Dynamic BUN Patterns in the ICU: Risk Stratification and Prognosis Following Cardiac Surgery

Dynamic BUN Patterns in the ICU: Risk Stratification and Prognosis Following Cardiac Surgery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamic BUN Patterns in the ICU: Risk Stratification and Prognosis Following Cardiac Surgery Hao Yuan, Ting Zhang, Qiang Li, Jianggui Shan, Song Xue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6794317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2026 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted 9 You are reading this latest preprint version Abstract Background Postoperative blood urea nitrogen (BUN) dynamics may reflect multisystem physiological stress after cardiac surgery, but their prognostic value remains underexplored. Methods This retrospective cohort study analyzed adult cardiac surgery patients from the MIMIC-IV database. BUN trajectories in the first 0–96 hours post-admission to ICU after surgery were categorized by Latent class mixed models (LCMM). The study outcome was postoperative 360-day all-cause mortality. Kaplan-Meier survival analysis model and the log-rank test were used to evaluate the differences in the outcome among different trajectories. The Cox proportional hazards model was then applied to find the relationship between trajectories and the outcome, and to calculate the hazard ratio (HR). Finally, subgroup analysis was conducted to verify the stability of the results. Results A total of 1146 eligible patients were enrolled in this study, among whom 144 (12.6%) died during the follow-up period. 4 distinct trajectories were finally identified, with significant differences in postoperative 360-day all-cause mortality (log-rank p < 0.001). The Cox proportional hazards model revealed that, compared with the stable low-level trajectory (reference group), gradual increase trajectory (HR = 2.48, 95% CI: 1.70–3.60), rapid decline from high level trajectory (HR = 5.42, 95% CI 3.32–8.85), and marked critical elevation trajectory (HR = 3.25, 95% CI 1.54–6.87) were all associated with higher mortality risks. These differences persisted even after adjusting for variables in different models. In subgroup analysis the results persisted across most subgroups without any notable interaction (all p for interaction > 0.05). Conclusion Early dynamic BUN patterns after cardiac surgery, better stratified patient mortality risk, and may be useful for the early risk assessment, personalized monitoring and prognostication. cardiac surgery blood urea nitrogen trajectory MIMIC-IV database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 10 Figure 12 Introduction Cardiac surgery remains an indispensable intervention for cardiovascular diseases, with over 2 million procedures performed annually worldwide ( 1 ). Despite technological advances, postoperative 1-year mortality still persists at nearly 10%, driven partly by uncaptured physiological insults during the early recovery phase ( 2 , 3 ). This highlights the urgent need for dynamic biomarkers that can reflect real-time system stress, rather than relying solely on a certain static clinical data. Blood urea nitrogen (BUN) possesses outstanding kinetic advantages due to its relatively short plasma half-life (2–8 hours), which allows for earlier detection of renal hypoperfusion compared with creatinine ( 4 , 5 ). Additionally, BUN is highly sensitive to changes in cardiac output, the state of the sympathetic nervous system, and protein catabolism, thereby reflecting a more comprehensive statue of multiple physiological systems ( 6 ). Previous studies have demonstrated that constructing BUN trajectories can offer robust predictive value for mortality risk in patients with acute pancreatitis ( 7 ). Therefore, the dynamic patterns of BUN may provide more refined prognostic information compared with single-timepoint thresholds. For intensive care unit (ICU) patients, BUN is a readily accessible biomarker and exhibits a dynamic developmental pattern. Several studies have demonstrated the predictive value of BUN for postoperative mortality risk after cardiac surgery ( 8 – 10 ). However, longitudinal data on BUN in such patients are lacking. Therefore, this study aims to investigate the impact of early postoperative BUN dynamics for cardiac surgery patients on 360-day survival using Latent class mixed models (LCMM). Patients’ data was extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Materials and Methods Data Source and Study Population The MIMIC-IV database (version 3.1) is a publicly available critical care database that includes 364,627 patient records from 2008–2022. The Institutional Review Board of MIT and Beth Israel Deaconess Medical Center provided a waiver of the informed consent requirement and authorized the data sharing program ( 11 ). One of the authors fulfilled the requirements of the CITI Program to gain access to the data (Hao Y, Certification number: 53244072). This study was approved by the Ethical Committee of Xuzhou No.1 People's Hospital (XYYLL2025-089). Cardiac surgery patients were identified by use of ICD-9 and ICD-10 procedure codes (Supplementary Material Table S 1). The inclusion criteria were: ( 1 ) cardiac surgery patients with first ICU admission, ( 2 ) adult patients (age ≥ 18 years), ( 3 ) ICU stay ≥ 96 hours postoperatively, and ( 4 ) availability of ≥ 4 BUN measurements within the first 96 hours. Data Preparation and Study endpoint The data extraction and collection were done with the aid of PostgreSQL (version 16.0.6) and Navicat Premium (version 17). Dynamic data consisted of four postoperative BUN measurements collected at 24-hour intervals (at 24h, 48h, 72h, and 96h after ICU admission), with the first recorded value within each time window used for analysis. Covariates with > 20% missing values were excluded. Based on this, static data finally comprised: ( 1 ) baseline demographics: age, gender, race, height, weight; ( 2 ) comorbidities: hypertension, diabetes mellitus (DM), myocardial infarction (MI), congestive heart failure (CHF), peripheral vascular disease (PVD), chronic pulmonary disease (CPD), renal disease (RD), Charlson Comorbidity Index (CCI); ( 3 ) the first laboratory values following ICU admission: red blood cell count, white blood cell count, platelet count, hemoglobin, serum creatinine (Scr), potassium, lactate; ( 4 ) 24-hour average vital signs after admission to the ICU: blood pressure (BP), heart rate (HR), respiratory rate (RR); ( 5 ) ICU severity scores: Simplified Acute Physiology Score II (SAPS II), Outcome Prediction in Intensive Care (OASIS), Glasgow Coma Scale (GCS), Systemic Inflammatory Response Syndrome (SIRS); ( 6 ) postoperative complications: delirium and sepsis; ( 7 ) time: hospital length of stay and ICU length of stay. Missing data in the remaining variables (< 20%) were imputed (multiple imputation, 5 imputations, chained equations) under missing-at-random assumption. Meanwhile, we winsorized extreme values of BUN. This approach ensured standardized extraction of variables, minimized bias from missing data, and aligned with established critical care research methodologies. The main outcome was all-cause mortality at 360 days post-surgery that was obtained from the database. Statistical Analysis The statistical analyses were achieved in R (version 4.4.2) and IBM SPSS (version 24). The descriptive statistics of categorical data are displayed in terms of n (%) and the continuous data are displayed as mean ± SD (normally distributed) or median (IQR) (non-normally distributed), the normality was measured by the Kolmogorov-Smirnov test. For continuous variables, comparisons between classes were conducted using analysis of variance or the Student t-test for normally distributed data, and the Kruskal-Wallis H test or the Mann-Whitney U test for non-normally distributed data. Categorical variables were analyzed using the chi-square test. When the p value less than 0.05, it was considered statistically significant. LCMM was used for classifying BUN trajectories by the R lcmm package. Seven candidate models of different trajectory shapes with different number of classes were explored according to the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the entropy. The final model was selected by optimizing both statistical fit and clinical interpretability. Survival analyses included Kaplan-Meier curves with log-rank tests to compare 360-day survival across trajectories, and Cox proportional hazards regression to assess mortality risk associations according to three different models. Furthermore, we applied subgroup analysis and forest plots to certify the stability of the results. Results Patients Selection and LTA of BUN We finally selected 1146 patient that were eligible for analysis based on the inclusion criteria from the MIMIC-IV database (Fig. 1 ). We fitted LCMM to the postoperative BUN measurements (0–96 hours) and considered 7 candidate models (Table 1 ). Although the 4-class model did not produce the lowest BIC value, the relative difference in BIC values decreased significantly beyond the 4-class model. This suggested that additional classes did not provide more improvement in model fit. We further calculate the average posterior probability of the model which was greater than 0.85, indicating that the model fit was decent (Supplementary Material Table S 2). Additionally, clinical interpretability of the 4-class model was better. Thus, we finally selected this model to further research. In this model, the four BUN trajectories were characterized as follows: Trajectory 1 (32.98%) represented a pattern of gradual increase; Trajectory 2 (57.59%) was characterized by a stable low-level; Trajectory 3 (6.28%) was defined by a rapid decline from high level; and Trajectory 4 (3.14%) was characterized by a marked critical elevation (Fig. 2 ). Table 1 Statistics for choosing the best model of classes. G Loglik AIC BIC Entropy %Class1 %Class2 %Class3 %Class4 %Class5 %Class6 %Class7 1 -15920.24 31854.480 31889.790 1.000 100.00000 2 -15534.87 31093.743 31154.272 0.646 75.39267 24.60733 3 -15386.18 30806.363 30892.112 0.740 68.93543 7.766143 23.29843 4 -15284.04 30612.081 30723.050 0.750 32.98429 57.59162 6.282723 3.141361 5 -15236.22 30526.436 30662.625 0.773 53.40314 36.47469 4.188482 2.356021 3.577661 6 -15187.18 30438.355 30599.764 0.731 35.16579 38.13264 4.363002 17.53927 2.268761 2.530541 7 -15163.51 30401.014 30587.643 0.723 33.33333 34.81675 7.155323 3.141361 16.75393 2.356021 2.443281 AIC, Akaike information criterion; BIC, the Bayesian information criterion Baseline characteristics Baseline characteristics showed the differences between four trajectory groups (Table 2 ). Patients in Trajectory 1 were older (75.78 [68.80, 81.56] years, p < 0.001) and had a higher score of CCI (7.00 [5.00, 9.00], p < 0.001). Trajectory 3 had the highest prevalence of CHF (73.6%, p < 0.001), postoperative delirium rate (51.4%, p = 0.012), and the lowest postoperative BP. Trajectory 4 exhibited both the most frequent DM (63.9%, p < 0.001) and RD (86.1%, p < 0.001), coupled with postoperative declined hemoglobin (8.10 [7.25, 9.33] g/dL, p < 0.001), elevated Scr (3.60 [1.55, 5.60] mg/dL, p < 0.001), and the most impaired ICU severity scores. Trajectory 3 had longer total hospital stays (17.61 [9.98, 25.44] days, p < 0.001), while Trajectory 4 required extended ICU care (7.36 [4.81, 12.52] days, p < 0.001), underscoring their high-risk status. A total of 144 patients died during the follow-up period. Compared with survival patients, those who died were older, had a higher prevalence of comorbidities (CHF, PVD, CPD, RD, and had a higher CCI score. On the first day after surgery in the ICU, non-survival patients had higher serum creatinine levels, as well as higher ICU severity scores. Additionally, the incidence of delirium and sepsis was higher in the non-survival group, and they had longer hospital and ICU lengths of stay (Table 3 ). Table 2 Characteristics of patients in the 4 trajectories. Variables Trajectory 1 (n = 378) Trajectory 2 (n = 660) Trajectory 3 (n = 72) Trajectory 4 (n = 36) p Baseline demographics Age, (years) 75.78 [68.80, 81.56] 71.69 [63.30, 78.82] 70.05 [63.45, 79.04] 71.63 [60.36, 75.87] < 0.001 Gender, female, n (%) 139 (36.8) 267 (40.5) 27 (37.5) 7 (19.4) 0.069 Race, white, n (%) 286 (75.7) 461 (69.8) 48 (66.7) 23 (63.9) 0.016 Height, (cm) 170.00 [160.00, 