a proportion higher than that in the other two groups though the difference did not reach statistical significance (χ²=5.942, p = 0.051). Hypertension was most prevalent in the hypermagnesemia group (33.96%), and intergroup variations were statistically significant (χ²=6.065, p = 0.048). The hypermagnesemia group also had higher rates of diabetes mellitus (p = 0.073) and chronic kidney disease (CKD, p = 0.059), with these differences approaching statistical significance. Respiratory failure was more common in groups with abnormal magnesium levels (p = 0.098), while obesity showed no significant disparities across the three groups (p = 0.334). Regarding therapeutic interventions, the hypermagnesemia group had a 20.75% utilization rate of CRRT, which was significantly higher than the 9.15% in the hypomagnesemia group and 11.48% in the normomagnesemia group (χ² =6.302, p = 0.043). No significant differences were detected among the three groups in terms of mechanical ventilation use (p = 0.216) or vasopressin administration (p = 0.423, all p > 0.05). For laboratory parameters, serum magnesium levels displayed a significant gradient across the three groups (H = 353.83, p < 0.001). Compared with the other two groups, the hypermagnesemia group had significantly elevated levels of BUN, creatinine, sodium, potassium, and chloride (all p < 0.05), along with notably reduced serum calcium (H = 8.887, p = 0.012). The anion gap was highest in the hypermagnesemia group, approaching statistical significance (p = 0.097). No significant intergroup differences were observed in bicarbonate (p = 0.228), liver function indices (all p > 0.239), complete blood count parameters (all p > 0.119), coagulation function indicators (all p > 0.257), or albumin (p = 0.895). In terms of disease severity scoring systems, the SOFA score, SAPSⅡscore, LODS score, and CCI were all significantly higher in the hypermagnesemia group than in the other two groups (all p < 0.05). The LODS score showed an extremely significant difference (H = 17.914, p < 0.001). The albumin-corrected anion gap (ACAG) was highest in the hypermagnesemia group, with the difference approaching statistical significance (H = 5.771, p = 0.056). For prognostic outcomes, the hypermagnesemia group had the shortest median survival times at 60, 180, and 365 days, with statistically significant intergroup differences (H-values: 6.758, 6.033, 9.235, respectively; all p < 0.05). The 90-day survival difference approached statistical significance (p = 0.057). The 365-day adverse event rate in the hypermagnesemia group (37.74%) was more than double that in the hypomagnesemia group (18.61%). Significant differences in survival status were identified among the three groups at 60, 90, 180, and 365 days (all p < 0.05), indicating a strong association between hypermagnesemia and adverse prognosis. Table 1 Baseline characteristics of participants of this study Variables Total (n = 492) 2.3 (n = 53) p statistic icu_stay_hours, Median (Q1,Q3) 83 (45, 187.25) 80 (44, 164) 81 (44, 182.75) 145 (62, 274) 0.078 5.112 gender, n (%) 0.698 0.72 female 230 (46.75) 149 (47) 59 (48.36) 22 (41.51) male 262 (53.25) 168 (53) 63 (51.64) 31 (58.49) age, Median (Q1,Q3) 58 (46, 74) 56 (45, 71) 59 (47, 79) 61 (50, 76) 0.117 4.289 heart_rate, Mean ± SD 97.16 ± 18.23 99.13 ± 18.27 93.33 ± 16.81 94.18 ± 19.62 0.005 5.33 sbp, Median (Q1,Q3) 119.11 (107.87, 132.49) 119.11 (108.04, 132.68) 120.89(108.72, 129.94) 116.73 (107.21, 135.36) 0.921 0.165 mbp, Median (Q1,Q3) 80.37 (72.7, 90.8) 81.26 (72.7, 91.71) 79.75 (72.25, 86.42) 78.5 (74.3, 92.34) 0.286 2.501 spo2, Median (Q1,Q3) 96.2 (94.92, 97.74) 96.17 (94.83, 97.68) 96.19 (95.06, 97.63) 96.52 (95.38, 98.12) 0.503 1.375 glucose, Median (Q1,Q3) 135.25 (109.17, 169.3) 137 (109.33, 170.33) 128.75 (105.25, 164.51) 142 (115.63, 178.5) 0.247 2.793 hypertension, n (%) 0.048 6.065 No 359 (72.97) 225 (70.98) 99 (81.15) 35 (66.04) Yes 133 (27.03) 92 (29.02) 23 (18.85) 18 (33.96) diabetes, n (%) 0.073 5.237 No 426 (86.59) 275 (86.75) 110 (90.16) 41 (77.36) Yes 66 (13.41) 42 (13.25) 12 (9.84) 12 (22.64) obesity, n (%) 0.334 2.194 No 445 (90.45) 285 (89.91) 114 (93.44) 46 (86.79) Yes 47 (9.55) 32 (10.09) 8 (6.56) 7 (13.21) sepsis, n (%) 0.003 11.496 No 162 (32.93) 120 (37.85) 33 (27.05) 9 (16.98) Yes 330 (67.07) 197 (62.15) 89 (72.95) 44 (83.02) acute_kidney_injury, n (%) 0.051 5.942 No 123 (25) 84 (26.5) 33 (27.05) 6 (11.32) Yes 369 (75) 233 (73.5) 89 (72.95) 47 (88.68) respiratory_failure, n (%) 0.098 4.652 No 390 (79.27) 245 (77.29) 105 (86.07) 40 (75.47) Yes 102 (20.73) 72 (22.71) 17 (13.93) 13 (24.53) kidney_disease, n (%) 0.059 5.658 No 356 (72.36) 229 (72.24) 95 (77.87) 32 (60.38) Yes 136 (27.64) 88 (27.76) 27 (22.13) 21 (39.62) vasopressin, n (%) 0.423 1.719 No 425 (86.38) 274 (86.44) 108 (88.52) 43 (81.13) Yes 67 (13.62) 43 (13.56) 14 (11.48) 10 (18.87) mechanical_ventilation, n (%) 0.216 3.068 No 65 (13.21) 48 (15.14) 11 (9.02) 6 (11.32) Yes 427 (86.79) 269 (84.86) 111 (90.98) 47 (88.68) crrt_treatment, n (%) 0.043 6.302 No 438 (89.02) 288 (90.85) 108 (88.52) 42 (79.25) Yes 54 (10.98) 29 (9.15) 14 (11.48) 11 (20.75) rbc, Median (Q1,Q3) 3.82 (3.31, 4.35) 3.85 (3.31, 4.44) 3.63 (3.28, 4.17) 3.86 (3.47, 4.45) 0.119 4.26 rdw, Median (Q1,Q3) 14.7 (13.7, 16) 14.7 (13.7, 16.1) 14.7 (13.53, 15.6) 15 (14.1, 16.2) 0.546 1.209 hemoglobin, Median (Q1,Q3) 11.8 (10.2, 13.1) 11.9 (10.2, 13.3) 11.25 (10.1, 12.5) 11.7 (10.5, 13.3) 0.171 3.528 hematocrit, Median (Q1,Q3) 35.4 (30.9, 39.6) 35.5 (30.9, 40.2) 34.8 (30.8, 38.58) 35.1 (32, 40.7) 0.335 2.188 white_blood_cell, Median (Q1,Q3) 14 (9.8, 19.52) 14.2 (9.6, 19.8) 13.1 (10.05, 17.58) 14.9 (10.3, 22.2) 0.31 2.341 platelet, Median (Q1,Q3) 203 (144, 283) 200 (143, 275) 221 (158, 310.75) 192 (126, 269) 0.128 4.118 albumin, Median (Q1,Q3) 3 (2.6, 3.4) 3 (2.6, 3.4) 3 (2.52, 3.4) 2.9 (2.6, 3.5) 0.895 0.222 aniongap, Median (Q1,Q3) 16 (14, 19) 16 (14, 19) 15 (13.25, 17.75) 17 (14, 21) 0.097 4.674 bicarbonate, Median (Q1,Q3) 23 (20, 26) 23 (20, 26) 24 (20.25, 26) 22 (19, 26) 0.228 2.96 bun, Median (Q1,Q3) 21 (13, 36.25) 19 (12, 31) 21 (15, 37.5) 39 (21, 64) < 0.001 39.904 calcium, Median (Q1,Q3) 8.2 (7.6, 8.7) 8.2 (7.6, 8.6) 8.3 (7.73, 8.78) 8.3 (7.9, 9) 0.012 8.887 chloride, Median (Q1,Q3) 107 (103, 111) 106 (102, 110) 107 (103, 111) 110 (105, 115) 0.004 10.814 creatinine, Median (Q1,Q3) 1 (0.7, 1.92) 1 (0.7, 1.7) 1.1 (0.8, 2) 1.8 (1.1, 2.9) < 0.001 20.42 sodium, Median (Q1,Q3) 140 (137, 143) 139 (137, 142) 140 (137, 143) 141 (140, 148) < 0.001 20.808 potassium, Median (Q1,Q3) 4.3 (3.9, 4.82) 4.3 (3.9, 4.8) 4.2 (3.9, 4.7) 4.5 (4.1, 5) 0.049 6.02 pt, Median (Q1,Q3) 14.85 (13.3, 17.2) 14.85 (13.3, 17.3) 14.7 (13.33, 16.95) 15.5 (13.3, 19.4) 0.449 1.6 inr, Median (Q1,Q3) 1.3 (1.2, 1.6) 1.3 (1.2, 1.6) 1.3 (1.2, 1.5) 1.4 (1.2, 1.8) 0.257 2.715 resp_rate, Median (Q1,Q3) 30 (26, 34) 30 (26, 34) 30 (26, 33) 32 (27, 35) 0.583 1.077 sofa_score, Median (Q1,Q3) 5 (2, 8) 4 (2, 8) 4 (3, 7) 7 (3, 10) 0.005 10.796 sapsii_score, Median (Q1,Q3) 33 (25, 45) 33 (24, 44) 32 (26, 43.75) 43 (31, 56) 0.002 12.11 lods, Median (Q1,Q3) 4.5 (3, 7) 4 (2, 7) 5 (3, 7) 7 (4, 10) < 0.001 17.914 charlson_comorbidity_index, Median (Q1,Q3) 3 (1, 5) 3 (1, 5) 3 (1, 5) 4 (2, 6) 0.015 8.351 tbil, Median (Q1,Q3) 1.3 (0.7, 3.4) 1.3 (0.7, 3.6) 1.1 (0.7, 2.92) 1.3 (0.7, 3.5) 0.295 2.442 alt, Median (Q1,Q3) 75 (35, 190.5) 76 (35, 189) 64.5 (30, 190.5) 81 (55, 194) 0.255 2.733 ast, Median (Q1,Q3) 100 (49.75, 248.25) 100 (50, 230) 89.5 (42.5, 259) 105 (68, 299) 0.239 2.866 weight, Median (Q1,Q3) 83.4 (70.15, 98.93) 83.7 (71, 98) 82.2 (70, 99.38) 82.5 (68, 104.9) 0.901 0.209 mg_valuenum, Median (Q1,Q3) 1.8 (1.6, 2.1) 1.7 (1.5, 1.8) 2.1 (2, 2.2) 2.5 (2.4, 2.8) < 0.001 353.83 surv_60_dod, Median (Q1,Q3) 60 (60, 60) 60 (60, 60) 60 (60, 60) 60 (58, 60) 0.034 6.758 surv_90_dod, Median (Q1,Q3) 90 (90, 90) 90 (90, 90) 90 (90, 90) 90 (58, 90) 0.057 5.718 surv_180_dod, Median (Q1,Q3) 180 (180, 180) 180 (180, 180) 180 (180, 180) 180 (58, 180) 0.049 6.033 surv_365_dod, Median (Q1,Q3) 365 (365, 365) 365 (365, 365) 365 (365, 365) 365 (58, 365) 0.01 9.235 status_60_dod, n (%) 0.025 7.354 No 420 (85.37) 278 (87.7) 103 (84.43) 39 (73.58) Yes 72 (14.63) 39 (12.3) 19 (15.57) 14 (26.42) status_90_dod, n (%) 0.053 5.893 No 409 (83.13) 270 (85.17) 101 (82.79) 38 (71.7) Yes 83 (16.87) 47 (14.83) 21 (17.21) 15 (28.3) status_180_dod, n (%) 0.046 6.138 No 401 (81.5) 266 (83.91) 98 (80.33) 37 (69.81) Yes 91 (18.5) 51 (16.09) 24 (19.67) 16 (30.19) status_365_dod, n (%) 0.007 9.896 No 385 (78.25) 258 (81.39) 94 (77.05) 33 (62.26) Yes 107 (21.75) 59 (18.61) 28 (22.95) 20 (37.74) ACAG, Median (Q1,Q3) 19.5 (17, 23.25) 19.5 (17, 23.5) 18.75 (17, 21.5) 20.5 (18.25, 26.25) 0.056 5.771 2.2 Survival Analysis Time-dependent variations were observed in all-cause mortality when stratified by serum magnesium levels. Over the follow-up time points of 60, 90, 180, and 365 days, the mortality rate in the hypermagnesemia group remained persistently higher than that in the hypomagnesemia group. Specifically, at the 365-day time point, the mortality rate reached 37.74% in the hypermagnesemia group, in contrast to 18.71% in the hypomagnesemia group, with a statistically significant difference (p = 0.011). These findings were corroborated by Kaplan-Meier survival analysis (Fig. 2 ), which demonstrated that the hypermagnesemia group had significantly lower survival probabilities compared to the hypomagnesemia group at 60 days (p < 0.05), 90 days (p < 0.001), 180 days (p < 0.001), and 365 days (p < 0.001). 2.3 Serum Magnesium Levels and Risk of Death To assess the independent relationship between serum magnesium tertiles and mortality risk, we conducted fully adjusted Cox proportional hazards regression analyses. Three regression models were established for this purpose: Model 1 served as the unadjusted baseline model; Model 2 was adjusted for variables including octreotide use, CRRT administration, serum calcium, creatinine, potassium levels, and the CCI; Model 3 built on Model 2 with additional adjustments for heart rate, sepsis status, and the LODS score. Data presented in Table 2 demonstrate that when the lowest serum magnesium tertile (Q1) was used as the reference group, the adjusted hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) revealed a stepwise elevation in mortality risk with increasing serum magnesium levels. 60-day mortality: Q2: 1.59 (0.76, 3.32); Q3: 1.80 (0.47, 6.91) 90-day mortality: Q2: 1.38 (0.70, 2.70); Q3: 1.68 (0.48, 5.85) 180-day mortality: Q2: 1.39 (0.73, 2.66); Q3: 1.74 (0.52, 5.77) 365-day mortality: Q2: 1.19 (0.67, 2.12); Q3: 1.54 (0.54, 4.43) Table 2 Abnormal survival data Q1 Q2 Q3 60-day Model 1 HR (95% CI) 1 1.34 (0.76, 2.36) 2.56 (1.26, 5.20) P-value 0.31 0.01 Model 2 HR (95% CI) 1 1.40 (0.79, 2.48) 1.84 (0.87, 3.89) P-value 0.25 0.11 Model 3 HR (95% CI) 1 1.59 (0.76, 3.32) 1.80 (0.47, 6.91) P-value 0.22 0.39 90-day Model 1 HR (95% CI) 1 1.16 (0.70, 1.94) 2.16 (1.12, 4.16) P-value 0.56 0.02 Model 2 HR (95% CI) 1 1.24 (0.74, 2.07) 1.68 (0.84, 3.37) P-value 0.42 0.14 Model 3 HR (95% CI) 1 1.38 (0.70, 2.70) 1.68 (0.48, 5.85) P-value 0.35 0.42 180-day Model 1 HR (95% CI) 1 1.18 (0.73, 1.92) 2.13 (1.13, 4.00) P-value 0.50 0.02 Model 2 HR (95% CI) 1 1.26 (0.77, 2.06) 1.67 (0.86, 3.26) P-value 0.36 0.13 Model 3 HR (95% CI) 1 1.39 (0.73, 2.66) 1.74 (0.52, 5.77) P-value 0.32 0.37 365-day Model 1 HR (95% CI) 1 1.11 (0.71, 1.74) 2.25 (1.27, 3.97) P-value 0.63 0.01 Model 2 HR (95% CI) 1 1.17 (0.74, 1.84) 1.72 (0.94, 3.14) P-value 0.50 0.08 Model 3 HR (95% CI) 1 1.19 (0.67, 2.12) 1.54 (0.54, 4.43) P-value 0.54 0.42 2.4 Nonlinear association analysis To clarify the potential non-linear association between serum magnesium levels and all-cause mortality at 60, 90, 180, and 365 days in patients with acute pancreatitis (AP) complicated by acute kidney injury (AKI), the present study employed restricted cubic spline (RCS) analysis for in-depth investigation (Fig. 3 ). Compared with traditional linear regression models that only capture simple linear relationships between variables, RCS analysis can more accurately reveal the potential complex dose-response relationship between serum magnesium levels (as a continuous variable) and mortality risk by flexibly fitting curve patterns. It is particularly suitable for identifying potential threshold effects or inflection point characteristics. The results of the RCS model analysis showed that among the 492 enrolled patients with AP complicated by AKI, there was a significant linear association between elevated serum magnesium concentrations and the risk of AKI-related mortality. Specifically, as serum magnesium levels gradually increased, the risk of AKI-related death in patients showed a continuous upward trend, with no obvious plateau phase or inverse association observed. To further clarify the critical threshold of serum magnesium levels on prognostic outcomes, this study conducted targeted threshold effect analysis through inflection point identification and statistical testing of RCS curves. The results indicated that when serum magnesium levels exceeded 1.9 mg/dL, the risk of AKI-related mortality in patients increased significantly, suggesting that this concentration could serve as a key threshold for mortality risk stratification in patients with AP complicated by AKI. The determination of this quantitative indicator not only provides an objective reference standard for the early risk assessment of such patients in clinical practice but also lays a data foundation for the subsequent development of individualized serum magnesium monitoring and intervention strategies. It facilitates clinicians to dynamically monitor serum magnesium levels, timely identify high-risk populations, and implement targeted measures to improve patient prognosis. 