Association between lactate-to-albumin ratio and 30-day mortality in patients with polytrauma: a retrospective study

Association between lactate-to-albumin ratio and 30-day mortality in patients with polytrauma: a retrospective study | 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 Association between lactate-to-albumin ratio and 30-day mortality in patients with polytrauma: a retrospective study Feng Zhou, Huazhong Cai, Zhenjun Miao, Qixiang Yin, Xiaoyun Pan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6938557/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The lactate-to-albumin ratio (LAR) has recently been proposed as a new prognostic marker for various conditions. However, not many studies have used it to predict the 30-day mortality rate in polytrauma patients. This study aimed to establish a link between the LAR and 30-day mortality in polytrauma patients. Methods : This retrospective cohort study was conducted on 477 adult polytrauma patients admitted to the intensive care unit (ICU) of Jiangsu University Affliated Hospital. To establish the association between the LAR and 30-day mortality in patients in the ICU, multivariate Cox regression and Kaplan-Meier survival analyses were performed. The restricted cubic spline (RCS) curve was used to further explore the association between the LAR and 30-day in-hospital mortality. The predictive ability of various indicators of 30-day in-hospital mortality was evaluated through receiver operating characteristic (ROC) curves, and the area under the curve (AUC) of each indicator was calculated. Subgroup analyses were also performed. Results : Of the 477 polytrauma patients, 101 died after 30 days of hospitalization, giving a 30-day in-hospital mortality rate of 21.17%. The COX regression model revealed a strong association between the LAR (0.26–0.67) and higher mortality [odds ratio (OR) being 1.92 (95%CI: 1.22–2.81)]. The RCS curve revealed a nonlinear relationship between the LAR and 30-day mortality (p <0.001). The 30-day mortality was higher in the 3 rd quartile (Q3) group compared to the 1 st (Q1) and 2 nd (Q2) quartile groups. The log-rank test yielded p <0.001, signifying that the differences in survival curves across the three groups were non-random. The AUCs for the ROC curves of the LAR, lactate, and albumin were 0.859 (95%CI 81.9–90.1), 0.817 (95%CI 77.2–86.2), and 0.707 (95%CI 64.6–76.9), respectively (p <0.001 for all). Subgroup analyses showed no significant interaction between the LAR and different subgroups (p = 0.107–0.876), indicating an association between the LAR and 30-day mortality in polytrauma patients regardless of subgroup differences. Conclusion : The LAR might be the a predictor of 30-day fatality for hospitalized polytrauma patients. Future prospective studies need to verify the predictive value of the LAR on 30-day mortality in polytrauma patients. lactate-to-albumin ratio 30-day mortality polytrauma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Background The global incidence of polytrauma remains high[ 1 ]. For polytrauma patients who have a high injury severity score (ISS) of > 16, will also develop systemic inflammatory response syndrome (SIRS), multifactorial shock[ 2 ], coagulopathy, and multiple organ dysfunction syndrome (MODS)[ 3 ]. The mortality rate of these patients could be as high as 20–40%, which significantly stresses out the economy and people. Therefore, current research focuses on early risk stratification. Some classic assessment metrics like the ISS[ 4 ], APACHE II[ 5 ], and others are widely used; however, they have downsides such as their static nature and slow response. In recent years, biomarkers have emerged as a focal point of research for prognostic prediction, owing to their capacity to dynamically reflect pathophysiological changes. An elevated quantity of lactic acid—the end product of glycolysis—in the blood shows tissue hypoxia and anaerobic metabolism. Trauma, shock, microcirculation disorders, and mitochondrial dysfunction may result in serum lactic acid accumulation to levels of > 2 mmol/L, a condition known as hyperlactatemia[ 5 – 8 ]. Albumin, a plasma protein produced by hepatocytes, accounts for approximately 60% of all plasma proteins. It is the main protein maintaining the colloidal osmotic pressure, transporting metabolic substances, and regulating the inflammatory response [ 9 , 10 ]. Polytraumatized patients have hypoalbuminemia—due to bleeding, capillary leaks, and reduced hepatic production. The more severe the hypoalbuminemia, the more likely there is to be an infection and MODS. As for the lack of dynamic observation in the ISS and APACHE II score systems, and also many factors that could affect the interpretation of isolated lactate or albumin levels, we suggest using a kind of combination of the indicators mentioned above to assess the 30-day mortality rate in polytrauma patients. Currently, there is evidence demonstrating significant predictive power from the LAR in multiple conditions, including respiratory failure [ 11 ], cancer [ 12 ], sepsis [ 13 , 14 ], critical illness [ 15 , 16 ], and burn injury [ 17 ]. However, the predictive power of the LAR still has limited application in the case of polytrauma. To avoid the shortcomings of a single indicator, this study will explore the correlation between LAR and the 30-day mortality rate in multiple trauma patients. We used the intensive care unit (ICU) database of the Jiangsu University Affiliated Hospital, which contains data from 2012 to 2022, to study the connection between the LAR and the 30-day mortality rate for hospitalized polytrauma patients. 2 Methods 2.1 Data source In this retrospective study, information on patients with polytrauma admitted to the ICU of Jiangsu University Affiliated Hospital from July 2012 to July 2022 was collected. The group consisted of 355 males and 122 females, with 101 deaths and 376 survivors. 2.2 Study population The study inclusion criteria were as follows: (1) Age ≥ 18 years; (2) ICU admission; (3) Multiple injury, ISS ≥ 16 points; (4) Serum Lactate, Albumin, and Complete Clinical Information Records; (5) ICU stay > 24 hours. The exclusion criteria were as follows: (1) insufficient clinical information; (2) a prior history of liver problems or malignancies causing hypoalbuminemia (3) age < 18 years; (4) transfer to other medical institutions during hospitalization. 2.3 Data collection In this research, a comprehensive array of variables was gathered, including but not limited to: age, gender, duration of ICU stay, systolic and diastolic blood pressure, heart rate, patient assessment through questioning, volume of red blood cells transfused within 24 hours of admission, volume of plasma transfused, Revised Trauma Score (RTS), Acute Physiology and Chronic Health Evaluation II (APACHE II), Trauma Injury Severity Score (TRISS), Injury Severity Score (ISS), white blood cell count, neutrophil count, lymphocyte count, C-reactive protein levels, hematocrit, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen levels, thrombin time (TT), international normalized ratio (INR), D-dimer levels, pH value, partial pressure of oxygen (PaO2), partial pressure of carbon dioxide (PaCO2), lactate levels, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood glucose, urea nitrogen, serum creatinine, lactate dehydrogenase, calcium ions, albumin levels, Sequential Organ Failure Assessment (SOFA) score, length of hospital stay, and the presence of conditions such as hypertension, diabetes, emergency surgery, mechanical ventilation, vasopressor drug administration, and acute kidney injury. For variables exhibiting missing data of less than 15%, multiple imputation techniques were employed to identify the most suitable dataset for addressing the gaps in the data. 2.4 Endpoint events and clinical definition Follow-up began when the patient was admitted to the ICU for the first time and ended 30 days after the first ICU admission. The primary study endpoint was whether the patient was alive or dead after 30 days. Polytrauma refers to multiple injuries affecting at least two different anatomical areas or organs at the same time as a result of the same high-energy traumatic event[ 18 ]. 2.5 Statistical analysis Descriptive statistics were split into endpoints. Continuous variables with non-normal distributions were summarized using the median and interquartile range [M(Q1, Q3)] and analyzed using the Mann-Whitney U test. To establish the connection between the LAR and mortality in polytrauma patients, the multivariable Cox regression analysis was performed and the LAR was put into quartiles or taken as a continuous value. The RSC analysis was also performed on the LAR and 30-day mortality in this category of patients. Kaplan–Meier survival curves were also used to study the association between the LAR and mortality. Receiver operatic characteristic (ROC) curves were drawn to judge the prediction correctness of each individual indicator for the 30-day in-hospital mortality, and the area under the curve (AUC) was calculated for comparison. For subgroup analyses, we analyzed the effect of the LAR on different demographic and clinical characteristics—including gender, age, hypertension, diabetes, operation performed, vasopressor use, mechanical ventilation, and AKI, and the interaction P-values were determined by taking the product of two groups. All statistical analyses were performed using R software version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). The threshold for statistical significance was set at p < 0.05. 3 Results 3.1 Baseline characteristics of patients Figure 1 shows the flowchart of the participant inclusion process. Regarding cases of polytrauma, the analysis included 477 patients, and 101 of them did not survive. Groups were constituted based on quartiles of the LAR—Q1, Q2, and Q3. The 157 cases in the Q1 group had a median LAR value of 0.48 (0.26–0.67). The Q2 group median LAR value was 0.86 (0.68–1.44), and the Q3 group median LAR value was 2.27 (1.46–10.78).