178.00] 170.00 [163.00, 178.00] 170.00 [163.00, 178.00] 174.00 [167.25, 180.00] 0.27 Weight, (Kg) 83.20 [70.00, 98.00] 81.00 [68.15, 96.60] 82.00 [68.75, 98.18] 88.60 [75.75, 100.65] 0.188 Comorbidities Hypertension, n (%) 115 (30.4) 436 (66.1) 10 (13.9) 6 (16.7) < 0.001 Diabetes mellitus, n (%) 195 (51.6) 235 (35.6) 41 (56.9) 23 (63.9) < 0.001 Myocardial infarction, n (%) 136 (36.0) 204 (30.9) 19 (26.4) 16 (44.4) 0.095 Congestive heart failure, n (%) 247 (65.3) 321 (48.6) 53 (73.6) 20 (55.6) < 0.001 Peripheral vascular disease, n (%) 91 (24.1) 147 (22.3) 22 (30.6) 7 (19.4) 0.401 Chronic pulmonary disease, n (%) 115 (30.4) 188 (28.5) 22 (30.6) 13 (36.1) 0.735 Renal disease, n (%) 233 (61.6) 103 (15.6) 59 (81.9) 31 (86.1) < 0.001 Charlson comorbidity index 7.00 [5.00, 9.00] 5.00 [3.75, 6.00] 7.00 [6.00, 8.25] 7.00 [6.00, 8.00] < 0.001 First laboratory values after admission to ICU Red blood cell count, (×10 12 /L) 2.81 [2.50, 3.23] 2.94 [2.58, 3.35] 2.94 [2.49, 3.33] 2.73 [2.33, 3.07] 0.002 White blood cell count, (×10 9 /L) 12.80 [9.60, 17.60] 12.70 [9.50, 17.30] 12.20 [8.47, 16.60] 9.90 [7.65, 13.58] 0.025 Platelet count, (×10 9 /L) 137.00 [108.25, 173.00] 136.00 [105.00, 172.00] 146.50 [114.75, 174.25] 115.50 [90.75, 151.75] 0.077 Hemoglobin, (g/dL) 8.40 [7.50, 9.40] 8.80 [7.80, 10.10] 8.40 [7.47, 10.00] 8.10 [7.25, 9.33] < 0.001 Serum creatinine, (mg/dL) 1.20 [1.00, 1.67] 0.80 [0.70, 1.00] 2.15 [1.40, 4.12] 3.60 [1.55, 5.60] < 0.001 Potassium, (mmol/L) 4.40 [4.00, 4.80] 4.30 [3.98, 4.70] 4.40 [3.98, 4.90] 4.70 [4.40, 5.32] 0.001 Lactate, (mmol/L) 2.00 [1.50, 2.98] 2.20 [1.60, 3.00] 2.10 [1.58, 3.30] 2.20 [1.67, 3.25] 0.611 ICU first 24-hour average vital signs Systolic pressure, (mmHg) 110.16 [104.93, 117.38] 108.68 [104.01, 114.51] 107.15 [102.81, 113.52] 107.10 [104.00, 114.76] 0.014 Diastolic pressure, (mmHg) 52.77 ± 6.93 56.19 ± 6.64 53.10 ± 7.97 53.50 ± 6.84 < 0.001 Mean arterial pressure, (mmHg) 70.45 [66.47, 74.59] 73.20 [69.31, 77.02] 70.18 [65.79, 73.83] 70.36 [67.91, 74.39] < 0.001 Heart rate, (per minute) 80.17 [75.14, 84.93] 81.31 [76.22, 87.21] 81.69 [74.92, 86.71] 80.54 [75.52, 88.28] 0.055 Respiratory rate, (per minute) 18.10 [16.60, 19.68] 17.98 [16.27, 19.63] 18.48 [16.59, 19.80] 19.24 [16.45, 20.51] 0.315 ICU severity scores SAPS II score 45.00 [38.00, 52.00] 39.00 [33.00, 45.00] 49.00 [43.75, 59.00] 50.50 [45.00, 57.25] < 0.001 OASIS score 35.00 [30.00, 40.00] 33.00 [28.00, 37.00] 36.00 [32.00, 40.25] 36.50 [31.00, 41.25] < 0.001 GCS score 15.00 [15.00, 15.00] 15.00 [15.00, 15.00] 15.00 [15.00, 15.00] 15.00 [15.00, 15.00] 0.05 SIRS score 3.00 [2.00, 3.00] 3.00 [2.00, 3.00] 3.00 [2.00, 3.00] 3.00 [2.00, 3.00] 0.109 Postoperative complications Delirium, n (%) 134 (35.4) 228 (34.5) 37 (51.4) 18 (50.0) 0.012 Sepsis, n (%) 218 (57.7) 347 (52.6) 48 (66.7) 21 (58.3) 0.147 Time Hospital length of stay, (days) 14.02 [10.13, 20.88] 11.91 [8.79, 15.93] 17.61 [9.98, 25.44] 16.89 [12.52, 25.31] < 0.001 ICU length of stay, (days) 5.70 [4.40, 8.34] 5.23 [4.27, 7.06] 6.34 [4.80, 11.52] 7.36 [4.81, 12.52] < 0.001 SAPS II, Simplified Acute Physiology Score II; OASIS, Outcome Prediction in Intensive Care; GCS, Glasgow Coma Scale; SIRS, Systemic Inflammatory Response Syndrome Table 3 Comparison between survival and non-survival patients. Variables Survival (n = 1002) Non-survival (n = 144) p Baseline demographics Age, (years) 72.49 [64.36, 79.65] 76.89 [69.88, 83.87] < 0.001 Gender, female, n (%) 381 (38.0) 59 (41.0) 0.556 Race, white, n (%) 710 (70.9) 108 (75.0) 0.353 Height, (cm) 170.00 [163.00, 178.00] 170.00 [160.00, 178.00] 0.423 Weight, (Kg) 83.00 [69.93, 97.97] 79.40 [65.00, 91.10] 0.019 Comorbidities Hypertension, n (%) 522 (52.1) 45 (31.2) < 0.001 Diabetes mellitus, n (%) 432 (43.1) 62 (43.1) 1 Myocardial infarction, n (%) 318 (31.7) 57 (39.6) 0.075 Congestive heart failure, n (%) 539 (53.8) 102 (70.8) < 0.001 Peripheral vascular disease, n (%) 216 (21.6) 51 (35.4) < 0.001 Chronic pulmonary disease, n (%) 283 (28.2) 55 (38.2) 0.019 Renal disease, n (%) 340 (33.9) 86 (59.7) < 0.001 Charlson comorbidity index 6.00 [4.00, 7.00] 7.00 [6.00, 9.00] < 0.001 First laboratory values after admission to ICU Red blood cell count, (×10 12 /L) 2.90 [2.53, 3.31] 2.86 [2.52, 3.34] 0.987 White blood cell count, (×10 9 /L) 12.60 [9.40, 17.17] 12.80 [8.50, 17.72] 0.773 Platelet count, (×10 9 /L) 136.00 [107.00, 172.00] 135.00 [102.75, 171.50] 0.848 Hemoglobin, (g/dL) 8.60 [7.70, 9.80] 8.60 [7.68, 9.93] 0.923 Serum creatinine, (mg/dL) 1.00 [0.70, 1.30] 1.20 [0.90, 1.80] < 0.001 Potassium, (mmol/L) 4.40 [4.00, 4.70] 4.30 [3.80, 4.82] 0.533 Lactate, (mmol/L) 2.20 [1.52, 3.00] 2.10 [1.50, 3.30] 0.363 ICU first 24-hour average vital signs Systolic pressure, (mmHg) 109.28 [104.16, 115.45] 108.98 [104.84, 116.57] 0.779 Diastolic pressure, (mmHg) 54.89 ± 6.77 54.04 ± 8.53 0.172 Mean arterial pressure, (mmHg) 72.32 [68.10, 76.15] 70.61 [67.37, 76.79] 0.415 Heart rate, (per minute) 80.57 [75.82, 86.28] 81.49 [75.07, 87.81] 0.225 Respiratory rate, (per minute) 18.05 [16.35, 19.60] 18.34 [16.70, 20.16] 0.09 ICU severity scores SAPS II score 41.00 [35.00, 48.00] 47.50 [40.00, 54.25] < 0.001 OASIS score 34.00 [28.00, 38.00] 37.00 [32.00, 41.00] < 0.001 GCS score 15.00 [15.00, 15.00] 15.00 [15.00, 15.00] 0.351 SIRS score 3.00 [2.00, 3.00] 3.00 [2.00, 3.00] 0.907 Postoperative complications Delirium, n (%) 340 (33.9) 77 (53.5) < 0.001 Sepsis, n (%) 526 (52.5) 108 (75.0) < 0.001 Time Hospital length of stay, (days) 12.72 [9.01, 17.33] 16.81 [11.85, 31.03] < 0.001 ICU length of stay, (days) 5.25 [4.31, 7.18] 8.72 [5.21, 17.67] < 0.001 SAPS II, Simplified Acute Physiology Score II; OASIS, Outcome Prediction in Intensive Care; GCS, Glasgow Coma Scale; SIRS, Systemic Inflammatory Response Syndrome Survival analysis The Kaplan-Meier survival analysis demonstrated that patients in Trajectory 3 had the worst prognosis for survival, as 33.3% of the patients were died at 360 days’ postoperatively. There are significant differences among the 4 trajectory classes (log-rank p < 0.001) (Fig. 3 ). Meanwhile, Trajectory 2 exhibited the most favorable prognosis, with the most stable trend of BUN and the lowest mortality rate during follow-up. Based on this finding, we selected Trajectory 2 as the reference group for subsequent Cox proportional hazards models (Table 4 ). In the unadjusted model (Model 1), all three other trajectories showed significantly higher mortality risks compared to Trajectory 2. Notably, Trajectory 3 demonstrated the highest mortality risk (HR = 5.42, 95% CI: 3.32–8.85, p < 0.001), while Trajectory 4 also exhibited elevated mortality risk (HR = 3.25, 95% CI: 1.54–6.87, p = 0.002). After adjusting for demographic characteristics in Model 2, the mortality risks remained essentially unchanged, with Trajectory 3 maintaining the highest risk (HR = 5.70, 95% CI: 3.49–9.32, p < 0.001) and Trajectory 4 showing a persistently elevated risk (HR = 3.57, 95% CI: 1.68–7.57, p = 0.001). Further adjustment for clinical variables in Model 3 yielded consistent results, with Trajectory 3 continuing to show the highest mortality risk (HR = 2.90, 95% CI: 1.56–5.39, p < 0.001), and Trajectory 4 remaining at a higher risk compared to the reference group (HR = 2.40, 95% CI: 1.02–5.62, p = 0.045). Table 4 Results of cox proportional hazards regression model. Model 1 Model 2 a Model 3 b Class Unadjusted HR (95% CI) p Adjusted HR (95% CI) p Adjusted HR (95% CI) p Class 2 1.00 Reference 1.00 Reference 1.00 Reference Class 1 2.48 [1.70, 3.60] < 0.001 2.15 [1.48, 3.15] < 0.001 1.77 [1.15, 2.74] 0.01 Class 3 5.42 [3.32, 8.85] < 0.001 5.70 [3.49, 9.32] < 0.001 2.90 [1.56, 5.39] 0.001 Class 4 3.25 [1.54, 6.87] 0.002 3.57 [1.68, 7.57] 0.001 2.40 [1.02, 5.62] 0.045 HR, hazard ratio; CI, confidence interval. a Model 2 adjusted for demographic variables (age, gender, race). b Model 3 adjusted for variables including age, gender, race, weight, hypertension, myocardial infarction, congestive heart failure, peripheral vascular disease, chronic pulmonary disease, renal disease, Charlson comorbidity index, red blood cell count, white blood cell count, serum creatinine, mean arterial pressure, SAPS II score, OASIS score, delirium, and sepsis. Subgroup analysis and forest plots We performed subgroup analysis to assess the stability of associations between BUN trajectories and 360-day mortality across main covariates and clinical subgroups, such as age, gender, race, MI, CHF, PVD, CPD, delirium, and sepsis (Supplementary Material Table S 3) (Fig. 4 ). Trajectory 3 was associated with a higher risk of mortality in the subgroup excluding patients who were ≤ 65 years old, female, non-white, without MI, with CPD, and with delirium. Within Trajectory group 1, patients aged > 65 years, male, white, with or without CHF, without PVD, without CPD, with delirium, and with sepsis exhibited a higher risk of mortality. In Trajectory group 4, patients who were male, white, without CPD, and with sepsis had a higher risk of mortality. Notably, the impact of trajectory on mortality risk did not significantly differ across all subgroups. (all p for interaction > 0.05). Discussion In this cohort study, we employed LCMM to characterize dynamic changes in BUN levels over the first 96 hours after ICU admission following cardiac surgery and identified four distinct BUN trajectories that effectively stratified patients into clinically meaningful risk subgroups. The gradual increase, rapid decline from high level, and marked critical elevation trajectories all compared with the stable low-level trajectory (reference group) were associated with a statistically significant higher 360-day mortality hazard. As mentioned, particularly the last two trends had a poorer prognosis despite affecting only 6.28% and 3.14% of the cohort, respectively. These associations were potent after thorough adjustment for demographics, comorbidities and other covariates and were robust across most subgroups. Our findings indicate that monitoring for dynamic BUN may be used to identify high risk phenotypes early in the postoperative course, thus, enabling the use of individualized interventions to minimize unfavorable outcomes. It has been previously demonstrated that high BUN was correlated with a higher risk of mortality in coronary artery diseases, heart failure and other cardiovascular diseases ( 12 – 14 ). It’s important to identify the prognostic factors with deep insight as well in this area because the physiology of the circulation is dramatically affected by performing the cardiac surgery. Liu et al. ( 8 ) has demonstrated that high BUN level was correlated with a longer time hospitalization (n = 192) for aortic dissection surgery patients. Ye et al. ( 9 ) reported that high BUN level correlated with elevated mortality at 28 days postoperatively in cardiac surgery patients. More recently, research in large numbers have expanded beyond short-term outcomes to evaluate mid- and long-term survival for cardiac surgery patients ( 15 , 16 ). Yu et al. ( 17 ), who reviewed 7,368 cardiac surgery patients, reported that BUN was identified an independent predictor of mid- to long-term mortality. Nevertheless, these previous studies were constrained by their single-timepoint BUN observation, that is, restricted to distinguish between temporary exchange versus chronic high-risk state. In this analysis we selected 360 days postoperatively as the time to endpoint, and derived four distinct BUN profiles post-cardiac surgery to potentially more accurately stratify mortality risk. This approach may be particularly applicable to ICU patients. For example, Zhang et al. ( 18 ) demonstrated that serial serum phosphate trajectories outperformed single measurements in predicting ICU mortality, while Jiang et al. ( 19 ) showed that C-reactive protein trajectories provided superior risk classification in sepsis compared to isolated values. As we all know, BUN and Scr are used as the most common indexes as a renal index in clinical practice, but prior study has verified the high-impact of BUN from non-renal ( 20 ). In our study, the BUN trajectory was independent and had an impact on prognosis even adjusting renal variables such as RD and Scr. This indicated that the prognostic value of BUN was less related to renal function and could therefore be multidimensional including hemodynamic stability, inflammatory process, and metabolic stress ( 21 – 23 ). Compared