The triglyceride paradox: a retrospective analysis based on the MIMIC-Ⅳ database

The triglyceride paradox: a retrospective analysis based on the MIMIC-Ⅳ database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The triglyceride paradox: a retrospective analysis based on the MIMIC-Ⅳ database Yujie Huang, Zhengjie Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4006758/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2024 Read the published version in European Journal of Medical Research → Version 1 posted 8 You are reading this latest preprint version Abstract Background Previous studies suggest that sepsis patients often have elevated triglyceride levels due to various factors, and higher levels may indicate a poorer prognosis. However, few studies have investigated whether lower triglycerides are associated with a better prognosis. Methods The Medical Information Mart for Intensive Care IV (MIMIC-IV) database provided all the data. To assess the association between triglycerides and prognosis, we used logistic regression analysis (LR) and Cox proportional hazards models. We further controlled for confounders using propensity score matching (PSM). Results Inclusion criteria were met by a total of 804 patients with a mean triglyceride of 103. We found that patients had a higher risk of 30-day ICU mortality and 30-day in-hospital mortality when triglycerides were in the second percentile (74 mg/dL-103 mg/dL). Interestingly, this group of patients seems to benefit more from the use of atorvastatin. Conclusion The relationship between triglyceride levels and prognosis in patients with sepsis is complex. Our study indicates that a poor prognosis is often associated with triglyceride levels in the range of 74 mg/dL-103 mg/dL. MIMIC-Ⅳ database triglyceride sepsis propensity score matching lipid metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Sepsis is a syndrome of multiple organ dysfunction caused by dysregulation of the systemic inflammatory response due to infection. The causes of sepsis are diverse, and effective treatments are still lacking despite intensive research ( 1 – 3 ). Furthermore, sepsis imposes a significant financial burden on national healthcare systems ( 4 , 5 ). The current understanding of sepsis is that it is a dysregulation of the systemic inflammatory or immune response. This view has gained widespread acceptance through in-depth study ( 6 ). Lipopolysaccharide, the primary component of bacterial endotoxin, plays a crucial role in the onset and development of sepsis. It induces a dramatic reaction in the host body, leading to metabolic changes that often accompany sepsis patients to varying degrees. Changes in lipid metabolism can result in reduced levels of circulating cholesterol, as well as alterations in high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A-I (Apo A-I), triglycerides (TG), and free fatty acids (FFAs) ( 7 , 8 ). Previous studies have primarily focused on changes in HDL and LDL levels in sepsis, with less attention given to triglycerides and their impact on patient prognosis. Furthermore, the role of triglycerides, which are essential to the body, in the development and progression of sepsis remains poorly understood. Elevated triglyceride levels are associated with a poorer prognosis, but it is unclear whether lower triglyceride levels indicate a better prognosis. The objective of this study was to examine the role of triglycerides in the onset and progression of sepsis. Our findings offer new insights into the development of strategies to improve the prognosis of sepsis patients. Methods Data Sources All data were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Of these, MIMIC-IV version 2.0, released in 2022, recorded information on 50,000 patients between 2008 and 2019. This database anonymizes all personal patient information so that informed consent is not required. Approval has also been granted for the sharing of research resources. Access to the database (certification number 9244195) was granted to one of the authors (yjh). Study Population The following criteria were required for inclusion in the study population 1. Sepsis and septic shock (sepsis-3) defined according to the Third International Consensus Definitions, that is, suspected or documented infection and an increase of at least 2 points in the total Sequential Organ Failure Assessment (SOFA) score. 2. Age > 18 years. 3. ICU stay > 24 hours. 4. Triglyceride data available on the day of ICU admission. 5. Study variables not missing. We extracted basic demographic information, comorbidities, laboratory parameters, medications, details of ICU stay, days of hospitalization, and prognosis. Outcomes Our primary outcome was 30-day all-cause ICU mortality, and secondary outcome included ICU mortality and 30-day all-cause in-hospital mortality. Data Extraction Structured Query Language (SQL) was used for data extraction. Patient demographics including age, gender, height, weight, and body mass index (BMI) were collected, and information on comorbidities such as cerebrovascular disease, congestive heart failure, chronic lung disease, diabetes, and acute respiratory distress syndrome (ARDS) were extracted according to the International Classification of Diseases (ICD) coding system. Initial records at the onset of sepsis including Systemic Inflammatory Response Syndrome score (SIRS), SOFA score, Charlson comorbidity index (CCI) score, vital signs (heart rate (HR), saturation of peripheral oxygen (Spo2), respiratory rate (RR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MBP)) and laboratory tests (cholesterol, triglycerides, LDL, HDL, hematocrit, hemoglobin, platelets, white blood cell (WBC), anion gap, bicarbonate, blood urea nitrogen (BUN), calcium, glucose, sodium, potassium, international normalized ratio (INR), prothrombin time (PT)). Statistical analysis Covariates were expressed in different ways depending on their category. Categorical variables were expressed as numbers (percentages) and tested by chi-squared test (or Fisher's exact test). Continuous variables were expressed as mean ± standard deviation or median (25%-75%) and tested by Student's t-test or Wilcoxon rank sum test. Propensity score matching (PSM) was performed using the "MatchIt" package, and survival analysis was performed using the "survival" and "survminer" packages. All analyses were performed with R 4.0.1. A two-tailed P value of less than 0.05 was considered statistically significant. Propensity Score Matching Propensity score matching was used to further adjust for the effects of confounders on primary and secondary outcomes. Matched variables included age, gender, BMI, SOFA score, SIRS score, CCI score, cholesterol, LDL, and HDL. PSM was performed using the MatchIt software package with selection parameters, nearest neighbor matching method, 1:1 matching ratio, and a caliper value of 0.2 without replacement. Standardized mean difference (SMD) was used to assess the balance of variables between groups before and after matching, and SMD less than 0.25 was considered "balanced". The "cobalt" was also used to test whether the matched data were balanced. Results Baseline data Figure 1 shows the patient selection process. A total of 804 patients with sepsis were included in our study, the mean age of the patients was 68 years, of which 460 were males and 344 were females. The patients' mean ICU stay was 8.13 days and the average length of stay (LOS) was 15.37 days. Regarding the outcome, 112 patients died in the ICU with an ICU mortality rate of 13.93%. 