{"paper_id":"2b1b03d6-7e65-47c9-82c8-8ff60d9cd016","body_text":"Integrating Conventional and Machine Learning Approaches to Evaluate the Prognostic Value of the Lactate-to-Albumin Ratio in Mechanically Ventilated ICU Patients: A Retrospective Analysis from the MIMIC-IV Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Conventional and Machine Learning Approaches to Evaluate the Prognostic Value of the Lactate-to-Albumin Ratio in Mechanically Ventilated ICU Patients: A Retrospective Analysis from the MIMIC-IV Database Yunkai Mu, Fangqin Lou, Guibo Feng, Mi Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7251795/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 27 You are reading this latest preprint version Abstract Background The lactate-to-albumin ratio (LAR), a composite biomarker reflecting both inflammatory burden and nutritional status, has been associated with adverse outcomes in various critically ill populations. However, its prognostic value in patients receiving invasive mechanical ventilation (IMV) remains unclear. Methods A retrospective cohort study was conducted using the MIMIC-IV database. Adult ICU patients who received IMV and had lactate and albumin measurements within 24 hours of admission were included. The primary outcome was 1-year all-cause mortality. Multivariable Cox proportional hazards and modified Poisson regression models were used to assess associations between LAR and mortality. Restricted cubic spline (RCS) analysis evaluated dose–response relationships. Subgroup, interaction, and sensitivity analyses were also performed. The Boruta algorithm was applied to compare LAR's predictive importance with traditional severity scores. Results A total of 9,195 IMV patients were included, with a mean age of 63.8 years; 40.3% were female. Patients were stratified into high and low LAR groups based on an optimal cutoff of 1.48. Higher LAR was independently associated with increased 1-year mortality (adjusted HR: 1.31, 95% CI: 1.20–1.43) and ICU mortality (adjusted RR: 1.27, 95% CI: 1.17–1.38). RCS analysis showed a linear positive association between LAR and 1-year mortality. Subgroup analyses demonstrated stronger associations in younger patients and those with lower RDW. The Boruta feature selection algorithm confirmed LAR as an important predictor, ranking above the SOFA score. Overlap weighting analysis further validated the robustness of the findings. Conclusions Elevated LAR is independently associated with increased short- and long-term mortality in ICU patients receiving IMV. While not a replacement for comprehensive scoring systems, LAR may serve as a convenient and valuable supplementary biomarker, especially in patients with indeterminate risk profiles. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Lactate-to-albumin ratio Invasive mechanical ventilation Critical care Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In critical care medicine,respiratory support remains a cornerstone of life-sustaining treatment 1 . Among various modalities, invasive mechanical ventilation (IMV) is a key intervention widely used in intensive care units (ICUs) to manage acute respiratory failure and prevent multi-organ dysfunction 2,3 . Despite ongoing advances in critical care strategies, the in-hospital mortality rate among IMV-treated patients remains as high as 38% 4 . During the coronavirus disease 2019 (COVID-19) pandemic, this rate further increased to approximately 49% 5 , underscoring the importance of accurate prognostic assessment in this high-risk population.Currently, several scoring systems are routinely used in the ICU to estimate illness severity and predict outcomes, including the Sequential Organ Failure Assessment (SOFA) score and the Acute Physiology Score III (APS III) 6,7 . Although these tools provide useful guidance in prognostication, substantial variability may still exist between patients with similar clinical presentations. Moreover, the reliance of these systems on numerous variables,some of which are subject to clinician judgment or data availability, can complicate clinical decision-making.Therefore, there is a pressing need for simple, objective, and easily accessible markers to aid in identifying high-risk individuals among IMV-treated patients and support timely therapeutic interventions. Systemic inflammation is a key driver of disease progression and adverse outcomes in critically ill patients 8 . Among those receiving IMV,inflammation is often accompanied by nutritional deficits, contributing synergistically to worse prognosis through mechanisms such as ventilator-induced lung injury, oxidative stress, and organ failure 9 . In this context,integrated evaluation of inflammatory and nutritional status is gaining attention. Lactate and albumin are routinely measured biochemical parameters commonly used to assess illness severity and prognosis in the ICU. Elevated lactate levels reflect tissue hypoperfusion and inflammatory decompensation 10 , while hypoalbuminemia is indicative of both chronic undernutrition and acute inflammatory burden 11 , and has been independently associated with poor clinical outcomes in critical illness 12 . The lactate-to-albumin ratio (LAR), which combines these two biomarkers into a single composite index, has recently been proposed as a novel integrative indicator of metabolic stress and nutritional status. Previous studies have suggested that LAR may provide additional prognostic information in various critical conditions such as sepsis, cardiac arrest, and traumatic brain injury 13–15 . However,evidence regarding the prognostic utility of LAR in mechanically ventilated ICU patients, particularly with respect to long-term mortality,remains limited. Therefore, in this study,we aimed to investigate the association between LAR and 1-year all-cause mortality among ICU patients receiving IMV, using data from the MIMIC-IV database. Additionally,we compared the predictive performance of LAR with established severity scores such as SOFA and APS III to explore its potential as a complementary prognostic tool. Materials and Methods Data source This retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. MIMIC-IV is a publicly available critical care database jointly maintained by the Massachusetts Institute of Technology and Harvard Medical School. It contains comprehensive clinical information for 65,353 ICU admissions at the Beth Israel Deaconess Medical Center in Boston,Massachusetts,between 2008 and 2019, including demographic characteristics, vital signs, comorbidities, laboratory values,treatment data (including mechanical ventilation duration),and clinical outcomes. Study population Patients were included if they met the following criteria: (1) received invasive mechanical ventilation (IMV) via endotracheal intubation during their first ICU admission, and (2) had available plasma lactate and serum albumin measurements within the first 24 hours of ICU admission. Exclusion criteria were: (1) age < 18 years; (2) ICU length of stay less than 24 hours; and (3) missing data on lactate or albumin. After applying these criteria, a total of 9,195 patients were included in the final analysis (Fig. 1 ). Clinical variables Clinical data were initially extracted within the first 24 hours following ICU admission. These included demographic characteristics (age, sex, weight, and ICU length of stay); vital signs [heart rate (HR), respiratory rate (RR), systolic and diastolic blood pressure (SBP/DBP), and peripheral oxygen saturation (SpO 2 )]; laboratory tests [white blood cell (WBC) count, hemoglobin, platelet count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), albumin, total bilirubin, creatinine, blood urea nitrogen (BUN), sodium, potassium, calcium, chloride, lactate, pH, partial pressure of carbon dioxide (pCO 2 ), partial pressure of oxygen (pO 2 ), anion gap, and bicarbonate]; comorbidities [hypertension, diabetes, chronic kidney disease (CKD), cirrhosis, cancer, heart failure, COVID-19 infection, and sepsis based on Sepsis-3 criteria]; clinical interventions [use of antibiotics, continuous renal replacement therapy (CRRT), CRRT duration, and duration of mechanical ventilation]; and disease severity scores [Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APS III), and Charlson Comorbidity Index].The primary outcomes were ICU all-cause mortality and 1-year all-cause mortality following ICU discharge. The lactate-to-albumin ratio was calculated as:LAR = lactate (mmol/L) / albumin (g/dL). Statistical Analysis The proportion of missing data for each variable was assessed using a missing value distribution histogram, and the missing data mechanism was further examined via a missing pattern heatmap (Supplementary Fig. 1). Multiple imputation (m = 5 imputations) was applied to handle missing values and ensure data completeness. Continuous variables were expressed as mean ± standard deviation (SD) if normally distributed and compared using independent-sample t tests; otherwise, they were reported as medians with interquartile ranges (IQRs) and compared using the Mann–Whitney U test. Categorical variables were presented as frequencies (percentages) and compared using the Chi-square test or Fisher’s exact test as appropriate. Survival analysis was first performed using Kaplan–Meier curves and the log-rank test to assess the primary outcome across different LAR levels. The association between the LAR and 1-year all-cause mortality was evaluated using Cox proportional hazards regression models, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. To assess the association between LAR and ICU mortality (a binary outcome), a modified Poisson regression model was used to directly estimate relative risks (RRs) and their corresponding 95% CIs, thereby avoiding overestimation of RR by odds ratios (ORs) in outcomes with high incidence.To evaluate multicollinearity among covariates in multivariate models, the variance inflation factor (VIF) was calculated. A VIF greater than 10 was considered indicative of severe multicollinearity. If multicollinearity was identified, redundant variables were removed or handled using principal component analysis (PCA). LAR was analyzed in two formats: (1) as a categorical variable based on the optimal cutoff value, dividing patients into high and low LAR groups (low LAR group served as reference); and (2) as a continuous variable to estimate the effect of each 1-standard deviation (SD) increase in LAR on mortality risk. Four progressively adjusted models were constructed for multivariate analysis:Model 0: unadjusted, including only LAR;Model 1: adjusted for demographic and comorbidity variables (age, sex, diabetes, hypertension, heart failure, AKI stage, CKD, cirrhosis, sepsis according to Sepsis-3 criteria, COVID-19, and cancer);Model 2: further adjusted for WBC count, hemoglobin, red cell distribution width (RDW), lymphocyte count, AST, ALT, total bilirubin (TBil), albumin, creatinine, BUN, serum chloride, anion gap, lactate dehydrogenase (LDH), antibiotic use, and CRRT;Model 3: additionally adjusted for disease severity scores including SOFA, APS III, and Charlson Comorbidity Index based on Model 2. Restricted cubic spline (RCS) analysis was employed to explore the dose–response relationship between LAR and 1-year all-cause mortality. Subgroup analyses and interaction tests were performed based on Model 3 to investigate whether the prognostic impact of LAR varied across different clinical subpopulations. Additionally, the Boruta feature selection algorithm, based on a random forest model, was applied to identify the most important predictors of 1-year all-cause mortality among IMV patients.To assess the robustness of our findings, sensitivity analyses were conducted. The overlap weighting method based on propensity scores was used to balance covariates between high and low LAR groups. Standardized mean differences (SMDs) were calculated before and after weighting, with an SMD < 0.1 indicating adequate balance.All statistical analyses were performed using R software (version 4.3.1), and a two-sided P value < 0.05 was considered statistically significant. Results Demographic and Clinical Characteristics A total of 9,195 patients receiving invasive mechanical ventilation (IMV) were included in this study. The mean age was 63.8 years (standard deviation: 16.3), and 40.3% of the patients were female. Based on the optimal cutoff value of LAR, patients were divided into a low LAR group (≤ 1.48, n = 7,775) and a high LAR group (> 1.48, n = 1,420). As shown in Table 1 , some baseline characteristics showed no significant differences between the two groups. For example, sex distribution, prevalence of diabetes, chronic kidney disease, malignancy, and serum sodium levels were comparable between the groups (all P > 0.05), indicating a relatively balanced distribution of these variables post-stratification. However, significant differences were observed in several other clinical and laboratory indicators.Compared with the low LAR group, patients in the high LAR group had higher respiratory and heart rates, but lower