HDL-C levels are associated with the development of acute respiratory distress syndrome and hospital mortality in critically ill patients: a prospective cohort study

HDL-C levels are associated with the development of acute respiratory distress syndrome and hospital mortality in critically ill patients: a prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HDL-C levels are associated with the development of acute respiratory distress syndrome and hospital mortality in critically ill patients: a prospective cohort study Hsin-Hsien Li, Tien-Ming Chan, How-Wen Ko, Chung-Shu Lee, Ping-Chih Hsu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9329846/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Metabolic perturbations frequently occur during the acute phase of critical illness and may disrupt lipid homeostasis. Acute respiratory distress syndrome (ARDS) is a severe condition that contributes to multiple organ failure and is associated with high morbidity and mortality in critically ill patients. This study aimed to evaluate the association between plasma lipid profiles and the development of ARDS, as well as clinical outcomes in critically ill patients. Methods We conducted a prospective observational cohort study of critically ill patients in Taiwan between October 2020 and July 2025. Plasma lipid profiles and clinical variables were measured at intensive care unit (ICU) admission. Clinical outcomes were compared between patients stratified by plasma HDL-C levels. Results A total of 285 critically ill patients were included, comprising 62 without ARDS and 223 with ARDS. The all-cause in-hospital mortality rates were 19.4% in patients without ARDS and 43.0% in those with ARDS. Patients who developed ARDS had significantly higher neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and interleukin-6 (IL-6) levels, higher risk of organ failure (i.e., APACHE II and SOFA scores), and lower plasma HDL-C levels (all p < 0.05). Non-survivors exhibited significantly higher NLR, greater organ failure severity, and lower HDL-C levels compared with survivors (all p < 0.05). Patients with low HDL-C (≤ 22.5 mg/dL; n = 162, 56.8%) had significantly higher CRP and IL-6, greater organ failure severity, and increased 28-, 60-, 90-day, and all-cause hospital mortality, compared with those with high HDL-C (> 22.5 mg/dL; n = 123, 43.2%) (all p < 0.05). In multivariable logistic regression analyses, elevated NLR and low HDL-C were independently associated with ARDS development and in-hospital mortality. Notably, HDL-C ≤ 22.5 mg/dL demonstrated the strongest predictive value among all variables (adjusted OR 3.383 [95% CI 1.278–8.952], p = 0.014; adjusted OR 3.451 [95% CI 1.194–9.975], p = 0.022, respectively). Conclusions Plasma HDL-C levels at ICU admission are independently associated with ARDS development and in-hospital mortality in critically ill patients. HDL-C may serve as a simple, accessible, and valuable early prognostic biomarker in this population. Critical illness Acute respiratory distress syndrome Lipid profiles High-density lipoprotein cholesterol Outcomes Mortality Figures Figure 1 Figure 2 Background Critical illness encompasses life-threatening conditions that require prompt intensive monitoring, support, and treatment of vital organ functions. Sepsis and acute respiratory distress syndrome (ARDS) represent two of the most common causes of critical illness among patients admitted to the intensive care unit (ICU) and are major contributors to multiple organ failure, with persistently high morbidity and mortality. ARDS is a life-threatening form of acute hypoxemic respiratory failure characterized by dysregulated airway and systemic inflammatory cascades and immune activation [ 1 – 4 ]. To date, no effective pharmacotherapies have been established, and lung-protective mechanical ventilation remains the cornerstone of evidence-based management for ARDS, with demonstrated benefits in improving clinical outcomes [ 5 ]. Despite significant advances in ARDS management, morbidity and mortality remain unacceptably high. Early identification of potential risk factors and prognostic determinants during the course of critical illness is therefore essential and may offer opportunities to reduce the burden of ARDS and improve patient outcomes. Lipid synthesis, transport, and metabolism are frequently disrupted during the acute phase of critical illness, thereby affecting immune function, hormone and vitamin production, as well as cell membrane integrity and signaling. Hypocholesterolemia commonly occurs during critical illness and has been associated with disease severity and adverse clinical outcomes. The underlying pathophysiological mechanisms are multifactorial, complex, and not yet fully elucidated. Sepsis is the primary predisposing factor for the development of ARDS, and prior studies have largely focused on the impact of lipid profiles on clinical outcomes in critically ill populations, particularly those with sepsis or septic shock [ 6 – 9 ]. However, the relationship between hypocholesterolemia and the development of ARDS, as well as its associated clinical outcomes in critically ill patients, remains incompletely understood and warrants further investigation. Therefore, the objective of this study was to evaluate the association between plasma lipid profiles at ICU