Association of serum osmolality levels with all-cause mortality risk in patients with DKA

Association of serum osmolality levels with all-cause mortality risk in patients with DKA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of serum osmolality levels with all-cause mortality risk in patients with DKA Jian Liao, Dingyu Lu, Zhi Liang, Hongwei Tan, Maojuan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5869644/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Background The purpose of this study was to investigate the relationship between serum osmolality levels and 28-day mortality in patients with DKA. Method Data for this observational cohort study were obtained from the MIMIC-IV3.0 database. The participants were divided into five groups based on the serum osmolality quintiles. The primary outcome was 28-day mortality. We employed Cox proportional hazards regression analysis and threshold effect analysis to assess the relationship between serum osmolality levels and 28-day mortality in patients with DKA. Results The study included 1026 patients; the mean age was 52 years, 55.0% were male. Our findings indicate that serum osmolality is associated with an increased risk of 28-day mortality, exhibiting a U-shaped relationship. Altered serum osmolality levels, whether lower or higher, are linked to a heightened risk of mortality. When osmolality < 308.9 mmol/L, the 28-day mortality risk decreased by 5.4% (HR 0.946, 95% CI 0.938–0.959) for every 1 mmol/L increase. At osmolality ≥ 308.9 mmol/L, there was a 3.5% (HR 1.035, 95% CI 1.030–1.039) increase in the 28-day mortality risk for every 1 mmol/L increase in osmolality. The risk of mortality was lower at osmolarity of 297–314 mmol/L. Conclusion A U-shaped correlation between initial serum osmolality and 28-days all-cause mortality in patients with DKA was identified. These results underscore serum osmolality’s critical role in early mortality among patients with DKA. Health sciences/Endocrinology Health sciences/Risk factors Health sciences/Diseases/Endocrine system and metabolic diseases DKA serum osmolality U-shaped correlation Intensive Care Unit Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Diabetic ketoacidosis (DKA) is a common diabetic emergency characterized by hyperglycemia, ketosis, and acidosis. This severe metabolic disorder results from the combined effects of insulin deficiency, insulin antagonism, and excess insulin hormone. While type 1 diabetes is prone to spontaneous DKA, type 2 diabetes can also develop DKA [ 1 ]. DKA is linked to considerable morbidity and a high demand for health-care resources, representing 4 ~ 9% of all hospital discharges among patients whose primary cause for acute hospitalization is diabetes mellitus [ 2 ]. The treatment of DKA remains a costly endeavor. In the United States, the estimated expense for a single DKA episode is around US $ 26,566 [ 3 ]. Conversely, in the United Kingdom, the cost of treating one episode of DKA is approximated at £2,064 for adults and £1,387 for adolescents aged 11 to 18 years [ 4 , 5 ]. Elevated blood glucose levels and high blood ketone concentrations increase serum osmolality, leading to the movement of intracellular fluid outside the cells. This process results in cellular dehydration and osmotic diuresis. The severe depletion of water can lead to inadequate blood volume, decreased blood pressure, and potentially culminate in circulatory failure [ 6 , 7 ]. Serum osmolality refers to the total osmolality of ions and particles dissolved in body fluid, which is influenced by the concentrations of sodium (Na), potassium (K), glucose, and urea [ 8 ]. Elevated serum osmolality have been linked to negative outcomes in various clinical conditions, including heart failure [ 9 ], acute myocardial infarction [ 10 ], cerebral ischemia [ 11 ] or sepsis [ 12 ]. However, there is a scarcity of research investigating the relationship between serum osmolality levels and the prognosis in patients with DKA. In order to address this gap, an observational cohort study was conducted to investigate the potential of serum osmolality levels in predicting mortality in patients with DKA. Methods Study population The researchers conducted an observational study using data from the publicly accessible Medical Information Mart for Intensive Care IV (MIMIC-IV 3.0) database. MIMIC-IV 3.0 (released 2022) is a publicly available critical care database containing de-identified ICU data from 2008–2022 at Beth Israel Deaconess Medical Center. It is an important database in the feld of critical care. The database includes information such as demographic data, vital signs, laboratory parameters, comorbidities, and more [ 13 ]. The patient cohort for this research comprised individuals with a confirmed diagnosis of DKA, following the guidelines outlined in the International Classification of Diseases, 9th and 10th Revision (25013, 25012, 25011, 24911, 25010, E1010, E1110, E1310, E0910, E0810, E1011, E1311). Ethical review and approval were waived for this study, due to reason: The use of the MIMIC-IV database was approved by the review committee of Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center and patient’s data were anonymized prior to publication. The research excluded individuals below the age of 18 during their initial admission, individuals who experienced multiple admissions to the ICU due to DKA (only data from the first admission were considered). We excluded patients who met the following criteria: (1) less than 18 years; (2) length of ICU stay < 24h (we excluded these patients due to the substantial amount of missing laboratory test data for these patients); (3) patients diagnosed with Hyperosmolar Hyperglycemic State; (4) patients with missing baseline sodium, potassium, ureanitrogen, and glucose at admission; (5) data for which the calculated serum osmolality value is anomalous. Data collection To conduct the data extraction, we utilized PostgresSQL software and pgAdmin 4 tool by employing Structured Query Language (SQL). The extraction process prioritized four distinct categories of potential variables: demographic factors, vital signs, laboratory parameters, severity of illness score, comorbidities and treatment during ICU stay. All laboratory tests and vital signs data were measured for the first time within 24 hours of ICU admission. Serum osmolality was calculated using the equation [2 × (Na + + K + ) + (glucose/18) + (ureanitrogen/2.8)]. Variables with more than 20% missingness were removed from the model to avoid bias that might result from directly filling in missing values. Outcomes The main outcome of this study was 28-day mortality after ICU admission. Secondary outcomes focused on hospital length of stay and ICU length of stay. Statistical analysis The patients were categorized into five groups based on their admission serum osmolality levels: Quintile 1 ( 326 mmol/L). Continuous variables were presented as the mean ± SD or median and interquartile range (IQR). Categorical variables were expressed as numbers or percentages (%). ANOVA analysis, the Kruskal-Wallis test for continuous variables, or the chi-square test for categorical data, as applicable. Kaplan-Meier survival analysis was used to assess the incidence rate of primary outcome events in different stratified groups based on the serum osmolality. The log-rank test was employed to examine any observed disparities. Binary logistic regression analysis was conducted to evaluate factors influencing the risk of all-cause death. Cox regression analysis was conducted to investigate the relationship between serum osmolality and 28-day mortality. We included all variables in the survival analysis. Model 1: unadjusted; Model 2 was adjusted for age, gender, temperature, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and saturation of peripheral oxygen (SPO2); Model 3 was further adjusted for white blood cell (WBC), platelet, hemoglobin, sodium, potassium, glucose, ureanitrogen, creatinine, lactate, Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APS III), Simplified Acute Physiology Score II ( SAPSII), Charlson Comorbidity Index (Charlson), type 2 diabetes, type 1 diabetes, sepsis, acute myocardial infarction (AMI), chronic kidney disease (CKD), acute kidney injury (AKI), the use of continuous renal replacement therapy (CRRT) and ventilation. HRs were counted and the findings were presented with 95% confidence intervals (CI). Furthermore, an analysis was conducted to examine the association between serum osmolality and 28-day mortality among patients diagnosed with DKA, utilizing a multivariate-adjusted restricted cubic spline method. To identify the inflection point, a recursive algorithm was applied, and a two-piecewise Cox proportional hazards regression model was established. This model incorporated the values from both sides of the inflection point to assess the threshold impact of serum osmolality on 28-day mortality. The data analyses were conducted using R software (version 4.2.2). For all analyses, a 2-side P < 0.05 was considered statistically significant. Results Baseline characteristics The study enrolled a total of 1026 critically ill patients with DKA from the MIMIC-IV database, as depicted in Fig. 1 . Table 1 presents the baseline characteristics of all patients. The median age of the study participants was 52 ± 16 years, with males comprising 55.0% of the sample. The patients in the group with higher serum osmolality were older, and had higher levels of WBC, sodium, glucose, ureanitrogen, creatinine, lactate, aniongap. These patients had higher disease severity scores, and comorbidities such as sepsis and acute AKI were relatively more common. There was no statistical difference in vital signs among the five groups (all p > 0.05). There was a higher utilization of CRRT and ventilation among patients with a higher serum osmolality. Overall, the all-cause mortality rate within 28 days of admission was 44.6%, and the difference in mortality rates between the five groups was statistically significant (51.2% vs. 32.2% vs. 31.2% vs. 48.3 vs. 60.2%, p < 0.001), indicating that lower or higher serum osmolality is associated with a higher risk of 28-day mortality. Primary outcomes Survival analysis The 28-day risk of mortality among groups was analyzed using Kaplan-Meier survival analysis curves, based on the serum osmolality quintile as presented. Patients had a higher short-term survival rate when serum osmolality was