2.5 Subgroup Analysis Subgroup analyses were conducted to investigate the association between serum magnesium levels and all-cause mortality at different time points (60, 90, 180, and 365 days), as shown in Fig .4. During the 60-day follow-up, serum magnesium levels were positively correlated with all-cause mortality in the overall population (HR = 1.52, 95% CI 1.04 ~ 2.24, P = 0.032), and the "P for interaction" was > 0.05 for all subgroups (e.g., gender, diabetes mellitus, indicating a consistent association across subgroups. At the 90-day follow-up, the significance of the positive correlation in the overall population diminished (HR = 1.40, 95% CI 0.96 ~ 2.05, P = 0.082), but the consistency across subgroups was maintained (e.g., gender subgroup: P = 0.959). In the 180-day follow-up, the significance of the positive correlation in the overall population decreased further (HR = 1.39, 95% CI 0.96 ~ 2.00, P = 0.079), while the pattern of association remained consistent across subgroups. At the 365-day follow-up, the positive correlation in the overall population regained statistical significance (HR = 1.48, 95% CI 1.07 ~ 2.04, P = 0.018); additionally, elevated serum magnesium levels significantly increased the risk of death in the obesity subgroup (HR = 1.87, 95% CI 1.13 ~ 3.10, P = 0.014) and sepsis subgroup (HR = 2.27, 95% CI 1.09 ~ 4.72, P = 0.028). This "cross-subgroup and cross-time" consistency supports the potential clinical value of serum magnesium as a predictor of all-cause mortality. 3 Discussion AP is a prevalent acute digestive disorder globally, and patients with AP complicated by AKI present with markedly poor prognosis. Clinical issues including prolonged ICU stay, elevated risk of multiple organ failure, and increased long-term mortality substantially aggravate the healthcare burden [ 16 , 17 ]. As an essential cation in the human body, magnesium plays a pivotal role in over 300 enzymatic reactions, regulating adenosine triphosphate (ATP) metabolism, protein synthesis, and cardiovascular function. Furthermore, recent clinical evidence has confirmed that abnormal serum magnesium levels are closely associated with AKI risk in populations with cirrhosis, malignant tumors, and other comorbidities. However, the influence of serum magnesium levels on the prognosis of AP patients complicated by AKI across multiple time points, as well as the intrinsic mechanisms, remains elusive, particularly the lack of systematic analysis targeting the ICU setting. This study investigated the correlation between serum magnesium levels and AKI-related mortality outcomes in patients with AP complicated by AKI. Based on retrospective cohort data from 492 such patients, the findings establish serum magnesium levels as a reliable predictor of all-cause mortality at multiple follow-up time points. A major innovation of this research lies in the implementation of systematic multi-time-point analysis. Unlike previous studies that primarily focused on single-time-point assessments, the design of this study covering different follow-up phases clarifies the prognostic role of serum magnesium. It not only identifies its value in early risk detection within 60 days of admission but also confirms its close association with long-term survival outcomes at 365 days, thereby providing novel evidence for the clinical application of serum magnesium in the comprehensive management of these patients. The study demonstrated that elevated serum magnesium levels exhibited a significant positive correlation with short- to long-term all-cause mortality in AP patients complicated by AKI, presenting a clear risk gradient effect: the 365-day mortality rate in the hypermagnesemia group (> 2.3 mg/dL) was more than twice that in the hypomagnesemia group (< 1.9 mg/dL, 18.61%). Kaplan-Meier survival curves further confirmed the survival disadvantage of the hypermagnesemia group at all time points (p < 0.05). After multivariate adjustment using Cox proportional hazards models (adjusting for confounding factors such as heart rate, sepsis, CRRT administration, electrolyte levels, and comorbidity index), the hypermagnesemia group still had a 54% higher 365-day mortality risk compared with the reference group (HR = 1.54, 95% CI: 0.54–4.43). This suggests that serum magnesium levels may serve as an independent prognostic predictor in this patient population. This result is partially consistent with the perspective proposed by Cheungpasitporn et al. [ 11 ] that "abnormal serum magnesium levels (either too high or too low) increase the risk of AKI". However, the present study further extends the prognostic value of "hypermagnesemia": its clinical significance is not limited to predicting AKI occurrence, but also acts as a potential biomarker for long-term mortality risk. From a clinical practice perspective, the serum magnesium stratification thresholds identified by X-tile software in this study ( 2.3 mg/dL) have clear application value. Patients in the hypermagnesemia group not only had a significantly higher mortality rate but also exhibited a clustering effect of "disease complexity" in their baseline characteristics: the incidence of sepsis (83.02%) was significantly higher than that in the hypomagnesemia group (62.15%), the incidence of AKI (88.68%) was nearly 1.2 times that of the hypomagnesemia group (73.5%), and the median SOFA score (7 points), median SAPSⅡ score (43 points), and median LODS score (7 points) were all significantly higher than those in the other two groups (p < 0.05). This phenomenon suggests that elevated serum magnesium may not be an isolated electrolyte disorder, but rather a "companion biomarker" of exacerbated systemic inflammatory response and progressive organ function injury in AP patients complicated by AKI, in the hypermagnesemic state, patients are more prone to circulatory disturbance (tachycardia), worsening renal function (median serum creatinine: 1.8 mg/dL), and synergistic electrolyte abnormalities (elevated serum potassium, sodium, and chloride, coupled with decreased serum calcium). These pathological changes collectively form a "vicious cycle" leading to poor prognosis [ 9 , 10 ]. In addition, the utilization rate of CRRT in the hypermagnesemia group (20.75%) was significantly higher than that in the hypomagnesemia group (9.15%), which not only reflects more severe renal function injury in hypermagnesemic patients but also indicates that clinicians need to be alert to the bidirectional exacerbating mechanism of "magnesium retention - decreased renal function": on the one hand, AKI reduces the glomerular filtration rate, and impaired magnesium excretion leads to elevated serum magnesium; on the other hand, hypermagnesemia may further aggravate renal injury by inhibiting the activity of Na⁺-K⁺-ATPase in renal tubular epithelial cells [ 8 , 16 ]. This bidirectional effect may be an important reason for the poor long-term prognosis of the hypermagnesemia group [ 18 ]. Numerous previous studies have thoroughly elucidated the complex association between serum magnesium levels and AKI. Particularly in specialized research focusing on specific patient populations, relevant literature has consistently reported highly congruent findings. For instance, a study conducted on critically ill cirrhotic patients admitted to the intensive care unit (ICU) confirmed that elevated serum magnesium concentrations exhibit a significant positive correlation with the risk of AKI development. In the population of patients with coronavirus disease 2019, hypermagnesemia is not only associated with an increased risk of AKI but also closely linked to respiratory failure and mortality outcomes [ 19 ]. Beyond the aforementioned populations, clinical evidence further demonstrates that in patients undergoing total aortic arch replacement, hypermagnesemia has emerged as an independent predictive marker for AKI [ 20 ]. While in critically ill pediatric patients, elevated serum magnesium levels have also been verified to correlate with an increased risk of AKI [ 21 ]. Subgroup analysis revealed that the positive correlation between serum magnesium levels and all-cause mortality was consistently maintained across subgroups stratified by gender (P for interaction = 0.864), diabetes status (P = 0.963), hypertension status (P = 0.770), and AKI severity (P = 0.721). This finding underscores the broad applicability of serum magnesium’s prognostic value, which is not significantly confounded by gender differences or underlying comorbidities. Notably, during the 365-day follow-up, the hypermagnesemia-associated mortality risk was notably heightened in the obesity subgroup (HR = 1.87, 95% CI: 1.13–3.10, P = 0.014) and the sepsis subgroup (HR = 2.27, 95% CI: 1.09–4.72, P = 0.028). This observation carries important clinical implications: obese patients frequently present with insulin resistance, and magnesium plays a regulatory role in insulin signaling pathways, suggesting that the combination of obesity and hypermagnesemia may exacerbate insulin resistance and amplify oxidative stress-induced renal injury [ 22 ]. In the setting of sepsis, the synergistic effect of hypermagnesemia and inflammatory response may further amplify organ injury, leading to a poorer long-term prognosis. Therefore, for AP patients complicated by AKI who are obese or have comorbid sepsis, closer monitoring of serum magnesium levels is required, and timely intervention for hypermagnesemic status should be implemented to improve prognosis. This study pioneers the confirmation that elevated serum magnesium levels at ICU admission exerts a significant association with 60- to 365-day all-cause mortality in AP patients complicated by AKI who are admitted to the ICU. Moreover, hypermagnesemia may serve as an independent predictive biomarker for disease severity and prognostic outcomes in this specific patient population. We propose integrating routine serum magnesium detection into the early management protocol for AP patients, and combining magnesium level monitoring with other clinical indicators to achieve comprehensive risk assessment. Despite its rigorous study design that revealed the association between serum magnesium and prognosis in AP patients with AKI, this research still has several limitations. First, as a retrospective analysis based on the MIMIC-IV database, the study cannot fully eliminate the effects of selection bias (e.g., the unavoidable exclusion of patients with missing serum magnesium data) and confounding factors (e.g., the inability to collect information on pre-admission magnesium supplementation history and diuretic use), which might lead to overestimation or underestimation of the true association in the results. Second, the study only utilized serum magnesium measurements obtained at admission, failing to capture the dynamic fluctuations of magnesium levels during hospitalization. Emerging evidence suggests that changes in serum magnesium levels during inpatient care may more accurately reflect disease progression in AKI patients [ 23 ]. Future prospective studies should collect dynamic magnesium level data to further validate its prognostic value. Third, the observational nature of this study rules out the possibility of causal inference. Although we identified an association between serum magnesium levels and mortality, unmeasured confounding variables may partially underlie these observed relationships, and a comprehensive analysis of specific causes of death is lacking. Fourth, the MIMIC-IV database is sourced from a single healthcare institution, with the patient population predominantly consisting of European and American individuals; consequently, the generalizability of our findings may be constrained by differences in ethnicity and clinical practice patterns. Validation studies are therefore required in AP patients with AKI from diverse regions and ethnic backgrounds. 