systolic and diastolic blood pressures (all P < 0.001), suggesting greater cardiovascular and respiratory instability. Laboratory findings showed elevated white blood cell counts, liver enzymes (AST, ALT), total bilirubin, lactate, and lactate dehydrogenase levels in the high LAR group, along with significantly lower serum albumin levels (2.57 ± 0.63 vs. 3.03 ± 0.61 g/dL, P < 0.001). These findings suggest a greater burden of systemic inflammation and hepatic–renal dysfunction in the high LAR group. Additionally, the proportion of patients with stage 3 acute kidney injury (AKI) was higher in the high LAR group (57.5% vs. 34.8%), and the use of continuous renal replacement therapy (CRRT) was also more frequent (28.7% vs. 11.2%, P < 0.001). Regarding disease severity, both SOFA and APS III scores were significantly higher in the high LAR group (P < 0.001).Notably, both ICU mortality (46.4% vs. 22.2%) and 1-year all-cause mortality (62.5% vs. 41.3%) were substantially higher in the high LAR group (both P < 0.001). Despite similar distributions of certain demographic and comorbidity-related variables, elevated LAR was associated with more severe physiological derangements, laboratory abnormalities, and worse clinical outcomes, highlighting the potential prognostic relevance of LAR in this patient population.Furthermore, Kaplan–Meier survival curve analysis demonstrated a significantly lower cumulative survival rate in the high LAR group compared with the low LAR group (log-rank P < 0.001; Fig. 2 ). Association Between LAR and All-Cause Mortality Risk Multicollinearity diagnostics showed that the variance inflation factor (VIF) for all covariates was < 5, indicating no significant multicollinearity (Supplementary Fig. 2). As presented in Table 2 , a higher LAR was significantly associated with worse prognosis among IMV patients. In the unadjusted model (Model 0), patients in the high LAR group (> 1.48) had a 2.06-fold increased risk of 1-year all-cause mortality compared to those in the low LAR group (95% CI: 1.91–2.13). This association remained robust after sequential adjustment for demographic factors, comorbidities, laboratory parameters, and disease severity scores (Model 3), with an adjusted hazard ratio (HR) of 1.31 (95% CI: 1.20–1.43). When LAR was analyzed as a continuous variable, each one standard deviation (SD) increase in LAR was associated with a 12% higher risk of 1-year mortality (HR = 1.12, 95% CI: 1.08–1.16, Model 3). Similarly, in the modified Poisson regression analysis for ICU mortality, the unadjusted relative risk (RR) for high LAR was 2.09 (95% CI: 1.95–2.24), and the association remained significant after full adjustment (RR = 1.27, 95% CI: 1.17–1.38, Model 3). Each SD increase in LAR was associated with a 12% higher risk of ICU mortality (RR = 1.12, 95% CI: 1.08–1.16, Model 3). These findings suggest that elevated LAR is independently associated with both short-term and 1-year all-cause mortality in IMV patients, supporting its potential utility as a prognostic marker in this high-risk population. Dose–Response Relationship and Subgroup Analyses After full multivariable adjustment (Model 3), including demographic factors, comorbidities, laboratory tests, treatments, and severity scores, a restricted cubic spline (RCS) regression (Fig. 3 A) revealed a linear positive association between LAR and 1-year all-cause mortality (P for overall trend < 0.001; P for nonlinearity = 0.779), with no evidence of a nonlinear threshold effect. Subgroup analyses (Fig. 3 B) consistently demonstrated that a high LAR (> 1.48) was associated with increased mortality risk across all predefined strata (all HRs > 1). Notably, statistically significant associations were observed in subgroups including females (HR = 1.37, 95% CI: 1.20–1.56), patients with diabetes (HR = 1.34, 95% CI: 1.20–1.49), hypertension (HR = 1.41, 95% CI: 1.22–1.64), and heart failure (HR = 1.31, 95% CI: 1.11–1.55). Although associations in the cirrhosis (HR = 1.20, 95% CI: 0.98–1.47) and CRRT (HR = 1.09, 95% CI: 0.92–1.29) subgroups did not reach statistical significance, the point estimates still suggested a trend toward increased risk. Interaction analysis showed no significant interactions across most subgroups (all P for interaction > 0.05), except for Sepsis 3.0, indicating that the association between LAR and mortality risk remained consistent across different clinical profiles. Additionally, further interaction tests (Fig. 4 ) suggested that the association between LAR and 1-year mortality was more pronounced among relatively younger individuals and those with lower RDW levels (both P for interaction < 0.001). Comparison with conventional severity scoring systems To further identify the most critical predictors of mortality, the Boruta feature selection algorithm was applied to evaluate the relative importance of all candidate variables. Figure 5 illustrates the Z-score–based importance rankings. In the analysis with 1-year all-cause mortality as the outcome (Fig. 5 A), variables such as the Charlson Comorbidity Index, APS III score, age, RDW, BUN, duration of mechanical ventilation, AKI stage, and PaO₂ were identified as top contributors. Notably, the LAR was confirmed as an important predictor and ranked tenth, ahead of the SOFA score. In the ICU all-cause mortality model (Fig. 5 B), APS III score, duration of mechanical ventilation, CRRT, LDH, BUN, SOFA score, and Charlson Comorbidity Index remained among the top predictors. LAR was again classified as a \"confirmed\" feature, ranking ninth—above lactate and albumin individually. These findings further support the potential of LAR as an independent prognostic indicator in critically ill patients. Sensitivity analysis Based on the pre-specified optimal cutoff value (LAR = 1.48), patients were categorized into low LAR (n = 7,775) and high LAR (n = 1,420) groups. Baseline comparisons revealed significant imbalances in several key clinical and laboratory variables, including age, disease severity scores (SOFA and APS III), comorbidity burden (Charlson Comorbidity Index), laboratory parameters (e.g., lactate, albumin, creatinine), and comorbid conditions (e.g., sepsis, heart failure). Most variables had absolute standardized mean differences (ASMDs) > 0.1, with some exceeding 1.0 (Fig. 6 A), indicating potential confounding. To mitigate these baseline differences, overlap weighting based on propensity scores was applied. Post-weighting, all covariates achieved good balance with ASMDs < 0.1. Kaplan–Meier survival curves based on the weighted population showed significantly lower 1-year survival in the high LAR group compared to the low LAR group (log-rank p < 0.001). The weighted Cox proportional hazards model further confirmed that high LAR was independently associated with increased 1-year all-cause mortality (adjusted HR = 1.27; 95% CI: 1.16–1.39; Fig. 6 B). Discussion This study demonstrated that an elevated LAR was independently associated with an increased risk of 1-year all-cause mortality among ICU patients receiving IMV. This association remained robust even after comprehensive adjustment for demographic characteristics, comorbidities, laboratory parameters, and illness severity scores. To our knowledge, this is the first large-scale cohort study to systematically evaluate the prognostic value of LAR in this high-risk population. Previous research has suggested that LAR may serve as a useful prognostic biomarker in critically ill patients. For example, Wang et al. reported that elevated LAR levels were significantly associated with higher 28-day and in-hospital mortality among ICU patients with acute respiratory distress syndrome (ARDS) 16 .Similarly, a large retrospective study found that higher LAR levels were strongly correlated with poor outcomes in ICU patients with sepsis 17 , and subsequent analyses confirmed its role as an independent predictor of sepsis-related mortality 18,19 . Moreover, Kokkoris et al. demonstrated that LAR levels measured on the first day of ICU admission were positively associated with ICU length of stay and mortality risk in patients with severe COVID-19, suggesting its utility in early risk stratification 20 . Comparable findings have also been reported in patients with acute myocardial infarction and traumatic brain injury 21,22 . Extending beyond these disease-specific investigations, the present study focused on a particularly vulnerable and clinically complex subgroup—ICU patients undergoing IMV—and aimed to assess the prognostic significance of LAR for long-term mortality in this setting. Using multiple analytical approaches, including multivariable adjustment, trend analysis, restricted cubic spline modeling, and subgroup analyses, our findings consistently showed that higher LAR levels were strongly associated with increased 1-year all-cause mortality. The robustness of this association was further supported by sensitivity analyses. In addition, the association between elevated LAR and ICU in-hospital mortality, confirmed via modified Poisson regression, underscores the predictive utility of this biomarker across both short- and long-term outcomes, highlighting its stability over different time horizons. After comprehensive adjustment for potential confounders, elevated LAR remained independently associated with an increased risk of 1-year all-cause mortality, regardless of whether it was modeled as a categorical or continuous variable. This consistency across modeling strategies underscores the robustness of LAR as a prognostic indicator. Further dose–response analysis using restricted cubic spline modeling revealed a linear positive association between LAR and mortality risk, with no evidence of nonlinear inflection points or threshold effects. These findings suggest that modeling LAR as a continuous variable may better capture its incremental risk association. This pattern is consistent with previous research on subarachnoid hemorrhage patients, which also reported a linear association between LAR and mortality risk 23 . Subgroup analyses indicated that the prognostic value of LAR was generally stable across various clinical strata. Notably, the predictive strength of LAR appeared more pronounced among younger patients compared to older individuals. Although elderly patients have traditionally drawn greater clinical attention due to a higher prevalence of malnutrition, immunosuppression, and comorbidities, our findings emphasize the importance of also assessing risk in younger IMV patients. Similarly, we observed that LAR was more predictive of mortality in patients with lower red cell distribution width (RDW). Previous studies have shown that elevated RDW itself is strongly associated with adverse outcomes in critically ill populations 24–26 . however, our results suggest that LAR may offer additional risk stratification value even before RDW elevation occurs. Taken together, these results provide a comprehensive view of the interaction between LAR and mortality risk across subgroups and support the potential utility of LAR in facilitating personalized risk stratification and precision prognostication in ICU patients undergoing invasive mechanical ventilation. The SOFA and APS III scores are widely utilized to assess illness severity in ICU patients, while the Charlson Comorbidity Index is commonly used to quantify chronic disease burden and its impact on mortality risk. These scoring systems have been extensively validated and are routinely employed in prognostic evaluations of critically ill individuals 6,7,27 .In the present study, the Boruta feature selection algorithm further confirmed their strong predictive value for adverse outcomes among patients receiving invasive mechanical ventilation (IMV), demonstrating consistent performance across different modeling frameworks.Although LAR cannot replace these established tools in clinical decision-making, it offers certain complementary advantages. As a composite biomarker derived from routinely available laboratory parameters, LAR may provide additional insights in specific clinical contexts. Notably, we observed that approximately 20% of patients had comparable SOFA, APS III, or Charlson scores but exhibited markedly different clinical outcomes. This finding suggests that in so-called “gray zones,” where traditional scoring systems may fail to adequately discriminate risk, LAR could help uncover latent prognostic differences and support more refined risk stratification. While LAR does not replicate the multidimensional scope of conventional scoring systems, its stable independent association with mortality after comprehensive adjustment indicates that it may serve as a valuable adjunct—particularly in settings where existing tools show limited discriminatory power. In critical care settings, patients undergoing invasive mechanical ventilation (IMV) often experience a highly complex state of metabolic stress, characterized by the concurrent activation of systemic inflammation and nutritional compromise. In severe conditions such as sepsis and cardiac arrest, tissue hypoxia and inflammatory