between 306–314 mmol/L, a lower survival rate when serum osmolality was below 297 mmol/L(Fig. 2 ). Multivariable Cox regression analysis Table 2 presents the results of the Cox regression conducted to assess the risk of all-cause death in patients with DKA. In the unadjusted model, the risk of mortality was increased by 64% (HR 1.64, 95%CI 1.20–2.24) in Q1 when compared with serum osmolality in Q3, and the risk of mortality was elevated by 93% (HR 1.93, 95%CI 1.43–2.61) in the highest quintile Q5. In model 2 (adjusted Table 1 Baseline characteristics of participants according to osmolality levels Variables Baseline serum osmolality levels (mmol/L) P Overall n = 1026 Q1( 326) n = 206 Demographic Age,year 52 ± 16 43 ± 16 47 ± 17 53 ± 16 58 ± 14 58 ± 14 < 0.001 Male,n(%) 564 (55.0 ) 89 (43.4 ) 106 (51.7 ) 121 (59.0 ) 126 (61.5 ) 122 (59.2 ) 0.001 Vital signs Temperature,℃ 36.89 ± 0.59 36.90 ± 0.48 36.93 ± 0.51 36.89 ± 0.58 36.92 ± 0.69 36.80 ± 0.66 0.168 HR,bpm 100 ± 19 100 ± 16 100 ± 19 101 ± 19 101 ± 19 99 ± 20 0.692 SBP,mmHg 130 ± 24 127 ± 21 130 ± 21 133 ± 26 134 ± 26 131 ± 26 0.053 DBP,mmHg 73 ± 18 73 ± 17 75 ± 17 73 ± 18 72 ± 18 72 ± 21 0.473 SPO2(%) 97.54 ± 3.85 97.94 ± 2.98 97.70 ± 3.38 97.63 ± 2.55 97.37 ± 3.38 97.07 ± 5.96 0.192 Laboratory tests WBC,K/uL 13 ± 7 10 ± 5 12 ± 6 13 ± 7 14 ± 6 14 ± 7 < 0.001 Platelet,K/uL 236 ± 102 228 ± 89 245 ± 103 246 ± 116 243 ± 107 218 ± 92 0.016 Hemoglobin,g/dL 11.19 ± 2.18 11.35 ± 2.10 11.39 ± 2.01 11.21 ± 2.25 11.22 ± 2.10 10.77 ± 2.37 0.030 Sodium,mmol/L 138 ± 7 133 ± 4 136 ± 4 137 ± 4 139 ± 5 144 ± 11 < 0.001 Potassium,mmol/L 4.29 ± 0.86 3.95 ± 0.68 4.12 ± 0.63 4.40 ± 0.91 4.46 ± 0.96 4.51 ± 0.95 < 0.001 Glucose,mg/dL 311 ± 216 206 ± 84 223 ± 88 274 ± 118 333 ± 150 520 ± 344 < 0.001 Ureanitrogen,mg/dL 32 ± 26 14 ± 10 20 ± 14 29 ± 16 37 ± 19 62 ± 31 < 0.001 Creatinine,mg/dL 1.88 ± 1.95 1.00 ± 0.69 1.23 ± 1.10 1.67 ± 1.43 2.33 ± 2.26 3.19 ± 2.66 < 0.001 Serum osmolality 313 ± 22 290 ± 5 301 ± 2 310 ± 3 320 ± 3 347 ± 20 < 0.001 PH 7.30 ± 0.10 7.31 ± 0.09 7.32 ± 0.09 7.31 ± 0.11 7.29 ± 0.11 7.28 ± 0.12 0.005 Lactate,mmol/L 2.44 ± 2.12 1.80 ± 1.12 2.01 ± 1.30 2.58 ± 1.97 2.71 ± 2.49 3.07 ± 2.89 < 0.001 Aniongap,mEq/L 19 ± 7 17 ± 5 18 ± 5 19 ± 6 20 ± 6 23 ± 8 < 0.001 Severity of illness score SOFA 4.1 ± 3.4 2.5 ± 2.5 2.7 ± 2.4 3.8 ± 2.9 4.9 ± 3.7 6.5 ± 3.6 < 0.001 APSIII 49 ± 20 39 ± 14 40 ± 16 48 ± 17 54 ± 21 63 ± 22 < 0.001 SAPSII 32 ± 14 23 ± 10 26 ± 12 31 ± 12 36 ± 13 42 ± 15 < 0.001 GCS 13.88 ± 2.25 14.52 ± 1.12 14.43 ± 1.45 13.95 ± 1.97 13.85 ± 2.35 12.67 ± 3.24 < 0.001 Charlson 4.3 ± 3.0 2.8 ± 2.6 3.8 ± 3.2 4.2 ± 2.8 5.1 ± 2.7 5.5 ± 3.1 < 0.001 Complication Type 2 Diabetes,n% 463 (45.1) 85 (41.5 ) 82 (40.0 ) 91 (44.4 ) 96 (46.8 ) 109 (52.9 ) 0.071 Type 1 Diabetes,n% 478 (46.6 ) 105 (51.2 ) 108 (52.7 ) 95 (46.3 ) 93 (45.4 ) 77 (37.4 ) 0.018 Sepsis,n% 455 (44.3 ) 52 (25.4 ) 62 (30.2 ) 90 (43.9 ) 118 (57.6 ) 133 (64.6 ) < 0.001 Hypertension,n% 348 (33.9 ) 63 (30.7 ) 56 (27.3 ) 84 (41.0 ) 73 (35.6 ) 72 (35.0 ) 0.043 Heart failure,n% 193 (18.8 ) 18 (8.8 ) 32 (15.6 ) 35 (17.1 ) 55 (26.8 ) 53 (25.7 ) < 0.001 AMI,n% 86 (8.4 ) 4 (2.0 ) 12 (5.9 ) 11 (5.4 ) 32 (15.6 ) 27 (13.1 ) < 0.001 Stroke,n% 63 (6.1 ) 6 (2.9 ) 6 (2.9 ) 14 (6.8 ) 20 (9.8 ) 17 (8.3 ) 0.008 COPD,n% 46 (4.5 ) 9 (4.4 ) 6 (2.9 ) 6 (2.9 ) 14 (6.8 ) 11 (5.3 ) 0.257 CKD,n% 233 (22.7 ) 14 (6.8 ) 40 (19.5 ) 42 (20.5 ) 64 (31.2 ) 73 (35.4 ) < 0.001 AKI,n% 579 (56.4 ) 73 (35.6 ) 94 (45.9 ) 126 (61.5 ) 141 (68.8 ) 145 (70.4 ) < 0.001 AKI stage,n% < 0.001 1 199 (19.4 ) 23 (11.2 ) 48 (23.4 ) 44 (21.5 ) 48 (23.4 ) 36 (17.5 ) 2 220 (21.4 ) 32 (15.6 ) 29 (14.1 ) 60 (29.3 ) 46 (22.4 ) 53 (25.7 ) 3 160 (15.6 ) 18 (8.8 ) 17 (8.3 ) 22 (10.7 ) 47 (22.9 ) 56 (27.2 ) Treatment during hospitalization CRRT,n% 48 (4.7 ) 2 (1.0 ) 3 (1.5 ) 6 (2.9 ) 18 (8.7 ) 19 (9.3 ) < 0.001 Ventilation,n% 189 (18.4 ) 19 (9.3 ) 22 (10.7 ) 39 (19.0 ) 52 (25.4 ) 57 (27.7 ) < 0.001 Insulin,n% 535 (52.1 ) 104 (50.7 ) 105 (51.2 ) 105 (51.2 ) 106 (51.7 ) 115 (55.8 ) 0.837 Fluid balance 24h,ml 4043 ± 3194 3397 ± 2571 3488 ± 2304 4195 ± 3554 4533 ± 3250 4600 ± 3842 < 0.001 Outcomes Hospital-day 10 ± 19 8 ± 9 8 ± 8 10 ± 11 13 ± 37 10 ± 9 0.027 ICU-day 3.56 ± 5.00 2.83 ± 4.06 2.88 ± 3.58 3.41 ± 5.11 4.45 ± 5.98 4.25 ± 5.68 < 0.001 28-day mortality,n% 458 (44.6 ) 105 (51.2 ) 66 (32.2 ) 64 (31.2 ) 99 (48.3 ) 124 (60.2 ) < 0.001 HR:heart rate; SBP:systolic blood pressure; DBP:diastolic blood pressure; SPO2:saturation of peripheral oxygen; WBC:white blood cell; SOFA:Sequential Organ Failure Assessment; APS III:Acute Physiology Score III; SAPSII:Simplified Acute Physiology Score II; GCS:Glasgow Coma Scale; Charlson:Charlson Comorbidity Index; COPD: chronic obstructive pulmonary disease; AMI:acute myocardial infarction; CKD:chronic kidney disease; AKI:acute kidney injury; CRRT:continuous renal replacement therapy for age, gender, temperature, heart rate, SBP, DBP, SPO2), the results remained significant. In model 3 (adjusted for age, gender, temperature, heart rate, SBP, DBP, SPO2 and potential confounders), the risk of mortality was elevated by 88% (HR 1.88, 95%CI 1.35–2.61) in Q1 and 46% (HR 1.46, 95%CI 1.01–2.01) in Q5. Table 2 Association between serum osmolality and 28-days all-cause mortality in patients with DKA Modle 1 Modle 2 Modle 3 HR(95% CI) P HR(95% CI) P HR(95% CI) P Serum osmolality quintiles Q1 (< 297) 1.64(1.20–2.24) 0.002 1.74(1.26–2.38) < 0.001 1.88(1.35–2.61) < 0.001 Q2 (297–305) 1.03(0.73–1.45) 0.861 1.06(0.75–1.50) 0.721 1.13(0.79–1.61) 0.496 Q3 (306–314) 1(reference) 1(reference) 1(reference) Q4 (315–326) 1.55(1.13–2.12) 0.007 1.51(1.10–2.07) 0.010 1.41(1.02–1.94) 0.036 Q5 (> 326) 1.93(1.43–2.61) < 0.001 1.88(1.39–2.54) < 0.001 1.46(1.01–2.11) 0.046 P for trend < 0.001 < 0.001 < 0.001 Model 1 unadjusted Model 2 adjusted for Age, gender, temperature, heart rate, SBP, DBP, SPO2 Model 3 adjusted for Model 2 + laboratory tests + complications + severity of illness score + treatments during hospitalization HR: hazard ratio, CI: confdence interval A lower and similar risk of mortality in Q2 and Q3 of osmolarity was observed. As a result, Q2 and Q3 were merged, and Q2 + Q3 served as a reference for a multivariable Cox regression analysis. After adjusting for the covariates in model 3, we found that there was a 70% (HR 1.70, 95%CI 1.31–2.21) higher risk of mortality at 28 days in Q1 compared to Q2 + Q3, and a 51% (HR 1.51, 95%CI 1.16–1.96) ) higher in the Q5 (Table 3 ). Table 3 Association between serum osmolality and 28-days all-cause mortality in patients with DKA after combining Q2 and Q3 Modle 1 Modle 2 Modle 3 HR(95% CI) P HR(95% CI) P HR(95% CI) P Serum osmolality quintiles Q1 (< 297) 1.57(1.21–2.02) < 0.001 1.63(1.26–2.11) < 0.001 1.70(1.31–2.21) 326) 1.86(1.46–2.37) < 0.001 1.78(1.39–2.28) < 0.001 1.51(1.16–1.96) 0.002 P for trend < 0.001 < 0.001 < 0.001 Model 1 unadjusted Model 2 adjusted for Age, gender, temperature, heart rate, SBP, DBP, SPO2 Model 3 adjusted for Model 2 + laboratory tests + complications + severity of illness score + treatments during hospitalization HR: hazard ratio, CI: confdence interval Threshold effect analysis for the relationship between serum osmolality levels and 28-day mortality The study employed a restricted cubic splines regression model to investigate the relationship between serum osmolality and the risk of 28-day mortality. After adjusting for covariates in model 3, we found a U-shaped relationship between serum osmolality and 28-day mortality in curve ftting (P for non-linearity < 0.001). Additional examination employing a segmented regression model indicated a turning point at a serum osmolality level of 308.9 mmol/L in relation to the risk of 28-day mortality (Fig. 3 ). When osmolality < 308.9 mmol/L, the 28-day mortality risk decreased by 5.4% (HR 0.946, 95% CI 0.938–0.959) for every 1 mmol/L increase. At osmolality ≥ 308.9 mmol/L, there was a 3.5% (HR 1.035, 95% CI 1.030–1.039) increase in the 28-day mortality risk for every 1 mmol/L increase in osmolality (Table 4 ). Table 4 Threshold efect analysis of the relationship between serum osmolality and 28-day mortality of patients with DKA Threshold of osmolality HR (95% CI) P < 308.9 mmol/L 0.946 0.938–0.959 < 0.001 ≥ 308.9 mmol/L 1.035 1.030–1.039 < 0.001 Nonlinear test < 0.001 The data were adjusted for all the covariates of Model 3 HR: hazard ratio, CI: confdence interval Sensitivity analysis In order to confirm the stability of the U-shaped association between serum osmolality and mortality at 28 days, we conducted curve fitting stratified by diabetes, AKI, CKD, hypertension, and sepsis. The results demonstrated that this U-shaped association remained consistent across various subgroups (Fig. 4 ). Discussion To the best of our knowledge, this study represents the first investigation into the association between serum osmolality and the risk of 28-day mortality in patients with DKA. Our findings indicate that serum osmolality is associated with an increased risk of 28-day mortality, exhibiting a U-shaped relationship. This association remains significant even after adjusting for potential confounding factors. Altered serum osmolality levels, whether lower or higher, are linked to a heightened risk of mortality. When osmolality < 308.9 mmol/L, the 28-day mortality risk decreased by 5.4% (HR 0.946, 95% CI 0.938–0.959) for every 1 mmol/L increase. At osmolality ≥ 308.9 mmol/L, there was a 3.5% (HR 1.035, 95% CI 1.030–1.039) increase in the 28-day mortality risk for every 1 mmol/L increase in osmolality. The risk of mortality was lower at osmolarity of 297–314 mmol/L. Consequently, these results indicate that serum osmolality holds promise as a valuable decision-making tool for clinicians and may serve as an independent prognostic factor in patients with DKA. Our study found that lower serum osmolality is associated with a significantly higher risk of mortality compared to higher serum osmolality. Patients with DKA may experience a shift of water from intracellular to extracellular spaces due to hyperglycemia, resulting in dilutional hyponatremia [ 14 ]. In such cases, patients may suffer from severe dehydration, but the absence of a significant increase in osmolality may mask the true extent of dehydration, leading to inadequate treatment and an increased risk of mortality. In addition, rapid declines in serum glucose concentration without concurrent rise in serum sodium may precipitate cerebral edema by creating an osmotic gradient that drives water into brain cells [ 15 ]. The level of serum osmolality is determined by the number of solute particles present in a given volume of solution, independent of the type and size of the solute particles [ 16 ]. Serum osmolality plays a critical role in regulating water balance within and outside of cells and blood vessels, maintaining normal cell morphology, and ensuring proper water distribution in blood vessels, thereby sustaining blood volume [ 17 ]. Numerous studies have previously investigated the predictive value of serum osmolality across various clinical conditions. Nicholson conducted a retrospective analysis examining the correlation between the initial calculated blood serum osmolality and the risk of 30-day in-hospital mortality among 24232 patients admitted to the emergency department. Their findings revealed that the first recorded serum osmolality in the emergency department was associated with an elevated risk of 30-day mortality [ 18 ]. Shen et al. conducted a large retrospective cohort study demonstrating that a hyperosmolar state at the time of admission was associated with increased mortality among critically ill patients suffering from cardiac, cerebral, vascular, and gastrointestinal diseases in the ICU, with a threshold of 300 mmol/L [ 19 ]. Similarly, the study by Holtfreter et al. reached a comparable conclusion, indicating that elevated serum osmolarity is associated with an increased risk of mortality among critically ill individuals. The mortality prediction value of serum osmolarity falls between that of the APACHE II and SOFA scores. Notably, when the outcome prediction is restricted to long-term ICU patients, serum osmolality demonstrates superior performance compared to both the APACHE II and SOFA scores. Furthermore,serumosmolality is a more cost-effective and rapid measure than the clinical scoring systems currently employed in ICUs for predicting disease severity [ 20 ]. Increased serum osmolarity plays a key role in activating intrarenal (polyol-fructokinase) [ 21 ] and extrarenal (vasopressin) [ 22 ] pathways, leading to renal injury. Elevated serum osmolality and reduced serum osmolality are viewed as independently linked to a greater likelihood of developing acute kidney injury (AKI) [ 23 ].We found that as serum osmolarity increased, the incidence of AKI also increased, reaching 70.4% in the Q5 (> 326mmol/L) group, and the utilization rate of CRRT was the highest, reaching 9.3%. Serum osmolality is maintained in a narrow range both by neuroendocrine functions through hypothalamic-pituitary axis and the kidney on controlling thirst and regulating water and electrolyte balance [ 24 ]. Serum osmolality is often elevated in patients with DKA. Under normal circumstances, the serum osmolarity ranges from 280-310mmol/L. The average effective serum osmolarity among patients with DKA is (299.73 ± 13.99) mmol/L [ 25 ]. It has been reported that high osmolality is independently associated with mortality in DKA [ 25 , 26 ]. Hyperosmolality primarily arises from hyperglycemia in DKA, leading to significant impacts on CNS function and consciousness. The most critical complication of DKA is brain edema, and untreated DKA can result in cardiac arrest and death. Among the various factors contributing to altered mental status, such as hyperglycemia, ketonemia, or metabolic acidosis, serum osmolality serves as the key determinant of the level of awareness in these patients [ 27 , 28 ]. Therefore, abnormal serum osmolality should be promptly diagnosed and treated clinically. Overall, this study used a large and publicly available critical-care database to first assess the relationship between serum osmolality and the risk of 28-day mortality in patients with DKA. However, our study has several limitations. (1) As this study was observational in nature, we were unable to definitively establish causality. Despite the use of multivariate adjustment and subgroup analyses, there is still a possibility of residual confounding factors influencing the clinical outcomes. (2) Serum osmolarity obtained from the initial measurements of blood glucose, sodium, potassium, and ureanitrogen might not completely reflect the overall change in the body. It is important to consider this limitation when interpreting the results. (3) The estimation of serum osmolality was conducted through a formula instead of directmeasurement, which hindered the ability to identify ‘delta osmolality’ or the ‘osmolal gap’. Even though the formula was selected carefully, it might not accurately represent the actual osmolality values. (4) Previous studies have shown that anion gap and ketone are important factors affecting patient prognosis. MIMIC-IV lacks ketone the data of ketone, so we were unable to perform relevant statistical analyses. (5) The individuals involved in the study were patients in the ICU, and additional validation is necessary to assess the relevance of serum osmolality for patients who are admitted to general wards. (6) As the study data came from a single center (the MIMIC-IV database), our findings may not generalise to different healthcare settings, particularly those with different ICU resources or patient demographics. (7) With electronic health records data, there may be potential inaccuracies or inconsistencies in data entry, missing data, or variable measurements that may affect study validity. Therefore, further research is essential to comprehensively explore how this bias impacts clinical outcomes. Conclusion Our research demonstrates a U-shaped relationship between serum osmolality levels and 28-day all-cause mortality in patients with DKA following ICU admission, while accounting for confounding variables. Due to its simplicity, accessibility, and cost-effectiveness, measuring serum osmolality highlights the importance of managing osmolality, assisting physicians in recognizing high-risk patients. However, further studies are crucial to determine if interventions targeting serum osmolality levels can positively impact clinical outcomes within this group. Declarations Competing interests The authors declare no competing interests. Funding This research received no external funding. Author Contribution Conceptualization, Jian Liao; Methodology, Dingyu Lu; Validation, Zhi Liang; Formal Analysis, Jian Liao; Data Curation, Hongwei Tan; Writing-Original Draft Preparation, Jian Liao; Writing-Review & Editing, Dingyu Lu; Supervision, Maojuan Wang. All authors read and approved the final draft. Acknowledgements We are particularly grateful to all the people who have given us help on our article. Data Availability Data is provided within the manuscript or supplementary information files（MIMIC IV 3.0： https://mimic.mit.edu). References Dhatariya KK,Glaser NS,Codner E, et al. Diabetic ketoacidosis. Nat Rev Dis Primers. 2020;6 (1):40. Centers for Disease Control and Prevention. Age-adjusted hospital discharge rates for diabetic ketoacidosis as first-listed diagnosis per 10,000 population, United States, 1988-2009. CDC https://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html (2013). Desai, D., Mehta, D., Mathias, P., Menon, G. & Schubart, U. K. Health care utilization and burden of diabetic ketoacidosis in the U.S. over the past decade: a nationwide analysis. Diabetes Care. 2018; 41:1631-1638. Dhatariya, K.K., Skedgel, C. & Fordham, R. The cost of treating diabetic ketoacidosis in the UK: a national survey of hospital resource use. Diabet. Med. 2017;34:1361-1366. Dhatariya, K. K. et al. The cost of treating diabetic ketoacidosis in an adolescent population in the UK: a national survey of hospital resource use. Diabet. Med. 2019;36:982-987. Vellanki, P. & Umpierrez, G. E. Increasing hospitalizations for DKA: A need for prevention programs. Diabetes Care. 2018;41:1839-1841. Kitabchi, A. E., Umpierrez, G. E., Miles, J. M. & Fisher, J. N. Hyperglycemic crises in adult patients with diabetes. Diabetes Care. 2009;32:1335-1343 Büyükkaragöz B, Bakkaloğlu SA. Serum osmolality and hyperosmolar states. Pediatr Nephrol. 2023;38:1013-25. Zou Q, Li J, Lin P, et al. Association between serum osmolality and 28-day all-cause mortality in patients with heart failure and reduced ejection fraction:a retrospective cohort study from the MIMIC-IV database. Front Endocrinol (Lausanne). 2024;15:1397329. Luo X, Tang Y, Shu Y, et al. Association between serum osmolality and deteriorating renal function in patients with acute myocardial infarction: analysis of thenMIMIC-IV database. BMC Cardiovasc Disord. 2024;24 (1):490. Brathwaite S,Macdonald RL. Current management of delayed cerebral ischemia: update from results of recent clinical trials. Transl Stroke Res. 2014;5(2):207-26. Liang M, Xu Y, Ren X, et al. The U-shaped association between serum osmolality and 28-day mortality in patients with sepsis: a retrospective cohort study.Infection. 2024;52(5):1931-1939. Johnson, A., Bulgarelli, L., Pollard, T., Gow, B., Moody, B., Horng, S., Celi, L. A., & Mark, R. (2024). MIMIC-IV (version 3.0). PhysioNet. https://doi.org/10.13026/hxp0-hg59. Abbas E. Kitabchi, Guillermo E. Umpierrez, John M. Miles, Joseph N. Fisher; Hyperglycemic Crises in Adult Patients With Diabetes. Diabetes Care.2009;32(7): 1335-1343. Wolfsdorf JI, Glaser N, Agus M, et al. ISPAD Clinical Practice Consensus Guidelines 2018:Diabetic ketoacidosis and the hyperglycemic hyperosmolar state.Pediatr Diabetes. 2018;19(Suppl.27):155-177. Star RA. Southwestern internal medicine conference: hyperosmolar states. Am J Med Sci. 1990;300:402-12. Argyropoulos C, et al. Hypertonicity: pathophysiologic concept and experimental studies. Cureus. 2016;8:e596. Nicholson T, Bennett K, Silke B. Serum osmolarity as an outcome predictor in hospital emergency medical admissions. Eur J Intern Med. 2012;23(2):e39-43. Shen Y, Chen X, Ying M, et al. Association between serum osmolality and mortality in patients who are critically ill:a retrospective cohort study. Holtfreter B, Bandt C, Kuhn SO, et al. Serum osmolarity and outcome in intensive care unit patients. Acta Anaesthesiol Scand. 