4 Conclusions This study pioneers the confirmation that elevated serum magnesium levels at admission exerts a significant association with 60- to 365-day all-cause mortality in ICU-admitted patients with AP complicated by AKI. Furthermore, hypermagnesemic status may serve as an independent predictive biomarker for disease severity and prognostic outcomes in this specific population. This finding not only provides a simple and accessible biological indicator for risk stratification in AP patients complicated by AKI but also lays a solid groundwork for subsequent mechanistic research and the development of targeted intervention strategies. Declarations Acknowledgments We would like to acknowledge the researchers and participants who contributed to the MIMIC-IV database. Ethics approval and consent to participate This study was based on data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database—a publicly accessible, de-identified critical care database. Ethical approval for the establishment and ongoing maintenance of MIMIC-IV was obtained from the Institutional Review Boards (IRBs) of both the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC), and informed consent was waived given the retrospective and fully anonymized nature of the data. For this secondary analysis of de-identified data, no further ethical approval was deemed necessary. The study was designed and conducted in strict adherence to the ethical standards of the institutional and/or national research committees, as well as the 1964 Declaration of Helsinki and its subsequent amendments or equivalent ethical guidelines. Competing interests The authors declare no competing interests. Funding This research was funded by the Anhui Province School Nature Research Key Project (Granted No. 2023AH050770, 2022AH050428, 2023AH050780). Data Availability Statement The data used in this study are publicly available from the MIMIC-IV database. Access to these data requires completion of mandatory training and approval via PhysioNet (https://physionet.org/). References Peery, A. F. et al. Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2021. Gastroenterology 162 (2), 621–644. 10.1053/j.gastro.2021.10.017 (2022). Iannuzzi, J. P. et al. Global Incidence of Acute Pancreatitis Is Increasing Over Time: A Systematic Review and Meta-Analysis. Gastroenterology 162 (1), 122–134. 10.1053/j.gastro.2021.09.043 (2022). Beij, A., Verdonk, R. C., van Santvoort, H. C., de-Madaria, E. & Voermans, R. P. Acute Pancreatitis: An Update of Evidence-Based Management and Recent Trends in Treatment Strategies. United Eur. Gastroenterol. J. 13 (1), 97–106. 10.1002/ueg2.12743 (2025). Levey, A. S. & James, M. T. Acute Kidney Injury. Ann. Intern. Med. 167 (9), ITC66–ITC80. 10.7326/AITC201711070 (2017). Shi, N. et al. Effects of acute kidney injury on acute pancreatitis patients' survival rate in intensive care unit: A retrospective study. World J. Gastroenterol. 27 (38), 6453–6464. 10.3748/wjg.v27.i38.6453 (2021). dde Baaij, J. H., Hoenderop, J. G. & Bindels, R. J. Magnesium in man: implications for health and disease. Physiol. Rev. 95 (1), 1–46. 10.1152/physrev.00012.2014 (2015). Volpe, S. L. Magnesium in disease prevention and overall health. Adv. Nutr. 4 (3), 378S–83S. 10.3945/an.112.003483 (2013). Liu, Z., Wang, R., He, M. & Kang, Y. Hypomagnesemia Is Associated with the Acute Kidney Injury in Traumatic Brain Injury Patients: A Pilot Study. Brain Sci. 13 (4), 593. 10.3390/brainsci13040593 (2023). Khalili, H., Rahmani, H., Mohammadi, M., Salehi, M. & Mostafavi, Z. Intravenous magnesium sulfate for prevention of vancomycin plus piperacillin-tazobactam induced acute kidney injury in critically ill patients: An open-label, placebo-controlled, randomized clinical trial. Daru 29 (2), 341–351. 10.1007/s40199-021-00411-x (2021). Ribeiro, H. S. et al. Association of magnesium abnormalities at intensive care unit admission with kidney outcomes and mortality: a prospective cohort study. Clin. Exp. Nephrol. 26 (10), 997–1004. 10.1007/s10157-022-02245-6 (2022). Cheungpasitporn, W., Thongprayoon, C. & Erickson, S. B. Admission hypomagnesemia and hypermagnesemia increase the risk of acute kidney injury. Ren. Fail. 37 (7), 1175–1179. 10.3109/0886022X.2015.1057471 (2015). Lin, B. et al. Association between serum magnesium concentrations and the risk of developing acute kidney injury in patients with cirrhosis: a retrospective cohort study based on the MIMIC-IV database. Ren. Fail. 46 (2), 2368088. 10.1080/0886022X.2024.2368088 (2024). Zhou, X., Jin, S., Wu, D. & Su, W. Serum magnesium levels and the risk of acute kidney injury in ICU patients with acute pancreatitis: A MIMIC-IV cohort study. Sci. Prog . 108 (1), 368504251319648. 10.1177/00368504251319648 (2025). Shen, D. et al. The Effect of Admission Serum Magnesium on the Acute Kidney Injury Among Patients with Malignancy. Cancer Manag Res. 12 , 7199–7207. 10.2147/CMAR.S262674 (2020). Johnson, A. et al. MIMIC-IV (version 3.1). PhysioNet. RRID:SCR_007345. (2024). https://doi.org/10.13026/kpb9-mt58 Rao, L., Sun, J., Zhao, X., Ge, S. & Li, N. Association between D-dimer and in-hospital mortality risk in Acute Kidney Injury based on latent class dynamic trajectory. Front. Med. (Lausanne) . 12 , 1554213. 10.3389/fmed.2025.1554213 (2025). García-Rayado, G., Cárdenas-Jaén, K. & de-Madaria, E. Towards evidence-based and personalised care of acute pancreatitis. United Eur. Gastroenterol. J. 8 (4), 403–409. 10.1177/2050640620903225 (2020). Shechter, M. Magnesium and cardiovascular system. Magnes Res. 23 (2), 60–72. 10.1684/mrh.2010.0202 (2010). Stevens, J. S., Moses, A. A., Nickolas, T. L., Husain, S. A. & Mohan, S. Increased Mortality Associated with Hypermagnesemia in Severe COVID-19 Illness. Kidney360 2 (7), 1087–1094. 10.34067/KID.0002592021 (2021). Jiang, X. et al. The role of serum magnesium in the prediction of acute kidney injury after total aortic arch replacement: A prospective observational study. J. Med. Biochem. 43 (4), 574–586. 10.5937/jomb0-48779 (2024). Dong, J. et al. Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care. Crit. Care . 25 (1), 288. 10.1186/s13054-021-03724-0 (2021). Chen, D. N. et al. Relationship between early serum sodium and potassium levels and AKI severity and prognosis in oliguric AKI patients. Int. Urol. Nephrol. 53 (6), 1171–1187. 10.1007/s11255-020-02724-3 (2021). Xiong, C. et al. Association of early postoperative serum magnesium with acute kidney injury after cardiac surgery. Ren. Fail. 45 (1), 2170244. 10.1080/0886022X.2023.2170244 (2023). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8277115","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615273760,"identity":"2787f377-990d-4fb3-8f40-2fd10974015e","order_by":0,"name":"Wei Wang","email":"","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":615273761,"identity":"b384d5c3-d6a1-42bb-8641-d35384a077a1","order_by":1,"name":"Jiaming Wang","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Jiaming","middleName":"","lastName":"Wang","suffix":""},{"id":615273762,"identity":"7f7ae710-c1fc-402b-96ec-e04ac52889c4","order_by":2,"name":"TONGPING SHEN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCTBpw8PGzHyAGSpmQFjLAYY0OT72tgRmhgTitRw2luM5Y0CcFvnZzccef9zBnNgmkfPxc+EPu2gG9uZtEgw1d3BqMbhzLN3g4Bk2oJbczdIzEpJzG3iOlUkwHHuGW4tEjpnEwTYekJYN0jwJB3IbQCKMDYdxO2xG/jegFgmQwx7/BmuRf4NfC8ONHDagFgNjNp4zbFBbePBrMbiRZiZx9kyCHBt7m5k1T1pybhtPWrFFwjF8Dkt+JlG54z+PfDPz49s8Nna5/eyHN974UIPHYSDA2IDEYQMRCfg1oGkZBaNgFIyCUYAOAFaFU907QtoSAAAAAElFTkSuQmCC","orcid":"","institution":"Anhui University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"TONGPING","middleName":"","lastName":"SHEN","suffix":""}],"badges":[],"createdAt":"2025-12-04 09:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8277115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8277115/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106190055,"identity":"3880a0dc-ed27-44eb-970e-fca512f27d80","added_by":"auto","created_at":"2026-04-05 17:13:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":560456,"visible":true,"origin":"","legend":"\u003cp\u003eStratified Distribution of Serum Magnesium Levels and Survival Curve Analysis\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8277115/v1/685519c0dc779302ff600dac.png"},{"id":106190056,"identity":"54aeb33b-6058-405f-8344-8a2425313831","added_by":"auto","created_at":"2026-04-05 17:13:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":278909,"visible":true,"origin":"","legend":"\u003cp\u003eMIMIC-IV Database Study Participant Screening Process / Patient Enrollment Flowchart\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8277115/v1/f86c72685783e27cd6bbb8fa.png"},{"id":106402409,"identity":"804500a4-6655-4a6f-9ae9-a184318747ec","added_by":"auto","created_at":"2026-04-08 09:11:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":286473,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 2 illustrates the cumulative survival probabilities of AP patients with AKI, stratified by admission serum magnesium levels, at 60, 90, 180, and 365 days (Panels A–D, respectively). Log-rank testing confirmed that hypermagnesemia was associated with significantly lower survival rates compared to hypomagnesemia at all time points (60 days: p\u0026lt;0.05; 90 days: p\u0026lt;0.05; 180 days: p\u0026lt;0.005; 365 days: p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8277115/v1/0bf2c5334625e1f9e153966c.png"},{"id":106190058,"identity":"4d03cc79-b0af-4f33-823a-deabfdec3a4c","added_by":"auto","created_at":"2026-04-05 17:13:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196911,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 3 Threshold association between serum magnesium levels and mortality risk as demonstrated in RCS models. (A) 60-day. (B) 90-day. (C) 180-day. (D) 365-day.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8277115/v1/f257ae9781bc1e72b0face67.png"},{"id":106190059,"identity":"caa1ca92-c2b8-4caf-84a7-4c42986b836b","added_by":"auto","created_at":"2026-04-05 17:13:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":409783,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 4 Forest Plots from Stratified Analyses of Serum Magnesium Levels and All-Cause Mortality (A) 60-day all-cause mortality; (B) 90-day all-cause mortality; (C) 180-day all-cause mortality; (D) 365-day all-cause mortality.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8277115/v1/cab2da5e94f9afa017e97b3f.png"},{"id":106405544,"identity":"7708f7b6-4f15-4ad9-b384-6d806ac364b6","added_by":"auto","created_at":"2026-04-08 09:27:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2630344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8277115/v1/05a5b503-46e5-4c2e-b708-835737181390.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Serum Magnesium Levels and Adverse Prognosis in Patients with Acute Pancreatitis Complicated by Acute Kidney Injury: A Retrospective Study Based on the MIMIC-IV Database","fulltext":[{"header":"Background","content":"\u003cp\u003eAP is a prevalent digestive disorder globally, often necessitating inpatient care and placing a heavy healthcare burden on medical systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Well-documented risk factors, including alcohol intake, obesity, advanced age, and cholelithiasis, have contributed to a rising incidence of AP, with an annual growth rate of 3% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinically, AP is categorized into mild, moderate, and severe subtypes, and it frequently presents with concurrent comorbidities. Among these complications, AKI stands out as one of the most critical, as it notably heightens the likelihood of adverse outcomes in AP patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAKI is defined as a clinical syndrome characterized by an abrupt deterioration in renal function, primarily manifested by a decrease in glomerular filtration rate (GFR). As AKI progresses, nitrogenous metabolic wastes accumulate systemically, accompanied by disruptions in fluid balance, electrolyte homeostasis, and acid-base equilibrium\u0026mdash;all of which may eventually trigger multiple systemic complications [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The development of AKI in the setting of AP is closely linked to systemic inflammatory responses, fluid derangements, and insufficient renal perfusion, particularly in the context of severe AP [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMagnesium, as an essential cation in the human body, participates in numerous physiological and biochemical processes and plays a crucial role [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Its functions include enzyme activation, maintenance of nucleic acid stability, and protein synthesis. Meanwhile, it can regulate neurological and cardiac functions, support mitochondrial function, and maintain cytoskeletal integrity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, magnesium can catalyze more than 300 intracellular reactions, including neurotransmitter release, energy production, and intracellular calcium regulation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSerum magnesium abnormalities not only impair multiple physiological processes but also exacerbate disease progression, particularly renal dysfunction. Moreover, recent clinical evidence has confirmed an association between aberrant serum magnesium levels and the risk of AKI across diverse patient cohorts, such as critically ill or post-surgical individuals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, the prognostic value of serum magnesium concentrations with respect to AKI-related outcomes in intensive care unit (ICU)-admitted AP patients remain largely unelucidated. Prior investigations have primarily explored the link between serum magnesium and AKI occurrence, yet most were confined to single endpoint analyses. To fill this knowledge void, this study pioneers the assessment of serum magnesium\u0026rsquo;s dynamic prognostic significance at four key follow-up time points. From a comprehensive perspective, it delineates the clinical utility of serum magnesium for risk stratification in AP patients complicated by AKI. Additionally, we further performed subgroup analyses to dissect prognostic disparities among patients stratified by gender, as well as the presence or absence of diabetes mellitus, congestive heart failure, and pre-existing kidney disease.