143 patients died in the hospital with an in-hospital mortality rate of 17.79% (Table 1 ). Table 1 Baseline characteristics of sepsis patients Variables N Age (year), mean (sd) 68(14.86) Gender, n (%) Male 460(57.21) Female 344(42.79) Cholesterol level (mg dl − 1 ), mean (sd) 145.44(51.17) HDL level (mg dl − 1 ), mean (sd) 43.56(18.30) LDL level (mg dl − 1 ), mean (sd) 77.85(41.72) Triglyceride level (mg dl − 1 ), mean (sd) 120.23(65.70) Hematocrit (%) 34.95(6.37) Hemoglobin level (g dl − 1 ), mean (sd) 11.54(2.21) Platelets count (10 9 L − 1 ), mean (sd) 205.78(94.55) WBC count (10 9 L − 1 ), mean (sd) 12.44(10.17) Anion gap (mEq L − 1 ), mean (sd) 15.42(3.29) Bicarbonate (mEq L − 1 ), mean (sd) 22.64(4.01) BUN level (mg dl − 1 ), mean (sd) 26.27(18.38) Calcium level (mg dl − 1 ), mean (sd) 8.54(0.71) Glucose level (mg dl − 1 ), mean (sd) 151.27(61.37) Sodium level (mg dl − 1 ), mean (sd) 139.28(4.57) Potassium level (mg dl − 1 ), mean (sd) 4.16(0.55) INR, mean (sd) 1.42(0.71) PT level (sec), mean (sd) 15.46(7.29) HR (beats min − 1 ), mean (sd) 83( 17 ) MBP (mm Hg), mean (sd) 82( 12 ) SBP (mm Hg), mean (sd) 124( 20 ) DBP (mm Hg), mean (sd) 65( 12 ) RR (times min − 1 ) 20( 4 ) Spo2 (%) 98( 2 ) Weight (kg) 83.6(23.7) Height (cm) 168.9(12.0) BMI (kg m − 2 ) 29.38(9.79) LOS ICU (days) 8.13(8.16) LOS Hospital (days) 15.37(15.12) ICU Expire, n (%) Dead 112(13.93) Alive 692(86.07) Hospital_Expire, n (%) Dead 143(17.79) Alive 661(82.21) SIRS, median (IQR) 3( 2 – 3 ) CCI, median (IQR) 7( 5 – 9 ) SOFA, median (IQR) 4( 2 – 4 ) Triglycerides and 30-day ICU mortality Cox proportional hazards model was used to examine the association between triglycerides and 30-day ICU mortality. 31 potential variables were first subjected to univariate Cox regression analysis (Table 2 ), and 16 variables with P < 0.2 were included in the subsequent multivariate Cox regression analysis, and finally we found that triglycerides were a potential protective factor (HR:0.996, 95% CI:0.99-1.00, P = 0.030) (Table 3 ). Table 2 Univariate Cox analysis for 30-day ICU mortality Variables HR (95%CI) P Value Age 1.02 (1.01–1.04) 0.002 Gender 1.34 (0.90-2.00) 0.144 Weight 1.01 (1.00-1.01) 0.211 Height 1.01 (0.99–1.03) 0.250 BMI 1.00 (0.98–1.02) 0.951 Cholesterol 1.00 (1.00–1.00) 0.465 HDL 1.00 (0.99–1.01) 0.983 LDL 1.00 (1.00-1.01) 0.126 Triglyceride 1.00 (0.99-1.00) 0.043 Hematocrit 0.99 (0.96–1.02) 0.480 Hemoglobin 0.96 (0.89–1.04) 0.327 Platelets 1.00 (1.00–1.00) 0.171 WBC 1.00 (0.98–1.02) 0.822 Anion gap 1.05 (1.00-1.10) 0.063 Bicarbonate 0.94 (0.89–0.98) 0.008 BUN 1.01 (1.00-1.02) 0.076 Calcium 1.03 (0.79–1.34) 0.811 Glucose 1.00 (1.00-1.01) < 0.001 Sodium 1.01 (0.96–1.05) 0.812 Potassium 1.63 (1.24–2.15) < 0.001 INR 1.17 (0.97–1.42) 0.104 PT 1.01 (1.00-1.03) 0.144 HR 1.00 (0.99–1.01) 0.841 MBP 0.98 (0.97-1.00) 0.030 SBP 0.99 (0.98-1.00) 0.115 DBP 0.99 (0.98–1.01) 0.311 RR 1.01 (0.97–1.06) 0.496 Spo2 1.03 (0.94–1.13) 0.571 SIRS 1.27 (1.03–1.57) 0.026 CCI 1.08 (1.01–1.16) 0.024 SOFAscore 1.04 (0.95–1.13) 0.396 Table 3 Multivariate Cox analysis for 30-day ICU mortality Variables HR (95%CI) P Value Age 1.03 (1.01–1.04) 0.003 Gender 1.43 (0.93–2.20) 0.100 LDL 1.01 (1.00-1.01) 0.001 Triglyceride 1.00 (0.99-1.00) 0.032 Platelets 1.00 (1.00–1.00) 0.094 Anion gap 0.99 (0.92–1.06) 0.718 Bicarbonate 0.92 (0.86–0.98) 0.015 BUN 1.00 (0.99–1.01) 0.445 Glucose 1.00 (1.00-1.01) 0.014 Potassium 1.49 (1.05–2.13) 0.028 INR 3.01 (0.36–25.39) 0.311 PT 0.92 (0.74–1.14) 0.438 MBP 0.98 (0.95–1.01) 0.125 SBP 1.01 (0.99–1.02) 0.321 SIRS 1.36 (1.08–1.70) 0.009 CCI 1.01 (0.92–1.10) 0.839 We then transformed triglycerides into categorical variables based on quartiles, that is, 0%-25% for TC1 (19 mg/dL-74 mg/dL), 25%-50% for TC2 (74 mg/dL-103 mg/dL), 50%-75% for TC3 (103 mg/dL-144 mg/dL), and 75%-100% for TC4 (144 mg/dL-481 mg/dL). We first analyzed the cumulative incidence of 30-day ICU mortality using the Kaplan-Meier (KM) method and evaluated it with the log-rank test Fig. 2 . We found that the prognosis of the TC2 group was worse than that of the TC1, TC3, and TC4 groups (P = 0.044). Based on this phenomenon, we further simplified the grouping, the TC2 group was defined as TCA, and the three groups, TC1, TC3 and TC4, were redefined as TCB. The KM curve was then redrawn, and the results showed that TCA had a worse prognosis (P = 0.010) (Fig. 3 ). Since there was an intersection point in the KM curve (around 5.5 days), landmark was used to further analyze the "jskm" based package, and the results showed that TCA had a higher risk of death (P < 0.001) from 5.5 days to 30 days Figure S1 . Cox proportional hazards analysis showed that the TCB group had a better prognosis in model 1 adjusted for 3 variables (includes gender, age and BMI) (HR:0.65, 95% CI: 0.44–0.96, P = 0.033). Similarly, in model 2 adjusted for 16 variables (includes age, cholesterol, LDL, WBC, anion gap, bicarbonate, BUN, glucose, potassium, INR, PT, CCI, MBP, SBP, SIRS and RR), the risk of ICU death was lower in the TCB group (HR:0.63,95% CI:0.41–0.95, P = 0.026) (Table 4 ). Table 4 The relationship between different triglyceride groups and 30-day ICU mortality Categories Model 1 Model 2 HR (95%CI) P value HR (95%CI) P value TC A Ref. Ref. TC B 0.65(0.44–0.96) 0.033 0.63(0.41–0.95) 0.026 Triglycerides and 30-day in-hospital mortality The in-hospital mortality rate was 23.38% (47/201) in the TCA group and 14.43% (87/603) in the TCB group. Both univariate Cox regression analysis (HR:0.60,95% CI:0.42–0.86, P = 0.005) (Table 5 ) and multivariate Cox regression analysis (HR:0.58,95% CI:0.40–0.83, P = 0.003) (Table 6 ) suggested that the risk of 30-day in-hospital mortality was lower in the TCB group than in the TCA group. Table 5 Univariate Cox analysis for 30-day in-hospital mortality Variables HR (95%CI) P Value Age 1.02 (1.01–1.04) < 0.001 Gender 1.23 (0.87–1.75) 0.245 Weight 1.00 (0.99–1.01) 0.609 Height 1.01 (0.99–1.02) 0.416 BMI 1.00 (0.98–1.01) 0.714 Cholesterol 1.00 (1.00-1.01) 0.136 HDL 1.00 (0.99–1.01) 0.749 LDL 1.00 (1.00-1.01) 0.020 Triglyceride 0.60 (0.42–0.86) 0.005 Hematocrit 1.01 (0.98–1.04) 0.475 Hemoglobin 1.01 (0.94–1.09) 0.777 Platelets 1.00 (1.00–1.00) 0.562 WBC 1.01 (1.00-1.02) 0.030 Aniongap 1.07 (1.02–1.12) 0.003 Bicarbonate 0.93 (0.89–0.97) 0.001 BUN 1.01 (1.00-1.02) 0.033 Calcium 1.00 (0.78–1.28) 0.996 Glucose 1.00 (1.00-1.01) 0.002 Sodium 1.02 (0.98–1.06) 0.260 Potassium 1.53 (1.18–1.98) 0.001 INR 1.16 (0.97–1.38) 0.112 PT 1.01 (0.99–1.03) 0.164 HR 1.00 (0.99–1.01) 0.941 MBP 0.99 (0.97-1.00) 0.063 SBP 0.99 (0.99-1.00) 0.193 DBP 0.99 (0.98–1.01) 0.264 RR 1.03 (0.99–1.07) 0.126 Spo2 1.02 (0.94–1.11) 0.663 Sirs 1.34 (1.10–1.63) 0.004 CCI 1.06 (1.00-1.12) 0.064 Sofascore 1.01 (0.94–1.08) 0.812 Table 6 Multivariate Cox analysis for 30-day in-hospital mortality Variables HR (95%CI) P Value Age 1.03 (1.01–1.04) < 0.001 Cholesterol 1.00 (0.99–1.01) 0.475 LDL 1.01 (1.00-1.02) 0.059 TC B 0.57 (0.39–0.84) 0.004 WBC 1.00 (0.99–1.01) 0.825 Aniongap 1.01 (0.95–1.07) 0.835 Bicarbonate 0.92 (0.88–0.98) 0.004 BUN 1.00 (0.99–1.01) 0.727 Glucose 1.00 (1.00–1.00) 0.114 Potassium 1.44 (1.05–1.98) 0.025 INR 3.76 (0.66–21.43) 0.137 PT 0.89 (0.75–1.06) 0.197 MBP 0.98 (0.96–1.01) 0.152 SBP 1.00 (0.99–1.02) 0.596 RR 1.01 (0.96–1.05) 0.792 Sirs 1.35 (1.09–1.67) 0.006 CCI 0.99 (0.92–1.06) 0.724 Triglycerides and ICU all-cause mortality We further investigated the relationship between triglycerides and ICU all-cause mortality. The in-hospital mortality was 18.91% (38/201) in the TCA group and 12.27% (74/603) in the TCB group. Logistic regression analysis suggested a lower risk of all-cause ICU mortality in the TCB group compared to the TCA group (OR:0.60,95% CI:0.39–0.92, P = 0.020) (Fig. 4 ). Propensity score matching showed that a total of 400 patients were successfully matched, 200 each in the TCA and TCB groups, and all data were balanced after matching compared to the pre-matching data (Table 7 ). Logistic regression analysis of the matched data showed that the risk of all-cause ICU death was lower in the TCB group compared to the TCA group (OR:0.55,95% CI:0.31–0.96, P = 0.039) (Fig. 4 ). Table 7 Standardized mean difference pre-post propensity score matching Variables Unmatched Matched P SMD P SMD Age 0.118 0.128 0.613 0.051 CCI 0.042 0.165 0.248 0.116 SOFA 0.682 0.032 0.769 0.029 Gender 0.285 0.094 0.044 0.213 BMI 0.116 0.142 0.514 0.065 SIRS 0.644 0.037 0.830 0.022 HDL 0.149 0.118 0.416 0.082 LDL 0.010 0.226 0.412 0.082 Cholesterol 0.001 0.289 0.325 0.099 Subgroup analysis We performed a subgroup analysis of 804 patients with the study endpoint of 30-day ICU mortality, and the subgroup analysis showed that among these patients with CCI score > 6, SOFA score > 2, BMI < 30, combined congestive heart failure, diabetes and uncomplicated ARDS, cerebrovascular disease, and chronic lung disease, the TCB group had a lower risk of ICU mortality (Fig. 5 ). Further analysis showed that there were also differences in lipid levels between the TCA and TCB groups, with both cholesterol and LDL levels significantly lower in the TCA group than in the TCB group (Fig. 6 ). Statin drugs In the TCA and TCB groups, we then analyzed the different responses to statins. There were three statins in the analysis: atorvastatin, rosuvastatin and simvastatin. Our selection criteria were patients with more than 5 days of use and an initial measurement of more than 10 mg. Subgroup analysis showed that among patients using atorvastatin, the TCB group had a lower 30-day ICU mortality rate compared with the TCA group (HR: 0.15, 95% CI: 0.05–0.44, P = 0.001) (Table S1 ). However, this did not appear to be the case for rosuvastatin and simvastatin. Further survival analysis also showed that among patients treated with atorvastatin, the TCB group had a better prognosis (P < 0.001) (Fig. 7 ). Discussion Previous studies have shown that sepsis affects lipid metabolism and alters the lipid profile of patients, as evidenced by decreased levels of HDL and phospholipids and increased levels of cholesterol and triglycerides ( 9 – 12 ). Of these, most studies have been done on HDL, while few studies have been done on triglycerides, and the relationship between triglycerides and the prognosis of patients with sepsis is not very well established. Cetinkaya A et al. showed that triglyceride levels greater than 150 mg/dL at the time of septic attack were a significant predictive marker of sepsis mortality ( 13 ), while Lee SH et al. had a different view and their study showed that triglyceride levels greater than 150 mg/dL were a significant predictive marker of sepsis mortality ( 14 ). Van der Poll et al investigated the effect of hypertriglyceridemia on the human response to LPS and in vitro experiments showed that triglyceride-rich fat emulsions inhibited LPS-induced human whole blood cytokine production in vitro ( 15 ). Thus, the relationship between triglyceride levels and the prognosis of patients with sepsis is unclear, and although most studies tend to support an adverse role for triglycerides in sepsis, such studies tend to base their conclusions more on in vitro experiments or animal experiments, and very few studies are based on clinical data from large samples ( 16 ). Our study is a retrospective cohort analysis based on the MIMIC database. In our study, we found that triglyceride levels significantly affect the prognosis of sepsis patients. We discovered that triglycerides appeared to be a protective factor that reduced 30-day ICU mortality in sepsis patients. When triglycerides were further analyzed by converting them into categorical variables, an interesting finding was that patients had the worst prognosis when triglyceride levels were in the range of 74 mg/dL-103 mg/dL. While most previous studies support the conclusion that excessively high triglycerides tend to indicate a poor prognosis, few studies have focused on whether lower triglyceride levels are associated with a better prognosis. Our study provides a new idea. Additional studies have shown that patients in the TCA group also had significantly lower cholesterol and LDL levels than those in the TCB group. Numerous studies have shown that in patients with sepsis, there is often a decrease in total cholesterol, HDL and LDL cholesterol, HDL and LDL ( 17 – 19 ). LDL has been shown to bind to LPS, thereby attenuating the effects of sepsis on the body ( 20 ). Feingold KR et al. also found that pharmacologically increasing the number of hepatic LDL receptors in rats was able to reduce the mortality rate due to LPS-induced mortality in rats ( 21 ). Lower cholesterol levels also tend to predict poor outcomes, a phenomenon also supported by several studies ( 22 – 27 ). Since cholesterol itself plays an important role in several aspects of cell membrane function, immunomodulation, and hormone synthesis, this may also be the reason why cholesterol reduction is strongly associated with poor prognosis. Since both LDL and cholesterol levels were lower in the TCA group of patients, these factors may be important contributors to their poor prognosis. Several previous studies have come to less optimistic conclusions about whether statin drugs can improve the prognosis of patients with sepsis (28, 29). Our study discovered that patients taking atorvastatin had a better prognosis in the TCB group compared to the TCA group. However, we do not know the reason for this phenomenon. In addition, subgroup analyses showed that the TCB group had a lower risk of ICU death compared with the TCA group in patients with a Charlson comorbidity index greater than 6, a SOFA score greater than 2, a BMI less than 30, and a combination of congestive heart failure, diabetes mellitus and uncomplicated ARDS, cerebrovascular disease, and chronic lung disease. These differences may provide a basis for individualized treatment of patients with sepsis. Conclusion Our study showed that the relationship between triglyceride levels and prognosis in patients with sepsis is complex, with patients having the worst prognosis when triglyceride levels were 74–103 mg/dL. Declarations Data availability statement All data in this study are from open public databases. These data can be accessed by passing the appropriate tests. Ethics statement The data involved in this study were anonymized with patient personal information and passed the requirements of the local legislature, so no additional ethical approval was required. Author contributions YJH and ZJS designed the study. YJH was responsible for data extraction, screening and analysis. ZJS was responsible for drafting the manuscript. YJH and ZJS were responsible for data quality management and revision of the manuscript. Funding This work was funded by Jiangsu Province Ability Improvement Project through Science, Technology and Education. Jiangsu Provincial Medical Innovation Center (Grant ID: No. CXZX202231). References Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–211. doi: 10.1016/S0140-6736(19)32989-7 . Cavaillon JM, Singer M, Skirecki T. 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ApoE-containing high density lipoproteins and phospholipid transfer protein activity increase in patients with a systemic inflammatory response. J Lipid Res. 2001;42(2):281–290. Carpentier YA, Scruel O. Changes in the concentration and composition of plasma lipoproteins during the acute phase response. Curr Opin Clin Nutr Metab Care. 2002;5(2):153–158. doi: 10.1097/00075197-200203000-00006 . Cetinkaya A, Erden A, Avci D, et al. Is hypertriglyceridemia a prognostic factor in sepsis?. Ther Clin Risk Manag. 2014;10:147–150. Published 2014 Feb 27. doi: 10.2147/TCRM.S57791 . Lee SH, Park MS, Park BH, et al. Prognostic Implications of Serum Lipid Metabolism over Time during Sepsis. Biomed Res Int. 2015;2015:789298. doi: 10.1155/2015/789298 . van der Poll T, Braxton CC, Coyle SM, et al. Effect of hypertriglyceridemia on endotoxin responsiveness in humans. Infect Immun. 