，See Table 1. Table 2 presents a comparison of the different LAR groups. Certain variables differed significantly among the LAR groups. These included age, ICU time, SBP, DBP, pulse, body temperature, RBC, plasma volume, GCS, respiration frequency, RTS, APACHEII, tria II, ISS, CRP, HCT, HB, PLT, APTT, PT, Fb, TT, INR, D-dimer, pH, PaO2, lactic acid, residual base amount, total bilirubin, ALT, AST, Glucose, Cr, lactate dehydrogenase, Ca 2 + ion, albumin, SOFA, Length of stay, emergency surgery, mechanical ventilation, vasoactive medication use, and AKI. 3.2 Association between the LAR and the risk of death in polytrauma patients In this analysis, the odds ratios (ORs) for LAR continuous variables across various models is >1 with all p < 0.05 As for the test of the LAR’s trend on categorical variables across different models, p is also less than 0.05 as well, showing that there is regularity of LAR assessment with these models. In the COX regression model, when we adjusted for age, gender, HNT, DM, SBP, DBP, RBC, GCS, RTS, APACHE, Surg, ISS, CRP, HCT, HB, PT, Fib, pH, PaO2, BE, GLU, and LDH, and using the LAR (0.26–0.67) as a reference, the latter showed an independent association with an increased risk of mortality in polytrauma patients with an OR (95%CI) of 1.92(1.22–2.81), as shown in Table 3. Additionally, the restricted cubic spline (RCS) curve shows the existence of a nonlinear relationship between the LAR and the risk of death in these patients, as can be seen in Figure 2 (p < 0.001). 3.3 Survival curve As shown in Figure 3, the survival rate of all groups declines with time. At the start time points, at 5 days, and at 10 days, the survival rate does not differ significantly among the groups. However, as time goes by, differences among groups get clearer. Time all the way, Q1 group is always at the top, meaning that patients in this group have a better chance of recovery. The survival rate of the Q2 group is between those of the Q1 and Q3 groups, indicating that patients of this class had a moderate prognosis. However, the Q3 group always had the lowest survival rate among all the groups at any point in time, indicating that the patients in the Q3 group had a poor prognosis. The log-rank test yielded p <0.001, indicating that the differences in survival curves among the three groups are non-random and statistically significant. The mortality rates are 4.46%, 12.96%, and 46.20% for the Q1, Q2, and Q3 cohorts, respectively, indicating that the mortality rate of the Q3 cohort was significantly higher than those of the other two cohorts. 3.4 ROC curve Figure 4 shows the ROC curve for predicting the 30-day mortality from the time of patient admission. For the LAR, AUC = 0.859(95% CI: 81.9% − 90.1%). For Lactate, AUC = 0.817 (95% CI: 77.2% − 86.2%). For Albumin, AUC = 0.707 (95% CI: 64.6% − 76.9%). Also, the AUC of the LAR differed significantly when compared to those of AL & LA (P < 0.05). 3.5 Subgroup analyses The relationships between the LAR and variables such as age, gender, hypertension, diabetes, surgery, mechanical ventilation, use of vasoactive drugs, and AKI were analyzed through the subgroup analysis. From the forest plot, after we performed a stratified analysis on the abovementioned factors, we can see that there is no considerable interaction between the LAR and each subgroup (the interaction p-value ranges from 0.107 to 0.876) (Figure 5). As far as the link between the LAR and 30-day in-hospital mortality is concerned, that too stays consistent. These results show that the LAR acts as an independent pre-nonsignificant predictor. 4 Discussion This study aimed to identify the relationship between the LAR and the 30-day mortality rate in patients who sustained polytrauma. According to this retrospective study, there is a positive correlation between the LAR and the 30-day mortality rate in this category of patients. We included 477 people diagnosed with polytraumas at Jiangsu University affiliated hospital between 2012 and 2022. A Cox regression model was run to see how the LAR was related to the 30-day mortality rate, and this was done before and after adjusting for confounders. The LAR was found to be an independent predictor of 30-day mortality in polytrauma patients. The RCS curve showed strong nonlinear relationships between the LAR and the mortality risk. The LAR survival curve showed that the survival rate of patients decreased with an increase in LAR. The ROC curve analysis showed that the LAR has greater predictive power than lactate or albumin at any single point for predicting the 30-day mortality rate. After adjusting for confounders, a forest plot was generated, proving that the LAR was an independent predictor of 30-day mortality. Thus, utilizing the LAR to forecast the 30-day mortality of polytrauma, we consider it reliable and can consider it a new biomarker. Lactic acid, an end product of glycolysis, comes from LDH synthesizing pyruvate under anaerobic conditions and a state of increased metabolism. Historically lactic acid has been regarded as a metabolic byproduct; however, more recently, it has been shown to play a polyvalent role as a signal. This may be involved in a number of processes—both physiological and pathological [19, 20]. In healthy cells, lactic acid plays a vital role in regulating energy metabolism and maintaining pH balance. In immune cells, it is a signaling molecule. In a clinical setting, lactic acid is most commonly used to evaluate conditions such as shock, infection, trauma, and severe illness, and also to predict possible adverse surgical outcomes [21, 22]. Monitoring the serum lactate levels of hospitalized patients is informative. However, a lot of different things that cause lactic acid level fluctuations— like a sick liver, hypoxia, and medicines. Such confounders complicate the use of the serum lactic acid level as a prognostic marker. Albumin, a plasma protein produced by the liver, accounts for about 60% of all plasma proteins. Albumin helps to maintain the colloidal osmotic pressure of plasma, transport hormones, fatty acids, and other molecules in the blood, and regulate the human body's immune response and metabolic reactions. Albumin levels are measured widely in clinical settings [4]; however, the serum albumin level also has its limitations. The specificity of albumin as a diagnostic marker is not very high because low plasma albumin concentrations can be due to many reasons. These include decreased albumin synthesis and increased albumin excretion. So, one set of test results does not suffice to pinpoint the etiology of an observed condition because it might not show the full story about a given health outcome. Therefore, we have to look at it in the light of other available pieces of information. It could be that albumin, by virtue of its longer half-life of approximately 15–20 days, may not promptly signal acute problems or changes from short-term events that occur suddenly, perhaps making it seem like the condition is not progressing significantly. Thus, relying solely on a polytrauma patient’s albumin level as a prognostic factor is risky. Recently, much work has been done on the LAR, most importantly, on determining whether it is useful in predicting the outcomes of different diseases [23, 24]. Güler et al. [16] performed emergency LAR assessments on a cohort of 2,310 non-traumatic elderly patients to determine its predictive potential for patient mortality. Their research findings proved that lactate levels, albumin levels, and LARs were all good predictors of the in-patient death rate. Also, note that the LAR was a better independent predictor of inpatient mortality than lactate or albumin levels. Also, Karampela et al. [25] looked at the prognostic ability of the LAR in patients with septic shock compared to patients with sepsis. They showed that the LAR was significantly higher in patients with septic shock and non-survivors than in patients with sepsis and survivors. Also, the baseline LAR displayed a positive correlation with the severity of the sepsis, where a higher LAR value was an independent predictor of 28-day mortality. Hence, the LAR appears to be a valid and promising prognostic marker of severe sepsis on the day of admission and one week later. Polytrauma often leads to severe illness, and quick changes in the patient’s condition make it hard for doctors to tell just from parameters such as the ISS, prothrombin time (PT), and blood lactate levels how well the person will do. So, we put blood lactate and albumin together to look at the link between the LAR and 30-day mortality in polytrauma patients. Concerning the prognosis, there is a paucity of available on the clinical importance of the LAR in this population. Notably, the LAR at ICU admission was found to be independently associated with mortality in critically injured patients by Arslan and Sahin [26], a finding that demonstrates its strong prognostic ability. In terms of mortality prediction, the LAR was superior to the serum lactate level and the 24-hour lactate clearance rate. In our study, we found that the LAR can be considered an independent predictor of 30-day mortality in polytrauma patients, unlike lactate and albumin levels that were less potent predictors and had more confounders than the LAR, as found by Arslan and Sahin. Nevertheless, this study has some limitations that should be acknowledged. First, this was a retrospective, single-center study; as such, it cannot fully explain the LAR with regard to polytrauma like an actual prospective study can. Second, we did not record the drugs and medical treatments that could have interfered with the LAR in polytrauma patients. This may render our findings less reliable. Also, we excluded patients with severe liver and kidney conditions but not cases of liver and kidney damage in the multi-hit group since hepatic and renal impairment can change the LAR. 5 Conclusion There is an association between a high LAR with 30-day mortality in polytrauma patients, and this association has been identified among different subgroups. Abbreviations AUC Area under the curve MODS Multiple organ dysfunction syndrome OR Odds ratio RCS Restricted cubic spline ROC Receiver operating characteristic SIRS Systemic inflammatory response syndrome ICU Intensive care unit ISS Injury severity score LAR Lactate-to-albumin ratio PT Prothrombin time Declarations Ethics approval and consent to participate This study, which entails an analysis of the hospital database, was approved by the Ethics Review Committee of Jiangsu University Affiliated Hospital. Furthermore, due to the retrospective nature of the study, the requirement for informed consent from patients, their legal guardians, or close relatives was waived. Consent for publication The Institutional Ethics Committee of Jiangsu University Affliated Hospital approved a waiver of consent for this study as it involved retrospective analysis of anonymized data. Availability of data and materials Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Major Scientific Research Project of Wuxi Municipal Health Commission (Z202108), Shanghai Key Laboratory of Peripheral Nerve and Microsurgery (20DZ2270200) NHC Key Laboratory of Hand Reconstruction (Fudan University), Zhenjiang Science and Technology Innovation Fund (Social Development–Key Project SH2024043), and Jiangsu University Medical Education Collaborative Innovation Fund (JDYY2023001). Authors’ contributions FZ contributed to the conceptualization, project administration, supervision, and writing of the original draft of the manuscript, as well as the review and editing process. CH, ZM, and QY were responsible for data curation, formal analysis, investigation, and methodology. XP and JM were involved in the conceptualization, project administration, supervision, and the review and editing of the manuscript. Acknowledgements We thank all participants and staff for their contributions. References Lansink KWW, Gunning AC, Spijkers ATE, Leenen LPH. Evaluation of trauma care in a mature level I trauma center in the Netherlands: outcomes in a Dutch mature level I trauma center. World J Surg. 2013;37:2353-9. Luo JL, Chen D, Tang LS, Deng H, Zhang C, Chen SY, et al. Multifactorial shock: a neglected situation in polytrauma patients. J Clin Med. 2022;11:6829. van Breugel JMM, Niemeyer MJS, Houwert RM, Groenwold RHH, Leenen LPH, van Wessem KJP. 