2006;50(8):970-977. Roncal Jimenez CA, Ishimoto T, Lanaspa MA, et al. Fructokinase activity mediates dehydration-induced renal injury. Kidney Int. 2014;86(2):294-302. García-Arroyo FE, Tapia E, Blas-Marron MG, et al. Vasopressin Mediates the Renal Damage Induced by Limited Fructose Rehydration in Recurrently Dehydrated Rats. Int J Biol Sci. 2017;13 (8):961-975. Yang J, Cheng Y, Wang R, Wang B. Association between serum osmolality and acute kidney injury in critically ill patients: a retrospective cohort study. Front Med. 2021;8:745803. Seay NW, Lehrich RW, Greenberg A. Diagnosis and management of disorders of body tonicity-hyponatremia and hypernatremia: core curriculum. Am J Kidney Dis. 2020;75:272-286. Wu XY , She DM , Wang F , et al. Clinical profiles, outcomes and risk factors among type 2 diabetic inpatients with diabetic ketoacidosis and hyperglycemic hyperosmolar state: a hospital-based analysis over a 6-year period. BMC Endocr Disord. 2020; 20(1):182. Blank SP, Blank RM, Ziegenfuss MD. The importance of hyperosmolarity in diabetic ketoacidosis. Diabet Med. 2020;37(12):2001-2008. Rondon-Berrios H, Argyropoulos C, Ing TS, et al. Hypertonicity: clinical entities, manifestations and treatment. World J Nephrol. 2017;6:1-13. Agrawal S, Baird GL, Quintos JB, et al. Pediatric diabetic ketoacidosis with hyperosmolarity: clinical characteristics and outcomes. Endocr Pract. 2018;24:26-732. Additional Declarations No competing interests reported. Supplementary Files PatientswithDKAfromtheMIMICIVdatabase.xlsx Cite Share Download PDF Status: Published Journal Publication published 29 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 May, 2025 Reviews received at journal 09 May, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviews received at journal 20 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers invited by journal 18 Apr, 2025 Submission checks completed at journal 18 Apr, 2025 First submitted to journal 08 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5869644","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":445772597,"identity":"0d23d200-92bd-4c60-8110-7879c8010f68","order_by":0,"name":"Jian Liao","email":"","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Liao","suffix":""},{"id":445772598,"identity":"3c461612-a615-4e6c-a907-6a510987d952","order_by":1,"name":"Dingyu Lu","email":"","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dingyu","middleName":"","lastName":"Lu","suffix":""},{"id":445772599,"identity":"89ed19f8-8266-4b26-9929-76d8e81203fe","order_by":2,"name":"Zhi Liang","email":"","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Liang","suffix":""},{"id":445772600,"identity":"19341a10-16ca-4031-898c-40b3ede423b0","order_by":3,"name":"Hongwei Tan","email":"","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Tan","suffix":""},{"id":445772601,"identity":"eb3757a3-7756-4e69-a347-122e96acd459","order_by":4,"name":"Maojuan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxmb+xw8//mGTY2xvIFILczsPm7FkA58xc88BIrWw9/MwSPA2yCWyz0ggUgtvM+8BA8kdZgm8Mx9vvMFQYxNNUItkM1/Cg8IzaXmSs9OKLRiOpeU2ENJi2MxgYCDBdqzYcHaOmQRjw2HCWuwPMxhI8LD9T9x/8wyRWhibeQwkeNvYEhtn8BCthS3NWOIMmzFjD9AvCcT4hbH/8OGHHypAUXl4440PNTaEtSADA4kEUpRDtJCqYxSMglEwCkYGAACCuT6KijC9BQAAAABJRU5ErkJggg==","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Maojuan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-21 03:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5869644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5869644/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-14405-1","type":"published","date":"2025-08-29T15:58:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81541434,"identity":"1fbbc3f7-db56-45c6-b3f5-480962af25a5","added_by":"auto","created_at":"2025-04-28 11:14:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33185,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study patients\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5869644/v1/66eb2fdbf872b0c8a2381855.png"},{"id":81541440,"identity":"41f2c554-6512-426f-a965-e5e4b3d9d93a","added_by":"auto","created_at":"2025-04-28 11:14:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174205,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis curves for all-cause 28-day ICU mortality.\u003c/p\u003e\n\u003cp\u003eQ1:＜297mmol/L; Q2: 297-305mmol/L; Q3: 306-314mmol/L; Q4: 315-326mmol/L; Q5:\u0026gt;326mmo/L.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5869644/v1/d42dffd128f00938d55f2fc8.jpeg"},{"id":81542770,"identity":"65a1c54c-4761-4320-83ff-7b6b5a461861","added_by":"auto","created_at":"2025-04-28 11:22:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182423,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between osmolality and 28-day mortality.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5869644/v1/daee5c29da94fc5dfe760832.jpeg"},{"id":81541444,"identity":"f5daf423-fd57-45b0-b363-a9c042a9405d","added_by":"auto","created_at":"2025-04-28 11:14:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62938,"visible":true,"origin":"","legend":"\u003cp\u003eU-shaped relationship between serum osmolality and 28-day mortality in different subgroups.\u003c/p\u003e\n\u003cp\u003e(a) Patients with diabetes; (b) Patients with AKI; (c) Patients with CKD; (d) Patients with hypertension; (e) Patients with sepsis.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5869644/v1/cabd09f6f86ddf74f5ce4bbf.jpeg"},{"id":90345004,"identity":"edb2eeba-f2bd-409f-bd23-b9dca97f6e92","added_by":"auto","created_at":"2025-09-01 16:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1711189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5869644/v1/c84ef210-4379-475c-9d6f-89648b6b47dc.pdf"},{"id":81542771,"identity":"b56eaba7-1b71-46f9-9bc6-b920f7e698d2","added_by":"auto","created_at":"2025-04-28 11:22:51","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":274869,"visible":true,"origin":"","legend":"","description":"","filename":"PatientswithDKAfromtheMIMICIVdatabase.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5869644/v1/e75326fccd8a055e13ce94b5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation of serum osmolality levels with all-cause mortality risk in patients with DKA\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic ketoacidosis (DKA) is a common diabetic emergency characterized by hyperglycemia, ketosis, and acidosis. This severe metabolic disorder results from the combined effects of insulin deficiency, insulin antagonism, and excess insulin hormone. While type 1 diabetes is prone to spontaneous DKA, type 2 diabetes can also develop DKA [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. DKA is linked to considerable morbidity and a high demand for health-care resources, representing 4\u0026thinsp;~\u0026thinsp;9% of all hospital discharges among patients whose primary cause for acute hospitalization is diabetes mellitus [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The treatment of DKA remains a costly endeavor. In the United States, the estimated expense for a single DKA episode is around US\u003cspan\u003e$\u003c/span\u003e26,566 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Conversely, in the United Kingdom, the cost of treating one episode of DKA is approximated at \u0026pound;2,064 for adults and \u0026pound;1,387 for adolescents aged 11 to 18 years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Elevated blood glucose levels and high blood ketone concentrations increase serum osmolality, leading to the movement of intracellular fluid outside the cells. This process results in cellular dehydration and osmotic diuresis. The severe depletion of water can lead to inadequate blood volume, decreased blood pressure, and potentially culminate in circulatory failure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Serum osmolality refers to the total osmolality of ions and particles dissolved in body fluid, which is influenced by the concentrations of sodium (Na), potassium (K), glucose, and urea [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Elevated serum osmolality have been linked to negative outcomes in various clinical conditions, including heart failure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], acute myocardial infarction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], cerebral ischemia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] or sepsis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, there is a scarcity of research investigating the relationship between serum osmolality levels and the prognosis in patients with DKA. In order to address this gap, an \u003cb\u003eobservational\u003c/b\u003e cohort study was conducted to investigate the potential of serum osmolality levels in predicting mortality in patients with DKA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe researchers conducted an observational study using data from the publicly accessible Medical Information Mart for Intensive Care IV (MIMIC-IV 3.0) database. MIMIC-IV 3.0 (released 2022) is a publicly available critical care database containing de-identified ICU data from 2008\u0026ndash;2022 at Beth Israel Deaconess Medical Center. It is an important database in the feld of critical care. The database includes information such as demographic data, vital signs, laboratory parameters, comorbidities, and more [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The patient cohort for this research comprised individuals with a confirmed diagnosis of DKA, following the guidelines outlined in the International Classification of Diseases, 9th and 10th Revision (25013, 25012, 25011, 24911, 25010, E1010, E1110, E1310, E0910, E0810, E1011, E1311). Ethical review and approval were waived for this study, due to reason: The use of the MIMIC-IV database was approved by the review committee of Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center and patient\u0026rsquo;s data were anonymized prior to publication.\u003c/p\u003e \u003cp\u003eThe research excluded individuals below the age of 18 during their initial admission, individuals who experienced multiple admissions to the ICU due to DKA (only data from the first admission were considered). We excluded patients who met the following criteria: (1) less than 18 years; (2) length of ICU stay\u0026thinsp;\u0026lt;\u0026thinsp;24h (we excluded these patients due to the substantial amount of missing laboratory test data for these patients); (3) patients diagnosed with Hyperosmolar Hyperglycemic State; (4) patients with missing baseline sodium, potassium, ureanitrogen, and glucose at admission; (5) data for which the calculated serum osmolality value is anomalous.