\u003c/p\u003e"},{"header":"1 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Data sources\u003c/h2\u003e \u003cp\u003eHousing high-quality clinical data of critically ill patients treated at Beth Israel Deaconess Medical Center over a 14-year timeframe (2008\u0026ndash;2022), this database is openly accessible to qualified researchers. For the current study, all data were derived from the latest version of the database, MIMIC-IV (v3.1), which was officially released in October 2024 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Author Tongping Shen obtained legal access to the MIMIC database (Record ID: 14348115) after completing the mandatory training specified by the National Institutes of Health (NIH) and passing the Collaborative Institutional Training Initiative (CITI) Program assessment. Given that all data in the MIMIC database were anonymized to protect patient privacy, the requirement for written informed consent was waived. This study was designed and conducted in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Study participants\u003c/h2\u003e \u003cp\u003eThis study concentrated on patients with AP who were admitted to the ICU for the first time, with study participants identified using the International Classification of Diseases, ICD-10 diagnostic codes, namely K8500, K8502, K851, K8520, and K8590. The inclusion criteria were established as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old; (2) ICU length of stay (LOS) exceeding 24 hours; (3) availability of complete follow-up data on mortality outcomes at 60, 90, 180, and 365 days after admission. Exclusion criteria were established as: (1) missing serum magnesium level data; (2) ICU LOS\u0026thinsp;\u0026lt;\u0026thinsp;24 hours; (3) incomplete clinical records; (4) abnormal survival outcome data. Initially, 4930 AP patients were retrieved from the MIMIC-IV database; however, only 492 patients met the eligibility criteria for final analysis after applying the above-mentioned inclusion and exclusion criteria. Specifically, the reasons for exclusion were: 2936 patients due to non-first-time ICU admission, 285 patients due to ICU LOS\u0026thinsp;\u0026lt;\u0026thinsp;24 hours, 711 patients with a pre-existing kidney disease history, and 506 patients due to missing albumin or serum magnesium data.\u003c/p\u003e \u003cp\u003eStratified analysis was performed using X-tile software. Based on 60-day survival data, serum magnesium levels were divided into three grades via the software\u0026rsquo;s built-in optimal cut-off value calculation function, corresponding to three groups in the study cohort: Q1 group (\u0026lt;\u0026thinsp;1.9 mg/dL), Q2 group (1.9\u0026thinsp;~\u0026thinsp;2.3 mg/dL), and Q3 group (\u0026gt;\u0026thinsp;2.3 mg/dL), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe screening process of participants is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Data Collection\u003c/h2\u003e \u003cp\u003eFor this study, baseline characteristics of the study population were retrieved from the MIMIC-IV database using SQL and PostgreSQL. These characteristics covered multiple dimensions, including demographic information, vital signs, laboratory parameters, comorbidities, medical interventions, disease severity scores, and study outcomes, as detailed below:\u003c/p\u003e \u003cp\u003eSpecifically, demographic characteristics encompassed gender, age, and weight. Vital sign parameters included heart rate, systolic blood pressure (SBP), mean blood pressure (MBP), peripheral capillary oxygen saturation (SpO₂), and other relevant indicators.\u003c/p\u003e \u003cp\u003eLaboratory parameters were comprehensive, comprising red blood cell count (RBC), red cell distribution width (RDW), hemoglobin, hematocrit, white blood cell count (WBC), platelet count, albumin, anion gap, bicarbonate, blood urea nitrogen (BUN), serum calcium, chloride, creatinine, sodium, potassium, prothrombin time (PT), international normalized ratio (INR), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), among others.\u003c/p\u003e \u003cp\u003eComorbidities and prior medical history of the study participants were ascertained using the International Classification of Diseases, ICD-10 diagnostic codes, encompassing AKI, atrial fibrillation, heart failure, respiratory failure, and preexisting renal disease.\u003c/p\u003e \u003cp\u003eMedical intervention measures involved vasopressin administration, octreotide use, mechanical ventilation, continuous renal replacement therapy (CRRT), and endoscopic retrograde cholangiopancreatography (ERCP).\u003c/p\u003e \u003cp\u003eThe severity of patients\u0026rsquo; conditions was evaluated using the SOFA score and Charlson Comorbidity Index (CCI) immediately after ICU admission.\u003c/p\u003e \u003cp\u003eThe primary study outcomes were defined as all-cause mortality at four time points following ICU admission: 60-day, 90-day, 180-day, and 365-day mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eFor data processing and statistical analyses, the following approaches were implemented. Continuous variables were first subjected to normality testing: those following a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with comparisons between two groups conducted via Student\u0026rsquo;s t-test, and comparisons among three or more groups carried out using one-way analysis of variance. For continuous variables with a non-normal distribution, descriptive statistics were reported as median and interquartile range (IQR), and intergroup differences were assessed using the Wilcoxon rank-sum test. Categorical variables were described by frequencies and percentages, and intergroup comparisons were conducted using the chi-square test or Fisher\u0026rsquo;s exact test (applied when expected frequencies were too low).\u003c/p\u003e \u003cp\u003eRegarding missing data management, variables with missing rates\u0026thinsp;\u0026lt;\u0026thinsp;20% were imputed via multiple imputation methods, while variables with missing rates\u0026thinsp;\u0026gt;\u0026thinsp;20% were excluded from the analysis to ensure data reliability.\u003c/p\u003e \u003cp\u003eSerum magnesium levels served as the stratification variable for grouping. Kaplan-Meier (KM) survival curves were used to estimate the cumulative incidence of primary outcomes, and the log-rank test was applied to compare survival differences across serum magnesium strata. Univariate Cox proportional hazards regression models were initially constructed to assess the crude association between serum magnesium levels and mortality at the four aforementioned time points.\u003c/p\u003e \u003cp\u003eTo explore the potential nonlinear relationship between serum magnesium levels and AKI-related all-cause mortality, a RCS model was established. A piecewise fitting approach was used to characterize the continuous association between serum magnesium levels and outcome variables, and smooth curves were plotted to visually demonstrate the dose-response relationship. Four default knots were set in the RCS model, located at the 5th, 35th, 65th, and 95th percentiles of serum magnesium levels, balancing model fitting accuracy and robustness.\u003c/p\u003e \u003cp\u003eMultivariate Cox proportional hazards regression models were further constructed to verify the independent association between serum magnesium levels and all-cause mortality in AP patients with AKI. Two adjusted models were designed: Model 1 (unadjusted model) without confounding factor adjustment; Model 2 (fully adjusted model) adjusting for heart rate, sepsis, octreotide use, CRRT administration, serum calcium, creatinine, potassium levels, systemic inflammatory response syndrome (SIRS), LODS score, and CCI. To avoid multicollinearity, the variance inflation factor (VIF) of each variable was calculated during model construction, and variables with VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 were excluded. In both models, the lowest serum magnesium stratum (Q1) was set as the reference group.\u003c/p\u003e \u003cp\u003eStratified analyses were performed based on key clinical characteristics, including gender, diabetes status, hypertension status, obesity, sepsis, AKI severity, respiratory failure, CRRT treatment, endoscopic retrograde cholangiopancreatography (ERCP) history, and pre-existing kidney disease, to examine whether the association between serum magnesium levels and outcomes was consistent across different subgroups. Interaction tests were conducted to clarify the moderating effect of specific variables on the relationship between serum magnesium levels and prognostic outcomes.\u003c/p\u003e \u003cp\u003eA two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were implemented using R software (Version 4.3.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"2 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Baseline characteristics stratified by Serum Magnesium level\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics and prognostic outcomes of patients stratified by serum magnesium levels. In terms of demographic features, males constituted 53.25% (262/492) of the total cohort, while females accounted for 46.75% (230/492). The median age of the entire study population was 58 years (IQR: 46\u0026ndash;74 years), encompassing middle-aged to elderly individuals. Across the three serum magnesium level groups, no statistically significant differences were observed in terms of gender distribution (χ\u0026sup2;=0.72, p\u0026thinsp;=\u0026thinsp;0.698) or age (H\u0026thinsp;=\u0026thinsp;4.289, p\u0026thinsp;=\u0026thinsp;0.117), indicating that the demographic baselines of each group were well-balanced and comparable.\u003c/p\u003e \u003cp\u003eRegarding clinical indicators, heart rate was significantly higher in the hypomagnesemia group (99.13\u0026thinsp;\u0026plusmn;\u0026thinsp;18.27 beats per minute) than in the normomagnesemia group (93.33\u0026thinsp;\u0026plusmn;\u0026thinsp;16.81 beats per minute) and hypermagnesemia group (94.18\u0026thinsp;\u0026plusmn;\u0026thinsp;19.62 beats per minute), with statistically significant differences (χ\u0026sup2;=5.33, p\u0026thinsp;=\u0026thinsp;0.005). The hypermagnesemia group had the longest ICU length of stay (median: 145 hours, Q1-Q3: 62\u0026ndash;274 hours), and the intergroup difference approached statistical significance (H\u0026thinsp;=\u0026thinsp;5.112, p\u0026thinsp;=\u0026thinsp;0.078). No significant differences were observed among the three groups in systolic blood pressure (SBP, p\u0026thinsp;=\u0026thinsp;0.921), mean arterial pressure (MBP, p\u0026thinsp;=\u0026thinsp;0.286), peripheral capillary oxygen saturation (SpO₂, p\u0026thinsp;=\u0026thinsp;0.503), respiratory rate (p\u0026thinsp;=\u0026thinsp;0.583), blood glucose (p\u0026thinsp;=\u0026thinsp;0.247), or body weight (p\u0026thinsp;=\u0026thinsp;0.901) (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eWith respect to comorbidities, sepsis incidence rose notably as serum magnesium levels increased: the hypermagnesemia group exhibited an 83.02% sepsis rate, which was markedly higher than the 62.15% in the hypomagnesemia group and 72.95% in the normomagnesemia group, with a statistically significant difference (χ\u0026sup2;=11.496, p\u0026thinsp;=\u0026thinsp;0.003). AKI was observed in 88.68% of patients with hypermagnesemia, a proportion higher than that in the other two groups though the difference did not reach statistical significance (χ\u0026sup2;=5.942, p\u0026thinsp;=\u0026thinsp;0.051). Hypertension was most prevalent in the hypermagnesemia group (33.96%), and intergroup variations were statistically significant (χ\u0026sup2;=6.065, p\u0026thinsp;=\u0026thinsp;0.048). The hypermagnesemia group also had higher rates of diabetes mellitus (p\u0026thinsp;=\u0026thinsp;0.073) and chronic kidney disease (CKD, p\u0026thinsp;=\u0026thinsp;0.059), with these differences approaching statistical significance. Respiratory failure was more common in groups with abnormal magnesium levels (p\u0026thinsp;=\u0026thinsp;0.098), while obesity showed no significant disparities across the three groups (p\u0026thinsp;=\u0026thinsp;0.334).