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Tables Table 1: LAR groups and values LAR N Median Min Max Q1 157 0.48 0.26 0.67 Q2 162 0.86 0.68 1.44 Q3 158 2.27 1.46 10.78 Table 2: Baseline characteristics of the patients Total (n=477) Q1 (n=157) Q2 (n=162) Q3 (n=158) p-value Age 57.0 (47.0, 66.0) 59.0 (49.0, 69.0) 56.0 (47.8, 65.3) 54.0 (44.0, 65.0) 9.15 0.010 ICU time 6.0 (2.0, 14.0) 4.0 (0.0, 9.0) 7.0 (3.0, 14.0) 9.0 (3.0, 20.5) 39.38 ＜0.001 SBP 121.0 (91.5, 144.8) 133.0 (116.5, 150.0) 128.0 (104.0, 149.0) 96.0 (84.0, 120.0) 79.67 ＜0.001 DBP 75.0 (60.0, 86.0) 80.0 (71.0, 89.0) 79.5 (66.0, 90.0) 60.0 (49.0, 75.5) 71.35 ＜0.001 Pulse 90.0 (78.0, 110.0) 82.0 (74.5, 97.5) 90.0 (79.0, 105.3) 105.0 (83.0, 119.0) 43.77 ＜0.001 Body temperature 36.7 (36.3, 36.9) 36.8 (36.5, 37.0) 36.7 (36.5, 36.9) 36.4 (36.0, 36.8) 57.26 ＜0.001 RBC (＜24h) 2.0 (0.0, 6.0) 0.0 (0.0, 1.8) 2.0 (0.0, 4.1) 6.0 (4.0, 9.0) 169.27 ＜0.001 Volume of plasma 400.0 (0.0, 800.0) 0.0 (0.0, 100.0) 375.0 (0.0, 700.0) 850.0 (400.0, 1385.0) 154.41 ＜0.001 GCS 12.0 (7.0, 14.0) 14.0 (10.0, 15.0) 12.0 (7.0, 14.0) 8.0 (4.0, 12.5) 69.42 ＜0.001 Frequency of respiration 20.0 (18.0, 22.0) 20.0 (18.0, 21.0) 20.0 (18.0, 21.3) 20.0 (18.0, 23.0) 6.81 0.033 RTS 6.9 (5.2, 7.8) 7.8 (6.9, 7.8) 6.9 (6.0, 7.8) 5.7 (4.5, 6.9) 79.33 ＜0.001 APACHEⅡ 22.0 (19.0, 25.0) 20.0 (17.5, 23.0) 21.0 (19.0, 23.0) 25.0 (23.0, 28.0) 86.07 ＜0.001 TRISS 0.4 (0.1, 0.8) 0.6 (0.2, 0.9) 0.5 (0.2, 0.8) 0.2 (0.0, 0.6) 36.29 ＜0.001 ISS 29.0 (25.0, 36.0) 25.0 (22.0, 29.0) 29.0 (25.0, 34.0) 36.0 (32.0, 41.0) 148.27 ＜0.001 WBC 13.1 (10.0, 17.5) 12.6 (10.1, 15.9) 13.3 (10.4, 18.8) 14.0 (9.6, 17.7) 4.39 0.111 Neutrophil count 11.3 (8.6, 15.2) 11.0 (8.6, 13.8) 11.6 (9.1, 16.2) 11.8 (8.0, 15.8) 4.29 0.117 Lymphocyte count 0.9 (0.6, 1.4) 0.8 (0.6, 1.3) 0.9 (0.6, 1.3) 1.1 (0.6, 1.8) 5.98 0.050 CRP 7.5 (1.3, 35.1) 8.0 (1.5, 38.4) 10.4 (2.2, 43.2) 5.0 (1.0, 19.8) 7.62 0.022 HCT 33.0 (27.3, 38.2) 35.5±6.1 33.6±6.5 28.2±8.7 44.83 ＜0.001 HB 110.5 (92.0, 128.0) 119.0±19.9 113.0±22.2 95.2±29.0 42.85 ＜0.001 PLT 142.0 (99.3, 187.5) 162.0 (128.5, 202.5) 144.0 (105.3, 186.8) 110.0 (72.0, 162.5) 49.16 ＜0.001 APTT 27.0 (23.7, 32.2) 24.6 (22.6, 27.3) 26.4 (23.5, 29.9) 32.5 (26.8, 49.0) 106.02 ＜0.001 PT 12.4 (11.4, 13.7) 11.7 (11.2, 12.4) 12.1 (11.4, 13.1) 14.0 (12.6, 16.1) 128.80 ＜0.001 Fibrinogen 1.9 (1.2, 2.6) 2.3 (1.8, 2.9) 1.9 (1.4, 2.6) 1.2 (0.8, 1.8) 101.28 ＜0.001 TT 18.2 (16.9, 20.2) 17.7 (16.6, 18.7) 17.9 (16.8, 19.5) 20.1 (18.0, 24.4) 66.33 ＜0.001 INR 1.1 (1.0, 1.2) 1.0 (1.0, 1.1) 1.1 (1.0, 1.1) 1.2 (1.1, 1.4) 126.54 ＜0.001 D-dimer 25.2 (10.0, 55.0) 21.2 (10.0, 45.5) 19.3 (8.9, 50.3) 30.9 (14.1, 71.8) 8.81 0.012 PH 7.4 (7.3, 7.4) 7.4 (7.4, 7.4) 7.4 (7.4,7.4) 7.3 (7.2, 7.4) 140.63 ＜0.001 PaO2 88.8 (76.2, 119.0) 82.7 (73.5, 98.4) 89.0 (77.4, 113.3) 99.9 (78.1, 150.1) 22.37 ＜0.001 PaCO2 38.4 (34.2, 42.5) 38.2 (34.7, 41.7) 38.4 (34.4, 42.2) 38.4 (33.4, 45.8) 1.32 0.516 Lactic acid 3.0 (2.1, 5.3) 1.8 (1.5, 2.1) 3.0 (2.5, 3.5) 6.4 (5.0, 9.7) 377.05 ＜0.001 Residual base -2.5 (-5.0, -1.3) -1.5 (-2.3, 0.2) -2.1 (-3.4, -1.4) -5.9 (-8.8, -4.0) 167.78 ＜0.001 Total bilirubin 13.4 (8.8, 18.7) 14.4 (11.6, 19.6) 14.1 (9.6, 18.6) 10.0 (6.3, 18.4) 24.34 ＜0.001 ALT 38.8 (23.0, 67.0) 33.0 (21.0, 58.0) 36.5 (21.5, 56.0) 48.2 (31.6, 92.0) 23.54 ＜0.001 AST 56.8 (35.0, 103.0) 47.0 (32.2, 74.8) 50.5 (31.9, 92.5) 78.0 (45.5, 149.9) 32.18 ＜0.001 Glucose 8.4 (7.0, 11.0) 7.6 (6.7, 9.6) 8.2 (7.2, 10.1) 9.8 (7.6, 13.8) 32.19 ＜0.001 Urea nitrogen 5.9 (4.6, 7.6) 5.9 (4.5, 7.3) 5.7 (4.5, 7.6) 6.1 (5.1, 7.7) 3.63 0.163 Cr 69.3 (57.1, 86.8) 65.4 (56.5, 78.8) 69.7 (55.8, 84.5) 75.9 (58.7, 98.9) 8.73 0.013 Lactate dehydrogenase 324.0 (256.0, 433.5) 307.0 (255.0, 377.0) 326.0 (252.0, 408.0) 355.0 (263.0, 492.0) 9.37 0.009 Calcium ion 2.1 (1.9, 2.2) 2.2 (2.1, 2.2) 2.1 (2.0, 2.2) 2.0 (1.8, 2.1) 76.13 ＜0.001 Albumin 33.5 (28.0, 38.0) 38.0 (34.0, 40.2) 33.6 (30.5, 36.7) 26.4 (20.7, 32.7) 155.43 ＜0.001 SOFA 7.0 (2.0, 11.0) 2.0 (1.0, 6.0) 6.0 (3.0, 8.0) 11.0 (9.0, 13.0) 185.57 ＜0.001 Length of stay 26.5 (14.0, 46.0) 28.0 (18.0, 43.5) 30.0 (18.0, 46.8) 17.0 (4.0, 47.0) 18.19 ＜0.001 Male 355 (73.6) 116 (73.0) 122 (76.3) 117 (71.8) 0.89 0.641 Hypertension 125 (25.9) 51 (32.1) 39 (24.4) 35 (21.5) 5.01 0.082 Diabetes 55 (11.4) 21 (13.2) 16 (10.0) 18 (11.0) 0.84 0.656 Emergency surgery 174 (36.1) 29 (18.2) 62 (38.8) 83 (50.9) 38.00 ＜0.001 Mechanical ventilation 267 (55.4) 42 (26.4) 80 (50.0) 145 (89.0) 130.23 ＜0.001 Vasoactive drugs 173 (35.9) 19 (12.0) 41 (25.6) 113 (69.3) 126.12 ＜0.001 AKI 70 (14.5) 12 (7.6) 14 (8.8) 44 (27.0) 30.95 ＜0.001 Table 3: Association between the LAR and the risk of death in multiply patients Model1 Model2 Model3 OR 95% CI P OR 95% CI P OR 95% CI P LAR, continuous 3.42 2.58, 4.53 ＜0.001 3.20 2.39, 4.27 ＜0.001 2.29 1.53, 3.44 ＜0.001 LAR, Classification 0.001 0.010 0.025 Q1 Ref Ref Ref Q2 2.90 1.23, 6.81 0.015 2.68 1.14, 6.35 0.025 1.65 0.66, 4.14 0.287 Q3 2.96 1.96, 8.16 ＜0.001 2.24 1.10, 4.77 ＜0.001 1.96 1.22, 2.81 ＜0.001 Model 1: Crude Model 2: Adjusted for Age, Gender, HNT, and DM Model 3: Adjusted for Age, Gender, HNT, DM, SBP, DBP, RBC, GCS, RTS, APACHE, Surg, ISS, CRP, HCT, HB, PT, Fib, pH, PaO2, BE, GLU, and LDH Additional Declarations No competing interests reported. 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University","correspondingAuthor":false,"prefix":"","firstName":"Huazhong","middleName":"","lastName":"Cai","suffix":""},{"id":486925261,"identity":"a76058ca-ae92-4ddb-b521-d6733269bfca","order_by":2,"name":"Zhenjun Miao","email":"","orcid":"","institution":"Affiliated Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Zhenjun","middleName":"","lastName":"Miao","suffix":""},{"id":486925262,"identity":"f55d1a41-e632-4721-9bb8-7f5249a99564","order_by":3,"name":"Qixiang Yin","email":"","orcid":"","institution":"Affiliated Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Qixiang","middleName":"","lastName":"Yin","suffix":""},{"id":486925263,"identity":"86ee965a-f574-41ad-bfca-babbf59a95a0","order_by":4,"name":"Xiaoyun Pan","email":"","orcid":"","institution":"Wuxi Ninth People's Hospital Affiliated with Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Pan","suffix":""},{"id":486925264,"identity":"fd02ee9c-b61c-480d-8b7f-b796c07c3e97","order_by":5,"name":"Jingyi Mi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACZoaEA0CKhx9ISAAxYwPRWiQbiNYCAwYHiNWi287w8HDBr8MyxufPGN74wWAju+EA87MH+LSYHWZIODyz7zCP2Y0cY8sehjTjDQfYzA0IauHtAWnhMZPgYTicuOEAD5sEUVqM+8+YSf5h+E+kFp4fh3kMGHLMpHkYDhBrS0M6j8SNtGJrGYNk45mH2czwazl/Jvkzzx9re/7+wxtvvqmwk+073vwMrxZgJCYwMLY1QzmgoGLGrx4I2A8wMPypI6hsFIyCUTAKRjAAAJckSzPAqHapAAAAAElFTkSuQmCC","orcid":"","institution":"Wuxi Ninth People's Hospital Affiliated with Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Mi","suffix":""}],"badges":[],"createdAt":"2025-06-20 11:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6938557/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6938557/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87271914,"identity":"b11e3194-562f-474d-98e4-b1bfd231a74d","added_by":"auto","created_at":"2025-07-22 08:24:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179491,"visible":true,"origin":"","legend":"\u003cp\u003eCase selection procedure\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6938557/v1/3a1e3de85e3fb02dcf19cf12.png"},{"id":87273870,"identity":"9b2fc8bd-2988-4e96-8492-d21434ce8e95","added_by":"auto","created_at":"2025-07-22 08:40:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132606,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between variables and predicted probabilities\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6938557/v1/19ad55dbd833bbba535cc2fa.png"},{"id":87272919,"identity":"729d916e-18ca-43b2-b15d-66ab5cb5c4e9","added_by":"auto","created_at":"2025-07-22 08:32:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54284,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for 30-day mortality\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6938557/v1/66f769bc746cbb9e567c27d9.png"},{"id":87271913,"identity":"3746fa44-e213-440f-932b-0f6b0ef84dde","added_by":"auto","created_at":"2025-07-22 08:24:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64174,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the LAR, Lactic acid, and Albumin for predicting 30-day mortality\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6938557/v1/8ef016409eed3e2706cdd834.png"},{"id":87272920,"identity":"a0d5983c-89d7-463a-9c22-7e3d864412d8","added_by":"auto","created_at":"2025-07-22 08:32:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106279,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating the subgroup analysis concerning the association between hospital mortality and the LAR\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6938557/v1/09a3ac10c6fabf5d81db39ef.png"},{"id":101398635,"identity":"3fa30010-b49e-4d28-8a7e-64163ab35729","added_by":"auto","created_at":"2026-01-29 09:43:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1266135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6938557/v1/bc373cb5-c0ea-48bc-84ae-6fa242f38ef9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between lactate-to-albumin ratio and 30-day mortality in patients with polytrauma: a retrospective study","fulltext":[{"header":"1 Background","content":"\u003cp\u003eThe global incidence of polytrauma remains high[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For polytrauma patients who have a high injury severity score (ISS) of \u0026gt;\u0026thinsp;16, will also develop systemic inflammatory response syndrome (SIRS), multifactorial shock[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], coagulopathy, and multiple organ dysfunction syndrome (MODS)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The mortality rate of these patients could be as high as 20\u0026ndash;40%, which significantly stresses out the economy and people. Therefore, current research focuses on early risk stratification. Some classic assessment metrics like the ISS[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], APACHE II[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and others are widely used; however, they have downsides such as their static nature and slow response. In recent years, biomarkers have emerged as a focal point of research for prognostic prediction, owing to their capacity to dynamically reflect pathophysiological changes.