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eTo conduct the data extraction, we utilized PostgresSQL software and pgAdmin 4 tool by employing Structured Query Language (SQL). The extraction process prioritized four distinct categories of potential variables: demographic factors, vital signs, laboratory parameters, severity of illness score, comorbidities and treatment during ICU stay. All laboratory tests and vital signs data were measured for the first time within 24 hours of ICU admission. Serum osmolality was calculated using the equation [2 \u0026times; (Na\u003csup\u003e+\u003c/sup\u003e + K\u003csup\u003e+\u003c/sup\u003e ) + (glucose/18) + (ureanitrogen/2.8)]. Variables with more than 20% missingness were removed from the model to avoid bias that might result from directly filling in missing values.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe main outcome of this study was 28-day mortality after ICU admission. Secondary outcomes focused on hospital length of stay and ICU length of stay.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe patients were categorized into five groups based on their admission serum osmolality levels: Quintile 1 (\u0026lt;\u0026thinsp;297 mmol/L), Quintile 2 (297\u0026ndash;305 mmol/L), Quintile 3 (306\u0026ndash;314 mmol/L), Quintile 4 (315\u0026ndash;326 mmol/L) and Quintile 5 (\u0026gt;\u0026thinsp;326 mmol/L). Continuous variables were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median and interquartile range (IQR). Categorical variables were expressed as numbers or percentages (%). ANOVA analysis, the Kruskal-Wallis test for continuous variables, or the chi-square test for categorical data, as applicable.\u003c/p\u003e \u003cp\u003eKaplan-Meier survival analysis was used to assess the incidence rate of primary outcome events in different stratified groups based on the serum osmolality. The log-rank test was employed to examine any observed disparities. Binary logistic regression analysis was conducted to evaluate factors influencing the risk of all-cause death.\u003c/p\u003e \u003cp\u003eCox regression analysis was conducted to investigate the relationship between serum osmolality and 28-day mortality. We included all variables in the survival analysis. Model 1: unadjusted; Model 2 was adjusted for age, gender, temperature, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and saturation of peripheral oxygen (SPO2); Model 3 was further adjusted for white blood cell (WBC), platelet, hemoglobin, sodium, potassium, glucose, ureanitrogen, creatinine, lactate, Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APS III), Simplified Acute Physiology Score II ( SAPSII), Charlson Comorbidity Index (Charlson), type 2 diabetes, type 1 diabetes, sepsis, acute myocardial infarction (AMI), chronic kidney disease (CKD), acute kidney injury (AKI), the use of continuous renal replacement therapy (CRRT) and ventilation. HRs were counted and the findings were presented with 95% confidence intervals (CI).\u003c/p\u003e \u003cp\u003eFurthermore, an analysis was conducted to examine the association between serum osmolality and 28-day mortality among patients diagnosed with DKA, utilizing a multivariate-adjusted restricted cubic spline method. To identify the inflection point, a recursive algorithm was applied, and a two-piecewise Cox proportional hazards regression model was established. This model incorporated the values from both sides of the inflection point to assess the threshold impact of serum osmolality on 28-day mortality. The data analyses were conducted using R software (version 4.2.2). For all analyses, a 2-side P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe study enrolled a total of 1026 critically ill patients with DKA from the MIMIC-IV database, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of all patients. The median age of the study participants was 52\u0026thinsp;\u0026plusmn;\u0026thinsp;16 years, with males comprising 55.0% of the sample. The patients in the group with higher serum osmolality were older, and had higher levels of WBC, sodium, glucose, ureanitrogen, creatinine, lactate, aniongap. These patients had higher disease severity scores, and comorbidities such as sepsis and acute AKI were relatively more common. There was no statistical difference in vital signs among the five groups (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). There was a higher utilization of CRRT and ventilation among patients with a higher serum osmolality. Overall, the all-cause mortality rate within 28 days of admission was 44.6%, and the difference in mortality rates between the five groups was statistically significant (51.2% vs. 32.2% vs. 31.2% vs. 48.3 vs. 60.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that lower or higher serum osmolality is associated with a higher risk of 28-day mortality.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary outcomes\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eThe 28-day risk of mortality among groups was analyzed using Kaplan-Meier survival analysis curves, based on the serum osmolality quintile as presented. Patients had a higher short-term survival rate when serum osmolality was between 306\u0026ndash;314 mmol/L, a lower survival rate when serum osmolality was below 297 mmol/L(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Cox regression analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the Cox regression conducted to assess the risk of all-cause death in patients with DKA. In the unadjusted model, the risk of mortality was increased by 64% (HR 1.64, 95%CI 1.20\u0026ndash;2.24) in Q1 when compared with serum osmolality in Q3, and the risk of mortality was elevated by 93% (HR 1.93, 95%CI 1.43\u0026ndash;2.61) in the highest quintile Q5. In model 2 (adjusted\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 according to osmolality levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eBaseline serum osmolality levels (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1026\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1(\u0026lt;\u0026thinsp;297)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;205\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2(297\u0026ndash;305)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;205\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3(306\u0026ndash;314)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;205\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4(315\u0026ndash;326)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;205\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ5(\u0026gt;\u0026thinsp;326)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;206\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e564 (55.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (43.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (51.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (59.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126 (61.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e122 (59.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature,℃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR,bpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP,mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134\u0026thinsp;\u0026plusmn;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131\u0026thinsp;\u0026plusmn;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP,mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPO2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC,K/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet,K/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236\u0026thinsp;\u0026plusmn;\u0026thinsp;102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228\u0026thinsp;\u0026plusmn;\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245\u0026thinsp;\u0026plusmn;\u0026thinsp;103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u0026thinsp;\u0026plusmn;\u0026thinsp;116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243\u0026thinsp;\u0026plusmn;\u0026thinsp;107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e218\u0026thinsp;\u0026plusmn;\u0026thinsp;92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin,g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e144\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311\u0026thinsp;\u0026plusmn;\u0026thinsp;216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206\u0026thinsp;\u0026plusmn;\u0026thinsp;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223\u0026thinsp;\u0026plusmn;\u0026thinsp;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274\u0026thinsp;\u0026plusmn;\u0026thinsp;118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e333\u0026thinsp;\u0026plusmn;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e520\u0026thinsp;\u0026plusmn;\u0026thinsp;344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUreanitrogen,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum osmolality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e320\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e347\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAniongap,mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity of illness score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPSIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 2 Diabetes,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463 (45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (41.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (40.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (44.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96 (46.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109 (52.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType 1 Diabetes,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478 (46.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (51.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108 (52.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95 (46.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93 (45.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77 (37.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455 (44.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (25.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (30.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (43.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118 (57.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133 (64.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348 (33.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (30.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (27.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (41.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (35.