with Trajectory 2, the remaining three trajectories exhibited higher mortality risk at 360 days postoperatively. For Trajectory 1, we noticed that the patients were generally old, with the highest mean systolic blood pressure and pulse pressure on day 1 in ICU. We speculate that elderly patients are more vulnerable to diastolic cardiac dysfunction and postoperative fluid overloads ( 24 ), that atherosclerosis in these patients are more sever and that large BP fluctuation in these patients may result in organ hypoperfusion ( 25 ). Trajectory 3 likely reflects the process of "transient ischemia-reperfusion injury during surgery." Patients in this group had higher fraction of CHF, higher mortality risk and incidence of postoperative delirium. Intra-operatively, in the case of low flow, low pressure and non-pulsatile perfusion encountered with cardiopulmonary by-pass renal perfusion is inadequate. That can induce oxidative stress, calcium overload and inflammation when cardiopulmonary by-pass is interrupted and renal reperfusion occurs ( 26 – 28 ). This process can induce acute tubular necrosis that results in an abrupt elevation of BUN after 24 hours, which reaches a peak and returns to normal values with stabilization of renal function. At the same time, ischemia-reperfusion induces systemic inflammatory response syndrome, and inflammatory factors, including tumor necrosis factor-α, and interleukin-6, can trigger the neuroinflammatory response, resulting in a dysfunction of nerve cells and ultimately postoperative delirium ( 29 ). Trajectory 4 may represent the pathological process of a "malignant cycle of cardiorenal syndrome." This cohort’s patients had the highest rates of DM and RD, and the postoperative Scr levels were also the highest. After cardiac surgery, cardiac function may be affected, leading to a decrease in cardiac output and thus reduced renal perfusion. The presence of RD, which often has a very poor renal reserve function, increases the probability of renal injury arising from inadequate renal perfusion ( 30 ). Moreover, renal dysfunction leads to retention of metabolic debris and stimulation of renin-angiotensin-aldosterone system producing water retention, overloaded the heart and worsened cardiac function leading to persistent elevation of BUN levels as in type 3 of cardiorenal syndrome ( 31 , 32 ). Although we fined prognostic value of postoperative BUN trajectories, several limitations should be acknowledged. Firstly, as a single-center retrospective study, our findings may be subject to selection bias. Future multicenter randomized controlled studies with larger sample sizes are needed. Secondly, we were unable to obtain data such as the EUROSCORE II and STS scores, which have been proven to be useful for assessing the prognosis of cardiac surgery patients. If these could be incorporated into our model, it might enhance the predictive accuracy for patient outcomes. Thirdly, although we adjusted for several variables, unmeasured variables such as detailed intraoperative hemodynamic parameters and specific vasoactive medication regimens could contribute to residual confounding. Finally, the requirement of ≥ 4 BUN measurements may have excluded patients who died early after surgery, potentially introducing survival bias. Conclusions We retrospectively determined four clinically distinct postoperative BUN trajectories that can be used to stratify the cardiac surgery patients into prognostically relevant groups. Using LCMM of high-frequency BUN measurements in 1146 patients, we found that dynamic BUN patterns (particularly the rapid decline from high level - Trajectory 3, and marked critical elevation - Trajectory 4 trajectories) were highly predictive for 360day mortality. These results remained robust in subgroup analysis and may be associated with pathophysiological processes including ischemia-reperfusion injury, systemic inflammation, and cardio-renal syndrome. Abbreviations BUN Blood urea nitrogen ICU Intensive care unit LCMM Latent class mixed models MIMIC-IV Medical Information Mart for Intensive Care IV DM Diabetes mellitus MI Myocardial infarction CHF Congestive heart failure PVD Peripheral vascular disease CPD Chronic pulmonary disease RD Renal disease CCI Charlson Comorbidity Index Scr Serum creatinine BP Blood pressure HR Heart rate RR Respiratory rate SAPS II Simplified Acute Physiology Score II OASIS Outcome Prediction in Intensive Care GCS Glasgow Coma Scale SIRS Systemic Inflammatory Response Syndrome AIC Akaike information criterion BIC Bayesian information criterion HR Hazard ratio CI Confidence interval Declarations Ethical approval The data for this study were extracted from the MIMIC-IV database (version 3.1). The privacy of all patients was protected, and the use of the database was approved by the Institutional Review Board of MIT and Beth Israel Deaconess Medical Center. This study was also approved by the Ethical Committee of Xuzhou No.1 People's Hospital (XYYLL2025-089). Data availability This study utilized data from a publicly available database, which can be accessed via https://www.physionet.org/content/mimiciv/3.1/ . Conflict of Interest The authors declare that they have no conflict of interest. Author Contributions HY, JS, and SX designed the experiment. HY obtained the permission to access the data and collaborated with TZ to complete the data extraction. HY, TZ and QL participated in the data statistical analysis. HY, JS and SX jointly wrote the paper. QL, JS and SX provided professional consultation on the problems encountered in the research. All authors participated in the final manuscript and approved the submitted version. Funding This study was supported by the National Nature Science Foundation of China (No. 82371584). Acknowledgments We are grateful to MIT and BIDMC for enabling us to complete this research using the MIMIC-IV database. References von Wyler MC, Kaneko T, Iribarne A, Kim KM, Arghami A, Fiedler A, et al. The Society of Thoracic Surgeons Adult Cardiac Surgery Database: 2023 Update on Procedure Data and Research. Ann Thorac Surg. 2024;117(2):260–70. Landoni G, Lomivorotov V, Silvetti S, Nigro Neto C, Pisano A, Alvaro G, et al. Nonsurgical Strategies to Reduce Mortality in Patients Undergoing Cardiac Surgery: An Updated Consensus Process. J Cardiothorac Vasc Anesth. 2018;32(1):225–35. Deo SV, Sundaram V, Sahadevan J, Selvaganesan P, Mohan SM, Rubelowsky J, et al. Outcomes of coronary artery bypass grafting in patients with heart failure with a midrange ejection fraction. J Thorac Cardiovasc Surg. 2023;165(1):149–e584. Arihan O, Wernly B, Lichtenauer M, Franz M, Kabisch B, Muessig J, et al. Blood Urea Nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU. PLoS ONE. 2018;13(1):e0191697. Lan Q, Zheng L, Zhou X, Wu H, Buys N, Liu Z, et al. The Value of Blood Urea Nitrogen in the Prediction of Risks of Cardiovascular Disease in an Older Population. Front Cardiovasc Med. 2021;8:614117. Hong C, Zhu H, Zhou X, Zhai X, Li S, Ma W et al. Association of Blood Urea Nitrogen with Cardiovascular Diseases and All-Cause Mortality in USA Adults: Results from NHANES 1999–2006. Nutrients. 2023;15(2). Wang Z, Wang W, Wang M, He Q, Xu J, Zou K, et al. Blood Urine Nitrogen Trajectories of Acute Pancreatitis Patients in Intensive Care Units. J Inflamm Res. 2024;17:3449–58. Liu J, Sun LL, Wang J, Ji G. Blood urea nitrogen in the prediction of in-hospital mortality of patients with acute aortic dissection. Cardiol J. 2018;25(3):371–6. Ye L, Shi H, Wang X, Duan Q, Ge P, Shao Y. Elevated Blood Urea Nitrogen to Serum Albumin Ratio Is an Adverse Prognostic Predictor for Patients Undergoing Cardiac Surgery. Front Cardiovasc Med. 2022;9:888736. Curran TF, Sunkara B, Leis A, Lim A, Haft J, Engoren M. Outcomes After Prolonged ICU Stays in Postoperative Cardiac Surgery Patients. Fed Pract. 2022;39(Suppl 5):S6–Sc11. Johnson A, Bulgarelli L, Pollard T, Gow B, Moody B, Horng S et al. MIMIC-IV (version 3.1). PhysioNet. 2024. Jujo K, Minami Y, Haruki S, Matsue Y, Shimazaki K, Kadowaki H, et al. Persistent high blood urea nitrogen level is associated with increased risk of cardiovascular events in patients with acute heart failure. ESC Heart Fail. 2017;4(4):545–53. Richter B, Sulzgruber P, Koller L, Steininger M, El-Hamid F, Rothgerber DJ, et al. Blood urea nitrogen has additive value beyond estimated glomerular filtration rate for prediction of long-term mortality in patients with acute myocardial infarction. Eur J Intern Med. 2019;59:84–90. Plakht Y, Gilutz H, Shiyovich A. Decreased admission serum albumin level is an independent predictor of long-term mortality in hospital survivors of acute myocardial infarction. Soroka Acute Myocardial Infarction II (SAMI-II) project. Int J Cardiol. 2016;219:20–4. McDonald B, van Walraven C, McIsaac DI. Predicting 1-Year Mortality After Cardiac Surgery Complicated by Prolonged Critical Illness: Derivation and Validation of a Population-Based Risk Model. J Cardiothorac Vasc Anesth. 2020;34(10):2628–37. Aktuerk D, McNulty D, Ray D, Begaj I, Howell N, Freemantle N, et al. National administrative data produces an accurate and stable risk prediction model for short-term and 1-year mortality following cardiac surgery. Int J Cardiol. 2016;203:196–203. Yu Y, Peng C, Zhang Z, Shen K, Zhang Y, Xiao J, et al. Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery. Front Cardiovasc Med. 2022;9:831390. Guo P, Ma Y, Su W, Xie D, Li X, Wang K, et al. Association between baseline serum bicarbonate and the risk of postoperative delirium in patients undergoing cardiac surgery in the ICU: a retrospective study from the MIMIC-IV database. BMC Anesthesiol. 2024;24(1):347. Jiang X, Zhang C, Pan Y, Cheng X, Zhang W. Effects of C-reactive protein trajectories of critically ill patients with sepsis on in-hospital mortality rate. Sci Rep. 2023;13(1):15223. Balestracci A, Meni Battaglia L, Toledo I, Martin SM, Alvarado C. Blood urea nitrogen to serum creatinine ratio as a prognostic factor in diarrhea-associated hemolytic uremic syndrome: a validation study. Eur J Pediatr. 2018;177(1):63–8. Wang Y, Xu X, Shi S, Gao X, Li Y, Wu H, et al. Blood urea nitrogen to creatinine ratio and long-term survival in patients with chronic heart failure. Eur J Med Res. 2023;28(1):343. Stark J. Interpretation of BUN and serum creatinine. An interactive exercise. Crit Care Nurs Clin North Am. 1998;10(4):491–6. Macedo E. Blood urea nitrogen beyond estimation of renal function. Crit Care Med. 2011;39(2):405–6. Friedrich I, Simm A, Kötting J, Thölen F, Fischer B, Silber RE. Cardiac surgery in the elderly patient. Dtsch Arztebl Int. 2009;106(25):416–22. Steppan J, Barodka V, Berkowitz DE, Nyhan D. Vascular stiffness and increased pulse pressure in the aging cardiovascular system. Cardiol Res Pract. 2011;2011:263585. Bellini MI, Charalampidis S, Herbert PE, Bonatsos V, Crane J, Muthusamy A, et al. Cold Pulsatile Machine Perfusion versus Static Cold Storage in Kidney Transplantation: A Single Centre Experience. Biomed Res Int. 2019;2019:7435248. Wang Y, Bellomo R. Cardiac surgery-associated acute kidney injury: risk factors, pathophysiology and treatment. Nat Rev Nephrol. 2017;13(11):697–711. Yu Y, Li C, Zhu S, Jin L, Hu Y, Ling X, et al. Diagnosis, pathophysiology and preventive strategies for cardiac surgery-associated acute kidney injury: a narrative review. Eur J Med Res. 2023;28(1):45. Mattimore D, Fischl A, Christophides A, Cuenca J, Davidson S, Jin Z et al. Delirium after Cardiac Surgery-A Narrative Review. Brain Sci. 2023;13(12). Boyer N, Eldridge J, Prowle JR, Forni LG. Postoperative Acute Kidney Injury. Clin J Am Soc Nephrol. 2022;17(10):1535–45. Bove T, Monaco F, Covello RD, Zangrillo A. Acute renal failure and cardiac surgery. HSR Proc Intensive Care Cardiovasc Anesth. 2009;1(3):13–21. Rangaswami J, Bhalla V, Blair JEA, Chang TI, Costa S, Lentine KL, et al. Cardiorenal Syndrome: Classification, Pathophysiology, Diagnosis, and Treatment Strategies: A Scientific Statement From the American Heart Association. Circulation. 2019;139(16):e840–78. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2026 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted Editorial decision: Revision requested 19 Nov, 2025 Reviews received at journal 26 Aug, 2025 Reviews received at journal 28 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor assigned by journal 02 Jun, 2025 Submission checks completed at journal 02 Jun, 2025 First submitted to journal 01 Jun, 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-6794317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487927895,"identity":"970aa5ea-cf26-4476-b4ea-3abcdeffb83c","order_by":0,"name":"Hao Yuan","email":"","orcid":"","institution":"Xuzhou No.1 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Yuan","suffix":""},{"id":487927897,"identity":"92b7e7f9-b4a3-41d1-8fb8-b788c6fadd75","order_by":1,"name":"Ting Zhang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zhang","suffix":""},{"id":487927899,"identity":"7ed5989d-bed9-4191-9117-ca60d2242292","order_by":2,"name":"Qiang Li","email":"","orcid":"","institution":"Second Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Li","suffix":""},{"id":487927900,"identity":"e514166c-d038-44fb-9e90-543b76e6c9a7","order_by":3,"name":"Jianggui Shan","email":"","orcid":"","institution":"Xuzhou No.1 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianggui","middleName":"","lastName":"Shan","suffix":""},{"id":487927901,"identity":"b62dfeca-b38c-4c30-8b3a-bf1bfb2a75fc","order_by":4,"name":"Song Xue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACgwMQWo6xvbHx4QdStBgz9xxuNpYgRotkA4ROZJ+R3ibAQ4wWfonkwx8+7qhN4J35sI1BgsFOTreBgBY2ibQ0yZlnjudJzk5se1DAkGxsdoCglhwzZt62Y8WGsxPbDSQYDiRuI6SFXyL/82eglsT9Nw+2SfAQo0VyRg6DNG9bTWLjDEYitRiceWYmObPtgDFjTyIwkA2I8IvB8eTHHz621QGj8vjDhx8q7OQIaoGCwzATiFMOAnXEKx0Fo2AUjIKRBwAFcEaAetiNgAAAAABJRU5ErkJggg==","orcid":"","institution":"Xuzhou No.1 People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Song","middleName":"","lastName":"Xue","suffix":""}],"badges":[],"createdAt":"2025-06-01 07:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6794317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6794317/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13019-026-03869-5","type":"published","date":"2026-02-12T15:57:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87364202,"identity":"ace26d50-ad44-4a8d-a101-668cd26ed9ce","added_by":"auto","created_at":"2025-07-23 06:14:08","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112423,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/46a248315ec744c758a97947.jpeg"},{"id":87363485,"identity":"0fd6817a-7fb8-4c0a-b325-4d79f7778782","added_by":"auto","created_at":"2025-07-23 06:06:08","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58748,"visible":true,"origin":"","legend":"\u003cp\u003eThe trajectory of BUN changes within 96 hours after cardiac surgery entering the ICU (Trajectory 1: gradual increase; Trajectory 2: stable low-level; Trajectory 3: decline from high level; Trajectory 4: marked critical elevation).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/fa9a841b0632cece95d82d23.jpeg"},{"id":87363491,"identity":"7fca39d5-f67b-4654-a0f8-9c51fc74e5a6","added_by":"auto","created_at":"2025-07-23 06:06:08","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180196,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan–Meier survival analysis curves for the 4 trajectories of patients at 360 days after surgery.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/c00de9600b689edc90dcf9e6.jpeg"},{"id":87364205,"identity":"c44ac80c-ab80-4107-bcaf-2e4a2becee8b","added_by":"auto","created_at":"2025-07-23 06:14:08","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89351,"visible":true,"origin":"","legend":"\u003cp\u003eThe forest plot after subgroup analysis. HR, hazard ratio.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/3f57d8bf9db20c21fce4c9a9.jpeg"},{"id":87363490,"identity":"0b46a27a-34dc-4a42-a228-01a21b94272c","added_by":"auto","created_at":"2025-07-23 06:06:08","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":112423,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/16d1ee9f9c9773df8b85959c.jpeg"},{"id":87364204,"identity":"64759a4c-d7f2-4d92-9023-8ea70447bd75","added_by":"auto","created_at":"2025-07-23 06:14:08","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":180196,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan–Meier survival analysis curves for the 4 trajectories of patients at 360 days after surgery.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/c24cb4fe405e2b7ff8598288.jpeg"},{"id":102786456,"identity":"091757fe-de00-4af2-95ff-53564dfe628d","added_by":"auto","created_at":"2026-02-16 16:13:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1782105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/e12f9cea-5b25-430e-81de-aa908bb7c5cc.pdf"},{"id":87364200,"identity":"5e107355-5b0c-48be-9afc-e742809289a9","added_by":"auto","created_at":"2025-07-23 06:14:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24685,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6794317/v1/4c1991ccf0e24ddba4c1b1d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic BUN Patterns in the ICU: Risk Stratification and Prognosis Following Cardiac Surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiac surgery remains an indispensable intervention for cardiovascular diseases, with over 2\u0026nbsp;million procedures performed annually worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite technological advances, postoperative 1-year mortality still persists at nearly 10%, driven partly by uncaptured physiological insults during the early recovery phase (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This highlights the urgent need for dynamic biomarkers that can reflect real-time system stress, rather than relying solely on a certain static clinical data.\u003c/p\u003e\u003cp\u003eBlood urea nitrogen (BUN) possesses outstanding kinetic advantages due to its relatively short plasma half-life (2\u0026ndash;8 hours), which allows for earlier detection of renal hypoperfusion compared with creatinine (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Additionally, BUN is highly sensitive to changes in cardiac output, the state of the sympathetic nervous system, and protein catabolism, thereby reflecting a more comprehensive statue of multiple physiological systems (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Previous studies have demonstrated that constructing BUN trajectories can offer robust predictive value for mortality risk in patients with acute pancreatitis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, the dynamic patterns of BUN may provide more refined prognostic information compared with single-timepoint thresholds.\u003c/p\u003e\u003cp\u003eFor intensive care unit (ICU) patients, BUN is a readily accessible biomarker and exhibits a dynamic developmental pattern. Several studies have demonstrated the predictive value of BUN for postoperative mortality risk after cardiac surgery (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, longitudinal data on BUN in such patients are lacking. Therefore, this study aims to investigate the impact of early postoperative BUN dynamics for cardiac surgery patients on 360-day survival using Latent class mixed models (LCMM). Patients\u0026rsquo; data was extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData Source and Study Population\u003c/p\u003e\u003cp\u003eThe MIMIC-IV database (version 3.1) is a publicly available critical care database that includes 364,627 patient records from 2008\u0026ndash;2022. The Institutional Review Board of MIT and Beth Israel Deaconess Medical Center provided a waiver of the informed consent requirement and authorized the data sharing program (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). One of the authors fulfilled the requirements of the CITI Program to gain access to the data (Hao Y, Certification number: 53244072). This study was approved by the Ethical Committee of Xuzhou No.1 People's Hospital (XYYLL2025-089).\u003c/p\u003e\u003cp\u003eCardiac surgery patients were identified by use of ICD-9 and ICD-10 procedure codes (Supplementary Material Table S 1). The inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) cardiac surgery patients with first ICU admission, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) adult patients (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years), (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) ICU stay\u0026thinsp;\u0026ge;\u0026thinsp;96 hours postoperatively, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) availability of \u0026ge;\u0026thinsp;4 BUN measurements within the first 96 hours.\u003c/p\u003e\u003cp\u003eData Preparation and Study endpoint\u003c/p\u003e\u003cp\u003eThe data extraction and collection were done with the aid of PostgreSQL (version 16.0.6) and Navicat Premium (version 17). Dynamic data consisted of four postoperative BUN measurements collected at 24-hour intervals (at 24h, 48h, 72h, and 96h after ICU admission), with the first recorded value within each time window used for analysis. Covariates with \u0026gt;\u0026thinsp;20% missing values were excluded. Based on this, static data finally comprised: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) baseline demographics: age, gender, race, height, weight; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) comorbidities: hypertension, diabetes mellitus (DM), myocardial infarction (MI), congestive heart failure (CHF), peripheral vascular disease (PVD), chronic pulmonary disease (CPD), renal disease (RD), Charlson Comorbidity Index (CCI); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the first laboratory values following ICU admission: red blood cell count, white blood cell count, platelet count, hemoglobin, serum creatinine (Scr), potassium, lactate; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) 24-hour average vital signs after admission to the ICU: blood pressure (BP), heart rate (HR), respiratory rate (RR); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) ICU severity scores: Simplified Acute Physiology Score II (SAPS II), Outcome Prediction in Intensive Care (OASIS), Glasgow Coma Scale (GCS), Systemic Inflammatory Response Syndrome (SIRS); (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) postoperative complications: delirium and sepsis; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) time: hospital length of stay and ICU length of stay.\u003c/p\u003e\u003cp\u003eMissing data in the remaining variables (\u0026lt;\u0026thinsp;20%) were imputed (multiple imputation, 5 imputations, chained equations) under missing-at-random assumption. Meanwhile, we winsorized extreme values of BUN. This approach ensured standardized extraction of variables, minimized bias from missing data, and aligned with established critical care research methodologies.\u003c/p\u003e\u003cp\u003eThe main outcome was all-cause mortality at 360 days post-surgery that was obtained from the database.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe statistical analyses were achieved in R (version 4.4.2) and IBM SPSS (version 24). The descriptive statistics of categorical data are displayed in terms of n (%) and the continuous data are displayed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (normally distributed) or median (IQR) (non-normally distributed), the normality was measured by the Kolmogorov-Smirnov test. For continuous variables, comparisons between classes were conducted using analysis of variance or the Student t-test for normally distributed data, and the Kruskal-Wallis H test or the Mann-Whitney U test for non-normally distributed data. Categorical variables were analyzed using the chi-square test. When the p value less than 0.05, it was considered statistically significant.\u003c/p\u003e\u003cp\u003eLCMM was used for classifying BUN trajectories by the R lcmm package. Seven candidate models of different trajectory shapes with different number of classes were explored according to the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the entropy. The final model was selected by optimizing both statistical fit and clinical interpretability.