\u003c/p\u003e\u003cp\u003eAn elevated quantity of lactic acid\u0026mdash;the end product of glycolysis\u0026mdash;in the blood shows tissue hypoxia and anaerobic metabolism. Trauma, shock, microcirculation disorders, and mitochondrial dysfunction may result in serum lactic acid accumulation to levels of \u0026gt;\u0026thinsp;2 mmol/L, a condition known as hyperlactatemia[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Albumin, a plasma protein produced by hepatocytes, accounts for approximately 60% of all plasma proteins. It is the main protein maintaining the colloidal osmotic pressure, transporting metabolic substances, and regulating the inflammatory response [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Polytraumatized patients have hypoalbuminemia\u0026mdash;due to bleeding, capillary leaks, and reduced hepatic production. The more severe the hypoalbuminemia, the more likely there is to be an infection and MODS.\u003c/p\u003e\u003cp\u003eAs for the lack of dynamic observation in the ISS and APACHE II score systems, and also many factors that could affect the interpretation of isolated lactate or albumin levels, we suggest using a kind of combination of the indicators mentioned above to assess the 30-day mortality rate in polytrauma patients. Currently, there is evidence demonstrating significant predictive power from the LAR in multiple conditions, including respiratory failure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], sepsis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], critical illness [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and burn injury [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the predictive power of the LAR still has limited application in the case of polytrauma. To avoid the shortcomings of a single indicator, this study will explore the correlation between LAR and the 30-day mortality rate in multiple trauma patients. We used the intensive care unit (ICU) database of the Jiangsu University Affiliated Hospital, which contains data from 2012 to 2022, to study the connection between the LAR and the 30-day mortality rate for hospitalized polytrauma patients.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data source\u003c/h2\u003e\u003cp\u003eIn this retrospective study, information on patients with polytrauma admitted to the ICU of Jiangsu University Affiliated Hospital from July 2012 to July 2022 was collected. The group consisted of 355 males and 122 females, with 101 deaths and 376 survivors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study population\u003c/h2\u003e\u003cp\u003eThe study inclusion criteria were as follows: (1) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) ICU admission; (3) Multiple injury, ISS\u0026thinsp;\u0026ge;\u0026thinsp;16 points; (4) Serum Lactate, Albumin, and Complete Clinical Information Records; (5) ICU stay\u0026thinsp;\u0026gt;\u0026thinsp;24 hours. The exclusion criteria were as follows: (1) insufficient clinical information; (2) a prior history of liver problems or malignancies causing hypoalbuminemia (3) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (4) transfer to other medical institutions during hospitalization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data collection\u003c/h2\u003e\u003cp\u003eIn this research, a comprehensive array of variables was gathered, including but not limited to: age, gender, duration of ICU stay, systolic and diastolic blood pressure, heart rate, patient assessment through questioning, volume of red blood cells transfused within 24 hours of admission, volume of plasma transfused, Revised Trauma Score (RTS), Acute Physiology and Chronic Health Evaluation II (APACHE II), Trauma Injury Severity Score (TRISS), Injury Severity Score (ISS), white blood cell count, neutrophil count, lymphocyte count, C-reactive protein levels, hematocrit, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen levels, thrombin time (TT), international normalized ratio (INR), D-dimer levels, pH value, partial pressure of oxygen (PaO2), partial pressure of carbon dioxide (PaCO2), lactate levels, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood glucose, urea nitrogen, serum creatinine, lactate dehydrogenase, calcium ions, albumin levels, Sequential Organ Failure Assessment (SOFA) score, length of hospital stay, and the presence of conditions such as hypertension, diabetes, emergency surgery, mechanical ventilation, vasopressor drug administration, and acute kidney injury. For variables exhibiting missing data of less than 15%, multiple imputation techniques were employed to identify the most suitable dataset for addressing the gaps in the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Endpoint events and clinical definition\u003c/h2\u003e\u003cp\u003eFollow-up began when the patient was admitted to the ICU for the first time and ended 30 days after the first ICU admission. The primary study endpoint was whether the patient was alive or dead after 30 days. Polytrauma refers to multiple injuries affecting at least two different anatomical areas or organs at the same time as a result of the same high-energy traumatic event[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were split into endpoints. Continuous variables with non-normal distributions were summarized using the median and interquartile range [M(Q1, Q3)] and analyzed using the Mann-Whitney U test. To establish the connection between the LAR and mortality in polytrauma patients, the multivariable Cox regression analysis was performed and the LAR was put into quartiles or taken as a continuous value. The RSC analysis was also performed on the LAR and 30-day mortality in this category of patients. Kaplan\u0026ndash;Meier survival curves were also used to study the association between the LAR and mortality. Receiver operatic characteristic (ROC) curves were drawn to judge the prediction correctness of each individual indicator for the 30-day in-hospital mortality, and the area under the curve (AUC) was calculated for comparison. For subgroup analyses, we analyzed the effect of the LAR on different demographic and clinical characteristics\u0026mdash;including gender, age, hypertension, diabetes, operation performed, vasopressor use, mechanical ventilation, and AKI, and the interaction P-values were determined by taking the product of two groups. All statistical analyses were performed using R software version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). The threshold for statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline characteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the flowchart of the participant inclusion process. Regarding cases of polytrauma, the analysis included 477 patients, and 101 of them did not survive. Groups were constituted based on quartiles of the LAR\u0026mdash;Q1, Q2, and Q3. The 157 cases in the Q1 group had a median LAR value of 0.48 (0.26\u0026ndash;0.67). The Q2 group median LAR value was 0.86 (0.68\u0026ndash;1.44), and the Q3 group median LAR value was 2.27 (1.46\u0026ndash;10.78).，See Table 1. Table 2 presents a comparison of the different LAR groups. Certain variables differed significantly among the LAR groups. These included age, ICU time, SBP, DBP, pulse, body temperature, RBC, plasma volume, GCS, respiration frequency, RTS, APACHEII, tria II, ISS, CRP, HCT, HB, PLT, APTT, PT, Fb, TT, INR, D-dimer, pH, PaO2, lactic acid, residual base amount, total bilirubin, ALT, AST, Glucose, Cr, lactate dehydrogenase, Ca\u003csup\u003e2 +\u003c/sup\u003e ion, albumin, SOFA, Length of stay, emergency surgery, mechanical ventilation, vasoactive medication use, and AKI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Association between the LAR and the risk of death in polytrauma patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this analysis, the odds ratios (ORs) for LAR continuous variables across various models is \u0026gt;1 with all p \u0026lt; 0.05 As for the test of the LAR\u0026rsquo;s trend on categorical variables across different models, p is also less than 0.05 as well, showing that there is regularity of LAR assessment with these models. In the COX regression model, when we adjusted for age, gender, HNT, DM, SBP, DBP, RBC, GCS, RTS, APACHE, Surg, ISS, CRP, HCT, HB, PT, Fib, pH, PaO2, BE, GLU, and LDH, and using the LAR (0.26\u0026ndash;0.67) as a reference, the latter showed an independent association with an increased risk of mortality in polytrauma patients with an OR (95%CI) of 1.92(1.22\u0026ndash;2.81), as shown in Table 3. Additionally, the restricted cubic spline (RCS) curve shows the existence of a nonlinear relationship between the LAR and the risk of death in these patients, as can be seen in Figure 2 (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Survival curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 3, the survival rate of all groups declines with time. At the start time points, at 5 days, and at 10 days, the survival rate does not differ significantly among the groups. However, as time goes by, differences among groups get clearer. Time all the way, Q1 group is always