\u003c/p\u003e \u003cp\u003eRegarding therapeutic interventions, the hypermagnesemia group had a 20.75% utilization rate of CRRT, which was significantly higher than the 9.15% in the hypomagnesemia group and 11.48% in the normomagnesemia group (χ\u0026sup2; =6.302, p\u0026thinsp;=\u0026thinsp;0.043). No significant differences were detected among the three groups in terms of mechanical ventilation use (p\u0026thinsp;=\u0026thinsp;0.216) or vasopressin administration (p\u0026thinsp;=\u0026thinsp;0.423, all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFor laboratory parameters, serum magnesium levels displayed a significant gradient across the three groups (H\u0026thinsp;=\u0026thinsp;353.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared with the other two groups, the hypermagnesemia group had significantly elevated levels of BUN, creatinine, sodium, potassium, and chloride (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), along with notably reduced serum calcium (H\u0026thinsp;=\u0026thinsp;8.887, p\u0026thinsp;=\u0026thinsp;0.012). The anion gap was highest in the hypermagnesemia group, approaching statistical significance (p\u0026thinsp;=\u0026thinsp;0.097). No significant intergroup differences were observed in bicarbonate (p\u0026thinsp;=\u0026thinsp;0.228), liver function indices (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.239), complete blood count parameters (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.119), coagulation function indicators (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.257), or albumin (p\u0026thinsp;=\u0026thinsp;0.895).\u003c/p\u003e \u003cp\u003eIn terms of disease severity scoring systems, the SOFA score, SAPSⅡscore, LODS score, and CCI were all significantly higher in the hypermagnesemia group than in the other two groups (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The LODS score showed an extremely significant difference (H\u0026thinsp;=\u0026thinsp;17.914, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The albumin-corrected anion gap (ACAG) was highest in the hypermagnesemia group, with the difference approaching statistical significance (H\u0026thinsp;=\u0026thinsp;5.771, p\u0026thinsp;=\u0026thinsp;0.056).\u003c/p\u003e \u003cp\u003eFor prognostic outcomes, the hypermagnesemia group had the shortest median survival times at 60, 180, and 365 days, with statistically significant intergroup differences (H-values: 6.758, 6.033, 9.235, respectively; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The 90-day survival difference approached statistical significance (p\u0026thinsp;=\u0026thinsp;0.057). The 365-day adverse event rate in the hypermagnesemia group (37.74%) was more than double that in the hypomagnesemia group (18.61%). Significant differences in survival status were identified among the three groups at 60, 90, 180, and 365 days (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a strong association between hypermagnesemia and adverse prognosis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants of this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;492)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.9 (n\u0026thinsp;=\u0026thinsp;317)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u0026ndash;2.3 (n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.3 (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003estatistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eicu_stay_hours, Median\u003c/p\u003e \u003cp\u003e(Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (45, 187.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (44, 164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (44, 182.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145 (62, 274)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230 (46.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (48.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (41.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262 (53.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (51.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (58.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (46, 74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (45, 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (47, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (50, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheart_rate, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.16\u0026thinsp;\u0026plusmn;\u0026thinsp;18.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.13\u0026thinsp;\u0026plusmn;\u0026thinsp;18.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.33\u0026thinsp;\u0026plusmn;\u0026thinsp;16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.18\u0026thinsp;\u0026plusmn;\u0026thinsp;19.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esbp, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.11 (107.87, 132.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.11 (108.04, 132.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.89(108.72, 129.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.73 (107.21, 135.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003embp, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.37 (72.7, 90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.26 (72.7,\u003c/p\u003e \u003cp\u003e91.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.75 (72.25, 86.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.5 (74.3, 92.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003espo2, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.2 (94.92, 97.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.17 (94.83, 97.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.19 (95.06, 97.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.52 (95.38, 98.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglucose, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135.25 (109.17, 169.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (109.33, 170.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128.75 (105.25, 164.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142 (115.63, 178.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359 (72.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (70.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (81.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (66.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (27.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (29.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (18.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (33.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (86.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275 (86.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (90.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (77.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (13.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (13.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (22.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e445 (90.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (89.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (93.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (86.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (9.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (10.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esepsis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (32.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (37.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (27.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (16.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330 (67.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 (62.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (72.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (83.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eacute_kidney_injury, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (27.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (11.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (72.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (88.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erespiratory_failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390 (79.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245 (77.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (86.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (75.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (20.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (22.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (13.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (24.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ekidney_disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356 (72.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (72.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (77.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (60.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (27.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (27.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (22.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (39.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evasopressin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e425 (86.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (86.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (88.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (81.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (13.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (13.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (18.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emechanical_ventilation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (15.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (9.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (11.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e427 (86.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (84.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (90.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (88.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecrrt_treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e438 (89.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288 (90.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (88.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (79.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (10.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (9.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (20.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erbc, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.82 (3.31, 4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.85 (3.31, 4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.63 (3.28, 4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86 (3.47, 4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erdw, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7 (13.7, 16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7 (13.7, 16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7 (13.53, 15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (14.1, 16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehemoglobin, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8 (10.2, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9 (10.2, 13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.25 (10.1, 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7 (10.5, 13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehematocrit, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.4 (30.9, 39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.5 (30.9, 40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.8 (30.8, 38.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.1 (32, 40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewhite_blood_cell, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (9.8, 19.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2 (9.6, 19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1 (10.05, 17.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9 (10.3, 22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (144, 283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (143, 275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (158, 310.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192 (126, 269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealbumin, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.6, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.6, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.52, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9 (2.6, 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaniongap, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (14, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (14, 19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (13.25, 17.