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72 (35.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (18.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (15.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (17.1 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (26.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53 (25.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMI,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (8.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (5.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (5.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (15.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27 (13.1 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (6.1 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (6.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (9.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17 (8.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (4.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (2.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (2.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (6.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (5.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233 (22.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (6.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (19.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (20.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (31.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73 (35.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579 (56.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (35.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (45.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (61.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (68.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e145 (70.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI stage,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=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199 (19.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (11.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (23.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (21.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (23.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36 (17.5 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (21.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (15.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (14.1 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (29.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 (22.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53 (25.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (15.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (8.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (10.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (22.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56 (27.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment during hospitalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (4.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.0 )\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\u003e6 (2.9 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (8.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (9.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189 (18.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (9.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (10.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (19.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52 (25.4 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57 (27.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e535 (52.1 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (50.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (51.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105 (51.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106 (51.7 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e115 (55.8 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluid balance 24h,ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4043\u0026thinsp;\u0026plusmn;\u0026thinsp;3194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3397\u0026thinsp;\u0026plusmn;\u0026thinsp;2571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3488\u0026thinsp;\u0026plusmn;\u0026thinsp;2304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4195\u0026thinsp;\u0026plusmn;\u0026thinsp;3554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4533\u0026thinsp;\u0026plusmn;\u0026thinsp;3250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4600\u0026thinsp;\u0026plusmn;\u0026thinsp;3842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU-day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality,n%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e458 (44.6 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (51.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (32.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (31.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99 (48.3 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e124 (60.2 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eHR:heart rate; SBP:systolic blood pressure; DBP:diastolic blood pressure; SPO2:saturation of peripheral oxygen; WBC:white blood cell; SOFA:Sequential Organ Failure Assessment; APS III:Acute Physiology Score III; SAPSII:Simplified Acute Physiology Score II; GCS:Glasgow Coma Scale; Charlson:Charlson Comorbidity Index; COPD: chronic obstructive pulmonary disease; AMI:acute myocardial infarction; CKD:chronic kidney disease; AKI:acute kidney injury; CRRT:continuous renal replacement therapy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003efor age, gender, temperature, heart rate, SBP, DBP, SPO2), the results remained significant. In model 3 (adjusted for age, gender, temperature, heart rate, SBP, DBP, SPO2 and potential confounders), the risk of mortality was elevated by 88% (HR 1.88, 95%CI 1.35\u0026ndash;2.61) in Q1 and 46% (HR 1.46, 95%CI 1.01\u0026ndash;2.01) in Q5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between serum osmolality and 28-days all-cause mortality in patients with DKA\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModle 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModle 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModle 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum osmolality quintiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (\u0026lt;\u0026thinsp;297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64(1.20\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74(1.26\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88(1.35\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (297\u0026ndash;305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03(0.73\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06(0.75\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13(0.79\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (306\u0026ndash;314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (315\u0026ndash;326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55(1.13\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51(1.10\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41(1.02\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5 (\u0026gt;\u0026thinsp;326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93(1.43\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88(1.39\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46(1.01\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cp\u003eModel 1 unadjusted\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003cp\u003eModel 2 adjusted for Age, gender, temperature, heart rate, SBP, DBP, SPO2\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003cp\u003eModel 3 adjusted for Model 2\u0026thinsp;+\u0026thinsp;laboratory tests\u0026thinsp;+\u0026thinsp;complications\u0026thinsp;+\u0026thinsp;severity of illness score\u0026thinsp;+\u0026thinsp;treatments during hospitalization\u003c/p\u003e \u003cp\u003eHR: hazard ratio, CI: confdence interval\u003c/p\u003e \u003cp\u003eA lower and similar risk of mortality in Q2 and Q3 of osmolarity was observed. As a result, Q2 and Q3 were merged, and Q2\u0026thinsp;+\u0026thinsp;Q3 served as a reference for a multivariable Cox regression analysis. After adjusting for the covariates in model 3, we found that there was a 70% (HR 1.70, 95%CI 1.31\u0026ndash;2.21) higher risk of mortality at 28 days in Q1 compared to Q2\u0026thinsp;+\u0026thinsp;Q3, and a 51% (HR 1.51, 95%CI 1.16\u0026ndash;1.96) ) higher in the Q5 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between serum osmolality and 28-days all-cause mortality in patients with DKA after combining Q2 and Q3\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModle 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModle 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModle 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum osmolality quintiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (\u0026lt;\u0026thinsp;297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57(1.21\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63(1.26\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70(1.31\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u0026thinsp;+\u0026thinsp;Q3 (297\u0026ndash;314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (315\u0026ndash;326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51(1.16\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46(1.11\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33(1.01\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5 (\u0026gt;\u0026thinsp;326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86(1.46\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78(1.39\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51(1.16\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cp\u003eModel 1 unadjusted\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003cp\u003eModel 2 adjusted for Age, gender, temperature, heart rate, SBP, DBP, SPO2\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section4\"\u003e \u003cp\u003eModel 3 adjusted for Model 2\u0026thinsp;+\u0026thinsp;laboratory tests\u0026thinsp;+\u0026thinsp;complications\u0026thinsp;+\u0026thinsp;severity of illness score\u0026thinsp;+\u0026thinsp;treatments during