at the top, meaning that patients in this group have a better chance of recovery. The survival rate of the Q2 group is between those of the Q1 and Q3 groups, indicating that patients of this class had a moderate prognosis. However, the Q3 group always had the lowest survival rate among all the groups at any point in time, indicating that the patients in the Q3 group had a poor prognosis. The log-rank test yielded p \u0026lt;0.001, indicating that the differences in survival curves among the three groups are non-random and statistically significant. The mortality rates are 4.46%, 12.96%, and 46.20% for the Q1, Q2, and Q3 cohorts, respectively, indicating that the mortality rate of the Q3 cohort was significantly higher than those of the other two cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 ROC curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 shows the ROC curve for predicting the 30-day mortality from the time of patient admission. For the LAR, AUC = 0.859(95% CI: 81.9% \u0026minus; 90.1%). For Lactate, AUC = 0.817 (95% CI: 77.2% \u0026minus; 86.2%). For Albumin, AUC = 0.707 (95% CI: 64.6% \u0026minus; 76.9%). Also, the AUC of the LAR differed significantly when compared to those of AL \u0026amp; LA (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Subgroup analyses \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationships between the LAR and variables such as age, gender, hypertension, diabetes, surgery, mechanical ventilation, use of vasoactive drugs, and AKI were analyzed through the subgroup analysis. From the forest plot, after we performed a stratified analysis on the abovementioned factors, we can see that there is no considerable interaction between the LAR and each subgroup (the interaction p-value ranges from 0.107 to 0.876) (Figure 5). As far as the link between the LAR and 30-day in-hospital mortality is concerned, that too stays consistent. These results show that the LAR acts as an independent pre-nonsignificant predictor.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study aimed to identify the relationship between the LAR and the 30-day mortality rate in patients who sustained polytrauma. According to this retrospective study, there is a positive correlation between the LAR and the 30-day mortality rate in this category of patients. We included 477 people diagnosed with polytraumas at Jiangsu University affiliated hospital between 2012 and 2022. A Cox regression model was run to see how the LAR was related to the 30-day mortality rate, and this was done before and after adjusting for confounders. The LAR was found to be an independent predictor of 30-day mortality in polytrauma patients. The RCS curve showed strong nonlinear relationships between the LAR and the mortality risk. The LAR survival curve showed that the survival rate of patients decreased with an increase in LAR. The ROC curve analysis showed that the LAR has greater predictive power than lactate or albumin at any single point for predicting the 30-day mortality rate. After adjusting for confounders, a forest plot was generated, proving that the LAR was an independent predictor of 30-day mortality. Thus, utilizing the LAR to forecast the 30-day mortality of polytrauma, we consider it reliable and can consider it a new biomarker.\u003c/p\u003e\n\u003cp\u003eLactic acid, an end product of glycolysis, comes from LDH synthesizing pyruvate under anaerobic conditions and a state of increased metabolism. Historically lactic acid has been regarded as a metabolic byproduct; however, more recently, it has been shown to play a polyvalent role as a signal. This may be involved in a number of processes\u0026mdash;both physiological and pathological [19, 20]. In healthy cells, lactic acid plays a vital role in regulating energy metabolism and maintaining pH balance. In immune cells, it is a signaling molecule. In a clinical setting, lactic acid is most commonly used to evaluate conditions such as shock, infection, trauma, and severe illness, and also to predict possible adverse surgical outcomes [21, 22]. Monitoring the serum lactate levels of hospitalized patients is informative. However, a lot of different things that cause lactic acid level fluctuations\u0026mdash; like a sick liver, hypoxia, and medicines. Such confounders complicate the use of the serum lactic acid level as a prognostic marker.\u003c/p\u003e\n\u003cp\u003eAlbumin, a plasma protein produced by the liver, accounts for about 60% of all plasma proteins. Albumin helps to maintain the colloidal osmotic pressure of plasma, transport hormones, fatty acids, and other molecules in the blood, and regulate the human body's immune response and metabolic reactions. Albumin levels are measured widely in clinical settings [4]; however, the serum albumin level also has its limitations. The specificity of albumin as a diagnostic marker is not very high because low plasma albumin concentrations can be due to many reasons. These include decreased albumin synthesis and increased albumin excretion. So, one set of test results does not suffice to pinpoint the etiology of an observed condition because it might not show the full story about a given health outcome. Therefore, we have to look at it in the light of other available pieces of information. It could be that albumin, by virtue of its longer half-life of approximately 15\u0026ndash;20 days, may not promptly signal acute problems or changes from short-term events that occur suddenly, perhaps making it seem like the condition is not progressing significantly. Thus, relying solely on a polytrauma patient\u0026rsquo;s albumin level as a prognostic factor is risky.\u003c/p\u003e\n\u003cp\u003eRecently, much work has been done on the LAR, most importantly, on determining whether it is useful in predicting the outcomes of different diseases [23, 24]. G\u0026uuml;ler et al. [16] performed emergency LAR assessments on a cohort of 2,310 non-traumatic elderly patients to determine its predictive potential for patient mortality. Their research findings proved that lactate levels, albumin levels, and LARs were all good predictors of the in-patient death rate. Also, note that the LAR was a better independent predictor of inpatient mortality than lactate or albumin levels. Also, Karampela et al. [25] looked at the prognostic ability of the LAR in patients with septic shock compared to patients with sepsis. They showed that the LAR was significantly higher in patients with septic shock and non-survivors than in patients with sepsis and survivors. Also, the baseline LAR displayed a positive correlation with the severity of the sepsis, where a higher LAR value was an independent predictor of 28-day mortality. Hence, the LAR appears to be a valid and promising prognostic marker of severe sepsis on the day of admission and one week later.\u003c/p\u003e\n\u003cp\u003ePolytrauma often leads to severe illness, and quick changes in the patient\u0026rsquo;s condition make it hard for doctors to tell just from parameters such as the ISS, prothrombin time (PT), and blood lactate levels how well the person will do. So, we put blood lactate and albumin together to look at the link between the LAR and 30-day mortality in polytrauma patients. Concerning the prognosis, there is a paucity of available on the clinical importance of the LAR in this population. Notably, the LAR at ICU admission was found to be independently associated with mortality in critically injured patients by Arslan and Sahin [26], a finding that demonstrates its strong prognostic ability. In terms of mortality prediction, the LAR was superior to the serum lactate level and the 24-hour lactate clearance rate. In our study, we found that the LAR can be considered an independent predictor of 30-day mortality in polytrauma patients, unlike lactate and albumin levels that were less potent predictors and had more confounders than the LAR, as found by Arslan and Sahin.\u003c/p\u003e\n\u003cp\u003eNevertheless, this study has some limitations that should be acknowledged. First, this was a retrospective, single-center study; as such, it cannot fully explain the LAR with regard to polytrauma like an actual prospective study can. Second, we did not record the drugs and medical treatments that could have interfered with the LAR in polytrauma patients. This may render our findings less reliable. Also, we excluded patients with severe liver and kidney conditions but not cases of liver and kidney damage in the multi-hit group since hepatic and renal impairment can change the LAR.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThere is an association between a high LAR with 30-day mortality in polytrauma patients, and this association has been identified among different subgroups.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Area under the curve\u003c/p\u003e\n\u003cp\u003eMODS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Multiple organ dysfunction syndrome\u003c/p\u003e\n\u003cp\u003eOR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp; Odds ratio\u003c/p\u003e\n\u003cp\u003eRCS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Restricted cubic spline\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSIRS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Systemic inflammatory response syndrome\u003c/p\u003e\n\u003cp\u003eICU\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Intensive care unit\u003c/p\u003e\n\u003cp\u003eISS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Injury severity score\u003c/p\u003e\n\u003cp\u003eLAR\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Lactate-to-albumin ratio\u003c/p\u003e\n\u003cp\u003ePT\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; Prothrombin time\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study, which entails an analysis of the hospital database, was approved by the Ethics Review Committee of Jiangsu University Affiliated Hospital. Furthermore, due to the retrospective nature of the study, the requirement for informed consent from patients, their legal guardians, or close relatives was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Institutional Ethics Committee of \u003c/em\u003eJiangsu University Affliated Hospital\u003cem\u003e approved a waiver of consent for this study as it involved retrospective analysis of anonymized data.