1995;63(9):3396–3400. doi: 10.1128/iai.63.9.3396-3400.1995 . 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Van Lenten BJ, Fogelman AM, Haberland ME, Edwards PA. The role of lipoproteins and receptor-mediated endocytosis in the transport of bacterial lipopolysaccharide. Proc Natl Acad Sci U S A. 1986;83(8):2704–2708. doi: 10.1073/pnas.83.8.2704 . Feingold KR, Funk JL, Moser AH, Shigenaga JK, Rapp JH, Grunfeld C. Role for circulating lipoproteins in protection from endotoxin toxicity. Infect Immun. 1995;63(5):2041–2046. doi: 10.1128/iai.63.5.2041-2046.1995 . Cirstea M, Walley KR, Russell JA, Brunham LR, Genga KR, Boyd JH. Decreased high-density lipoprotein cholesterol level is an early prognostic marker for organ dysfunction and death in patients with suspected sepsis. J Crit Care. 2017;38:289–294. doi: 10.1016/j.jcrc.2016.11.041 . Lekkou A, Mouzaki A, Siagris D, Ravani I, Gogos CA. Serum lipid profile, cytokine production, and clinical outcome in patients with severe sepsis. J Crit Care. 2014;29(5):723–727. doi: 10.1016/j.jcrc.2014.04.018 . Chien JY, Jerng JS, Yu CJ, Yang PC. Low serum level of high-density lipoprotein cholesterol is a poor prognostic factor for severe sepsis [published correction appears in Crit Care Med. 2005;33(11):2727]. Crit Care Med. 2005;33(8):1688–1693. doi: 10.1097/01.ccm.0000171183.79525.6b . Walley KR, Boyd JH, Kong HJ, Russell JA. Low Low-Density Lipoprotein Levels Are Associated With, But Do Not Causally Contribute to, Increased Mortality in Sepsis. Crit Care Med. 2019;47(3):463–466. doi: 10.1097/CCM.0000000000003551 . Vavrova L, Rychlikova J, Mrackova M, Novakova O, Zak A, Novak F. Increased inflammatory markers with altered antioxidant status persist after clinical recovery from severe sepsis: a correlation with low HDL cholesterol and albumin. Clin Exp Med. 2016;16(4):557–569. doi: 10.1007/s10238-015-0390-1 . Kruger P, Bailey M, Bellomo R, et al. A multicenter randomized trial of atorvastatin therapy in intensive care patients with severe sepsis. Am J Respir Crit Care Med. 2013;187(7):743–750. doi: 10.1164/rccm.201209-1718OC . National Heart, Lung, and Blood Institute ARDS Clinical Trials Network, Truwit JD, Bernard GR, et al. Rosuvastatin for sepsis-associated acute respiratory distress syndrome. N Engl J Med. 2014;370(23):2191–2200. doi: 10.1056/NEJMoa1401520 . Additional Declarations No competing interests reported. Supplementary Files figs1.tif Figure S1. Landmark analyses for 30-day ICU mortality plotted after re-division into two groups. Cut-off value is time = 5.5 days. TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 01 Apr, 2024 Reviews received at journal 13 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviewers invited by journal 09 Mar, 2024 Editor assigned by journal 08 Mar, 2024 Submission checks completed at journal 06 Mar, 2024 First submitted to journal 02 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4006758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276731232,"identity":"6a0221af-209a-4971-9ef8-1983bfdc13cb","order_by":0,"name":"Yujie Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Huang","suffix":""},{"id":276731233,"identity":"99f45ce0-c102-44b7-a0c4-b00bca2a1698","order_by":1,"name":"Zhengjie Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJACZhDB2MzA+ADKNiBaC7MBaVqAgE2CKC3yM3IPfy6ouGPX3M58rPJnm7U8A3vzNgmGmjs4tRjcyEswnnHmWXJjM1vabd62dMMGnmNlEgzHnuHWIpFjkMzbdjiZsZnH7DZj22HGBokcMwnGhsN4HJZjcJj3H0gL/7fCn22H7Rvk3+DXwnAjx7CZt+GwHdAWNgagdYkNEjz4tRiceWPMzHPscAJjM5uxNM+59OQ2nrRii4RjeBzWnmP8mafmsL1h/+GHH3+UWdv2sx/eeONDDR6HQUHixgYoiw1EJBDUwMBgL0+EolEwCkbBKBihAABdq1EvBfp5cwAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Zhengjie","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-03-02 15:16:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4006758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4006758/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-024-02159-x","type":"published","date":"2024-11-26T15:58:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52296115,"identity":"9b60061e-6888-4cc7-ae9b-0e67e1ee5aef","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":350034,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart to illustrate the selection of patients from the MIMIC IV database\u003c/p\u003e","description":"","filename":"Onlinefig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/953420983510b143af4c423d.png"},{"id":52296117,"identity":"7cdd24dc-d087-449a-aca1-9a9f6769f628","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123143,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve for plotting 30-day ICU mortality based on triglyceride quartiles. TC1 (19 mg/dL-74 mg/dL), TC2 (74 mg/dL-103 mg/dL), TC3 (103 mg/dL-144 mg/dL), TC4 (144 mg/dL-481 mg/dL).\u003c/p\u003e","description":"","filename":"Onlinefig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/6872be901006496ff1078256.png"},{"id":52296118,"identity":"fb2a8bd5-790e-4a1e-b015-1d5fedcb3366","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122098,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for 30-day ICU mortality plotted after re-division into two groups. TC2 is defined as TCA, TC1, TC3 and TC4 were redefined as TCB.\u003c/p\u003e","description":"","filename":"Onlinefig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/379478c04c87915e78cf94a3.png"},{"id":52296121,"identity":"522ff76c-cde9-42f7-b4a8-b834fe33d883","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327025,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic regression analysis was conducted to examine the odds ratios of mortality in the original and PSM-adjusted cohorts. PSM: propensity score matching.\u003c/p\u003e","description":"","filename":"Onlinefig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/b26ca305d50b65e08caf46e9.png"},{"id":52296120,"identity":"019ddcec-daec-4b47-b6e1-b6337627bac9","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":730919,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots displaying hazard ratios for the primary endpoint across various subgroups. HR, hazard ratio; CI, confidence interval; CCI, Charlson comorbidity index; BMI, body mass index.\u003c/p\u003e","description":"","filename":"Onlinefig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/28e0f1141b85a35aeeae733e.png"},{"id":52296119,"identity":"21090241-54e8-42b8-956f-6f1eebe837bd","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":157993,"visible":true,"origin":"","legend":"\u003cp\u003eCholesterol and LDL levels in different subgroups.\u003c/p\u003e","description":"","filename":"Onlinefig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/d2cc6a68e769379c5c8e5836.png"},{"id":52296122,"identity":"2ccf3574-5cac-4f9d-86b6-55750629cfda","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":111926,"visible":true,"origin":"","legend":"\u003cp\u003eAtorvastatin and prognosis by subgroup.\u003c/p\u003e","description":"","filename":"Onlinefig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/1a78ac6db32bed6e778b381a.png"},{"id":70389382,"identity":"79d09f6d-81a8-48ca-8528-482a69211b14","added_by":"auto","created_at":"2024-12-02 17:28:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4123187,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/755f6a44-4259-4314-ae52-93fe686684b6.pdf"},{"id":52296123,"identity":"a9b06af0-081e-45e7-b541-05bec4708c22","added_by":"auto","created_at":"2024-03-08 18:04:58","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2672296,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Landmark analyses for 30-day ICU mortality plotted after re-division into two groups. Cut-off value is time = 5.5 days.