responses synergistically contribute to the development of multiple organ dysfunction, necessitating IMV support. Under these circumstances, metabolic derangements and impaired hepatic synthetic function lead to elevated lactate levels and reduced serum albumin concentrations, jointly contributing to an increased LAR 28–30 .Importantly, IMV itself may exacerbate preexisting inflammation. Prior studies have shown that inappropriate ventilator settings—such as excessive tidal volume or positive end-expiratory pressure (PEEP)—can induce ventilator-induced lung injury (VILI) 31,32 , Moreover, sedatives and neuromuscular blocking agents may impair host defense mechanisms, increasing the risk of secondary infections. Compromise of intestinal barrier function and translocation of gut microbiota may further trigger a so-called “second hit” of inflammation, amplifying the systemic inflammatory burden. These processes not only promote lactate overproduction but also contribute to persistent albumin depletion, reflected in a sustained elevation of LAR. On the other hand, albumin is a well-recognized marker of both inflammation and nutritional status. It plays a vital role in maintaining intravascular oncotic pressure, antioxidative defense, and the transport of endogenous and exogenous substances 30 . Hypoalbuminemia is commonly associated with negative nitrogen balance, immune suppression, and impaired tissue repair, and has been independently linked to prolonged ICU stay, increased need for organ support, and higher mortality risk 33 . Taken together, LAR serve as an integrative biomarker reflecting both inflammatory and nutritional status, and may holds potential as a prognostic indicator. Future studies are warranted to explore its role in guiding individualized nutritional support, anti-inflammatory strategies, and optimization of ventilation management in critically ill patients. Several limitations of this study should be acknowledged. First, this was a retrospective observational cohort study based on a single-center ICU database. Although multiple statistical adjustments and sensitivity analyses were conducted to mitigate potential confounding, residual confounders cannot be entirely ruled out. Second, LAR was calculated using lactate and albumin values obtained within the first 24 hours after ICU admission; this single-time-point measurement may not capture the dynamic changes in metabolic and inflammatory status over the course of illness. Third, certain clinically relevant variables—such as comprehensive nutritional assessments, broader inflammatory biomarkers, and detailed data on ventilator settings or sedative use—were not available in the database, which may limit the completeness of risk adjustment. Finally, external validation using data from other centers or prospective trials is warranted to confirm the prognostic utility of LAR in this high-risk population. Conclusions This study demonstrated that elevated LAR was independently associated with increased 1-year all-cause mortality among ICU patients receiving IMV, with robust associations observed across multiple modeling strategies. Moreover, the prognostic value of LAR appeared to be more pronounced in younger patients and those with lower RDW, indicating its potential utility in specific subpopulations. Although LAR is not a substitute for established severity scoring systems such as SOFA or APS III, it may serve as a valuable and easily accessible supplementary biomarker, particularly when traditional tools provide limited discriminatory power. Abbreviations AKI – Acute kidney injury ALP – Alkaline phosphatase ALT – Alanine aminotransferase AST – Aspartate aminotransferase APS III – Acute Physiology Score III BUN – Blood urea nitrogen CKD – Chronic kidney disease CRRT – Continuous renal replacement therapy HR – Hazard ratio ICD – International Classification of Diseases ICU – Intensive care unit IMV – Invasive mechanical ventilation IQR – Interquartile range KM – Kaplan–Meier LAR – Lactate-to-albumin ratio LDH – Lactate dehydrogenase OR – Odds ratio RR – Relative risk RCS – Restricted cubic spline RDW – Red cell distribution width SOFA – Sequential Organ Failure Assessment TBil – Total bilirubin VIF – Variance inflation factor WBC – White blood cell Declarations Ethics approval and consent to participate The requirement for informed consent was waived because this study was a retrospective secondary analysis of de-identified data from the publicly available MIMIC-IV database, which poses no risk to participants. The use of the MIMIC-IV database has been approved by the Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. All procedures were conducted in accordance with the ethical standards of the Declaration of Helsinki. Clinical Trial Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://physionet.org/content/mimiciv/2.2/. Competing interests The authors declare that they have no competing interests. Funding The authors declare that no financial support was received for the research, authorship, and/or publication of this article. Authors' contributions YM and FL participated in the conception and design of the study and wrote the manuscript. MH participated in key revisions, as well as the statistical analysis of data. YM and GF verified the methods and steps of statistical analysis. All authors participated in the revision of the manuscript, read and approved the submitted version, and agreed to take responsibility for all aspects of the study to ensure the accuracy of this research. All authors agreed to publish this manuscript. Acknowledgements The authors would like to thank the patients for participating in this study. References Martin-Loeches I, Bos LD, Goligher EC. Will all ARDS patients be receiving mechanical ventilation in 2035? Yes. Intensive Care Med . 2017;43(4):568-569. doi:10.1007/s00134-016-4461-x Jaber S, Bellani G, Blanch L, et al. The intensive care medicine research agenda for airways, invasive and noninvasive mechanical ventilation. Intensive Care Med . 2017;43(9):1352-1365. doi:10.1007/s00134-017-4896-8 Antonelli M, Azoulay E, Bonten M, et al. Year in review in Intensive Care Medicine, 2008: II. 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Baseline information according to the levels of lactate - albumin ratio. Characteristic Overall n = 9195 Low (≤ 1.48) n = 7775 High (> 1.48) n = 1420 p value age (years) 63.83 ± 16.34 64.00 ± 16.30 62.86 ± 16.57 0.017 female 3702 (40.3%) 3104 (39.9%) 598 (42.1%) 0.122 respiratory rate (/min) 19.90 ± 6.72 19.69 ± 6.53 21.05 ± 7.57 <0.001 heart rate (/min) 91.24 ± 21.40 89.86 ± 20.89 98.78 ± 22.56 <0.001 systolic blood pressure (mmHg) 120.00 ± 25.14 121.19 ± 24.84 113.53 ± 25.77 <0.001 diastolic blood pressure (mmHg) 66.00 (55.00 79.00) 67.00 (56.00 79.00) 64.00 (53.00 77.00) <0.001 saturation of peripheral oxygen (%) 99.00 (95.00 100.00) 98.00 (95.00 100.00) 99.00 (95.00 100.00) 0.536 hypertension 3531 (38.4%) 3045 (39.2%) 486 (34.2%) <0.001 diabetes 2742 (29.8%) 2327 (29.9%) 415 (29.2%) 0.594 chronic kidney disease 1685 (18.3%) 1440 (18.5%) 245 (17.3%) 0.256 AKI stage <0.001 0 1096 (11.9%) 1023 (13.2%) 73 (5.1%) 1 1240 (13.5%) 1099 (14.1%) 141 (9.9%) 2 3338 (36.3%) 2948 (37.9%) 390 (27.5%) 3 3521 (38.3%) 2705 (34.8%) 816 (57.5%) heart failure 2678 (29.1%) 2324 (29.9%) 354 (24.9%) <0.001 cirrhosis 1088 (11.8%) 797 (10.3%) 291 (20.5%) <0.001 sepsis 7422 (80.7%) 6168 (79.3%) 1254 (88.3%) <0.001 cancer 1189 (12.9%) 994 (12.8%) 195 (13.7%) 0.328 COVID_19 160 (1.7%) 150 (1.9%) 10 (0.7%) 0.001 white blood cell (10^9/L) 12.10 (8.40 17.10) 11.90 (8.40 16.60) 13.60 (8.50 20.50) <0.001 hemoglobin (g/dL) 10.62 ± 2.42 10.67 ± 2.39 10.37 ± 2.59 <0.001 red cell distribution width (%) 14.60 (13.50 16.30) 14.60 (13.50 16.20) 15.10 (14.00 17.00) <0.001 platelets (10^9/L) 182.00 (124.00 253.00) 187.00 (130.00 256.00) 147.00 (92.00 224.00) <0.001 alanine aminotransferase (U/L) 30.00 (17.00 69.00) 27.00 (16.00 58.00) 63.00 (24.00 256.25) <0.001 aspartate aminotransferase (U/L) 46.00 (26.00 111.50) 42.00 (25.00 88.00) 126.50 (48.00 509.00) <0.001 albumin (g/dL) 2.96 ± 0.63 3.03 ± 0.61 2.57 ± 0.63 <0.001 total bilirubin (mg/dl) 0.70 (0.40 1.40) 0.60 (0.40 1.20) 1.10 (0.60 2.73) <0.001 creatinine (mg/dl) 1.10 (0.80 1.70) 1.00 (0.70 1.60) 1.40 (1.00 2.13) <0.001 blood urea nitrogen (mg/dl) 21.00 (14.00 36.00) 21.00 (14.00 35.00) 24.00 (16.00 39.00) <0.001 lactate dehydrogenase (U/L) 309.00 (227.50 472.00) 295.00 (222.00 431.00) 455.00 (283.75 965.25) <0.001 anion gap (mmol/L) 15.11 ± 5.08 14.29 ± 4.18 19.63 ± 6.87 <0.001 lactate (mmol/L) 1.90 (1.30 3.10) 1.70 (1.20 2.40) 5.90 (4.60 8.20) <0.001 calcium (mg/dL) 8.20 ± 0.96 8.23 ± 0.90 8.05 ± 1.24 <0.001 chloride (mEq/L) 104.18 ± 7.14 104.25 ± 7.01 103.77 ± 7.82 0.028 potassium (mEq/L) 4.27 ± 0.82 4.25 ± 0.79 4.39 ± 0.96 <0.001 sodium (mEq/L) 138.45 ± 5.79 138.44 ± 5.66 138.54 ± 6.43 0.593 SOFA 7.12 ± 4.03 6.57 ± 3.72 10.16 ± 4.30 <0.001 APS Ⅲ 55.54 ± 24.83 52.16 ± 22.67 74.05 ± 27.80 <0.001 CHARLSON 5.07 ± 2.99 5.02 ± 2.97 5.29 ± 3.09 0.003 antibiotics 8614 (93.7%) 7252 (93.3%) 1362 (95.9%) <0.001 continuous renal replacement therapy 1276 (13.9%) 868 (11.2%) 408 (28.7%) <0.001 in-ICU mortality 2385 (25.9%) 1726 (22.2%) 659 (46.4%) <0.001 one-year mortality 4097 (44.6%) 3210 (41.3%) 887 (62.5%) <0.001 Table 2. The association between lactate - albumin ratio and all-cause mortality. LAR N model 0 model 1 model 2 model 3 cut-off value = 10.59 9195 Hazard Ratio for one-year mortality (Cox regression) Low (≤1.48) 7775 reference reference reference reference High (>1.48) 1420 2.06 (1.91, 2.13) 1.73 (1.61, 1.87) 1.54 (1.41, 1.67) 1.31 (1.20, 1.43) Each SD increase - 1.35 (1.31, 1.39) 1.26 (1.22, 1.29) 1.21 (1.17, 1.25) 1.12 (1.08, 1.16) cut-off value = 10.59 9195 Relative Risk for in-ICU mortality (modified Poison regression) Low (≤1.48) 7775 reference reference reference reference High (>1.48) 1420 2.09 (1.95, 2.24) 1.71 (1.60, 1.84) 1.49 (1.38, 1.61) 1.27 (1.17, 1.38) Each SD increase - 1.38 (1.34, 1.41) 1.26 (1.23, 1.30) 1.20 (1.17, 1.24) 1.12 (1.08, 1.16) model 0: LAR was included; model 1: age, gender, diabetes, hypertension, heart failure, AKI stage, CKD, cirrhosis, sepsis3, COVID-19 and cancer were adjusted; model 2: WBC, hemoglobin, RDW, lymphocytes, AST, ALT, TBil, albumin, total bilirubin, creatinine, BUN, chloride, anion gap, LDH, antibiotics and CRRT and were further adjusted; model 3: SOFA, APS Ⅲ and CHARLSON were further adjusted. N - number of observation, SD - standard deviation. Additional Declarations No competing interests reported. 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07:08:48\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":296213,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlowchart of patient selection and grouping based on lactate/albumin ratio (LAR).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/ca1aea5b21c6176e7d51931d.png\"},{\"id\":96240218,\"identity\":\"a5c9205f-a729-438f-bf28-a11f7828fd4d\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:08:36\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":119733,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKaplan–Meier survival curves with log-rank test comparing 1-year all-cause mortality between patients with high and low LAR levels.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/11c8825d89b12f4cb8ebd856.png\"},{\"id\":95845875,\"identity\":\"6af2c4fd-39db-4d6a-8608-93cec6efaf0f\",\"added_by\":\"auto\",\"created_at\":\"2025-11-13 14:51:03\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":228545,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAssociation between LAR and 1-year all-cause mortality.\\u003cstrong\\u003eA\\u003c/strong\\u003e Adjusted restricted cubic spline showing the linear association between LAR and 1-year all-cause mortality.\\u003cstrong\\u003eB\\u003c/strong\\u003e Subgroup analyses of the association between LAR and 1-year all-cause mortality.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/75d47854536eb096691e9703.png\"},{\"id\":96240265,\"identity\":\"9895328a-60ca-482b-a8f5-d7783093fe29\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:08:43\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":148802,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eInteraction analyses between LAR and 1-year all-cause mortality.\\u003cstrong\\u003eA\\u003c/strong\\u003e Interaction between LAR and age.\\u003cstrong\\u003eB\\u003c/strong\\u003e Interaction between LAR and RDW.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/f8f6e8931fdc381ce7c00414.png\"},{\"id\":95845885,\"identity\":\"0cea462e-28e0-4b18-a992-46b4cd0a298a\",\"added_by\":\"auto\",\"created_at\":\"2025-11-13 14:51:03\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":325875,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eVariable importance ranked by the Boruta feature selection algorithm.\\u003cstrong\\u003eA \\u003c/strong\\u003eImportance of predictors for 1-year all-cause mortality.\\u003cstrong\\u003eB\\u003c/strong\\u003e Importance of predictors for ICU mortality.