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Major Scientific Research Project of Wuxi Municipal Health Commission (Z202108), Shanghai Key Laboratory of Peripheral Nerve and Microsurgery (20DZ2270200) NHC Key Laboratory of Hand Reconstruction (Fudan University), Zhenjiang Science and Technology Innovation Fund (Social Development\u0026ndash;Key Project SH2024043), and Jiangsu University Medical Education Collaborative Innovation Fund (JDYY2023001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFZ contributed to the conceptualization, project administration, supervision, and writing of the original draft of the manuscript, as well as the review and editing process. CH, ZM, and QY were responsible for data curation, formal analysis, investigation, and methodology. XP and JM were involved in the conceptualization, project administration, supervision, and the review and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants and staff for their contributions.\u003c/p\u003e"},{"header":"References","content":"\n\u003col\u003e\n\u003cli\u003eLansink KWW, Gunning AC, Spijkers ATE, Leenen LPH. Evaluation of trauma care in a mature level I trauma center in the Netherlands: outcomes in a Dutch mature level I trauma center. World J Surg. 2013;37:2353-9.\u003c/li\u003e\n\u003cli\u003eLuo JL, Chen D, Tang LS, Deng H, Zhang C, Chen SY, et al. Multifactorial shock: a neglected situation in polytrauma patients. J Clin Med. 2022;11:6829.\u003c/li\u003e\n\u003cli\u003evan Breugel JMM, Niemeyer MJS, Houwert RM, Groenwold RHH, Leenen LPH, van Wessem KJP. Global changes in mortality rates in polytrauma patients admitted to the ICU-a systematic review. World J Emerg Surg. 2020;15:55.\u003c/li\u003e\n\u003cli\u003eZhou P, Ling LJ, Xia XH, Yuan H, Guo ZQ, Feng QP, et al. Independent predictors of mortality for critically ill patients with polytrauma: a single center, retrospective study. Heliyon. 2024;10:e25163.\u003c/li\u003e\n\u003cli\u003eMijaljica DR, Gregoric P, Ivancevic N, Pavlovic V, Jovanovic B, Djukic V. Predicting mortality in severe polytrauma with limited resources. Ulus Travma Acil Cerrahi Derg. 2022;28:1404-11.\u003c/li\u003e\n\u003cli\u003eLi SH, Wang C, Hu P, Xu TM, Chen B, Jin FF, et al. Surgical management of multiple rib fractures in polytrauma patients: semi-damage control surgery. Int J Med Sci. 2024;21:2926-33.\u003c/li\u003e\n\u003cli\u003eScriven JW, Battaloglu E. The effectiveness of prehospital subcutaneous continuous lactate monitoring in adult trauma: a systematic review. 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Ann Med. 2025;57:2482024.\u003c/li\u003e\n\u003cli\u003eHe J, Tong L, Wu P, Wu YB, Shi WF, Chen L. Prognostic significance of preoperative lactate dehydrogenase to albumin ratio in breast cancer: a retrospective study. Int J Gen Med. 2023;16:507-14.\u003c/li\u003e\n\u003cli\u003eHu JH, Jin Q, Fang HL, Zhang WW. Evaluating the predictive value of initial lactate/albumin ratios in determining prognosis of sepsis patients. Medicine. 2024;103:e37535.\u003c/li\u003e\n\u003cli\u003eHua YM, Ding N, Jing HZ, Xie YF, Wu H, Wu Y, et al. Association between the lactate-to-albumin ratio (LAR) index and risk of acute kidney injury in critically ill patients with sepsis: analysis of the MIMIC-IV database. Front Physiol. 2025;16:1469866.\u003c/li\u003e\n\u003cli\u003eArslan G, Besci T, \u0026Ouml;zdemir G, Evren G, T\u0026uuml;zen HI, Prencuva P, et al. Predictive value of PRISM-4, PIM-3, CRP, albumin, CRP/albumin ratio and lactate in critically ill children. Children. 2023;10:1731.\u003c/li\u003e\n\u003cli\u003eG\u0026uuml;ler S, Kocasaban D, Erciyas Z, Demirtas E, G\u0026uuml;naydin YK. Role of lactate-to-albumin ratio in predicting in-hospital mortality of geriatric patients admitted to the emergency department. Notfall Rettungsmed. 2025;28:26-33.\u003c/li\u003e\n\u003cli\u003eCadarso-Saez V, Ramirez-Zavala C, P\u0026eacute;rez-Pino MA, Toro-Huamanchumo CJ. Lactate to albumin ratio has limited prognostic value for complications in children under five with burn injuries. Sci Rep. 2025;15:2551.\u003c/li\u003e\n\u003cli\u003ePape HC, Lefering R, Butcher N, Peitzman A, Leenen L, Marzi I, et al. The definition of polytrauma revisited: an international consensus process and proposal of the new 'Berlin definition'. J Trauma Acute Care Surg. 2014;77:780-86.\u003c/li\u003e\n\u003cli\u003eWang HM, Wu X, Yu S, Zhang JW, Wen J, Wang Y, et al. Lactate promotes the epithelial-mesenchymal transition of liver cancer cells via TWIST1 lactylation. Exp Cell Res. 2025;447:114474.\u003c/li\u003e\n\u003cli\u003eWu XX, Liu CY, Zhang CY, Kuai L, Hu S, Jia N, et al. The role of lactate and lactylation in the dysregulation of immune responses in psoriasis. Clin Rev Allergy Immunol. 2025;68:28.\u003c/li\u003e\n\u003cli\u003eAlqu\u0026eacute;zar-Arbe A, P\u0026eacute;rez-Baena S, Fern\u0026aacute;ndez C, Aguil\u0026oacute; S, Burillo G, Jacob J, et al. Association between lactate determined at emergency department arrival and the probability of inhospital mortality and intensive care admission in elderly patients. Eur J Emerg Med. 2025;32:171-9.\u003c/li\u003e\n\u003cli\u003eAn R, Wu X, Bie DY, Ding J, Li YN, Jia Y, et al. Association between the highest lactate level on the first postoperative day and postoperative delirium in cardiac surgery patients. CNS Neurosci Ther. 2025;31:e70380.\u003c/li\u003e\n\u003cli\u003eZhang S, Chen N, Ma LS. Lactate-to-albumin ratio: a promising predictor of 28-day all-cause mortality in critically ill patients with acute ischemic stroke. J Stroke Cerebrovasc Dis. 2024;33:107536.\u003c/li\u003e\n\u003cli\u003eWu DJ, Shen SY, Luo DM. Association of lactate-to-albumin ratio with in-hospital and intensive care unit mortality in patients with intracerebral hemorrhage. Front Neurol. 2023;14:1198741.\u003c/li\u003e\n\u003cli\u003eKarampela I, Kounatidis D, Vallianou NG, Panagopoulos F, Tsilingiris D, Dalamaga M. Kinetics of the lactate to albumin ratio in new onset sepsis: prognostic implications. Diagnostics. 2024;14:1988.\u003c/li\u003e\n\u003cli\u003eArslan K, Sahin AS. Lactate, lactate clearance, and lactate-to-albumin ratio in predicting mortality in patients with critical polytrauma: a retrospective observational study. Medicine. 2024;103:e40704.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e LAR groups and values\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eLAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eMin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eMax\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e2.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e10.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: \u003c/strong\u003eBaseline characteristics of the patients\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003eTotal (n=477)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eQ1 (n=157)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eQ2 (n=162)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003eQ3 (n=158)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e57.0 (47.0, 66.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e59.0 (49.0, 69.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e56.0 (47.8, 65.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e54.0 (44.0, 65.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e9.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eICU time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e6.0 (2.0, 14.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e4.0 (0.0, 9.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e7.0 (3.0, 14.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e9.0 (3.0, 20.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e39.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eSBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e121.0 (91.5, 144.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e133.0 (116.5, 150.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e128.0 (104.0, 149.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e96.0 (84.0, 120.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e79.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eDBP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e75.0 (60.0, 86.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e80.0 (71.0, 89.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e79.5 (66.0, 90.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e60.0 (49.0, 75.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e71.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ePulse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e90.0 (78.0, 110.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e82.0 (74.5, 97.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e90.0 (79.0, 105.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e105.0 (83.0, 119.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e43.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eBody temperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e36.7 (36.3, 36.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e36.8 (36.5, 37.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e36.7 (36.5, 36.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e36.4 (36.0, 36.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e57.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eRBC (＜24h)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e2.0 (0.0, 6.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.0 (0.0, 1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2.0 (0.0, 4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e6.0 (4.0, 9.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e169.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eVolume of plasma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e400.0 (0.0, 800.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.0 (0.0, 100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e375.0 (0.0, 700.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e850.0 (400.0, 1385.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e154.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eGCS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e12.0 (7.0, 14.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e14.0 (10.0, 15.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e12.0 (7.0, 14.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e8.0 (4.0, 12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e69.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eFrequency of respiration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e20.0 (18.0, 22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e20.0 (18.0, 21.