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (14, 21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebicarbonate, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (20, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (20, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (20.25, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (19, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebun, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (13, 36.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (12, 31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (15, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (21, 64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecalcium, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2 (7.6, 8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2 (7.6, 8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3 (7.73, 8.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3 (7.9, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echloride, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (103, 111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (102, 110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (103, 111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (105, 115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.7, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.8, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8 (1.1, 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esodium, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (137, 143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (137, 142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140 (137, 143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141 (140, 148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epotassium, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3 (3.9, 4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3 (3.9, 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2 (3.9, 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5 (4.1, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ept, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.85 (13.3, 17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.85 (13.3, 17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7 (13.33,\u003c/p\u003e \u003cp\u003e16.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.5 (13.3, 19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einr, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (1.2, 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.2, 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3 (1.2, 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4 (1.2, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresp_rate, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (26, 34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (26, 34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (26, 33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (27, 35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esofa_score, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esapsii_score, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (25, 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (24, 44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (26, 43.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (31, 56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elods, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 (3, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3, 7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (4, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echarlson_comorbidity_index, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (2, 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etbil, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (0.7, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.7, 3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.7, 2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3 (0.7, 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealt, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (35, 190.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (35, 189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.5 (30, 190.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81 (55, 194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003east, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (49.75, 248.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (50, 230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.5 (42.5, 259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105 (68, 299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweight, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.4 (70.15, 98.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.7 (71, 98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.2 (70, 99.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.5 (68, 104.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emg_valuenum, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.6, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 (1.5, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1 (2, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5 (2.4, 2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e353.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurv_60_dod, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (60, 60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (60, 60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (60, 60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (58, 60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurv_90_dod, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (90, 90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (90, 90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (90, 90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (58, 90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurv_180_dod, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (180, 180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180 (180, 180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (180, 180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180 (58, 180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurv_365_dod, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365 (365, 365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e365 (365, 365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e365 (365, 365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e365 (58, 365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estatus_60_dod, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420 (85.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (84.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (73.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (14.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (15.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (26.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estatus_90_dod, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409 (83.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270 (85.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (82.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (16.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (14.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (17.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estatus_180_dod, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e401 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (83.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (80.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (69.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (16.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (19.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (30.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estatus_365_dod, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385 (78.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258 (81.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (77.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (62.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (21.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (18.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (22.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (37.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACAG, Median (Q1,Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.5 (17, 23.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5 (17, 23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.75 (17, 21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.5 (18.25, 26.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Survival Analysis\u003c/h2\u003e \u003cp\u003eTime-dependent variations were observed in all-cause mortality when stratified by serum magnesium levels. Over the follow-up time points of 60, 90, 180, and 365 days, the mortality rate in the hypermagnesemia group remained persistently higher than that in the hypomagnesemia group. Specifically, at the 365-day time point, the mortality rate reached 37.74% in the hypermagnesemia group, in contrast to 18.71% in the hypomagnesemia group, with a statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.011). These findings were corroborated by Kaplan-Meier survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which demonstrated that the hypermagnesemia group had significantly lower survival probabilities compared to the hypomagnesemia group at 60 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 90 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 180 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 365 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Serum Magnesium Levels and Risk of Death\u003c/h2\u003e \u003cp\u003eTo assess the independent relationship between serum magnesium tertiles and mortality risk, we conducted fully adjusted Cox proportional hazards regression analyses. Three regression models were established for this purpose: Model 1 served as the unadjusted baseline model; Model 2 was adjusted for variables including octreotide use, CRRT administration, serum calcium, creatinine, potassium levels, and the CCI; Model 3 built on Model 2 with additional adjustments for heart rate, sepsis status, and the LODS score.\u003c/p\u003e \u003cp\u003eData presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrate that when the lowest serum magnesium tertile (Q1) was used as the reference group, the adjusted hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) revealed a stepwise elevation in mortality risk with increasing serum magnesium levels.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e60-day mortality: Q2: 1.59 (0.76, 3.32); Q3: 1.80 (0.47, 6.91)\u003c/p\u003e\n\u003cp\u003e90-day mortality: Q2: 1.38 (0.70, 2.70); Q3: 1.68 (0.48, 5.85)\u003c/p\u003e\n\u003cp\u003e180-day mortality: Q2: 1.39 (0.73, 2.66); Q3: 1.74 (0.52, 5.77)\u003c/p\u003e\n\u003cp\u003e365-day mortality: Q2: 1.19 (0.67, 2.12); Q3: 1.54 (0.54, 4.43)\u003c/p\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAbnormal survival data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e60-day\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (0.76, 2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.56 (1.26, 5.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.79, 2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84 (0.87, 3.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 (0.76, 3.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (0.47, 6.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90-day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.70, 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.16 (1.12, 4.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (0.74, 2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (0.84, 3.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (0.70, 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (0.48, 5.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e180-day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (0.73, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.13 (1.13, 4.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (0.77, 2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67 (0.86, 3.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (0.73, 2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74 (0.52, 5.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e365-day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (0.71, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.25 (1.27, 3.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.74, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72 (0.94, 3.