\u003c/p\u003e\u003cp\u003eSurvival analyses included Kaplan-Meier curves with log-rank tests to compare 360-day survival across trajectories, and Cox proportional hazards regression to assess mortality risk associations according to three different models. Furthermore, we applied subgroup analysis and forest plots to certify the stability of the results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatients Selection and LTA of BUN\u003c/p\u003e\u003cp\u003eWe finally selected 1146 patient that were eligible for analysis based on the inclusion criteria from the MIMIC-IV database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We fitted LCMM to the postoperative BUN measurements (0\u0026ndash;96 hours) and considered 7 candidate models (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the 4-class model did not produce the lowest BIC value, the relative difference in BIC values decreased significantly beyond the 4-class model. This suggested that additional classes did not provide more improvement in model fit. We further calculate the average posterior probability of the model which was greater than 0.85, indicating that the model fit was decent (Supplementary Material Table S 2). Additionally, clinical interpretability of the 4-class model was better. Thus, we finally selected this model to further research. In this model, the four BUN trajectories were characterized as follows: Trajectory 1 (32.98%) represented a pattern of gradual increase; Trajectory 2 (57.59%) was characterized by a stable low-level; Trajectory 3 (6.28%) was defined by a rapid decline from high level; and Trajectory 4 (3.14%) was characterized by a marked critical elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistics for choosing the best model of classes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLoglik\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%Class1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%Class2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%Class3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e%Class4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e%Class5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e%Class6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e%Class7\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-15920.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31854.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31889.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100.00000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-15534.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31093.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31154.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75.39267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e24.60733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-15386.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30806.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30892.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68.93543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.766143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e23.29843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-15284.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30612.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30723.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e32.98429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e57.59162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.282723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.141361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-15236.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30526.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30662.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e53.40314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e36.47469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.188482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.356021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e3.577661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-15187.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30438.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30599.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.16579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e38.13264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.363002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e17.53927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e2.268761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.530541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-15163.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30401.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30587.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.33333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.81675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.155323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.141361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e16.75393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.356021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2.443281\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\u003eAIC, Akaike information criterion; BIC, the Bayesian information criterion\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBaseline characteristics\u003c/p\u003e\u003cp\u003eBaseline characteristics showed the differences between four trajectory groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients in Trajectory 1 were older (75.78 [68.80, 81.56] years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a higher score of CCI (7.00 [5.00, 9.00], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Trajectory 3 had the highest prevalence of CHF (73.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), postoperative delirium rate (51.4%, p\u0026thinsp;=\u0026thinsp;0.012), and the lowest postoperative BP. Trajectory 4 exhibited both the most frequent DM (63.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and RD (86.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), coupled with postoperative declined hemoglobin (8.10 [7.25, 9.33] g/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), elevated Scr (3.60 [1.55, 5.60] mg/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the most impaired ICU severity scores. Trajectory 3 had longer total hospital stays (17.61 [9.98, 25.44] days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Trajectory 4 required extended ICU care (7.36 [4.81, 12.52] days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), underscoring their high-risk status.\u003c/p\u003e\u003cp\u003eA total of 144 patients died during the follow-up period. Compared with survival patients, those who died were older, had a higher prevalence of comorbidities (CHF, PVD, CPD, RD, and had a higher CCI score. On the first day after surgery in the ICU, non-survival patients had higher serum creatinine levels, as well as higher ICU severity scores. Additionally, the incidence of delirium and sepsis was higher in the non-survival group, and they had longer hospital and ICU lengths of stay (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of patients in the 4 trajectories.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrajectory 1 (n\u0026thinsp;=\u0026thinsp;378)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTrajectory 2 (n\u0026thinsp;=\u0026thinsp;660)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTrajectory 3 (n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTrajectory 4 (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eBaseline demographics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.78 [68.80, 81.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.69 [63.30, 78.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.05 [63.45, 79.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.63 [60.36, 75.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eGender, female, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e267 (40.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace, white, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286 (75.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e461 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170.00 [160.00, 178.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170.00 [163.00, 178.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170.00 [163.00, 178.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e174.00 [167.25, 180.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, (Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.20 [70.00, 98.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.00 [68.15, 96.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.00 [68.75, 98.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.60 [75.75, 100.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\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\u003e115 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e436 (66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eDiabetes mellitus, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (51.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (56.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eMyocardial infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204 (30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247 (65.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (73.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003ePeripheral vascular disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic pulmonary disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188 (28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e233 (61.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (81.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (86.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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 comorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.00 [5.00, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.00 [3.75, 6.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.00 [6.00, 8.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.00 [6.00, 8.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFirst laboratory values after admission to ICU\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count, (\u0026times;10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.81 [2.50, 3.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.94 [2.58, 3.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.94 [2.49, 3.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.73 [2.33, 3.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count, (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.80 [9.60, 17.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.70 [9.50, 17.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.20 [8.47, 16.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.90 [7.65, 13.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count, (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137.00 [108.25, 173.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.00 [105.00, 172.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146.50 [114.75, 174.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115.50 [90.75, 151.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.077\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\u003e8.40 [7.50, 9.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.80 [7.80, 10.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.40 [7.47, 10.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.10 [7.25, 9.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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 creatinine, (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20 [1.00, 1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80 [0.70, 1.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.15 [1.40, 4.12]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.60 [1.55, 5.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003e4.40 [4.00, 4.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.30 [3.98, 4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.40 [3.98, 4.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.70 [4.40, 5.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\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\u003e2.00 [1.50, 2.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.20 [1.60, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.10 [1.58, 3.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.20 [1.67, 3.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICU first 24-hour average vital signs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic pressure, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110.16 [104.93, 117.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.68 [104.01, 114.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107.15 [102.81, 113.