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e20.0 (18.0, 21.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e20.0 (18.0, 23.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e6.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.033\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eRTS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e6.9 (5.2, 7.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e7.8 (6.9, 7.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e6.9 (6.0, 7.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e5.7 (4.5, 6.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e79.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eAPACHEⅡ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e22.0 (19.0, 25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e20.0 (17.5, 23.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e21.0 (19.0, 23.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e25.0 (23.0, 28.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e86.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eTRISS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e0.4 (0.1, 0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.6 (0.2, 0.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.5 (0.2, 0.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e0.2 (0.0, 0.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e36.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eISS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e29.0 (25.0, 36.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e25.0 (22.0, 29.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e29.0 (25.0, 34.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e36.0 (32.0, 41.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e148.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eWBC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e13.1 (10.0, 17.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e12.6 (10.1, 15.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e13.3 (10.4, 18.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e14.0 (9.6, 17.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e4.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eNeutrophil count\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e11.3 (8.6, 15.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e11.0 (8.6, 13.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e11.6 (9.1, 16.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e11.8 (8.0, 15.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e4.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eLymphocyte count\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e0.9 (0.6, 1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.8 (0.6, 1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.9 (0.6, 1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e1.1 (0.6, 1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e5.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eCRP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e7.5 (1.3, 35.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e8.0 (1.5, 38.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e10.4 (2.2, 43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e5.0 (1.0, 19.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e7.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eHCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e33.0 (27.3, 38.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e35.5\u0026plusmn;6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e33.6\u0026plusmn;6.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e28.2\u0026plusmn;8.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e44.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eHB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e110.5 (92.0, 128.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e119.0\u0026plusmn;19.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e113.0\u0026plusmn;22.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e95.2\u0026plusmn;29.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e42.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ePLT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e142.0 (99.3, 187.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e162.0 (128.5, 202.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e144.0 (105.3, 186.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e110.0 (72.0, 162.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e49.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eAPTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e27.0 (23.7, 32.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e24.6 (22.6, 27.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e26.4 (23.5, 29.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e32.5 (26.8, 49.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e106.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ePT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e12.4 (11.4, 13.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e11.7 (11.2, 12.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e12.1 (11.4, 13.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e14.0 (12.6, 16.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e128.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eFibrinogen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e1.9 (1.2, 2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2.3 (1.8, 2.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.9 (1.4, 2.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e1.2 (0.8, 1.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e101.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e18.2 (16.9, 20.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e17.7 (16.6, 18.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e17.9 (16.8, 19.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e20.1 (18.0, 24.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e66.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eINR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e1.1 (1.0, 1.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.0 (1.0, 1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.1 (1.0, 1.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e1.2 (1.1, 1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e126.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eD-dimer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e25.2 (10.0, 55.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e21.2 (10.0, 45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e19.3 (8.9, 50.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e30.9 (14.1, 71.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e8.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ePH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e7.4 (7.3, 7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e7.4 (7.4, 7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e7.4 (7.4,7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e7.3 (7.2, 7.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e140.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ePaO2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e88.8 (76.2, 119.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e82.7 (73.5, 98.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e89.0 (77.4, 113.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e99.9 (78.1, 150.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e22.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003ePaCO2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e38.4 (34.2, 42.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e38.2 (34.7, 41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e38.4 (34.4, 42.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e38.4 (33.4, 45.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.516\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eLactic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e3.0 (2.1, 5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.8 (1.5, 2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e3.0 (2.5, 3.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e6.4 (5.0, 9.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e377.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eResidual base\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e-2.5 (-5.0, -1.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e-1.5 (-2.3, 0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e-2.1 (-3.4, -1.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e-5.9 (-8.8, -4.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e167.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eTotal bilirubin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e13.4 (8.8, 18.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e14.4 (11.6, 19.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e14.1 (9.6, 18.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e10.0 (6.3, 18.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e24.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eALT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e38.8 (23.0, 67.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e33.0 (21.0, 58.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e36.5 (21.5, 56.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e48.2 (31.6, 92.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e23.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eAST\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e56.8 (35.0, 103.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e47.0 (32.2, 74.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e50.5 (31.9, 92.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e78.0 (45.5, 149.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e32.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eGlucose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e8.4 (7.0, 11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e7.6 (6.7, 9.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e8.2 (7.2, 10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e9.8 (7.6, 13.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e32.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eUrea nitrogen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e5.9 (4.6, 7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e5.9 (4.5, 7.