\u003c/p\u003e","description":"","filename":"figs1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/eaa54de037e7f7686df28c86.tif"},{"id":52296116,"identity":"739b6441-6ba6-4710-9c5a-3a714fb88b4b","added_by":"auto","created_at":"2024-03-08 18:04:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15694,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4006758/v1/3608b8a435ff838bbf46b544.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The triglyceride paradox: a retrospective analysis based on the MIMIC-Ⅳ database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a syndrome of multiple organ dysfunction caused by dysregulation of the systemic inflammatory response due to infection. The causes of sepsis are diverse, and effective treatments are still lacking despite intensive research (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Furthermore, sepsis imposes a significant financial burden on national healthcare systems (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current understanding of sepsis is that it is a dysregulation of the systemic inflammatory or immune response. This view has gained widespread acceptance through in-depth study (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Lipopolysaccharide, the primary component of bacterial endotoxin, plays a crucial role in the onset and development of sepsis. It induces a dramatic reaction in the host body, leading to metabolic changes that often accompany sepsis patients to varying degrees. Changes in lipid metabolism can result in reduced levels of circulating cholesterol, as well as alterations in high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A-I (Apo A-I), triglycerides (TG), and free fatty acids (FFAs) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have primarily focused on changes in HDL and LDL levels in sepsis, with less attention given to triglycerides and their impact on patient prognosis. Furthermore, the role of triglycerides, which are essential to the body, in the development and progression of sepsis remains poorly understood. Elevated triglyceride levels are associated with a poorer prognosis, but it is unclear whether lower triglyceride levels indicate a better prognosis.\u003c/p\u003e \u003cp\u003eThe objective of this study was to examine the role of triglycerides in the onset and progression of sepsis. Our findings offer new insights into the development of strategies to improve the prognosis of sepsis patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eAll data were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Of these, MIMIC-IV version 2.0, released in 2022, recorded information on 50,000 patients between 2008 and 2019. This database anonymizes all personal patient information so that informed consent is not required. Approval has also been granted for the sharing of research resources. Access to the database (certification number 9244195) was granted to one of the authors (yjh).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe following criteria were required for inclusion in the study population 1. Sepsis and septic shock (sepsis-3) defined according to the Third International Consensus Definitions, that is, suspected or documented infection and an increase of at least 2 points in the total Sequential Organ Failure Assessment (SOFA) score. 2. Age\u0026thinsp;\u0026gt;\u0026thinsp;18 years. 3. ICU stay\u0026thinsp;\u0026gt;\u0026thinsp;24 hours. 4. Triglyceride data available on the day of ICU admission. 5. Study variables not missing.\u003c/p\u003e \u003cp\u003eWe extracted basic demographic information, comorbidities, laboratory parameters, medications, details of ICU stay, days of hospitalization, and prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eOur primary outcome was 30-day all-cause ICU mortality, and secondary outcome included ICU mortality and 30-day all-cause in-hospital mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Extraction\u003c/h2\u003e \u003cp\u003eStructured Query Language (SQL) was used for data extraction. Patient demographics including age, gender, height, weight, and body mass index (BMI) were collected, and information on comorbidities such as cerebrovascular disease, congestive heart failure, chronic lung disease, diabetes, and acute respiratory distress syndrome (ARDS) were extracted according to the International Classification of Diseases (ICD) coding system. Initial records at the onset of sepsis including Systemic Inflammatory Response Syndrome score (SIRS), SOFA score, Charlson comorbidity index (CCI) score, vital signs (heart rate (HR), saturation of peripheral oxygen (Spo2), respiratory rate (RR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MBP)) and laboratory tests (cholesterol, triglycerides, LDL, HDL, hematocrit, hemoglobin, platelets, white blood cell (WBC), anion gap, bicarbonate, blood urea nitrogen (BUN), calcium, glucose, sodium, potassium, international normalized ratio (INR), prothrombin time (PT)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCovariates were expressed in different ways depending on their category. Categorical variables were expressed as numbers (percentages) and tested by chi-squared test (or Fisher's exact test). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (25%-75%) and tested by Student's t-test or Wilcoxon rank sum test.\u003c/p\u003e \u003cp\u003ePropensity score matching (PSM) was performed using the \"MatchIt\" package, and survival analysis was performed using the \"survival\" and \"survminer\" packages. All analyses were performed with R 4.0.1. A two-tailed P value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePropensity Score Matching\u003c/h2\u003e \u003cp\u003ePropensity score matching was used to further adjust for the effects of confounders on primary and secondary outcomes. Matched variables included age, gender, BMI, SOFA score, SIRS score, CCI score, cholesterol, LDL, and HDL. PSM was performed using the MatchIt software package with selection parameters, nearest neighbor matching method, 1:1 matching ratio, and a caliper value of 0.2 without replacement. Standardized mean difference (SMD) was used to assess the balance of variables between groups before and after matching, and SMD less than 0.25 was considered \"balanced\". The \"cobalt\" was also used to test whether the matched data were balanced.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline data\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the patient selection process. A total of 804 patients with sepsis were included in our study, the mean age of the patients was 68 years, of which 460 were males and 344 were females. The patients' mean ICU stay was 8.13 days and the average length of stay (LOS) was 15.37 days. Regarding the outcome, 112 patients died in the ICU with an ICU mortality rate of 13.93%. 143 patients died in the hospital with an in-hospital mortality rate of 17.79% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eBaseline characteristics of sepsis patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \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\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (year), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(14.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460(57.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e344(42.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCholesterol level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145.44(51.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.56(18.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.85(41.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.23(65.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHematocrit (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.95(6.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemoglobin level (g dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.54(2.