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107.10 [104.00, 114.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic pressure, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eMean arterial pressure, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.45 [66.47, 74.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.20 [69.31, 77.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.18 [65.79, 73.83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.36 [67.91, 74.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eHeart rate, (per minute)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.17 [75.14, 84.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.31 [76.22, 87.21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.69 [74.92, 86.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.54 [75.52, 88.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate, (per minute)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.10 [16.60, 19.68]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.98 [16.27, 19.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.48 [16.59, 19.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.24 [16.45, 20.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICU severity scores\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPS II score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.00 [38.00, 52.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.00 [33.00, 45.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.00 [43.75, 59.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.50 [45.00, 57.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOASIS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.00 [30.00, 40.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.00 [28.00, 37.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.00 [32.00, 40.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.50 [31.00, 41.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 [15.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 [15.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 [15.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.00 [15.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePostoperative complications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelirium, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e228 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37 (51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.012\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\u003e218 (57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e347 (52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital length of stay, (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.02 [10.13, 20.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.91 [8.79, 15.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.61 [9.98, 25.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.89 [12.52, 25.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eICU length of stay, (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.70 [4.40, 8.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.23 [4.27, 7.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.34 [4.80, 11.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.36 [4.81, 12.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eSAPS II, Simplified Acute Physiology Score II; OASIS, Outcome Prediction in Intensive Care; GCS, Glasgow Coma Scale; SIRS, Systemic Inflammatory Response Syndrome\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\u003eComparison between survival and non-survival patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvival (n\u0026thinsp;=\u0026thinsp;1002)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-survival (n\u0026thinsp;=\u0026thinsp;144)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eBaseline demographics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.49 [64.36, 79.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.89 [69.88, 83.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eGender, female, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e381 (38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace, white, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e710 (70.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170.00 [163.00, 178.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170.00 [160.00, 178.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, (Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.00 [69.93, 97.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.40 [65.00, 91.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\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\u003e522 (52.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eDiabetes mellitus, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e432 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyocardial infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e318 (31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e539 (53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (70.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003ePeripheral vascular disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216 (21.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eChronic pulmonary disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (38.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eCharlson comorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00 [4.00, 7.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.00 [6.00, 9.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFirst laboratory values after admission to ICU\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count, (\u0026times;10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.90 [2.53, 3.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.86 [2.52, 3.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count, (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.60 [9.40, 17.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.80 [8.50, 17.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count, (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136.00 [107.00, 172.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135.00 [102.75, 171.50]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.848\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\u003e8.60 [7.70, 9.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.60 [7.68, 9.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine, (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 [0.70, 1.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20 [0.90, 1.80]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003ePotassium, (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.40 [4.00, 4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.30 [3.80, 4.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.533\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\u003e2.20 [1.52, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.10 [1.50, 3.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICU first 24-hour average vital signs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic pressure, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109.28 [104.16, 115.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108.98 [104.84, 116.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic pressure, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean arterial pressure, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.32 [68.10, 76.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.61 [67.37, 76.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate, (per minute)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.57 [75.82, 86.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.49 [75.07, 87.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate, (per minute)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.05 [16.35, 19.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.34 [16.70, 20.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eICU severity scores\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPS II score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.00 [35.00, 48.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.50 [40.00, 54.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eOASIS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.00 [28.00, 38.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.00 [32.00, 41.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eGCS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 [15.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 [15.00, 15.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRS score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePostoperative complications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDelirium, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (53.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eSepsis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e526 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital length of stay, (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.72 [9.01, 17.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.81 [11.85, 31.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eICU length of stay, (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.25 [4.31, 7.18]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.72 [5.21, 17.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003eSAPS II, Simplified Acute Physiology Score II; OASIS, Outcome Prediction in Intensive Care; GCS, Glasgow Coma Scale; SIRS, Systemic Inflammatory Response Syndrome\u003c/p\u003e\u003cp\u003eSurvival analysis\u003c/p\u003e\u003cp\u003eThe Kaplan-Meier survival analysis demonstrated that patients in Trajectory 3 had the worst prognosis for survival, as 33.3% of the patients were died at 360 days\u0026rsquo; postoperatively. There are significant differences among the 4 trajectory classes (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Meanwhile, Trajectory 2 exhibited the most favorable prognosis, with the most stable trend of BUN and the lowest mortality rate during follow-up. Based on this finding, we selected Trajectory 2 as the reference group for subsequent Cox proportional hazards models (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the unadjusted model (Model 1), all three other trajectories showed significantly higher mortality risks compared to Trajectory 2. Notably, Trajectory 3 demonstrated the highest mortality risk (HR\u0026thinsp;=\u0026thinsp;5.42, 95% CI: 3.32\u0026ndash;8.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Trajectory 4 also exhibited elevated mortality risk (HR\u0026thinsp;=\u0026thinsp;3.25, 95% CI: 1.54\u0026ndash;6.87, p\u0026thinsp;=\u0026thinsp;0.002). After adjusting for demographic characteristics in Model 2, the mortality risks remained essentially unchanged, with Trajectory 3 maintaining the highest risk (HR\u0026thinsp;=\u0026thinsp;5.70, 95% CI: 3.49\u0026ndash;9.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Trajectory 4 showing a persistently elevated risk (HR\u0026thinsp;=\u0026thinsp;3.57, 95% CI: 1.68\u0026ndash;7.57, p\u0026thinsp;=\u0026thinsp;0.001). Further adjustment for clinical variables in Model 3 yielded consistent results, with Trajectory 3 continuing to show the highest mortality risk (HR\u0026thinsp;=\u0026thinsp;2.90, 95% CI: 1.56\u0026ndash;5.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Trajectory 4 remaining at a higher risk compared to the reference group (HR\u0026thinsp;=\u0026thinsp;2.40, 95% CI: 1.02\u0026ndash;5.62, p\u0026thinsp;=\u0026thinsp;0.045).\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\u003eResults of cox proportional hazards regression model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModel 3\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnadjusted HR (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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdjusted HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\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\u003eClass 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 Reference\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\u003cp\u003e1.00 Reference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00 Reference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.48 [1.70, 3.