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e5.7 (4.5, 7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e6.1 (5.1, 7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e3.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e69.3 (57.1, 86.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e65.4 (56.5, 78.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e69.7 (55.8, 84.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e75.9 (58.7, 98.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e8.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eLactate dehydrogenase\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e324.0 (256.0, 433.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e307.0 (255.0, 377.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e326.0 (252.0, 408.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e355.0 (263.0, 492.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e9.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eCalcium ion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e2.1 (1.9, 2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2.2 (2.1, 2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2.1 (2.0, 2.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e2.0 (1.8, 2.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e76.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eAlbumin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e33.5 (28.0, 38.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e38.0 (34.0, 40.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e33.6 (30.5, 36.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e26.4 (20.7, 32.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e155.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eSOFA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e7.0 (2.0, 11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e2.0 (1.0, 6.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e6.0 (3.0, 8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e11.0 (9.0, 13.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e185.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eLength of stay\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e26.5 (14.0, 46.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e28.0 (18.0, 43.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e30.0 (18.0, 46.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e17.0 (4.0, 47.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e18.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e355 (73.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e116 (73.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e122 (76.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e117 (71.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.641\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e125 (25.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e51 (32.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e39 (24.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e35 (21.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e5.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e55 (11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e21 (13.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e16 (10.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e18 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.656\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eEmergency surgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e174 (36.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e29 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e62 (38.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e83 (50.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e38.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eMechanical ventilation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e267 (55.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e42 (26.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e80 (50.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e145 (89.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e130.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eVasoactive drugs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e173 (35.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e19 (12.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e41 (25.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e113 (69.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e126.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003eAKI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e70 (14.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e12 (7.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e14 (8.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e44 (27.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e30.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: \u003c/strong\u003eAssociation between the LAR and the risk of death in multiply patients\u003c/p\u003e\n\u003ctable width=\"624\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"177\"\u003e\n\u003cp\u003eModel1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"185\"\u003e\n\u003cp\u003eModel2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"186\"\u003e\n\u003cp\u003eModel3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eLAR, continuous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e3.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e2.58, 4.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e3.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e2.39, 4.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e2.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.53, 3.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eLAR, Classification\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e2.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e1.23, 6.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e2.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.14, 6.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e1.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e0.66, 4.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e0.287\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e2.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e1.96, 8.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e2.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e1.10, 4.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e1.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e1.22, 2.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e＜0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable width=\"0%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eModel 1: Crude\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eModel 2: Adjusted for Age, Gender, HNT, and DM\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eModel 3: Adjusted for Age, Gender, HNT, DM, SBP, DBP, RBC, GCS, RTS, APACHE, Surg, ISS, CRP, HCT, HB, PT, Fib, pH, PaO2, BE, GLU, and LDH\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lactate-to-albumin ratio, 30-day, mortality, polytrauma","lastPublishedDoi":"10.21203/rs.3.rs-6938557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6938557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The lactate-to-albumin ratio (LAR) has recently been proposed as a new prognostic marker for various conditions. However, not many studies have used it to predict the 30-day mortality rate in polytrauma patients. This study aimed to establish a link between the LAR and 30-day mortality in polytrauma patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This retrospective cohort study was conducted on 477 adult polytrauma patients admitted to the intensive care unit (ICU) of Jiangsu University Affliated\u003c/p\u003e\n\u003cp\u003eHospital. To establish the association between the LAR and 30-day mortality in patients in the ICU, multivariate Cox regression and Kaplan-Meier survival analyses were performed. The restricted cubic spline (RCS) curve was used to further explore the association between the LAR and 30-day in-hospital mortality. The predictive ability of various indicators of 30-day in-hospital mortality was evaluated through receiver operating characteristic (ROC) curves, and the area under the curve (AUC) of each indicator was calculated. Subgroup analyses were also performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Of the 477 polytrauma patients, 101 died after 30 days of hospitalization, giving a 30-day in-hospital mortality rate of 21.17%. The COX regression model revealed a strong association between the LAR (0.26–0.67) and higher mortality [odds ratio (OR) being 1.92 (95%CI: 1.22–2.81)]. The RCS curve revealed a nonlinear relationship between the LAR and 30-day mortality (p \u0026lt;0.001). The 30-day mortality was higher in the 3\u003csup\u003erd\u003c/sup\u003e quartile (Q3) group compared to the 1\u003csup\u003est\u003c/sup\u003e (Q1) and 2\u003csup\u003end\u003c/sup\u003e (Q2) quartile groups. The log-rank test yielded p \u0026lt;0.001, signifying that the differences in survival curves across the three groups were non-random. The AUCs for the ROC curves of the LAR, lactate, and albumin were 0.859 (95%CI 81.9–90.1), 0.817 (95%CI 77.2–86.2), and 0.707 (95%CI 64.6–76.9), respectively (p \u0026lt;0.001 for all). Subgroup analyses showed no significant interaction between the LAR and different subgroups (p = 0.107–0.876), indicating an association between the LAR and 30-day mortality in polytrauma patients regardless of subgroup differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The LAR might be the a predictor of 30-day fatality for hospitalized polytrauma patients. Future prospective studies need to verify the predictive value of the LAR on 30-day mortality in polytrauma patients.\u003c/p\u003e","manuscriptTitle":"Association between lactate-to-albumin ratio and 30-day mortality in patients with polytrauma: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 08:24:50","doi":"10.21203/rs.3.rs-6938557/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49994b62-61dc-4c60-857a-da425cc33e24","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T01:53:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 08:24:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6938557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6938557","identity":"rs-6938557","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}