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelets count (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eL\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205.78(94.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC count (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eL\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.44(10.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnion gap (mEq L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.42(3.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBicarbonate (mEq L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.64(4.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.27(18.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalcium level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.54(0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.27(61.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.28(4.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotassium level (mg dl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.16(0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eINR, mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42(0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePT level (sec), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.46(7.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHR (beats min\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMBP (mm Hg), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP (mm Hg), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP (mm Hg), mean (sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRR (times min\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpo2 (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.6(23.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168.9(12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg m\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.38(9.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOS ICU (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.13(8.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOS Hospital (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.37(15.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU Expire, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDead\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112(13.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e692(86.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital_Expire, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDead\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143(17.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e661(82.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIRS, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTriglycerides and 30-day ICU mortality\u003c/h2\u003e \u003cp\u003eCox proportional hazards model was used to examine the association between triglycerides and 30-day ICU mortality. 31 potential variables were first subjected to univariate Cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and 16 variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.2 were included in the subsequent multivariate Cox regression analysis, and finally we found that triglycerides were a potential protective factor (HR:0.996, 95% CI:0.99-1.00, P\u0026thinsp;=\u0026thinsp;0.030) (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\u003eUnivariate Cox analysis for 30-day ICU mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34 (0.90-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.144\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.126\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.89\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.171\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.00-1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.063\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.89\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.076\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.79\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63 (1.24\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.97\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.104\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.144\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.97-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.97\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpo2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.94\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27 (1.03\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.01\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFAscore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.95\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eMultivariate Cox analysis for 30-day ICU mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43 (0.93\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.094\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.92\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.718\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.86\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.445\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49 (1.05\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.01 (0.36\u0026ndash;25.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.311\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.74\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.438\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.95\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.125\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.321\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.36 (1.08\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.92\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.839\u003c/b\u003e\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\u003eWe then transformed triglycerides into categorical variables based on quartiles, that is, 0%-25% for TC1 (19 mg/dL-74 mg/dL), 25%-50% for TC2 (74 mg/dL-103 mg/dL), 50%-75% for TC3 (103 mg/dL-144 mg/dL), and 75%-100% for TC4 (144 mg/dL-481 mg/dL).\u003c/p\u003e \u003cp\u003eWe first analyzed the cumulative incidence of 30-day ICU mortality using the Kaplan-Meier (KM) method and evaluated it with the log-rank test Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We found that the prognosis of the TC2 group was worse than that of the TC1, TC3, and TC4 groups (P\u0026thinsp;=\u0026thinsp;0.044). Based on this phenomenon, we further simplified the grouping, the TC2 group was defined as TCA, and the three groups, TC1, TC3 and TC4, were redefined as TCB. The KM curve was then redrawn, and the results showed that TCA had a worse prognosis (P\u0026thinsp;=\u0026thinsp;0.010) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Since there was an intersection point in the KM curve (around 5.5 days), landmark was used to further analyze the \"jskm\" based package, and the results showed that TCA had a higher risk of death (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) from 5.5 days to 30 days Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCox proportional hazards analysis showed that the TCB group had a better prognosis in model 1 adjusted for 3 variables (includes gender, age and BMI) (HR:0.65, 95% CI: 0.44\u0026ndash;0.96, P\u0026thinsp;=\u0026thinsp;0.033). Similarly, in model 2 adjusted for 16 variables (includes age, cholesterol, LDL, WBC, anion gap, bicarbonate, BUN, glucose, potassium, INR, PT, CCI, MBP, SBP, SIRS and RR), the risk of ICU death was lower in the TCB group (HR:0.63,95% CI:0.41\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.026) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eThe relationship between different triglyceride groups and 30-day ICU