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/a779cd5ae9442b4bcb7b5362.png\"},{\"id\":96240617,\"identity\":\"05a9ae71-d3a7-4d35-bb7b-8adacfb1a801\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:09:11\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":192181,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePropensity score–based overlap weighting and survival comparison by LAR level.\\u003cstrong\\u003eA\\u003c/strong\\u003eStandardized mean differences (SMD) of baseline covariates before and after overlap weighting.\\u003cstrong\\u003eB\\u003c/strong\\u003e Weighted Kaplan–Meier survival curves and adjusted hazard ratio comparing high vs. low LAR groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/692c0e48cc6ef2b6fe265651.png\"},{\"id\":100614789,\"identity\":\"ae8f2adb-36c3-4ae2-8f8b-f726b41d8b53\",\"added_by\":\"auto\",\"created_at\":\"2026-01-19 17:25:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2161544,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/5b3b0ed3-035e-4689-bc59-02cf182acde6.pdf\"},{\"id\":96240417,\"identity\":\"cf95c4d3-b24b-4f85-9af0-5540f1be2018\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:08:54\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":7255880,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementaryfigures.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7251795/v1/851b2795aa521a2c1787ebe9.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Integrating Conventional and Machine Learning Approaches to Evaluate the Prognostic Value of the Lactate-to-Albumin Ratio in Mechanically Ventilated ICU Patients: A Retrospective Analysis from the MIMIC-IV Database\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eIn critical care medicine,respiratory support remains a cornerstone of life-sustaining treatment\\u003csup\\u003e1\\u003c/sup\\u003e. Among various modalities, invasive mechanical ventilation (IMV) is a key intervention widely used in intensive care units (ICUs) to manage acute respiratory failure and prevent multi-organ dysfunction\\u003csup\\u003e2,3\\u003c/sup\\u003e. Despite ongoing advances in critical care strategies, the in-hospital mortality rate among IMV-treated patients remains as high as 38%\\u003csup\\u003e4\\u003c/sup\\u003e. During the coronavirus disease 2019 (COVID-19) pandemic, this rate further increased to approximately 49%\\u003csup\\u003e5\\u003c/sup\\u003e, underscoring the importance of accurate prognostic assessment in this high-risk population.Currently, several scoring systems are routinely used in the ICU to estimate illness severity and predict outcomes, including the Sequential Organ Failure Assessment (SOFA) score and the Acute Physiology Score III (APS III)\\u003csup\\u003e6,7\\u003c/sup\\u003e. Although these tools provide useful guidance in prognostication, substantial variability may still exist between patients with similar clinical presentations. Moreover, the reliance of these systems on numerous variables,some of which are subject to clinician judgment or data availability, can complicate clinical decision-making.Therefore, there is a pressing need for simple, objective, and easily accessible markers to aid in identifying high-risk individuals among IMV-treated patients and support timely therapeutic interventions.\\u003c/p\\u003e\\u003cp\\u003eSystemic inflammation is a key driver of disease progression and adverse outcomes in critically ill patients\\u003csup\\u003e8\\u003c/sup\\u003e. Among those receiving IMV,inflammation is often accompanied by nutritional deficits, contributing synergistically to worse prognosis through mechanisms such as ventilator-induced lung injury, oxidative stress, and organ failure\\u003csup\\u003e9\\u003c/sup\\u003e. In this context,integrated evaluation of inflammatory and nutritional status is gaining attention. Lactate and albumin are routinely measured biochemical parameters commonly used to assess illness severity and prognosis in the ICU. Elevated lactate levels reflect tissue hypoperfusion and inflammatory decompensation\\u003csup\\u003e10\\u003c/sup\\u003e, while hypoalbuminemia is indicative of both chronic undernutrition and acute inflammatory burden\\u003csup\\u003e11\\u003c/sup\\u003e, and has been independently associated with poor clinical outcomes in critical illness\\u003csup\\u003e12\\u003c/sup\\u003e. The lactate-to-albumin ratio (LAR), which combines these two biomarkers into a single composite index, has recently been proposed as a novel integrative indicator of metabolic stress and nutritional status. Previous studies have suggested that LAR may provide additional prognostic information in various critical conditions such as sepsis, cardiac arrest, and traumatic brain injury\\u003csup\\u003e13\\u0026ndash;15\\u003c/sup\\u003e. However,evidence regarding the prognostic utility of LAR in mechanically ventilated ICU patients, particularly with respect to long-term mortality,remains limited.\\u003c/p\\u003e\\u003cp\\u003eTherefore, in this study,we aimed to investigate the association between LAR and 1-year all-cause mortality among ICU patients receiving IMV, using data from the MIMIC-IV database. Additionally,we compared the predictive performance of LAR with established severity scores such as SOFA and APS III to explore its potential as a complementary prognostic tool.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eData source\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eThis retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. MIMIC-IV is a publicly available critical care database jointly maintained by the Massachusetts Institute of Technology and Harvard Medical School. It contains comprehensive clinical information for 65,353 ICU admissions at the Beth Israel Deaconess Medical Center in Boston,Massachusetts,between 2008 and 2019, including demographic characteristics, vital signs, comorbidities, laboratory values,treatment data (including mechanical ventilation duration),and clinical outcomes.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStudy population\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003ePatients were included if they met the following criteria: (1) received invasive mechanical ventilation (IMV) via endotracheal intubation during their first ICU admission, and (2) had available plasma lactate and serum albumin measurements within the first 24 hours of ICU admission. Exclusion criteria were: (1) age\\u0026thinsp;\\u0026lt;\\u0026thinsp;18 years; (2) ICU length of stay less than 24 hours; and (3) missing data on lactate or albumin. After applying these criteria, a total of 9,195 patients were included in the final analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eClinical variables\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eClinical data were initially extracted within the first 24 hours following ICU admission. These included demographic characteristics (age, sex, weight, and ICU length of stay); vital signs [heart rate (HR), respiratory rate (RR), systolic and diastolic blood pressure (SBP/DBP), and peripheral oxygen saturation (SpO\\u003csub\\u003e2\\u003c/sub\\u003e)]; laboratory tests [white blood cell (WBC) count, hemoglobin, platelet count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), albumin, total bilirubin, creatinine, blood urea nitrogen (BUN), sodium, potassium, calcium, chloride, lactate, pH, partial pressure of carbon dioxide (pCO\\u003csub\\u003e2\\u003c/sub\\u003e), partial pressure of oxygen (pO\\u003csub\\u003e2\\u003c/sub\\u003e), anion gap, and bicarbonate]; comorbidities [hypertension, diabetes, chronic kidney disease (CKD), cirrhosis, cancer, heart failure, COVID-19 infection, and sepsis based on Sepsis-3 criteria]; clinical interventions [use of antibiotics, continuous renal replacement therapy (CRRT), CRRT duration, and duration of mechanical ventilation]; and disease severity scores [Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APS III), and Charlson Comorbidity Index].The primary outcomes were ICU all-cause mortality and 1-year all-cause mortality following ICU discharge. The lactate-to-albumin ratio was calculated as:LAR\\u0026thinsp;=\\u0026thinsp;lactate (mmol/L) / albumin (g/dL).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eThe proportion of missing data for each variable was assessed using a missing value distribution histogram, and the missing data mechanism was further examined via a missing pattern heatmap (Supplementary Fig.\\u0026nbsp;1). Multiple imputation (m\\u0026thinsp;=\\u0026thinsp;5 imputations) was applied to handle missing values and ensure data completeness. Continuous variables were expressed as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD) if normally distributed and compared using independent-sample t tests; otherwise, they were reported as medians with interquartile ranges (IQRs) and compared using the Mann\\u0026ndash;Whitney U test. Categorical variables were presented as frequencies (percentages) and compared using the Chi-square test or Fisher\\u0026rsquo;s exact test as appropriate.\\u003c/p\\u003e\\u003cp\\u003eSurvival analysis was first performed using Kaplan\\u0026ndash;Meier curves and the log-rank test to assess the primary outcome across different LAR levels. The association between the LAR and 1-year all-cause mortality was evaluated using Cox proportional hazards regression models, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. To assess the association between LAR and ICU mortality (a binary outcome), a modified Poisson regression model was used to directly estimate relative risks (RRs) and their corresponding 95% CIs, thereby avoiding overestimation of RR by odds ratios (ORs) in outcomes with high incidence.To evaluate multicollinearity among covariates in multivariate models, the variance inflation factor (VIF) was calculated. A VIF greater than 10 was considered indicative of severe multicollinearity. If multicollinearity was identified, redundant variables were removed or handled using principal component analysis (PCA).\\u003c/p\\u003e\\u003cp\\u003eLAR was analyzed in two formats: (1) as a categorical variable based on the optimal cutoff value, dividing patients into high and low LAR groups (low LAR group served as reference); and (2) as a continuous variable to estimate the effect of each 1-standard deviation (SD) increase in LAR on mortality risk. Four progressively adjusted models were constructed for multivariate analysis:Model 0: unadjusted, including only LAR;Model 1: adjusted for demographic and comorbidity variables (age, sex, diabetes, hypertension, heart failure, AKI stage, CKD, cirrhosis, sepsis according to Sepsis-3 criteria, COVID-19, and cancer);Model 2: further adjusted for WBC count, hemoglobin, red cell distribution width (RDW), lymphocyte count, AST, ALT, total bilirubin (TBil), albumin, creatinine, BUN, serum chloride, anion gap, lactate dehydrogenase (LDH), antibiotic use, and CRRT;Model 3: additionally adjusted for disease severity scores including SOFA, APS III, and Charlson Comorbidity Index based on Model 2.\\u003c/p\\u003e\\u003cp\\u003eRestricted cubic spline (RCS) analysis was employed to explore the dose\\u0026ndash;response relationship between LAR and 1-year all-cause mortality. Subgroup analyses and interaction tests were performed based on Model 3 to investigate whether the prognostic impact of LAR varied across different clinical subpopulations. Additionally, the Boruta feature selection algorithm, based on a random forest model, was applied to identify the most important predictors of 1-year all-cause mortality among IMV patients.To assess the robustness of our findings, sensitivity analyses were conducted. The overlap weighting method based on propensity scores was used to balance covariates between high and low LAR groups. Standardized mean differences (SMDs) were calculated before and after weighting, with an SMD\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1 indicating adequate balance.All statistical analyses were performed using R software (version 4.3.1), and a two-sided P value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eDemographic and Clinical Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 9,195 patients receiving invasive mechanical ventilation (IMV) were included in this study. The mean age was 63.8 years (standard deviation: 16.3), and 40.3% of the patients were female. Based on the optimal cutoff value of LAR, patients were divided into a low LAR group (\\u0026le;\\u0026thinsp;1.48, n\\u0026thinsp;=\\u0026thinsp;7,775) and a high LAR group (\\u0026gt;\\u0026thinsp;1.48, n\\u0026thinsp;=\\u0026thinsp;1,420). As shown in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, some baseline characteristics showed no significant differences between the two groups. For example, sex distribution, prevalence of diabetes, chronic kidney disease, malignancy, and serum sodium levels were comparable between the groups (all P\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), indicating a relatively balanced distribution of these variables post-stratification. However, significant differences were observed in several other clinical and laboratory indicators.Compared with the low LAR group, patients in the high LAR group had higher respiratory and heart rates, but lower systolic and diastolic blood pressures (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), suggesting greater cardiovascular and respiratory instability. Laboratory findings showed elevated white blood cell counts, liver enzymes (AST, ALT), total bilirubin, lactate, and lactate dehydrogenase levels in the high LAR group, along with significantly lower serum albumin levels (2.