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.15 [1.48, 3.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.77 [1.15, 2.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.42 [3.32, 8.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.70 [3.49, 9.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.90 [1.56, 5.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.25 [1.54, 6.87]\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.57 [1.68, 7.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.40 [1.02, 5.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.045\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\u003eHR, hazard ratio; CI, confidence interval.\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 2 adjusted for demographic variables (age, gender, race).\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eModel 3 adjusted for variables including age, gender, race, weight, hypertension, myocardial infarction, congestive heart failure, peripheral vascular disease, chronic pulmonary disease, renal disease, Charlson comorbidity index, red blood cell count, white blood cell count, serum creatinine, mean arterial pressure, SAPS II score, OASIS score, delirium, and sepsis.\u003c/p\u003e\u003cp\u003eSubgroup analysis and forest plots\u003c/p\u003e\u003cp\u003eWe performed subgroup analysis to assess the stability of associations between BUN trajectories and 360-day mortality across main covariates and clinical subgroups, such as age, gender, race, MI, CHF, PVD, CPD, delirium, and sepsis (Supplementary Material Table S 3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Trajectory 3 was associated with a higher risk of mortality in the subgroup excluding patients who were \u0026le;\u0026thinsp;65 years old, female, non-white, without MI, with CPD, and with delirium. Within Trajectory group 1, patients aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years, male, white, with or without CHF, without PVD, without CPD, with delirium, and with sepsis exhibited a higher risk of mortality. In Trajectory group 4, patients who were male, white, without CPD, and with sepsis had a higher risk of mortality. Notably, the impact of trajectory on mortality risk did not significantly differ across all subgroups. (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cohort study, we employed LCMM to characterize dynamic changes in BUN levels over the first 96 hours after ICU admission following cardiac surgery and identified four distinct BUN trajectories that effectively stratified patients into clinically meaningful risk subgroups. The gradual increase, rapid decline from high level, and marked critical elevation trajectories all compared with the stable low-level trajectory (reference group) were associated with a statistically significant higher 360-day mortality hazard. As mentioned, particularly the last two trends had a poorer prognosis despite affecting only 6.28% and 3.14% of the cohort, respectively. These associations were potent after thorough adjustment for demographics, comorbidities and other covariates and were robust across most subgroups. Our findings indicate that monitoring for dynamic BUN may be used to identify high risk phenotypes early in the postoperative course, thus, enabling the use of individualized interventions to minimize unfavorable outcomes.\u003c/p\u003e\u003cp\u003eIt has been previously demonstrated that high BUN was correlated with a higher risk of mortality in coronary artery diseases, heart failure and other cardiovascular diseases (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). It\u0026rsquo;s important to identify the prognostic factors with deep insight as well in this area because the physiology of the circulation is dramatically affected by performing the cardiac surgery. Liu et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) has demonstrated that high BUN level was correlated with a longer time hospitalization (n\u0026thinsp;=\u0026thinsp;192) for aortic dissection surgery patients. Ye et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) reported that high BUN level correlated with elevated mortality at 28 days postoperatively in cardiac surgery patients. More recently, research in large numbers have expanded beyond short-term outcomes to evaluate mid- and long-term survival for cardiac surgery patients (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Yu et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), who reviewed 7,368 cardiac surgery patients, reported that BUN was identified an independent predictor of mid- to long-term mortality. Nevertheless, these previous studies were constrained by their single-timepoint BUN observation, that is, restricted to distinguish between temporary exchange versus chronic high-risk state. In this analysis we selected 360 days postoperatively as the time to endpoint, and derived four distinct BUN profiles post-cardiac surgery to potentially more accurately stratify mortality risk. This approach may be particularly applicable to ICU patients. For example, Zhang et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) demonstrated that serial serum phosphate trajectories outperformed single measurements in predicting ICU mortality, while Jiang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) showed that C-reactive protein trajectories provided superior risk classification in sepsis compared to isolated values.\u003c/p\u003e\u003cp\u003eAs we all know, BUN and Scr are used as the most common indexes as a renal index in clinical practice, but prior study has verified the high-impact of BUN from non-renal (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In our study, the BUN trajectory was independent and had an impact on prognosis even adjusting renal variables such as RD and Scr. This indicated that the prognostic value of BUN was less related to renal function and could therefore be multidimensional including hemodynamic stability, inflammatory process, and metabolic stress (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCompared with Trajectory 2, the remaining three trajectories exhibited higher mortality risk at 360 days postoperatively. For Trajectory 1, we noticed that the patients were generally old, with the highest mean systolic blood pressure and pulse pressure on day 1 in ICU. We speculate that elderly patients are more vulnerable to diastolic cardiac dysfunction and postoperative fluid overloads (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), that atherosclerosis in these patients are more sever and that large BP fluctuation in these patients may result in organ hypoperfusion (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Trajectory 3 likely reflects the process of \"transient ischemia-reperfusion injury during surgery.\" Patients in this group had higher fraction of CHF, higher mortality risk and incidence of postoperative delirium. Intra-operatively, in the case of low flow, low pressure and non-pulsatile perfusion encountered with cardiopulmonary by-pass renal perfusion is inadequate. That can induce oxidative stress, calcium overload and inflammation when cardiopulmonary by-pass is interrupted and renal reperfusion occurs (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This process can induce acute tubular necrosis that results in an abrupt elevation of BUN after 24 hours, which reaches a peak and returns to normal values with stabilization of renal function. At the same time, ischemia-reperfusion induces systemic inflammatory response syndrome, and inflammatory factors, including tumor necrosis factor-α, and interleukin-6, can trigger the neuroinflammatory response, resulting in a dysfunction of nerve cells and ultimately postoperative delirium (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Trajectory 4 may represent the pathological process of a \"malignant cycle of cardiorenal syndrome.\" This cohort\u0026rsquo;s patients had the highest rates of DM and RD, and the postoperative Scr levels were also the highest. After cardiac surgery, cardiac function may be affected, leading to a decrease in cardiac output and thus reduced renal perfusion. The presence of RD, which often has a very poor renal reserve function, increases the probability of renal injury arising from inadequate renal perfusion (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Moreover, renal dysfunction leads to retention of metabolic debris and stimulation of renin-angiotensin-aldosterone system producing water retention, overloaded the heart and worsened cardiac function leading to persistent elevation of BUN levels as in type 3 of cardiorenal syndrome (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough we fined prognostic value of postoperative BUN trajectories, several limitations should be acknowledged. Firstly, as a single-center retrospective study, our findings may be subject to selection bias. Future multicenter randomized controlled studies with larger sample sizes are needed. Secondly, we were unable to obtain data such as the EUROSCORE II and STS scores, which have been proven to be useful for assessing the prognosis of cardiac surgery patients. If these could be incorporated into our model, it might enhance the predictive accuracy for patient outcomes. Thirdly, although we adjusted for several variables, unmeasured variables such as detailed intraoperative hemodynamic parameters and specific vasoactive medication regimens could contribute to residual confounding. Finally, the requirement of \u0026ge;\u0026thinsp;4 BUN measurements may have excluded patients who died early after surgery, potentially introducing survival bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe retrospectively determined four clinically distinct postoperative BUN trajectories that can be used to stratify the cardiac surgery patients into prognostically relevant groups. Using LCMM of high-frequency BUN measurements in 1146 patients, we found that dynamic BUN patterns (particularly the rapid decline from high level - Trajectory 3, and marked critical elevation - Trajectory 4 trajectories) were highly predictive for 360day mortality. These results remained robust in subgroup analysis and may be associated with pathophysiological processes including ischemia-reperfusion injury, systemic inflammation, and cardio-renal syndrome.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBUN Blood urea nitrogen\u003c/p\u003e\n\u003cp\u003eICU Intensive care unit\u003c/p\u003e\n\u003cp\u003eLCMM Latent class mixed models\u003c/p\u003e\n\u003cp\u003eMIMIC-IV Medical Information Mart for Intensive Care IV\u003c/p\u003e\n\u003cp\u003eDM Diabetes mellitus\u003c/p\u003e\n\u003cp\u003eMI Myocardial infarction\u003c/p\u003e\n\u003cp\u003eCHF Congestive heart failure\u003c/p\u003e\n\u003cp\u003ePVD Peripheral vascular disease\u003c/p\u003e\n\u003cp\u003eCPD Chronic pulmonary disease\u003c/p\u003e\n\u003cp\u003eRD Renal disease\u003c/p\u003e\n\u003cp\u003eCCI Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003eScr Serum creatinine\u003c/p\u003e\n\u003cp\u003eBP Blood pressure\u003c/p\u003e\n\u003cp\u003eHR Heart rate\u003c/p\u003e\n\u003cp\u003eRR Respiratory rate\u003c/p\u003e\n\u003cp\u003eSAPS II Simplified Acute Physiology Score II\u003c/p\u003e\n\u003cp\u003eOASIS Outcome Prediction in Intensive Care\u003c/p\u003e\n\u003cp\u003eGCS Glasgow Coma Scale\u003c/p\u003e\n\u003cp\u003eSIRS Systemic Inflammatory Response Syndrome\u003c/p\u003e\n\u003cp\u003eAIC Akaike information criterion\u003c/p\u003e\n\u003cp\u003eBIC Bayesian information criterion\u003c/p\u003e\n\u003cp\u003eHR Hazard ratio\u003c/p\u003e\n\u003cp\u003eCI Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThe data for this study were extracted from the MIMIC-IV database (version 3.1). The privacy of all patients was protected, and the use of the database was approved by the Institutional Review Board of MIT and Beth Israel Deaconess Medical Center. This study was also approved by the Ethical Committee of Xuzhou No.1 People\u0026apos;s Hospital (XYYLL2025-089).\u003c/p\u003e\n\u003cp\u003eData\u0026nbsp;availability\u003c/p\u003e\n\u003cp\u003eThis study utilized data from a publicly available database, which can be accessed via https://www.physionet.org/content/mimiciv/3.1/ .