mortality\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\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\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65(0.44\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63(0.41\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTriglycerides and 30-day in-hospital mortality\u003c/h2\u003e \u003cp\u003eThe in-hospital mortality rate was 23.38% (47/201) in the TCA group and 14.43% (87/603) in the TCB group. Both univariate Cox regression analysis (HR:0.60,95% CI:0.42\u0026ndash;0.86, P\u0026thinsp;=\u0026thinsp;0.005) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and multivariate Cox regression analysis (HR:0.58,95% CI:0.40\u0026ndash;0.83, P\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) suggested that the risk of 30-day in-hospital mortality was lower in the TCB group than in the TCA group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox analysis for 30-day in-hospital mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.87\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.136\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.42\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.94\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAniongap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.02\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.89\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.78\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.98\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.53 (1.18\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.97\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.112\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.164\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.063\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.193\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.99\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.126\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpo2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.94\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSirs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34 (1.10\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.00-1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSofascore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.94\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox analysis for 30-day in-hospital mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.475\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.39\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.825\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAniongap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.95\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.835\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.727\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.114\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.05\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.76 (0.66\u0026ndash;21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.137\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.75\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.197\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.96\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.152\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.596\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.792\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSirs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.09\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.92\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.724\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTriglycerides and ICU all-cause mortality\u003c/h2\u003e \u003cp\u003eWe further investigated the relationship between triglycerides and ICU all-cause mortality. The in-hospital mortality was 18.91% (38/201) in the TCA group and 12.27% (74/603) in the TCB group. Logistic regression analysis suggested a lower risk of all-cause ICU mortality in the TCB group compared to the TCA group (OR:0.60,95% CI:0.39\u0026ndash;0.92, P\u0026thinsp;=\u0026thinsp;0.020) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePropensity score matching showed that a total of 400 patients were successfully matched, 200 each in the TCA and TCB groups, and all data were balanced after matching compared to the pre-matching data (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Logistic regression analysis of the matched data showed that the risk of all-cause ICU death was lower in the TCB group compared to the TCA group (OR:0.55,95% CI:0.31\u0026ndash;0.96, P\u0026thinsp;=\u0026thinsp;0.039) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized mean difference pre-post propensity score matching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.289\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eWe performed a subgroup analysis of 804 patients with the study endpoint of 30-day ICU mortality, and the subgroup analysis showed that among these patients with CCI score\u0026thinsp;\u0026gt;\u0026thinsp;6, SOFA score\u0026thinsp;\u0026gt;\u0026thinsp;2, BMI\u0026thinsp;\u0026lt;\u0026thinsp;30, combined congestive heart failure, diabetes and uncomplicated ARDS, cerebrovascular disease, and chronic lung disease, the TCB group had a lower risk of ICU mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis showed that there were also differences in lipid levels between the TCA and TCB groups, with both cholesterol and LDL levels significantly lower in the TCA group than in the TCB group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatin drugs\u003c/h2\u003e \u003cp\u003eIn the TCA and TCB groups, we then analyzed the different responses to statins. There were three statins in the analysis: atorvastatin, rosuvastatin and simvastatin. Our selection criteria were patients with more than 5 days of use and an initial measurement of more than 10 mg. Subgroup analysis showed that among patients using atorvastatin, the TCB group had a lower 30-day ICU mortality rate compared with the TCA group (HR: 0.15, 95% CI: 0.05\u0026ndash;0.44, P\u0026thinsp;=\u0026thinsp;0.001) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, this did not appear to be the case for rosuvastatin and simvastatin. Further survival analysis also showed that among patients treated with atorvastatin, the TCB group had a better prognosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have shown that sepsis affects lipid metabolism and alters the lipid profile of patients, as evidenced by decreased levels of HDL and phospholipids and increased levels of cholesterol and triglycerides (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Of these, most studies have been done on HDL, while few studies have been done on triglycerides, and the relationship between triglycerides and the prognosis of patients with sepsis is not very well established. Cetinkaya A et al. showed that triglyceride levels greater than 150 mg/dL at the time of septic attack were a significant predictive marker of sepsis mortality (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), while Lee SH et al. had a different view and their study showed that triglyceride levels greater than 150 mg/dL were a significant predictive marker of sepsis mortality (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Van der Poll et al investigated the effect of hypertriglyceridemia on the human response to LPS and in vitro experiments showed that triglyceride-rich fat emulsions inhibited LPS-induced human whole blood cytokine production in vitro (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Thus, the relationship between triglyceride levels and the prognosis of patients with sepsis is unclear, and although most studies tend to support an adverse role for triglycerides in sepsis, such studies tend to base their conclusions more on in vitro experiments or animal experiments, and very few studies are based on clinical data from large samples (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study is a retrospective cohort analysis based on the MIMIC database. In our study, we found that triglyceride levels significantly affect the prognosis of sepsis patients. We discovered that triglycerides appeared to be a protective factor that reduced 30-day ICU mortality in sepsis patients. When triglycerides were further analyzed by converting them into categorical variables, an interesting finding was that patients had the worst prognosis when triglyceride levels were in the range of 74 mg/dL-103 mg/dL. While most previous studies support the conclusion that excessively high triglycerides tend to indicate a poor prognosis, few studies have focused on whether lower triglyceride levels are associated with a better prognosis. Our study provides a new idea.