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.63 vs. 3.03\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.61 g/dL, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). These findings suggest a greater burden of systemic inflammation and hepatic\\u0026ndash;renal dysfunction in the high LAR group.\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, the proportion of patients with stage 3 acute kidney injury (AKI) was higher in the high LAR group (57.5% vs. 34.8%), and the use of continuous renal replacement therapy (CRRT) was also more frequent (28.7% vs. 11.2%, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Regarding disease severity, both SOFA and APS III scores were significantly higher in the high LAR group (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).Notably, both ICU mortality (46.4% vs. 22.2%) and 1-year all-cause mortality (62.5% vs. 41.3%) were substantially higher in the high LAR group (both P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Despite similar distributions of certain demographic and comorbidity-related variables, elevated LAR was associated with more severe physiological derangements, laboratory abnormalities, and worse clinical outcomes, highlighting the potential prognostic relevance of LAR in this patient population.Furthermore, Kaplan\\u0026ndash;Meier survival curve analysis demonstrated a significantly lower cumulative survival rate in the high LAR group compared with the low LAR group (log-rank P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAssociation Between LAR and All-Cause Mortality Risk\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMulticollinearity diagnostics showed that the variance inflation factor (VIF) for all covariates was \\u0026lt;\\u0026thinsp;5, indicating no significant multicollinearity (Supplementary Fig. 2). As presented in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, a higher LAR was significantly associated with worse prognosis among IMV patients. In the unadjusted model (Model 0), patients in the high LAR group (\\u0026gt;\\u0026thinsp;1.48) had a 2.06-fold increased risk of 1-year all-cause mortality compared to those in the low LAR group (95% CI: 1.91\\u0026ndash;2.13). This association remained robust after sequential adjustment for demographic factors, comorbidities, laboratory parameters, and disease severity scores (Model 3), with an adjusted hazard ratio (HR) of 1.31 (95% CI: 1.20\\u0026ndash;1.43). When LAR was analyzed as a continuous variable, each one standard deviation (SD) increase in LAR was associated with a 12% higher risk of 1-year mortality (HR\\u0026thinsp;=\\u0026thinsp;1.12, 95% CI: 1.08\\u0026ndash;1.16, Model 3). Similarly, in the modified Poisson regression analysis for ICU mortality, the unadjusted relative risk (RR) for high LAR was 2.09 (95% CI: 1.95\\u0026ndash;2.24), and the association remained significant after full adjustment (RR\\u0026thinsp;=\\u0026thinsp;1.27, 95% CI: 1.17\\u0026ndash;1.38, Model 3). Each SD increase in LAR was associated with a 12% higher risk of ICU mortality (RR\\u0026thinsp;=\\u0026thinsp;1.12, 95% CI: 1.08\\u0026ndash;1.16, Model 3). These findings suggest that elevated LAR is independently associated with both short-term and 1-year all-cause mortality in IMV patients, supporting its potential utility as a prognostic marker in this high-risk population.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDose\\u0026ndash;Response Relationship and Subgroup Analyses\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAfter full multivariable adjustment (Model 3), including demographic factors, comorbidities, laboratory tests, treatments, and severity scores, a restricted cubic spline (RCS) regression (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA) revealed a linear positive association between LAR and 1-year all-cause mortality (P for overall trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; P for nonlinearity\\u0026thinsp;=\\u0026thinsp;0.779), with no evidence of a nonlinear threshold effect. Subgroup analyses (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB) consistently demonstrated that a high LAR (\\u0026gt;\\u0026thinsp;1.48) was associated with increased mortality risk across all predefined strata (all HRs\\u0026thinsp;\\u0026gt;\\u0026thinsp;1). Notably, statistically significant associations were observed in subgroups including females (HR\\u0026thinsp;=\\u0026thinsp;1.37, 95% CI: 1.20\\u0026ndash;1.56), patients with diabetes (HR\\u0026thinsp;=\\u0026thinsp;1.34, 95% CI: 1.20\\u0026ndash;1.49), hypertension (HR\\u0026thinsp;=\\u0026thinsp;1.41, 95% CI: 1.22\\u0026ndash;1.64), and heart failure (HR\\u0026thinsp;=\\u0026thinsp;1.31, 95% CI: 1.11\\u0026ndash;1.55). Although associations in the cirrhosis (HR\\u0026thinsp;=\\u0026thinsp;1.20, 95% CI: 0.98\\u0026ndash;1.47) and CRRT (HR\\u0026thinsp;=\\u0026thinsp;1.09, 95% CI: 0.92\\u0026ndash;1.29) subgroups did not reach statistical significance, the point estimates still suggested a trend toward increased risk. Interaction analysis showed no significant interactions across most subgroups (all P for interaction\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), except for Sepsis 3.0, indicating that the association between LAR and mortality risk remained consistent across different clinical profiles. Additionally, further interaction tests (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) suggested that the association between LAR and 1-year mortality was more pronounced among relatively younger individuals and those with lower RDW levels (both P for interaction\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eComparison with conventional severity scoring systems\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo further identify the most critical predictors of mortality, the Boruta feature selection algorithm was applied to evaluate the relative importance of all candidate variables. Figure \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e illustrates the Z-score\\u0026ndash;based importance rankings. In the analysis with 1-year all-cause mortality as the outcome (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA), variables such as the Charlson Comorbidity Index, APS III score, age, RDW, BUN, duration of mechanical ventilation, AKI stage, and PaO₂ were identified as top contributors. Notably, the LAR was confirmed as an important predictor and ranked tenth, ahead of the SOFA score. In the ICU all-cause mortality model (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB), APS III score, duration of mechanical ventilation, CRRT, LDH, BUN, SOFA score, and Charlson Comorbidity Index remained among the top predictors. LAR was again classified as a \\u0026quot;confirmed\\u0026quot; feature, ranking ninth\\u0026mdash;above lactate and albumin individually. These findings further support the potential of LAR as an independent prognostic indicator in critically ill patients.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSensitivity analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the pre-specified optimal cutoff value (LAR\\u0026thinsp;=\\u0026thinsp;1.48), patients were categorized into low LAR (n\\u0026thinsp;=\\u0026thinsp;7,775) and high LAR (n\\u0026thinsp;=\\u0026thinsp;1,420) groups. Baseline comparisons revealed significant imbalances in several key clinical and laboratory variables, including age, disease severity scores (SOFA and APS III), comorbidity burden (Charlson Comorbidity Index), laboratory parameters (e.g., lactate, albumin, creatinine), and comorbid conditions (e.g., sepsis, heart failure). Most variables had absolute standardized mean differences (ASMDs)\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.1, with some exceeding 1.0 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA), indicating potential confounding. To mitigate these baseline differences, overlap weighting based on propensity scores was applied. Post-weighting, all covariates achieved good balance with ASMDs\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1. Kaplan\\u0026ndash;Meier survival curves based on the weighted population showed significantly lower 1-year survival in the high LAR group compared to the low LAR group (log-rank p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The weighted Cox proportional hazards model further confirmed that high LAR was independently associated with increased 1-year all-cause mortality (adjusted HR\\u0026thinsp;=\\u0026thinsp;1.27; 95% CI: 1.16\\u0026ndash;1.39; Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study demonstrated that an elevated LAR was independently associated with an increased risk of 1-year all-cause mortality among ICU patients receiving IMV. This association remained robust even after comprehensive adjustment for demographic characteristics, comorbidities, laboratory parameters, and illness severity scores. To our knowledge, this is the first large-scale cohort study to systematically evaluate the prognostic value of LAR in this high-risk population.\\u003c/p\\u003e\\n\\u003cp\\u003ePrevious research has suggested that LAR may serve as a useful prognostic biomarker in critically ill patients. For example, Wang et al. reported that elevated LAR levels were significantly associated with higher 28-day and in-hospital mortality among ICU patients with acute respiratory distress syndrome (ARDS)\\u003csup\\u003e16\\u003c/sup\\u003e.Similarly, a large retrospective study found that higher LAR levels were strongly correlated with poor outcomes in ICU patients with sepsis \\u003csup\\u003e17\\u003c/sup\\u003e, and subsequent analyses confirmed its role as an independent predictor of sepsis-related mortality\\u003csup\\u003e18,19\\u003c/sup\\u003e. Moreover, Kokkoris et al. demonstrated that LAR levels measured on the first day of ICU admission were positively associated with ICU length of stay and mortality risk in patients with severe COVID-19, suggesting its utility in early risk stratification\\u003csup\\u003e20\\u003c/sup\\u003e. Comparable findings have also been reported in patients with acute myocardial infarction and traumatic brain injury\\u003csup\\u003e21,22\\u003c/sup\\u003e. Extending beyond these disease-specific investigations, the present study focused on a particularly vulnerable and clinically complex subgroup\\u0026mdash;ICU patients undergoing IMV\\u0026mdash;and aimed to assess the prognostic significance of LAR for long-term mortality in this setting. Using multiple analytical approaches, including multivariable adjustment, trend analysis, restricted cubic spline modeling, and subgroup analyses, our findings consistently showed that higher LAR levels were strongly associated with increased 1-year all-cause mortality. The robustness of this association was further supported by sensitivity analyses. In addition, the association between elevated LAR and ICU in-hospital mortality, confirmed via modified Poisson regression, underscores the predictive utility of this biomarker across both short- and long-term outcomes, highlighting its stability over different time horizons.\\u003c/p\\u003e\\n\\u003cp\\u003eAfter comprehensive adjustment for potential confounders, elevated LAR remained independently associated with an increased risk of 1-year all-cause mortality, regardless of whether it was modeled as a categorical or continuous variable. This consistency across modeling strategies underscores the robustness of LAR as a prognostic indicator. Further dose\\u0026ndash;response analysis using restricted cubic spline modeling revealed a linear positive association between LAR and mortality risk, with no evidence of nonlinear inflection points or threshold effects. These findings suggest that modeling LAR as a continuous variable may better capture its incremental risk association. This pattern is consistent with previous research on subarachnoid hemorrhage patients, which also reported a linear association between LAR and mortality risk\\u003csup\\u003e23\\u003c/sup\\u003e. Subgroup analyses indicated that the prognostic value of LAR was generally stable across various clinical strata. Notably, the predictive strength of LAR appeared more pronounced among younger patients compared to older individuals. Although elderly patients have traditionally drawn greater clinical attention due to a higher prevalence of malnutrition, immunosuppression, and comorbidities, our findings emphasize the importance of also assessing risk in younger IMV patients. Similarly, we observed that LAR was more predictive of mortality in patients with lower red cell distribution width (RDW). Previous studies have shown that elevated RDW itself is strongly associated with adverse outcomes in critically ill populations\\u003csup\\u003e24\\u0026ndash;26\\u003c/sup\\u003e. however, our results suggest that LAR may offer additional risk stratification value even before RDW elevation occurs. Taken together, these results provide a comprehensive view of the interaction between LAR and mortality risk across subgroups and support the potential utility of LAR in facilitating personalized risk stratification and precision prognostication in ICU patients undergoing invasive mechanical ventilation.