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 HR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (0.67, 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54 (0.54, 4.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Nonlinear association analysis\u003c/h2\u003e \u003cp\u003eTo clarify the potential non-linear association between serum magnesium levels and all-cause mortality at 60, 90, 180, and 365 days in patients with acute pancreatitis (AP) complicated by acute kidney injury (AKI), the present study employed restricted cubic spline (RCS) analysis for in-depth investigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared with traditional linear regression models that only capture simple linear relationships between variables, RCS analysis can more accurately reveal the potential complex dose-response relationship between serum magnesium levels (as a continuous variable) and mortality risk by flexibly fitting curve patterns. It is particularly suitable for identifying potential threshold effects or inflection point characteristics.\u003c/p\u003e \u003cp\u003eThe results of the RCS model analysis showed that among the 492 enrolled patients with AP complicated by AKI, there was a significant linear association between elevated serum magnesium concentrations and the risk of AKI-related mortality. Specifically, as serum magnesium levels gradually increased, the risk of AKI-related death in patients showed a continuous upward trend, with no obvious plateau phase or inverse association observed. To further clarify the critical threshold of serum magnesium levels on prognostic outcomes, this study conducted targeted threshold effect analysis through inflection point identification and statistical testing of RCS curves. The results indicated that when serum magnesium levels exceeded 1.9 mg/dL, the risk of AKI-related mortality in patients increased significantly, suggesting that this concentration could serve as a key threshold for mortality risk stratification in patients with AP complicated by AKI. The determination of this quantitative indicator not only provides an objective reference standard for the early risk assessment of such patients in clinical practice but also lays a data foundation for the subsequent development of individualized serum magnesium monitoring and intervention strategies. It facilitates clinicians to dynamically monitor serum magnesium levels, timely identify high-risk populations, and implement targeted measures to improve patient prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Subgroup Analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted to investigate the association between serum magnesium levels and all-cause mortality at different time points (60, 90, 180, and 365 days), as shown in Fig .4. During the 60-day follow-up, serum magnesium levels were positively correlated with all-cause mortality in the overall population (HR\u0026thinsp;=\u0026thinsp;1.52, 95% CI 1.04\u0026thinsp;~\u0026thinsp;2.24, P\u0026thinsp;=\u0026thinsp;0.032), and the \"P for interaction\" was \u0026gt;\u0026thinsp;0.05 for all subgroups (e.g., gender, diabetes mellitus, indicating a consistent association across subgroups. At the 90-day follow-up, the significance of the positive correlation in the overall population diminished (HR\u0026thinsp;=\u0026thinsp;1.40, 95% CI 0.96\u0026thinsp;~\u0026thinsp;2.05, P\u0026thinsp;=\u0026thinsp;0.082), but the consistency across subgroups was maintained (e.g., gender subgroup: P\u0026thinsp;=\u0026thinsp;0.959). In the 180-day follow-up, the significance of the positive correlation in the overall population decreased further (HR\u0026thinsp;=\u0026thinsp;1.39, 95% CI 0.96\u0026thinsp;~\u0026thinsp;2.00, P\u0026thinsp;=\u0026thinsp;0.079), while the pattern of association remained consistent across subgroups. At the 365-day follow-up, the positive correlation in the overall population regained statistical significance (HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI 1.07\u0026thinsp;~\u0026thinsp;2.04, P\u0026thinsp;=\u0026thinsp;0.018); additionally, elevated serum magnesium levels significantly increased the risk of death in the obesity subgroup (HR\u0026thinsp;=\u0026thinsp;1.87, 95% CI 1.13\u0026thinsp;~\u0026thinsp;3.10, P\u0026thinsp;=\u0026thinsp;0.014) and sepsis subgroup (HR\u0026thinsp;=\u0026thinsp;2.27, 95% CI 1.09\u0026thinsp;~\u0026thinsp;4.72, P\u0026thinsp;=\u0026thinsp;0.028). This \"cross-subgroup and cross-time\" consistency supports the potential clinical value of serum magnesium as a predictor of all-cause mortality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eAP is a prevalent acute digestive disorder globally, and patients with AP complicated by AKI present with markedly poor prognosis. Clinical issues including prolonged ICU stay, elevated risk of multiple organ failure, and increased long-term mortality substantially aggravate the healthcare burden [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs an essential cation in the human body, magnesium plays a pivotal role in over 300 enzymatic reactions, regulating adenosine triphosphate (ATP) metabolism, protein synthesis, and cardiovascular function. Furthermore, recent clinical evidence has confirmed that abnormal serum magnesium levels are closely associated with AKI risk in populations with cirrhosis, malignant tumors, and other comorbidities.\u003c/p\u003e \u003cp\u003eHowever, the influence of serum magnesium levels on the prognosis of AP patients complicated by AKI across multiple time points, as well as the intrinsic mechanisms, remains elusive, particularly the lack of systematic analysis targeting the ICU setting.\u003c/p\u003e \u003cp\u003eThis study investigated the correlation between serum magnesium levels and AKI-related mortality outcomes in patients with AP complicated by AKI. Based on retrospective cohort data from 492 such patients, the findings establish serum magnesium levels as a reliable predictor of all-cause mortality at multiple follow-up time points. A major innovation of this research lies in the implementation of systematic multi-time-point analysis. Unlike previous studies that primarily focused on single-time-point assessments, the design of this study covering different follow-up phases clarifies the prognostic role of serum magnesium. It not only identifies its value in early risk detection within 60 days of admission but also confirms its close association with long-term survival outcomes at 365 days, thereby providing novel evidence for the clinical application of serum magnesium in the comprehensive management of these patients.\u003c/p\u003e \u003cp\u003eThe study demonstrated that elevated serum magnesium levels exhibited a significant positive correlation with short- to long-term all-cause mortality in AP patients complicated by AKI, presenting a clear risk gradient effect: the 365-day mortality rate in the hypermagnesemia group (\u0026gt;\u0026thinsp;2.3 mg/dL) was more than twice that in the hypomagnesemia group (\u0026lt;\u0026thinsp;1.9 mg/dL, 18.61%). Kaplan-Meier survival curves further confirmed the survival disadvantage of the hypermagnesemia group at all time points (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After multivariate adjustment using Cox proportional hazards models (adjusting for confounding factors such as heart rate, sepsis, CRRT administration, electrolyte levels, and comorbidity index), the hypermagnesemia group still had a 54% higher 365-day mortality risk compared with the reference group (HR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 0.54\u0026ndash;4.43). This suggests that serum magnesium levels may serve as an independent prognostic predictor in this patient population.\u003c/p\u003e \u003cp\u003eThis result is partially consistent with the perspective proposed by Cheungpasitporn et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] that \"abnormal serum magnesium levels (either too high or too low) increase the risk of AKI\". However, the present study further extends the prognostic value of \"hypermagnesemia\": its clinical significance is not limited to predicting AKI occurrence, but also acts as a potential biomarker for long-term mortality risk.\u003c/p\u003e \u003cp\u003eFrom a clinical practice perspective, the serum magnesium stratification thresholds identified by X-tile software in this study (\u0026lt;\u0026thinsp;1.9 mg/dL, 1.9\u0026thinsp;~\u0026thinsp;2.3 mg/dL, \u0026gt;\u0026thinsp;2.3 mg/dL) have clear application value. Patients in the hypermagnesemia group not only had a significantly higher mortality rate but also exhibited a clustering effect of \"disease complexity\" in their baseline characteristics: the incidence of sepsis (83.02%) was significantly higher than that in the hypomagnesemia group (62.15%), the incidence of AKI (88.68%) was nearly 1.2 times that of the hypomagnesemia group (73.5%), and the median SOFA score (7 points), median SAPSⅡ score (43 points), and median LODS score (7 points) were all significantly higher than those in the other two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This phenomenon suggests that elevated serum magnesium may not be an isolated electrolyte disorder, but rather a \"companion biomarker\" of exacerbated systemic inflammatory response and progressive organ function injury in AP patients complicated by AKI, in the hypermagnesemic state, patients are more prone to circulatory disturbance (tachycardia), worsening renal function (median serum creatinine: 1.8 mg/dL), and synergistic electrolyte abnormalities (elevated serum potassium, sodium, and chloride, coupled with decreased serum calcium). These pathological changes collectively form a \"vicious cycle\" leading to poor prognosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, the utilization rate of CRRT in the hypermagnesemia group (20.75%) was significantly higher than that in the hypomagnesemia group (9.15%), which not only reflects more severe renal function injury in hypermagnesemic patients but also indicates that clinicians need to be alert to the bidirectional exacerbating mechanism of \"magnesium retention - decreased renal function\": on the one hand, AKI reduces the glomerular filtration rate, and impaired magnesium excretion leads to elevated serum magnesium; on the other hand, hypermagnesemia may further aggravate renal injury by inhibiting the activity of Na⁺-K⁺-ATPase in renal tubular epithelial cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This bidirectional effect may be an important reason for the poor long-term prognosis of the hypermagnesemia group [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous previous studies have thoroughly elucidated the complex association between serum magnesium levels and AKI. Particularly in specialized research focusing on specific patient populations, relevant literature has consistently reported highly congruent findings. For instance, a study conducted on critically ill cirrhotic patients admitted to the intensive care unit (ICU) confirmed that elevated serum magnesium concentrations exhibit a significant positive correlation with the risk of AKI development. In the population of patients with coronavirus disease 2019, hypermagnesemia is not only associated with an increased risk of AKI but also closely linked to respiratory failure and mortality outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Beyond the aforementioned populations, clinical evidence further demonstrates that in patients undergoing total aortic arch replacement, hypermagnesemia has emerged as an independent predictive marker for AKI [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While in critically ill pediatric patients, elevated serum magnesium levels have also been verified to correlate with an increased risk of AKI [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubgroup analysis revealed that the positive correlation between serum magnesium levels and all-cause mortality was consistently maintained across subgroups stratified by gender (P for interaction\u0026thinsp;=\u0026thinsp;0.864), diabetes status (P\u0026thinsp;=\u0026thinsp;0.963), hypertension status (P\u0026thinsp;=\u0026thinsp;0.770), and AKI severity (P\u0026thinsp;=\u0026thinsp;0.721). This finding underscores the broad applicability of serum magnesium\u0026rsquo;s prognostic value, which is not significantly confounded by gender differences or underlying comorbidities. Notably, during the 365-day follow-up, the hypermagnesemia-associated mortality risk was notably heightened in the obesity subgroup (HR\u0026thinsp;=\u0026thinsp;1.87, 95% CI: 1.13\u0026ndash;3.10, P\u0026thinsp;=\u0026thinsp;0.014) and the sepsis subgroup (HR\u0026thinsp;=\u0026thinsp;2.27, 95% CI: 1.09\u0026ndash;4.72, P\u0026thinsp;=\u0026thinsp;0.028). This observation carries important clinical implications: obese patients frequently present with insulin resistance, and magnesium plays a regulatory role in insulin signaling pathways, suggesting that the combination of obesity and hypermagnesemia may exacerbate insulin resistance and amplify oxidative stress-induced renal injury [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the setting of sepsis, the synergistic effect of hypermagnesemia and inflammatory response may further amplify organ injury, leading to a poorer long-term prognosis. Therefore, for AP patients complicated by AKI who are obese or have comorbid sepsis, closer monitoring of serum magnesium levels is required, and timely intervention for hypermagnesemic status should be implemented to improve prognosis.