\u003c/p\u003e \u003cp\u003eAdditional studies have shown that patients in the TCA group also had significantly lower cholesterol and LDL levels than those in the TCB group. Numerous studies have shown that in patients with sepsis, there is often a decrease in total cholesterol, HDL and LDL cholesterol, HDL and LDL (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). LDL has been shown to bind to LPS, thereby attenuating the effects of sepsis on the body (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Feingold KR et al. also found that pharmacologically increasing the number of hepatic LDL receptors in rats was able to reduce the mortality rate due to LPS-induced mortality in rats (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Lower cholesterol levels also tend to predict poor outcomes, a phenomenon also supported by several studies (\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince cholesterol itself plays an important role in several aspects of cell membrane function, immunomodulation, and hormone synthesis, this may also be the reason why cholesterol reduction is strongly associated with poor prognosis. Since both LDL and cholesterol levels were lower in the TCA group of patients, these factors may be important contributors to their poor prognosis.\u003c/p\u003e \u003cp\u003eSeveral previous studies have come to less optimistic conclusions about whether statin drugs can improve the prognosis of patients with sepsis (28, 29). Our study discovered that patients taking atorvastatin had a better prognosis in the TCB group compared to the TCA group. However, we do not know the reason for this phenomenon. In addition, subgroup analyses showed that the TCB group had a lower risk of ICU death compared with the TCA group in patients with a Charlson comorbidity index greater than 6, a SOFA score greater than 2, a BMI less than 30, and a combination of congestive heart failure, diabetes mellitus and uncomplicated ARDS, cerebrovascular disease, and chronic lung disease. These differences may provide a basis for individualized treatment of patients with sepsis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study showed that the relationship between triglyceride levels and prognosis in patients with sepsis is complex, with patients having the worst prognosis when triglyceride levels were 74\u0026ndash;103 mg/dL.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in this study are from open public databases. These data can be accessed by passing the appropriate tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data involved in this study were anonymized with patient personal information and passed the requirements of the local legislature, so no additional ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYJH and ZJS designed the study. YJH was responsible for data extraction, screening and analysis. ZJS was responsible for drafting the manuscript. YJH and ZJS were responsible for data quality management and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Jiangsu Province Ability Improvement Project through Science, Technology and Education. Jiangsu Provincial Medical Innovation Center (Grant ID: No. CXZX202231).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200\u0026ndash;211. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(19)32989-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)32989-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavaillon JM, Singer M, Skirecki T. Sepsis therapies: learning from 30 years of failure of translational research to propose new leads. 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N Engl J Med. 2014;370(23):2191\u0026ndash;2200. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1401520\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1401520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MIMIC-Ⅳ database, triglyceride, sepsis, propensity score matching, lipid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4006758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4006758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious studies suggest that sepsis patients often have elevated triglyceride levels due to various factors, and higher levels may indicate a poorer prognosis. However, few studies have investigated whether lower triglycerides are associated with a better prognosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Medical Information Mart for Intensive Care IV (MIMIC-IV) database provided all the data. To assess the association between triglycerides and prognosis, we used logistic regression analysis (LR) and Cox proportional hazards models. We further controlled for confounders using propensity score matching (PSM).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eInclusion criteria were met by a total of 804 patients with a mean triglyceride of 103. We found that patients had a higher risk of 30-day ICU mortality and 30-day in-hospital mortality when triglycerides were in the second percentile (74 mg/dL-103 mg/dL). Interestingly, this group of patients seems to benefit more from the use of atorvastatin.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe relationship between triglyceride levels and prognosis in patients with sepsis is complex. Our study indicates that a poor prognosis is often associated with triglyceride levels in the range of 74 mg/dL-103 mg/dL.\u003c/p\u003e","manuscriptTitle":"The triglyceride paradox: a retrospective analysis based on the MIMIC-Ⅳ database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 18:04:52","doi":"10.21203/rs.3.rs-4006758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-01T22:58:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-14T02:54:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3b613f93-0317-4a25-a487-9b471ff7fc9c","date":"2024-03-12T18:05:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3625a554-3781-4815-9fe7-9acb65859249","date":"2024-03-12T14:24:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-09T11:03:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-08T09:24:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-06T09:42:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2024-03-02T15:08:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d5c2f8f-ca5e-4dd8-b42a-72a7c894f8b3","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:23:35+00:00","versionOfRecord":{"articleIdentity":"rs-4006758","link":"https://doi.org/10.1186/s40001-024-02159-x","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2024-11-26 15:58:17","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-03-08 18:04:52","video":"","vorDoi":"10.1186/s40001-024-02159-x","vorDoiUrl":"https://doi.org/10.1186/s40001-024-02159-x","workflowStages":[]},"version":"v1","identity":"rs-4006758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4006758","identity":"rs-4006758","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}