\\u003c/p\\u003e\\n\\u003cp\\u003eThe SOFA and APS III scores are widely utilized to assess illness severity in ICU patients, while the Charlson Comorbidity Index is commonly used to quantify chronic disease burden and its impact on mortality risk. These scoring systems have been extensively validated and are routinely employed in prognostic evaluations of critically ill individuals\\u003csup\\u003e6,7,27\\u003c/sup\\u003e.In the present study, the Boruta feature selection algorithm further confirmed their strong predictive value for adverse outcomes among patients receiving invasive mechanical ventilation (IMV), demonstrating consistent performance across different modeling frameworks.Although LAR cannot replace these established tools in clinical decision-making, it offers certain complementary advantages. As a composite biomarker derived from routinely available laboratory parameters, LAR may provide additional insights in specific clinical contexts. Notably, we observed that approximately 20% of patients had comparable SOFA, APS III, or Charlson scores but exhibited markedly different clinical outcomes. This finding suggests that in so-called \\u0026ldquo;gray zones,\\u0026rdquo; where traditional scoring systems may fail to adequately discriminate risk, LAR could help uncover latent prognostic differences and support more refined risk stratification. While LAR does not replicate the multidimensional scope of conventional scoring systems, its stable independent association with mortality after comprehensive adjustment indicates that it may serve as a valuable adjunct\\u0026mdash;particularly in settings where existing tools show limited discriminatory power.\\u003c/p\\u003e\\n\\u003cp\\u003eIn critical care settings, patients undergoing invasive mechanical ventilation (IMV) often experience a highly complex state of metabolic stress, characterized by the concurrent activation of systemic inflammation and nutritional compromise. In severe conditions such as sepsis and cardiac arrest, tissue hypoxia and inflammatory responses synergistically contribute to the development of multiple organ dysfunction, necessitating IMV support. Under these circumstances, metabolic derangements and impaired hepatic synthetic function lead to elevated lactate levels and reduced serum albumin concentrations, jointly contributing to an increased LAR\\u003csup\\u003e28\\u0026ndash;30\\u003c/sup\\u003e.Importantly, IMV itself may exacerbate preexisting inflammation. Prior studies have shown that inappropriate ventilator settings\\u0026mdash;such as excessive tidal volume or positive end-expiratory pressure (PEEP)\\u0026mdash;can induce ventilator-induced lung injury (VILI)\\u003csup\\u003e31,32\\u003c/sup\\u003e, Moreover, sedatives and neuromuscular blocking agents may impair host defense mechanisms, increasing the risk of secondary infections. Compromise of intestinal barrier function and translocation of gut microbiota may further trigger a so-called \\u0026ldquo;second hit\\u0026rdquo; of inflammation, amplifying the systemic inflammatory burden. These processes not only promote lactate overproduction but also contribute to persistent albumin depletion, reflected in a sustained elevation of LAR. On the other hand, albumin is a well-recognized marker of both inflammation and nutritional status. It plays a vital role in maintaining intravascular oncotic pressure, antioxidative defense, and the transport of endogenous and exogenous substances\\u003csup\\u003e30\\u003c/sup\\u003e. Hypoalbuminemia is commonly associated with negative nitrogen balance, immune suppression, and impaired tissue repair, and has been independently linked to prolonged ICU stay, increased need for organ support, and higher mortality risk\\u003csup\\u003e33\\u003c/sup\\u003e. Taken together, LAR serve as an integrative biomarker reflecting both inflammatory and nutritional status, and may holds potential as a prognostic indicator. Future studies are warranted to explore its role in guiding individualized nutritional support, anti-inflammatory strategies, and optimization of ventilation management in critically ill patients.\\u003c/p\\u003e\\n\\u003cp\\u003eSeveral limitations of this study should be acknowledged. First, this was a retrospective observational cohort study based on a single-center ICU database. Although multiple statistical adjustments and sensitivity analyses were conducted to mitigate potential confounding, residual confounders cannot be entirely ruled out. Second, LAR was calculated using lactate and albumin values obtained within the first 24 hours after ICU admission; this single-time-point measurement may not capture the dynamic changes in metabolic and inflammatory status over the course of illness. Third, certain clinically relevant variables\\u0026mdash;such as comprehensive nutritional assessments, broader inflammatory biomarkers, and detailed data on ventilator settings or sedative use\\u0026mdash;were not available in the database, which may limit the completeness of risk adjustment. Finally, external validation using data from other centers or prospective trials is warranted to confirm the prognostic utility of LAR in this high-risk population.\\u003c/p\\u003e\\n\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study demonstrated that elevated LAR was independently associated with increased 1-year all-cause mortality among ICU patients receiving IMV, with robust associations observed across multiple modeling strategies. Moreover, the prognostic value of LAR appeared to be more pronounced in younger patients and those with lower RDW, indicating its potential utility in specific subpopulations. Although LAR is not a substitute for established severity scoring systems such as SOFA or APS III, it may serve as a valuable and easily accessible supplementary biomarker, particularly when traditional tools provide limited discriminatory power.\\u003c/p\\u003e\\n\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eAKI \\u0026ndash; Acute kidney injury\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;ALP \\u0026ndash; Alkaline phosphatase\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;ALT \\u0026ndash; Alanine aminotransferase\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;AST \\u0026ndash; Aspartate aminotransferase\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;APS III \\u0026ndash; Acute Physiology Score III\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;BUN \\u0026ndash; Blood urea nitrogen\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;CKD \\u0026ndash; Chronic kidney disease\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;CRRT \\u0026ndash; Continuous renal replacement therapy\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;HR \\u0026ndash; Hazard ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;ICD \\u0026ndash; International Classification of Diseases\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;ICU \\u0026ndash; Intensive care unit\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;IMV \\u0026ndash; Invasive mechanical ventilation\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;IQR \\u0026ndash; Interquartile range\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;KM \\u0026ndash; Kaplan\\u0026ndash;Meier\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;LAR \\u0026ndash; Lactate-to-albumin ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;LDH \\u0026ndash; Lactate dehydrogenase\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;OR \\u0026ndash; Odds ratio\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;RR \\u0026ndash; Relative risk\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;RCS \\u0026ndash; Restricted cubic spline\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;RDW \\u0026ndash; Red cell distribution width\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;SOFA \\u0026ndash; Sequential Organ Failure Assessment\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;TBil \\u0026ndash; Total bilirubin\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;VIF \\u0026ndash; Variance inflation factor\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;WBC \\u0026ndash; White blood cell\\u003c/p\\u003e\\n\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics approval and consent to participate The requirement for informed consent was waived because this study was a retrospective secondary analysis of de-identified data from the publicly available MIMIC-IV database, which poses no risk to participants. The use of the MIMIC-IV database has been approved by the Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. All procedures were conducted in accordance with the ethical standards of the Declaration of Helsinki.\\u003c/p\\u003e\\n\\u003cp\\u003eClinical Trial\\u0026nbsp;Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u0026nbsp;Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and materials\\u0026nbsp;The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below:\\u0026nbsp;https://physionet.org/content/mimiciv/2.2/.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests\\u0026nbsp;The authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u0026nbsp;The authors declare that no financial support was received for the research, authorship, and/or publication of this article.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; contributions\\u0026nbsp;YM and FL participated in the conception and design of the study and wrote the manuscript. MH participated in key revisions, as well as the statistical analysis of data. YM and GF verified the methods and steps of statistical analysis. All authors participated in the revision of the manuscript, read and approved the submitted version, and agreed to take responsibility for all aspects of the study to ensure the accuracy of this research. All authors agreed to publish this manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements The authors would like to thank the patients for participating in this study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eMartin-Loeches I, Bos LD, Goligher EC. Will all ARDS patients be receiving mechanical ventilation in 2035? Yes. \\u003cem\\u003eIntensive Care Med\\u003c/em\\u003e. 2017;43(4):568-569. doi:10.1007/s00134-016-4461-x\\u003c/li\\u003e\\n\\u003cli\\u003eJaber S, Bellani G, Blanch L, et al. The intensive care medicine research agenda for airways, invasive and noninvasive mechanical ventilation. \\u003cem\\u003eIntensive Care Med\\u003c/em\\u003e. 2017;43(9):1352-1365. doi:10.1007/s00134-017-4896-8\\u003c/li\\u003e\\n\\u003cli\\u003eAntonelli M, Azoulay E, Bonten M, et al. Year in review in Intensive Care Medicine, 2008: II. Experimental, acute respiratory failure and ARDS, mechanical ventilation and endotracheal intubation. \\u003cem\\u003eIntensive Care Med\\u003c/em\\u003e. 2009;35(2):215-231. doi:10.1007/s00134-008-1380-5\\u003c/li\\u003e\\n\\u003cli\\u003eMehta AB, Walkey AJ, Curran-Everett D, Matlock D, Douglas IS. Hospital Mechanical Ventilation Volume and Patient Outcomes: Too Much of a Good Thing? \\u003cem\\u003eCritical Care Medicine\\u003c/em\\u003e. 2019;47(3):360-368. doi:10.1097/ccm.0000000000003590\\u003c/li\\u003e\\n\\u003cli\\u003eLim ZJ, Subramaniam A, Ponnapa Reddy M, et al. Case Fatality Rates for Patients with COVID-19 Requiring Invasive Mechanical Ventilation. A Meta-analysis. \\u003cem\\u003eAm J Respir Crit Care Med\\u003c/em\\u003e. 2021;203(1):54-66. doi:10.1164/rccm.202006-2405oc\\u003c/li\\u003e\\n\\u003cli\\u003eLambden S, Laterre PF, Levy MM, Francois B. The SOFA score\\u0026mdash;development, utility and challenges of accurate assessment in clinical trials. \\u003cem\\u003eCrit Care\\u003c/em\\u003e. 2019;23(1). doi:10.1186/s13054-019-2663-7\\u003c/li\\u003e\\n\\u003cli\\u003eKeegan MT, Gajic O, Afessa B. Severity of illness scoring systems in the intensive care unit. \\u003cem\\u003eCritical Care Medicine\\u003c/em\\u003e. 2011;39(1):163-169. doi:10.1097/ccm.0b013e3181f96f81\\u003c/li\\u003e\\n\\u003cli\\u003eLelubre C, Vincent JL. Mechanisms and treatment of organ failure in sepsis. \\u003cem\\u003eNat Rev Nephrol\\u003c/em\\u003e. 2018;14(7):417-427. doi:10.1038/s41581-018-0005-7\\u003c/li\\u003e\\n\\u003cli\\u003eAttaway AH, Scheraga RG, Bhimraj A, Biehl M, Hatipoğlu U. Severe covid-19 pneumonia: pathogenesis and clinical management. \\u003cem\\u003eBMJ\\u003c/em\\u003e. Published online March 10, 2021:n436. doi:10.1136/bmj.n436\\u003c/li\\u003e\\n\\u003cli\\u003eVincent