hospitalization\u003c/p\u003e \u003cp\u003eHR: hazard ratio, CI: confdence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThreshold effect analysis for the relationship between serum osmolality levels and 28-day mortality\u003c/h2\u003e \u003cp\u003eThe study employed a restricted cubic splines regression model to investigate the relationship between serum osmolality and the risk of 28-day mortality. After adjusting for covariates in model 3, we found a U-shaped relationship between serum osmolality and 28-day mortality in curve ftting (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additional examination employing a segmented regression model indicated a turning point at a serum osmolality level of 308.9 mmol/L in relation to the risk of 28-day mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). When osmolality\u0026thinsp;\u0026lt;\u0026thinsp;308.9 mmol/L, the 28-day mortality risk decreased by 5.4% (HR 0.946, 95% CI 0.938\u0026ndash;0.959) for every 1 mmol/L increase. At osmolality\u0026thinsp;\u0026ge;\u0026thinsp;308.9 mmol/L, there was a 3.5% (HR 1.035, 95% CI 1.030\u0026ndash;1.039) increase in the 28-day mortality risk for every 1 mmol/L increase in osmolality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold efect analysis of the relationship between serum osmolality and 28-day mortality of patients with DKA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold of osmolality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;308.9 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.938\u0026ndash;0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;308.9 mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.030\u0026ndash;1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonlinear test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data were adjusted for all the covariates of Model 3\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003cp\u003eHR: hazard ratio, CI: confdence interval\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eIn order to confirm the stability of the U-shaped association between serum osmolality and mortality at 28 days, we conducted curve fitting stratified by diabetes, AKI, CKD, hypertension, and sepsis. The results demonstrated that this U-shaped association remained consistent across various subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study represents the first investigation into the association between serum osmolality and the risk of 28-day mortality in patients with DKA. Our findings indicate that serum osmolality is associated with an increased risk of 28-day mortality, exhibiting a U-shaped relationship. This association remains significant even after adjusting for potential confounding factors.\u003c/p\u003e \u003cp\u003eAltered serum osmolality levels, whether lower or higher, are linked to a heightened risk of mortality. When osmolality\u0026thinsp;\u0026lt;\u0026thinsp;308.9 mmol/L, the 28-day mortality risk decreased by 5.4% (HR 0.946, 95% CI 0.938\u0026ndash;0.959) for every 1 mmol/L increase. At osmolality\u0026thinsp;\u0026ge;\u0026thinsp;308.9 mmol/L, there was a 3.5% (HR 1.035, 95% CI 1.030\u0026ndash;1.039) increase in the 28-day mortality risk for every 1 mmol/L increase in osmolality. The risk of mortality was lower at osmolarity of 297\u0026ndash;314 mmol/L. Consequently, these results indicate that serum osmolality holds promise as a valuable decision-making tool for clinicians and may serve as an independent prognostic factor in patients with DKA. Our study found that lower serum osmolality is associated with a significantly higher risk of mortality compared to higher serum osmolality. Patients with DKA may experience a shift of water from intracellular to extracellular spaces due to hyperglycemia, resulting in dilutional hyponatremia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In such cases, patients may suffer from severe dehydration, but the absence of a significant increase in osmolality may mask the true extent of dehydration, leading to inadequate treatment and an increased risk of mortality. In addition, rapid declines in serum glucose concentration without concurrent rise in serum sodium may precipitate cerebral edema by creating an osmotic gradient that drives water into brain cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe level of serum osmolality is determined by the number of solute particles present in a given volume of solution, independent of the type and size of the solute particles [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Serum osmolality plays a critical role in regulating water balance within and outside of cells and blood vessels, maintaining normal cell morphology, and ensuring proper water distribution in blood vessels, thereby sustaining blood volume [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Numerous studies have previously investigated the predictive value of serum osmolality across various clinical conditions. Nicholson conducted a retrospective analysis examining the correlation between the initial calculated blood serum osmolality and the risk of 30-day in-hospital mortality among 24232 patients admitted to the emergency department. Their findings revealed that the first recorded serum osmolality in the emergency department was associated with an elevated risk of 30-day mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Shen et al. conducted a large retrospective cohort study demonstrating that a hyperosmolar state at the time of admission was associated with increased mortality among critically ill patients suffering from cardiac, cerebral, vascular, and gastrointestinal diseases in the ICU, with a threshold of 300 mmol/L [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, the study by Holtfreter et al. reached a comparable conclusion, indicating that elevated serum osmolarity is associated with an increased risk of mortality among critically ill individuals.\u003c/p\u003e \u003cp\u003eThe mortality prediction value of serum osmolarity falls between that of the APACHE II and SOFA scores. Notably, when the outcome prediction is restricted to long-term ICU patients, serum osmolality demonstrates superior performance compared to both the APACHE II and SOFA scores. Furthermore,serumosmolality is a more cost-effective and rapid measure than the clinical scoring systems currently employed in ICUs for predicting disease severity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Increased serum osmolarity plays a key role in activating intrarenal (polyol-fructokinase) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and extrarenal (vasopressin) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] pathways, leading to renal injury. Elevated serum osmolality and reduced serum osmolality are viewed as independently linked to a greater likelihood of developing acute kidney injury (AKI) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].We found that as serum osmolarity increased, the incidence of AKI also increased, reaching 70.4% in the Q5 (\u0026gt;\u0026thinsp;326mmol/L) group, and the utilization rate of CRRT was the highest, reaching 9.3%.\u003c/p\u003e \u003cp\u003eSerum osmolality is maintained in a narrow range both by neuroendocrine functions through hypothalamic-pituitary axis and the kidney on controlling thirst and regulating water and electrolyte balance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Serum osmolality is often elevated in patients with DKA. Under normal circumstances, the serum osmolarity ranges from 280-310mmol/L. The average effective serum osmolarity among patients with DKA is (299.73\u0026thinsp;\u0026plusmn;\u0026thinsp;13.99) mmol/L [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It has been reported that high osmolality is independently associated with mortality in DKA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Hyperosmolality primarily arises from hyperglycemia in DKA, leading to significant impacts on CNS function and consciousness. The most critical complication of DKA is brain edema, and untreated DKA can result in cardiac arrest and death. Among the various factors contributing to altered mental status, such as hyperglycemia, ketonemia, or metabolic acidosis, serum osmolality serves as the key determinant of the level of awareness in these patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, abnormal serum osmolality should be promptly diagnosed and treated clinically.\u003c/p\u003e \u003cp\u003eOverall, this study used a large and publicly available critical-care database to first assess the relationship between serum osmolality and the risk of 28-day mortality in patients with DKA. However, our study has several limitations. (1) As this study was \u003cb\u003eobservational\u003c/b\u003e in nature, we were unable to definitively establish causality. Despite the use of multivariate adjustment and subgroup analyses, there is still a possibility of residual confounding factors influencing the clinical outcomes. (2) Serum osmolarity obtained from the initial measurements of blood glucose, sodium, potassium, and ureanitrogen might not completely reflect the overall change in the body. It is important to consider this limitation when interpreting the results. (3) The estimation of serum osmolality was conducted through a formula instead of directmeasurement, which hindered the ability to identify \u0026lsquo;delta osmolality\u0026rsquo; or the \u0026lsquo;osmolal gap\u0026rsquo;. Even though the formula was selected carefully, it might not accurately represent the actual osmolality values. (4) Previous studies have shown that anion gap and ketone are important factors affecting patient prognosis. MIMIC-IV lacks ketone the data of ketone, so we were unable to perform relevant statistical analyses. (5) The individuals involved in the study were patients in the ICU, and additional validation is necessary to assess the relevance of serum osmolality for patients who are admitted to general wards. (6) As the study data came from a single center (the MIMIC-IV database), our findings may not generalise to different healthcare settings, particularly those with different ICU resources or patient demographics. (7) With electronic health records data, there may be potential inaccuracies or inconsistencies in data entry, missing data, or variable measurements that may affect study validity. Therefore, further research is essential to comprehensively explore how this bias impacts clinical outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research demonstrates a U-shaped relationship between serum osmolality levels and 28-day all-cause mortality in patients with DKA following ICU admission, while accounting for confounding variables. Due to its simplicity, accessibility, and cost-effectiveness, measuring serum osmolality highlights the importance of managing osmolality, assisting physicians in recognizing high-risk patients. However, further studies are crucial to determine if interventions targeting serum osmolality levels can positively impact clinical outcomes within this group.