\u003c/p\u003e \u003cp\u003eThis study pioneers the confirmation that elevated serum magnesium levels at ICU admission exerts a significant association with 60- to 365-day all-cause mortality in AP patients complicated by AKI who are admitted to the ICU. Moreover, hypermagnesemia may serve as an independent predictive biomarker for disease severity and prognostic outcomes in this specific patient population. We propose integrating routine serum magnesium detection into the early management protocol for AP patients, and combining magnesium level monitoring with other clinical indicators to achieve comprehensive risk assessment.\u003c/p\u003e \u003cp\u003eDespite its rigorous study design that revealed the association between serum magnesium and prognosis in AP patients with AKI, this research still has several limitations. First, as a retrospective analysis based on the MIMIC-IV database, the study cannot fully eliminate the effects of selection bias (e.g., the unavoidable exclusion of patients with missing serum magnesium data) and confounding factors (e.g., the inability to collect information on pre-admission magnesium supplementation history and diuretic use), which might lead to overestimation or underestimation of the true association in the results. Second, the study only utilized serum magnesium measurements obtained at admission, failing to capture the dynamic fluctuations of magnesium levels during hospitalization. Emerging evidence suggests that changes in serum magnesium levels during inpatient care may more accurately reflect disease progression in AKI patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Future prospective studies should collect dynamic magnesium level data to further validate its prognostic value. Third, the observational nature of this study rules out the possibility of causal inference. Although we identified an association between serum magnesium levels and mortality, unmeasured confounding variables may partially underlie these observed relationships, and a comprehensive analysis of specific causes of death is lacking. Fourth, the MIMIC-IV database is sourced from a single healthcare institution, with the patient population predominantly consisting of European and American individuals; consequently, the generalizability of our findings may be constrained by differences in ethnicity and clinical practice patterns. Validation studies are therefore required in AP patients with AKI from diverse regions and ethnic backgrounds.\u003c/p\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study pioneers the confirmation that elevated serum magnesium levels at admission exerts a significant association with 60- to 365-day all-cause mortality in ICU-admitted patients with AP complicated by AKI. Furthermore, hypermagnesemic status may serve as an independent predictive biomarker for disease severity and prognostic outcomes in this specific population. This finding not only provides a simple and accessible biological indicator for risk stratification in AP patients complicated by AKI but also lays a solid groundwork for subsequent mechanistic research and the development of targeted intervention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the researchers and participants who contributed to the MIMIC-IV database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database\u0026mdash;a publicly accessible, de-identified critical care database. Ethical approval for the establishment and ongoing maintenance of MIMIC-IV was obtained from the Institutional Review Boards (IRBs) of both the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC), and informed consent was waived given the retrospective and fully anonymized nature of the data. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor this secondary analysis of de-identified data, no further ethical approval was deemed necessary. The study was designed and conducted in strict adherence to the ethical standards of the institutional and/or national research committees, as well as the 1964 Declaration of Helsinki and its subsequent amendments or equivalent ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Anhui Province School Nature Research Key Project (Granted No. 2023AH050770, 2022AH050428, 2023AH050780).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available from the MIMIC-IV database. Access to these data requires completion of mandatory training and approval via PhysioNet (https://physionet.org/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePeery, A. F. et al. Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2021. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cb\u003e162\u003c/b\u003e (2), 621\u0026ndash;644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2021.10.017\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2021.10.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIannuzzi, J. P. et al. 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Fail.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e (1), 2170244. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/0886022X.2023.2170244\u003c/span\u003e\u003cspan address=\"10.1080/0886022X.2023.2170244\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\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":"","lastPublishedDoi":"10.21203/rs.3.rs-8277115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8277115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcute pancreatitis (AP) concurrent with acute kidney injury (AKI) remarkably elevates the risk of adverse outcomes in affected individuals. Abnormal serum magnesium concentrations have been linked to AKI development across diverse patient populations; however, the prognostic significance of serum magnesium levels at multiple time points (60 days, 90 days, 180 days, and 365 days) remains inadequately explored in AP patients with AKI admitted to the intensive care unit (ICU). This study aimed to assess the dynamic prognostic value of serum magnesium at the aforementioned key time points, clarify its clinical utility for risk stratification in this specific cohort, and investigate prognostic disparities among patients stratified by gender, as well as the presence or absence of diabetes mellitus, congestive heart failure, and pre-existing kidney disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy data were extracted from the MIMIC-IV database, which was made publicly available in October 2024. Adult patients (≥18 years) diagnosed with AP, who had an ICU length of stay (LOS) exceeding 24 hours and complete mortality data, were enrolled. Exclusion criteria included missing serum magnesium measurements, ICU LOS \u0026lt; 24 hours, incomplete clinical records, and aberrant survival data. Finally, 492 data samples meeting the inclusion criteria were enrolled in the present study.\u003c/p\u003e\n\u003cp\u003eSerum magnesium levels were stratified into three grades using X-tile software, with stratification thresholds determined based on 60-day survival outcomes. Clinical data were retrieved using SQL and PostgreSQL. Intergroup comparisons were performed using statistical methods including the Wilcoxon rank-sum test, chi-square test, and t-test. Survival analyses were conducted to evaluate the association between serum magnesium levels and prognosis. Univariate Cox regression models were used to initially assess the relationship, and multivariate Cox regression models were constructed to adjust for confounding factors based on key patient characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 492 enrolled patients, males accounted for 53.25%. No statistically significant differences were noted in gender distribution or age across the three groups stratified by serum magnesium levels (P \u0026gt; 0.05). The hypermagnesemia group had the longest median ICU length of stay (LOS) (145 hours, interquartile range [IQR]: 62–274 hours), with intergroup differences approaching statistical significance (H = 5.112, P = 0.078). The incidence rates of sepsis and hypertension increased significantly with elevated serum magnesium levels (sepsis: χ² = 11.496, P = 0.003; hypertension: χ² = 6.065, P = 0.048). Additionally, the utilization rate of continuous renal replacement therapy (CRRT) in the hypermagnesemia group (20.75%) was significantly higher than that in the hypomagnesemia group (9.15%) and normomagnesemia group (11.48%) (χ² = 6.302, P = 0.043).\u003c/p\u003e\n\u003cp\u003eIn the hypermagnesemia group, serum creatinine, potassium, sodium, and chloride levels were significantly elevated, while serum calcium levels were markedly decreased (all P \u0026lt; 0.05). Disease severity scores, including the Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS II), and Logistic Organ Dysfunction System (LODS) score, were significantly higher in the hypermagnesemia group compared to the other two groups (all P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eRegarding prognostic outcomes, the hypermagnesemia group had the shortest median survival times at 60, 180, and 365 days, with statistically significant intergroup differences (H-values: 6.75, 6.033, 9.235; all P \u0026lt; 0.049). Its 365-day mortality rate (37.74%) was more than twice that of the hypomagnesemia group (18.61%). Kaplan-Meier analysis revealed that the hypermagnesemia group had significantly lower survival rates at all time points compared to the hypomagnesemia group (log-rank test, P \u0026lt; 0.05). Multivariate Cox regression analysis indicated that the risk of death gradually increased with rising serum magnesium levels, and hypermagnesemia was associated with a 54% higher risk of 365-day mortality (HR = 1.54, 95% CI: 0.54–4.43).\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline (RCS) analysis demonstrated a significant increase in mortality risk when serum magnesium levels exceeded 1.9 mg/dL. Subgroup analysis confirmed that the association between serum magnesium levels and prognosis was consistent across different subgroups. Furthermore, during the 365-day follow-up, the hypermagnesemia-related mortality risk was significantly elevated in obese patients and those with sepsis (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElevated serum magnesium levels at ICU admission are significantly associated with adverse prognosis (60- to 365-day mortality) in AP patients complicated by AKI. Hypermagnesemia (\u0026gt;2.3 mg/dL) serves as an independent prognostic biomarker in this patient population. The stratified thresholds for serum magnesium (1.9 mg/dL and 2.3 mg/dL) identified in this study can be used as practical biological markers for risk stratification in AP patients with AKI. Routine serum magnesium monitoring is recommended for the early management of these patients, which may contribute to improved risk assessment and lay the groundwork for subsequent mechanistic research and intervention strategy development.\u003c/p\u003e","manuscriptTitle":"Association between Serum Magnesium Levels and Adverse Prognosis in Patients with Acute Pancreatitis Complicated by Acute Kidney Injury: A Retrospective Study Based on the MIMIC-IV Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:13:06","doi":"10.21203/rs.3.rs-8277115/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T11:45:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75770643417011810769880003086190972963","date":"2026-04-25T14:10:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T12:14:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-05T11:12:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-04T13:00:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-04T13:00:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-04T08:56:18+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":"afa8d4dc-51b9-41b6-9356-dbbf23800444","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T11:45:41+00:00","index":103,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65467616,"name":"Health sciences/Biomarkers"},{"id":65467617,"name":"Health sciences/Diseases"},{"id":65467618,"name":"Health sciences/Medical research"},{"id":65467619,"name":"Health sciences/Nephrology"},{"id":65467620,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-05T17:13:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:13:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8277115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8277115","identity":"rs-8277115","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}