JL, Quintairos E Silva A, Couto L, Taccone FS. The value of blood lactate kinetics in critically ill patients: a systematic review. \\u003cem\\u003eCrit Care\\u003c/em\\u003e. 2016;20(1). doi:10.1186/s13054-016-1403-5\\u003c/li\\u003e\\n\\u003cli\\u003eSoeters PB, Wolfe RR, Shenkin A. Hypoalbuminemia: Pathogenesis and Clinical Significance. \\u003cem\\u003eJ Parenter Enteral Nutr\\u003c/em\\u003e. 2019;43(2):181-193. doi:10.1002/jpen.1451\\u003c/li\\u003e\\n\\u003cli\\u003ePlakht Y, Gilutz H, Shiyovich A. Decreased admission serum albumin level is an independent predictor of long-term mortality in hospital survivors of acute myocardial infarction. Soroka Acute Myocardial Infarction II (SAMI-II) project. \\u003cem\\u003eInternational Journal of Cardiology\\u003c/em\\u003e. 2016;219:20-24. doi:10.1016/j.ijcard.2016.05.067\\u003c/li\\u003e\\n\\u003cli\\u003eWu D, Shen S, Luo D. Association of lactate-to-albumin ratio with in-hospital and intensive care unit mortality in patients with intracerebral hemorrhage. \\u003cem\\u003eFront Neurol\\u003c/em\\u003e. 2023;14. doi:10.3389/fneur.2023.1198741\\u003c/li\\u003e\\n\\u003cli\\u003eChen DL, Chung CM, Wang GJ, Chang KC. Lactate-to-albumin ratio and cholesterol levels predict neurological outcome in cardiac arrest survivors. \\u003cem\\u003eThe American Journal of Emergency Medicine\\u003c/em\\u003e. 2024;83:9-15. doi:10.1016/j.ajem.2024.06.029\\u003c/li\\u003e\\n\\u003cli\\u003eBou Chebl R, Jamali S, Sabra M, et al. Lactate/Albumin Ratio as a Predictor of In-Hospital Mortality in Septic Patients Presenting to the Emergency Department. \\u003cem\\u003eFront Med\\u003c/em\\u003e. 2020;7. doi:10.3389/fmed.2020.550182\\u003c/li\\u003e\\n\\u003cli\\u003eWang HX, Huang XH, Ma LQ, et al. Association between lactate-to-albumin ratio and short-time mortality in patients with acute respiratory distress syndrome. \\u003cem\\u003eJournal of Clinical Anesthesia\\u003c/em\\u003e. 2024;99:111632. doi:10.1016/j.jclinane.2024.111632\\u003c/li\\u003e\\n\\u003cli\\u003eLichtenauer M, Wernly B, Ohnewein B, et al. The Lactate/Albumin Ratio: A Valuable Tool for Risk Stratification in Septic Patients Admitted to ICU. \\u003cem\\u003eIJMS\\u003c/em\\u003e. 2017;18(9):1893. doi:10.3390/ijms18091893\\u003c/li\\u003e\\n\\u003cli\\u003eHu J, Jin Q, Fang H, Zhang W. Evaluating the predictive value of initial lactate/albumin ratios in determining prognosis of sepsis patients. \\u003cem\\u003eMedicine\\u003c/em\\u003e. 2024;103(12):e37535. doi:10.1097/MD.0000000000037535\\u003c/li\\u003e\\n\\u003cli\\u003eChen X, Zhou X, Zhao H, et al. Clinical Value of the Lactate/Albumin Ratio and Lactate/Albumin Ratio \\u0026times; Age Score in the Assessment of Prognosis in Patients With Sepsis. \\u003cem\\u003eFront Med\\u003c/em\\u003e. 2021;8:732410. doi:10.3389/fmed.2021.732410\\u003c/li\\u003e\\n\\u003cli\\u003eKokkoris S, Gkoufa A, Katsaros DE, et al. Lactate to Albumin Ratio and Mortality in Patients with Severe Coronavirus Disease-2019 Admitted to an Intensive Care Unit. \\u003cem\\u003eJCM\\u003c/em\\u003e. 2024;13(23):7106. doi:10.3390/jcm13237106\\u003c/li\\u003e\\n\\u003cli\\u003eChen Y, Lai W, Yang K, Wu B, Xie D, Peng C. Association between lactate/albumin ratio and prognosis in patients with acute myocardial infarction. \\u003cem\\u003eEur J Clin Investigation\\u003c/em\\u003e. 2024;54(1):e14094. doi:10.1111/eci.14094\\u003c/li\\u003e\\n\\u003cli\\u003eWang R, He M, Qu F, Zhang J, Xu J. Lactate Albumin Ratio Is Associated With Mortality in Patients With Moderate to Severe Traumatic Brain Injury. \\u003cem\\u003eFront Neurol\\u003c/em\\u003e. 2022;13:662385. doi:10.3389/fneur.2022.662385\\u003c/li\\u003e\\n\\u003cli\\u003eZhang GG, Hao JH, Yong Q, et al. Lactate-to-albumin ratio is associated with in-hospital mortality in patients with spontaneous subarachnoid hemorrhage and a nomogram model construction. \\u003cem\\u003eFront Neurol\\u003c/em\\u003e. 2022;13. doi:10.3389/fneur.2022.1009253\\u003c/li\\u003e\\n\\u003cli\\u003eHuang B, Yan J, Li C, et al. Red blood cell distribution width is a risk factor for multiple organ dysfunction syndrome in elderly patients with infection: a case control study. \\u003cem\\u003eAging Clin Exp Res\\u003c/em\\u003e. 2023;35(7):1577-1580. doi:10.1007/s40520-023-02431-w\\u003c/li\\u003e\\n\\u003cli\\u003eLee JJ, Montazerin SM, Jamil A, et al. Association between red blood cell distribution width and mortality and severity among patients with COVID‐19: A systematic review and meta‐analysis. \\u003cem\\u003eJournal of Medical Virology\\u003c/em\\u003e. 2021;93(4):2513-2522. doi:10.1002/jmv.26797\\u003c/li\\u003e\\n\\u003cli\\u003eUrben T, Amacher SA, Becker C, et al. Red blood cell distribution width for the prediction of outcomes after cardiac arrest. \\u003cem\\u003eSci Rep\\u003c/em\\u003e. 2023;13(1). doi:10.1038/s41598-023-41984-8\\u003c/li\\u003e\\n\\u003cli\\u003eTuty Kuswardhani RA, Henrina J, Pranata R, Anthonius Lim M, Lawrensia S, Suastika K. Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis. \\u003cem\\u003eDiabetes \\u0026amp; Metabolic Syndrome: Clinical Research \\u0026amp; Reviews\\u003c/em\\u003e. 2020;14(6):2103-2109. doi:10.1016/j.dsx.2020.10.022\\u003c/li\\u003e\\n\\u003cli\\u003eSinger M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). \\u003cem\\u003eJAMA\\u003c/em\\u003e. 2016;315(8):801. doi:10.1001/jama.2016.0287\\u003c/li\\u003e\\n\\u003cli\\u003eBakker J, Nijsten MW, Jansen TC. Clinical use of lactate monitoring in critically ill patients. \\u003cem\\u003eAnn Intensive Care\\u003c/em\\u003e. 2013;3(1):12. doi:10.1186/2110-5820-3-12\\u003c/li\\u003e\\n\\u003cli\\u003eEckart A, Struja T, Kutz A, et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. \\u003cem\\u003eThe American Journal of Medicine\\u003c/em\\u003e. 2020;133(6):713-722.e7. doi:10.1016/j.amjmed.2019.10.031\\u003c/li\\u003e\\n\\u003cli\\u003eSlutsky AS, Ranieri VM. Ventilator-Induced Lung Injury. \\u003cem\\u003eN Engl J Med\\u003c/em\\u003e. 2013;369(22):2126-2136. doi:10.1056/nejmra1208707\\u003c/li\\u003e\\n\\u003cli\\u003eJaber S, Jung B, Corne P, et al. An intervention to decrease complications related to endotracheal intubation in the intensive care unit: a prospective, multiple-center study. \\u003cem\\u003eIntensive Care Med\\u003c/em\\u003e. 2010;36(2):248-255. doi:10.1007/s00134-009-1717-8\\u003c/li\\u003e\\n\\u003cli\\u003eVincent JL, Dubois MJ, Navickis RJ, Wilkes MM. Hypoalbuminemia in Acute Illness: Is There a Rationale for Intervention?: A Meta-Analysis of Cohort Studies and Controlled Trials. \\u003cem\\u003eAnnals of Surgery\\u003c/em\\u003e. 2003;237(3):319-334. doi:10.1097/01.sla.0000055547.93484.87\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1.\\u003c/strong\\u003e Baseline information according to the levels of lactate - albumin ratio.\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"682\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCharacteristic\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eOverall\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003en = 9195\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLow (\\u0026le; 1.48)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003en = 7775\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHigh (\\u0026gt; 1.48)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003en = 1420\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003ep\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eage (years)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e63.83 \\u0026plusmn; 16.34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e64.00 \\u0026plusmn; 16.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e62.86 \\u0026plusmn; 16.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.017\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003efemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3702 (40.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3104 (39.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e598 (42.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.122\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003erespiratory rate (/min)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e19.90 \\u0026plusmn; 6.72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e19.69 \\u0026plusmn; 6.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e21.05 \\u0026plusmn; 7.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eheart rate (/min)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e91.24 \\u0026plusmn; 21.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e89.86 \\u0026plusmn; 20.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e98.78 \\u0026plusmn; 22.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003esystolic blood pressure (mmHg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e120.00 \\u0026plusmn; 25.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e121.19 \\u0026plusmn; 24.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e113.53 \\u0026plusmn; 25.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ediastolic blood pressure (mmHg)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e66.00 (55.00 79.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e67.00 (56.00 79.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e64.00 (53.00 77.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003esaturation of peripheral oxygen (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e99.00 (95.00 100.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e98.00 (95.00 100.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e99.00 (95.00 100.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.536\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ehypertension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3531 (38.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3045 (39.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e486 (34.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ediabetes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2742 (29.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2327 (29.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e415 (29.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.594\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003echronic kidney disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1685 (18.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1440 (18.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e245 (17.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.256\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eAKI stage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1096 (11.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1023 (13.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e73 (5.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1240 (13.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1099 (14.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e141 (9.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3338 (36.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2948 (37.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e390 (27.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3521 (38.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2705 (34.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e816 (57.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eheart failure\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2678 (29.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2324 (29.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e354 (24.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ecirrhosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1088 (11.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e797 (10.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e291 (20.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003esepsis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e7422 (80.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e6168 (79.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1254 (88.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ecancer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1189 (12.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e994 (12.8%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e195 (13.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.328\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eCOVID_19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e160 (1.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e150 (1.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e10 (0.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ewhite blood cell (10^9/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e12.10 (8.40 17.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e11.90 (8.40 16.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e13.60 (8.50 20.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ehemoglobin (g/dL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e10.62 \\u0026plusmn; 2.42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e10.67 \\u0026plusmn; 2.