\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eHY, JS, and SX designed the experiment. HY obtained the permission to access the data and collaborated with TZ to complete the data extraction. HY, TZ and QL participated in the data statistical analysis. HY, JS and SX jointly wrote the paper. QL, JS and SX provided professional consultation on the problems encountered in the research. All authors participated in the final manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Nature Science Foundation of China (No. 82371584).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe are grateful to MIT and BIDMC for enabling us to complete this research using the MIMIC-IV database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evon Wyler MC, Kaneko T, Iribarne A, Kim KM, Arghami A, Fiedler A, et al. The Society of Thoracic Surgeons Adult Cardiac Surgery Database: 2023 Update on Procedure Data and Research. Ann Thorac Surg. 2024;117(2):260\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandoni G, Lomivorotov V, Silvetti S, Nigro Neto C, Pisano A, Alvaro G, et al. Nonsurgical Strategies to Reduce Mortality in Patients Undergoing Cardiac Surgery: An Updated Consensus Process. J Cardiothorac Vasc Anesth. 2018;32(1):225\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeo SV, Sundaram V, Sahadevan J, Selvaganesan P, Mohan SM, Rubelowsky J, et al. Outcomes of coronary artery bypass grafting in patients with heart failure with a midrange ejection fraction. J Thorac Cardiovasc Surg. 2023;165(1):149\u0026ndash;e584.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArihan O, Wernly B, Lichtenauer M, Franz M, Kabisch B, Muessig J, et al. Blood Urea Nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU. PLoS ONE. 2018;13(1):e0191697.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLan Q, Zheng L, Zhou X, Wu H, Buys N, Liu Z, et al. The Value of Blood Urea Nitrogen in the Prediction of Risks of Cardiovascular Disease in an Older Population. Front Cardiovasc Med. 2021;8:614117.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHong C, Zhu H, Zhou X, Zhai X, Li S, Ma W et al. Association of Blood Urea Nitrogen with Cardiovascular Diseases and All-Cause Mortality in USA Adults: Results from NHANES 1999\u0026ndash;2006. Nutrients. 2023;15(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Z, Wang W, Wang M, He Q, Xu J, Zou K, et al. Blood Urine Nitrogen Trajectories of Acute Pancreatitis Patients in Intensive Care Units. J Inflamm Res. 2024;17:3449\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Sun LL, Wang J, Ji G. Blood urea nitrogen in the prediction of in-hospital mortality of patients with acute aortic dissection. Cardiol J. 2018;25(3):371\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe L, Shi H, Wang X, Duan Q, Ge P, Shao Y. Elevated Blood Urea Nitrogen to Serum Albumin Ratio Is an Adverse Prognostic Predictor for Patients Undergoing Cardiac Surgery. Front Cardiovasc Med. 2022;9:888736.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCurran TF, Sunkara B, Leis A, Lim A, Haft J, Engoren M. Outcomes After Prolonged ICU Stays in Postoperative Cardiac Surgery Patients. Fed Pract. 2022;39(Suppl 5):S6\u0026ndash;Sc11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson A, Bulgarelli L, Pollard T, Gow B, Moody B, Horng S et al. MIMIC-IV (version 3.1). PhysioNet. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJujo K, Minami Y, Haruki S, Matsue Y, Shimazaki K, Kadowaki H, et al. Persistent high blood urea nitrogen level is associated with increased risk of cardiovascular events in patients with acute heart failure. ESC Heart Fail. 2017;4(4):545\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichter B, Sulzgruber P, Koller L, Steininger M, El-Hamid F, Rothgerber DJ, et al. Blood urea nitrogen has additive value beyond estimated glomerular filtration rate for prediction of long-term mortality in patients with acute myocardial infarction. Eur J Intern Med. 2019;59:84\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlakht Y, Gilutz H, Shiyovich A. Decreased admission serum albumin level is an independent predictor of long-term mortality in hospital survivors of acute myocardial infarction. Soroka Acute Myocardial Infarction II (SAMI-II) project. Int J Cardiol. 2016;219:20\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcDonald B, van Walraven C, McIsaac DI. Predicting 1-Year Mortality After Cardiac Surgery Complicated by Prolonged Critical Illness: Derivation and Validation of a Population-Based Risk Model. J Cardiothorac Vasc Anesth. 2020;34(10):2628\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAktuerk D, McNulty D, Ray D, Begaj I, Howell N, Freemantle N, et al. National administrative data produces an accurate and stable risk prediction model for short-term and 1-year mortality following cardiac surgery. Int J Cardiol. 2016;203:196\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Y, Peng C, Zhang Z, Shen K, Zhang Y, Xiao J, et al. Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery. Front Cardiovasc Med. 2022;9:831390.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo P, Ma Y, Su W, Xie D, Li X, Wang K, et al. Association between baseline serum bicarbonate and the risk of postoperative delirium in patients undergoing cardiac surgery in the ICU: a retrospective study from the MIMIC-IV database. BMC Anesthesiol. 2024;24(1):347.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang X, Zhang C, Pan Y, Cheng X, Zhang W. Effects of C-reactive protein trajectories of critically ill patients with sepsis on in-hospital mortality rate. Sci Rep. 2023;13(1):15223.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalestracci A, Meni Battaglia L, Toledo I, Martin SM, Alvarado C. Blood urea nitrogen to serum creatinine ratio as a prognostic factor in diarrhea-associated hemolytic uremic syndrome: a validation study. Eur J Pediatr. 2018;177(1):63\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Xu X, Shi S, Gao X, Li Y, Wu H, et al. Blood urea nitrogen to creatinine ratio and long-term survival in patients with chronic heart failure. Eur J Med Res. 2023;28(1):343.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStark J. Interpretation of BUN and serum creatinine. An interactive exercise. Crit Care Nurs Clin North Am. 1998;10(4):491\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacedo E. Blood urea nitrogen beyond estimation of renal function. Crit Care Med. 2011;39(2):405\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedrich I, Simm A, K\u0026ouml;tting J, Th\u0026ouml;len F, Fischer B, Silber RE. Cardiac surgery in the elderly patient. Dtsch Arztebl Int. 2009;106(25):416\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteppan J, Barodka V, Berkowitz DE, Nyhan D. Vascular stiffness and increased pulse pressure in the aging cardiovascular system. Cardiol Res Pract. 2011;2011:263585.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBellini MI, Charalampidis S, Herbert PE, Bonatsos V, Crane J, Muthusamy A, et al. Cold Pulsatile Machine Perfusion versus Static Cold Storage in Kidney Transplantation: A Single Centre Experience. Biomed Res Int. 2019;2019:7435248.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Bellomo R. Cardiac surgery-associated acute kidney injury: risk factors, pathophysiology and treatment. Nat Rev Nephrol. 2017;13(11):697\u0026ndash;711.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Y, Li C, Zhu S, Jin L, Hu Y, Ling X, et al. Diagnosis, pathophysiology and preventive strategies for cardiac surgery-associated acute kidney injury: a narrative review. Eur J Med Res. 2023;28(1):45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMattimore D, Fischl A, Christophides A, Cuenca J, Davidson S, Jin Z et al. Delirium after Cardiac Surgery-A Narrative Review. Brain Sci. 2023;13(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoyer N, Eldridge J, Prowle JR, Forni LG. Postoperative Acute Kidney Injury. Clin J Am Soc Nephrol. 2022;17(10):1535\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBove T, Monaco F, Covello RD, Zangrillo A. Acute renal failure and cardiac surgery. HSR Proc Intensive Care Cardiovasc Anesth. 2009;1(3):13\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRangaswami J, Bhalla V, Blair JEA, Chang TI, Costa S, Lentine KL, et al. Cardiorenal Syndrome: Classification, Pathophysiology, Diagnosis, and Treatment Strategies: A Scientific Statement From the American Heart Association. Circulation. 2019;139(16):e840\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cardiac surgery, blood urea nitrogen, trajectory, MIMIC-IV database","lastPublishedDoi":"10.21203/rs.3.rs-6794317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6794317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePostoperative blood urea nitrogen (BUN) dynamics may reflect multisystem physiological stress after cardiac surgery, but their prognostic value remains underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study analyzed adult cardiac surgery patients from the MIMIC-IV database. BUN trajectories in the first 0\u0026ndash;96 hours post-admission to ICU after surgery were categorized by Latent class mixed models (LCMM). The study outcome was postoperative 360-day all-cause mortality. Kaplan-Meier survival analysis model and the log-rank test were used to evaluate the differences in the outcome among different trajectories. The Cox proportional hazards model was then applied to find the relationship between trajectories and the outcome, and to calculate the hazard ratio (HR). Finally, subgroup analysis was conducted to verify the stability of the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 1146 eligible patients were enrolled in this study, among whom 144 (12.6%) died during the follow-up period. 4 distinct trajectories were finally identified, with significant differences in postoperative 360-day all-cause mortality (log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Cox proportional hazards model revealed that, compared with the stable low-level trajectory (reference group), gradual increase trajectory (HR\u0026thinsp;=\u0026thinsp;2.48, 95% CI: 1.70\u0026ndash;3.60), rapid decline from high level trajectory (HR\u0026thinsp;=\u0026thinsp;5.42, 95% CI 3.32\u0026ndash;8.85), and marked critical elevation trajectory (HR\u0026thinsp;=\u0026thinsp;3.25, 95% CI 1.54\u0026ndash;6.87) were all associated with higher mortality risks. These differences persisted even after adjusting for variables in different models. In subgroup analysis the results persisted across most subgroups without any notable interaction (all p for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eEarly dynamic BUN patterns after cardiac surgery, better stratified patient mortality risk, and may be useful for the early risk assessment, personalized monitoring and prognostication.\u003c/p\u003e","manuscriptTitle":"Dynamic BUN Patterns in the ICU: Risk Stratification and Prognosis Following Cardiac Surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 06:06:03","doi":"10.21203/rs.3.rs-6794317/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-19T17:19:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T12:53:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T15:24:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64725379312079073367831886355909058765","date":"2025-07-22T09:47:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325929321230471204093829492973236886710","date":"2025-07-16T13:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T12:03:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-03T02:51:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-03T02:50:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2025-06-01T07:21:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"147a6554-2d54-4a56-9c75-85c61933a14f","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:10:09+00:00","versionOfRecord":{"articleIdentity":"rs-6794317","link":"https://doi.org/10.1186/s13019-026-03869-5","journal":{"identity":"journal-of-cardiothoracic-surgery","isVorOnly":false,"title":"Journal of Cardiothoracic Surgery"},"publishedOn":"2026-02-12 15:57:59","publishedOnDateReadable":"February 12th, 2026"},"versionCreatedAt":"2025-07-23 06:06:03","video":"","vorDoi":"10.1186/s13019-026-03869-5","vorDoiUrl":"https://doi.org/10.1186/s13019-026-03869-5","workflowStages":[]},"version":"v1","identity":"rs-6794317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6794317","identity":"rs-6794317","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}