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Jian Liao; Methodology, Dingyu Lu; Validation, Zhi Liang; Formal Analysis, Jian Liao; Data Curation, Hongwei Tan; Writing-Original Draft Preparation, Jian Liao; Writing-Review \u0026amp; Editing, Dingyu Lu; Supervision, Maojuan Wang. All authors read and approved the final draft.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe are particularly grateful to all the people who have given us help on our article.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files（MIMIC IV 3.0： https://mimic.mit.edu).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDhatariya KK,Glaser NS,Codner E, et al. Diabetic ketoacidosis. Nat Rev Dis Primers. 2020;6 (1):40.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Age-adjusted hospital discharge rates for diabetic ketoacidosis as first-listed diagnosis per 10,000 population, United States, 1988-2009. CDC https://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html (2013).\u003c/li\u003e\n\u003cli\u003eDesai, D., Mehta, D., Mathias, P., Menon, G. \u0026amp; Schubart, U. K. Health care utilization and burden of diabetic ketoacidosis in the U.S. over the past decade: a nationwide analysis. Diabetes Care. 2018; 41:1631-1638.\u003c/li\u003e\n\u003cli\u003eDhatariya, K.K., Skedgel, C. \u0026amp; Fordham, R. The cost of treating diabetic ketoacidosis in the UK: a national survey of hospital resource use. Diabet. Med. 2017;34:1361-1366.\u003c/li\u003e\n\u003cli\u003eDhatariya, K. K. et al. The cost of treating diabetic ketoacidosis in an adolescent population in the UK: a national survey of hospital resource use. Diabet. Med. 2019;36:982-987.\u003c/li\u003e\n\u003cli\u003eVellanki, P. \u0026amp; Umpierrez, G. E. Increasing hospitalizations for DKA: A need for prevention programs. Diabetes Care. 2018;41:1839-1841.\u003c/li\u003e\n\u003cli\u003eKitabchi, A. E., Umpierrez, G. E., Miles, J. M. \u0026amp; Fisher, J. N. Hyperglycemic crises in adult patients with diabetes. Diabetes Care. 2009;32:1335-1343\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;y\u0026uuml;kkarag\u0026ouml;z B, Bakkaloğlu SA. Serum osmolality and hyperosmolar states. Pediatr Nephrol. 2023;38:1013-25.\u003c/li\u003e\n\u003cli\u003eZou Q, Li J, Lin P, et al. Association between serum osmolality and 28-day all-cause mortality in patients with heart failure and reduced ejection fraction:a retrospective cohort study from the MIMIC-IV database. Front Endocrinol (Lausanne). 2024;15:1397329.\u003c/li\u003e\n\u003cli\u003eLuo X, Tang Y, Shu Y, et al. Association between serum osmolality and deteriorating renal function in patients with acute myocardial infarction: analysis of thenMIMIC-IV database. BMC Cardiovasc Disord. 2024;24 (1):490. \u003c/li\u003e\n\u003cli\u003eBrathwaite S,Macdonald RL. Current management of delayed cerebral ischemia: update from results of recent clinical trials. Transl Stroke Res. 2014;5(2):207-26.\u003c/li\u003e\n\u003cli\u003eLiang M, Xu Y, Ren X, et al. The U-shaped association between serum osmolality and 28-day mortality in patients with sepsis: a retrospective cohort study.Infection. 2024;52(5):1931-1939.\u003c/li\u003e\n\u003cli\u003eJohnson, A., Bulgarelli, L., Pollard, T., Gow, B., Moody, B., Horng, S., Celi, L. A., \u0026amp; Mark, R. (2024). MIMIC-IV (version 3.0). PhysioNet. https://doi.org/10.13026/hxp0-hg59.\u003c/li\u003e\n\u003cli\u003eAbbas E. Kitabchi, Guillermo E. Umpierrez, John M. Miles, Joseph N. Fisher; Hyperglycemic Crises in Adult Patients With Diabetes. Diabetes Care.2009;32(7): 1335-1343.\u003c/li\u003e\n\u003cli\u003eWolfsdorf JI, Glaser N, Agus M, et al. ISPAD Clinical Practice Consensus Guidelines 2018:Diabetic ketoacidosis and the hyperglycemic hyperosmolar state.Pediatr Diabetes. 2018;19(Suppl.27):155-177.\u003c/li\u003e\n\u003cli\u003eStar RA. Southwestern internal medicine conference: hyperosmolar states. Am J Med Sci. 1990;300:402-12. \u003c/li\u003e\n\u003cli\u003eArgyropoulos C, et al. Hypertonicity: pathophysiologic concept and experimental studies. Cureus. 2016;8:e596.\u003c/li\u003e\n\u003cli\u003eNicholson T, Bennett K, Silke B. Serum osmolarity as an outcome predictor in hospital emergency medical admissions. Eur J Intern Med. 2012;23(2):e39-43.\u003c/li\u003e\n\u003cli\u003eShen Y, Chen X, Ying M, et al. Association between serum osmolality and mortality in patients who are critically ill:a retrospective cohort study.\u003c/li\u003e\n\u003cli\u003eHoltfreter B, Bandt C, Kuhn SO, et al. Serum osmolarity and outcome in intensive care unit patients. Acta Anaesthesiol Scand. 2006;50(8):970-977.\u003c/li\u003e\n\u003cli\u003eRoncal Jimenez CA, Ishimoto T, Lanaspa MA, et al. Fructokinase activity mediates dehydration-induced renal injury. Kidney Int. 2014;86(2):294-302. \u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-Arroyo FE, Tapia E, Blas-Marron MG, et al. Vasopressin Mediates the Renal Damage Induced by Limited Fructose Rehydration in Recurrently Dehydrated Rats. Int J Biol Sci. 2017;13 (8):961-975.\u003c/li\u003e\n\u003cli\u003eYang J, Cheng Y, Wang R, Wang B. Association between serum osmolality and acute kidney injury in critically ill patients: a retrospective cohort study. Front Med. 2021;8:745803.\u003c/li\u003e\n\u003cli\u003eSeay NW, Lehrich RW, Greenberg A. Diagnosis and management of disorders of body tonicity-hyponatremia and hypernatremia: core curriculum. Am J Kidney Dis. 2020;75:272-286.\u003c/li\u003e\n\u003cli\u003eWu XY , She DM , Wang F , et al. Clinical profiles, outcomes and risk factors among type 2 diabetic inpatients with diabetic ketoacidosis and hyperglycemic hyperosmolar state: a hospital-based analysis over a 6-year period. BMC Endocr Disord. 2020; 20(1):182.\u003c/li\u003e\n\u003cli\u003eBlank SP, Blank RM, Ziegenfuss MD. The importance of hyperosmolarity in diabetic ketoacidosis. Diabet Med. 2020;37(12):2001-2008.\u003c/li\u003e\n\u003cli\u003eRondon-Berrios H, Argyropoulos C, Ing TS, et al. Hypertonicity: clinical entities, manifestations and treatment. World J Nephrol. 2017;6:1-13.\u003c/li\u003e\n\u003cli\u003eAgrawal S, Baird GL, Quintos JB, et al. Pediatric diabetic ketoacidosis with hyperosmolarity: clinical characteristics and outcomes. Endocr Pract. 2018;24:26-732.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"DKA, serum osmolality, U-shaped correlation, Intensive Care Unit","lastPublishedDoi":"10.21203/rs.3.rs-5869644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5869644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e The purpose of this study was to investigate the relationship between serum osmolality levels and 28-day mortality in patients with DKA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e Data for this observational cohort study were obtained from the MIMIC-IV3.0 database. The participants were divided into five groups based on the serum osmolality quintiles. The primary outcome was 28-day mortality. We employed Cox proportional hazards regression analysis and threshold effect analysis to assess the relationship between serum osmolality levels and 28-day mortality in patients with DKA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e The study included 1026 patients; the mean age was 52 years, 55.0% were male. Our findings indicate that serum osmolality is associated with an increased risk of 28-day mortality, exhibiting a U-shaped relationship. Altered serum osmolality levels, whether lower or higher, are linked to a heightened risk of mortality. When \u0026nbsp;osmolality \u0026lt; 308.9 mmol/L, the 28-day mortality risk decreased by 5.4% (HR 0.946, 95% CI 0.938–0.959) for every 1 mmol/L increase. At osmolality ≥ 308.9 mmol/L, there was a 3.5% (HR 1.035, 95% CI 1.030–1.039) increase in the 28-day mortality risk for every 1 mmol/L increase in osmolality. The risk of mortality was lower at osmolarity of 297–314 mmol/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e A U-shaped correlation between initial serum osmolality and 28-days all-cause mortality in patients with DKA was identified. These results underscore serum osmolality’s critical role in early mortality among patients with DKA.\u003c/p\u003e","manuscriptTitle":"Association of serum osmolality levels with all-cause mortality risk in patients with DKA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 11:14:46","doi":"10.21203/rs.3.rs-5869644/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-09T17:16:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-09T14:16:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123939127638230786398938371580464855636","date":"2025-04-21T13:06:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-20T13:54:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60199455622508325987036723804908911192","date":"2025-04-18T18:54:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-18T18:36:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-18T08:46:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-08T17:00:36+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":"331e4fec-cea2-4913-8202-97ef9b1c6548","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47483252,"name":"Health sciences/Endocrinology"},{"id":47483253,"name":"Health sciences/Risk factors"},{"id":47483254,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"}],"tags":[],"updatedAt":"2025-09-01T16:04:07+00:00","versionOfRecord":{"articleIdentity":"rs-5869644","link":"https://doi.org/10.1038/s41598-025-14405-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-08-29 15:58:21","publishedOnDateReadable":"August 29th, 2025"},"versionCreatedAt":"2025-04-28 11:14:46","video":"","vorDoi":"10.1038/s41598-025-14405-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-14405-1","workflowStages":[]},"version":"v1","identity":"rs-5869644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5869644","identity":"rs-5869644","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}