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e10.37 \\u0026plusmn; 2.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ered cell distribution width (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e14.60 (13.50 16.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e14.60 (13.50 16.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e15.10 (14.00 17.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eplatelets (10^9/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e182.00 (124.00 253.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e187.00 (130.00 256.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e147.00 (92.00 224.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ealanine aminotransferase (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e30.00 (17.00 69.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e27.00 (16.00 58.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e63.00 (24.00 256.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003easpartate aminotransferase (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e46.00 (26.00 111.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e42.00 (25.00 88.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e126.50 (48.00 509.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ealbumin (g/dL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2.96 \\u0026plusmn; 0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3.03 \\u0026plusmn; 0.61\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2.57 \\u0026plusmn; 0.63\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003etotal bilirubin (mg/dl)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e0.70 (0.40 1.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e0.60 (0.40 1.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1.10 (0.60 2.73)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ecreatinine (mg/dl)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1.10 (0.80 1.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1.00 (0.70 1.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1.40 (1.00 2.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eblood urea nitrogen (mg/dl)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e21.00 (14.00 36.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e21.00 (14.00 35.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e24.00 (16.00 39.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003elactate dehydrogenase (U/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e309.00 (227.50 472.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e295.00 (222.00 431.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e455.00 (283.75 965.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eanion gap (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e15.11 \\u0026plusmn; 5.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e14.29 \\u0026plusmn; 4.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e19.63 \\u0026plusmn; 6.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003elactate (mmol/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1.90 (1.30 3.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1.70 (1.20 2.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e5.90 (4.60 8.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ecalcium (mg/dL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e8.20 \\u0026plusmn; 0.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e8.23 \\u0026plusmn; 0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e8.05 \\u0026plusmn; 1.24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003echloride (mEq/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e104.18 \\u0026plusmn; 7.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e104.25 \\u0026plusmn; 7.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e103.77 \\u0026plusmn; 7.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003epotassium (mEq/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e4.27 \\u0026plusmn; 0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e4.25 \\u0026plusmn; 0.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e4.39 \\u0026plusmn; 0.96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003esodium (mEq/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e138.45 \\u0026plusmn; 5.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e138.44 \\u0026plusmn; 5.66\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e138.54 \\u0026plusmn; 6.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.593\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eSOFA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e7.12 \\u0026plusmn; 4.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e6.57 \\u0026plusmn; 3.72\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e10.16 \\u0026plusmn; 4.30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eAPS Ⅲ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e55.54 \\u0026plusmn; 24.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e52.16 \\u0026plusmn; 22.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e74.05 \\u0026plusmn; 27.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eCHARLSON\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e5.07 \\u0026plusmn; 2.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e5.02 \\u0026plusmn; 2.97\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e5.29 \\u0026plusmn; 3.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e0.003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eantibiotics\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e8614 (93.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e7252 (93.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1362 (95.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003econtinuous renal replacement therapy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1276 (13.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e868 (11.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e408 (28.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003ein-ICU mortality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e2385 (25.9%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1726 (22.2%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e659 (46.4%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 208px;\\\"\\u003e\\n \\u003cp\\u003eone-year mortality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e4097 (44.6%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e3210 (41.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e887 (62.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2.\\u003c/strong\\u003e The association between lactate - albumin ratio and all-cause mortality.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"557\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLAR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003emodel 0\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003emodel 1\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003emodel 2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003emodel 3\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003ecut-off value = 10.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e9195\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"top\\\" style=\\\"width: 378px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHazard Ratio\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;for one-year mortality (Cox regression)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eLow (\\u0026le;1.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e7775\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eHigh (\\u0026gt;1.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e1420\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e2.06 (1.91, 2.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.73 (1.61, 1.87)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.54 (1.41, 1.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.31 (1.20, 1.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eEach SD increase\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.35 (1.31, 1.39)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.26 (1.22, 1.29)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.21 (1.17, 1.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.12 (1.08, 1.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003ecut-off value = 10.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e9195\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"top\\\" style=\\\"width: 378px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eRelative Risk for in-ICU mortality (modified Poison regression)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eLow (\\u0026le;1.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e7775\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003ereference\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eHigh (\\u0026gt;1.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e1420\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e2.09 (1.95, 2.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.71 (1.60, 1.84)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.49 (1.38, 1.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.27 (1.17, 1.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003eEach SD increase\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 56px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.38 (1.34, 1.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.26 (1.23, 1.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.20 (1.17, 1.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.12 (1.08, 1.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003emodel 0: LAR was included; model 1: age, gender, diabetes, hypertension, heart failure, AKI stage, CKD, cirrhosis, sepsis3, COVID-19 and cancer were adjusted; model 2: WBC, hemoglobin, RDW, lymphocytes, AST, ALT, TBil, albumin, total bilirubin, creatinine, BUN, chloride, anion gap, LDH, antibiotics and CRRT and were further adjusted; model 3: SOFA, APS Ⅲ and CHARLSON were further adjusted. N - number of observation, SD - standard deviation.\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Lactate-to-albumin ratio, Invasive mechanical ventilation, Critical care, Machine learning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7251795/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7251795/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eThe lactate-to-albumin ratio (LAR), a composite biomarker reflecting both inflammatory burden and nutritional status, has been associated with adverse outcomes in various critically ill populations. However, its prognostic value in patients receiving invasive mechanical ventilation (IMV) remains unclear.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eA retrospective cohort study was conducted using the MIMIC-IV database. Adult ICU patients who received IMV and had lactate and albumin measurements within 24 hours of admission were included. The primary outcome was 1-year all-cause mortality. Multivariable Cox proportional hazards and modified Poisson regression models were used to assess associations between LAR and mortality. Restricted cubic spline (RCS) analysis evaluated dose\\u0026ndash;response relationships. Subgroup, interaction, and sensitivity analyses were also performed. The Boruta algorithm was applied to compare LAR's predictive importance with traditional severity scores.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eA total of 9,195 IMV patients were included, with a mean age of 63.8 years; 40.3% were female. Patients were stratified into high and low LAR groups based on an optimal cutoff of 1.48. Higher LAR was independently associated with increased 1-year mortality (adjusted HR: 1.31, 95% CI: 1.20\\u0026ndash;1.43) and ICU mortality (adjusted RR: 1.27, 95% CI: 1.17\\u0026ndash;1.38). RCS analysis showed a linear positive association between LAR and 1-year mortality. Subgroup analyses demonstrated stronger associations in younger patients and those with lower RDW. The Boruta feature selection algorithm confirmed LAR as an important predictor, ranking above the SOFA score. Overlap weighting analysis further validated the robustness of the findings.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e\\u003cp\\u003eElevated LAR is independently associated with increased short- and long-term mortality in ICU patients receiving IMV. While not a replacement for comprehensive scoring systems, LAR may serve as a convenient and valuable supplementary biomarker, especially in patients with indeterminate risk profiles.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Integrating Conventional and Machine Learning Approaches to Evaluate the Prognostic Value of the Lactate-to-Albumin Ratio in Mechanically Ventilated ICU Patients: A Retrospective Analysis from the MIMIC-IV Database\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-13 14:50:58\",\"doi\":\"10.21203/rs.3.rs-7251795/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision 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