admission and the development of ARDS, as well as related clinical outcomes, in critically ill patients. Methods Study design and patients This prospective observational study was conducted between October 2020 and July 2025 in the ICU of a tertiary referral center, Chang Gung Memorial Hospital (CGMH), Linkou branch, Taiwan. ARDS was defined according to the Berlin criteria [ 10 ]. All critically ill patients admitted to the ICU were screened, and those who met the Berlin criteria for ARDS (ARDS group) as well as those who did not (non-ARDS group) were enrolled. The exclusion criteria were as follows: (1) age < 20 years; (2) death within 7 days after ICU admission; and (3) inability to obtain informed consent from the patient or their legal representative. This study was conducted in accordance with the Declaration of Helsinki, and ethical approval was obtained from the Institutional Review Board of CGMH for Human Research (CGMH IRB No. 202000760A3, 202100595A3, 202201833A3, and 202300897A3). Data collection Demographic characteristics and underlying comorbidities were recorded for all participants. Blood samples were collected at ICU admission in 6 mL plastic tubes containing K₂EDTA as an anticoagulant (BD, Franklin Lakes, NJ, USA), and analyses were performed in the institutional biochemistry laboratory. Clinical and laboratory variables were obtained, including interleukin-6 (IL-6); plasma lipid profiles, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol, and triglycerides; recent statin use within the preceding six months; Acute Physiology and Chronic Health Evaluation II (APACHE II) score; and Sequential Organ Failure Assessment (SOFA) score. The dates of hospital and ICU admission, mechanical ventilator initiation and liberation, the development of ARDS, ICU and hospital discharge, and time of death were recorded. In addition, the presence of shock, use of inotropic agents, and requirement for renal replacement therapy during the ICU stay were documented. Outcome measurements Hospital mortality was defined as death from any cause during the index hospitalization. Patients who remained alive 90 days after hospital discharge were classified as survivors. Mortality outcomes included 28-day, 60-day, and 90-day mortality, as well as all-cause hospital mortality. Additional clinical outcomes included the incidence of shock, use of inotropic agents, development of acute kidney injury, requirement for renal replacement therapy, duration of mechanical ventilation, length of stay in the ICU, and total hospital length of stay. Statistical analysis Comparisons between groups were performed using analysis of variance or the Student’s t test for normally distributed variables, and the Kruskal–Wallis test or Mann–Whitney U test for non-normally distributed variables, as appropriate. All continuous variables were reported as mean (standard deviation) for normally distributed variables or median (interquartile range) for non-normally distributed variables. Categorical variables were reported as frequency (proportion) and were compared using the chi-square test for equal proportions or Fisher’s exact test. Receiver operating characteristic curves and the Youden index were conducted to determine the optimal cutoff values to dichotomize continuous variables. Univariate analysis was examined to identify the risk factors associated with the development of ARDS and hospital mortality in the first step, followed by the construction of multivariate logistic regression models with stepwise selection. The results were expressed using odds ratio (OR) and 95% confidence interval (CI). Kaplan–Meier method was employed to estimate the cumulative incidence of hospital survival for each group and the difference in survival curve was assessed by log-rank test. All statistical analyses were performed using SPSS version 29.0 (IBM Inc., Armonk, NY), and a two-sided p value less than 0.05 was considered statistically significant. Results A total of 6,238 critically ill patients admitted to the ICU were screened during the study period. Of these, 62 patients without ARDS and 223 patients with ARDS were included in the final analysis (Fig. 1 ). The overall all-cause in-hospital mortality among the study population was 37.9%. Comparisons between critically ill patients with and without ARDS As shown in Table 1 , there were no significant differences in age or gender distribution between patients with and without ARDS. Body mass index (BMI) was significantly higher in the ARDS group than in the non-ARDS group. Underlying comorbidities were generally comparable between the two groups, except for a higher prevalence of chronic lung disease among patients without ARDS. Table 1 Baseline characteristics and clinical variables: patients without ARDS versus with ARDS Variables Without ARDS With ARDS p value ( n = 62) ( n = 223) Age (years) 62.7 ± 14.3 64.7 ± 13.3 0.313 Male (gender) 39 (62.9%) 166 (74.4%) 0.074 Body mass index (kg/m 2 ) 22.6 ± 4.5 23.9 ± 4.4 0.044 Hypertension 27 (43.5%) 104 (46.6%) 0.666 Diabetes mellitus 29 (46.8%) 77 (34.5%) 0.078 Chronic heart disease 8 (12.9%) 30 (13.5%) 0.910 Chronic lung disease 23 (37.1%) 47(21.1%) 0.010 Chronic liver disease 8 (12.9%) 23 (10.3%) 0.562 Chronic kidney disease 7 (11.3%) 38 (17%) 0.272 Immunocompromised status 31 (50%) 105 (47.1%) 0.684 APACHE II score 14.1 ± 5.6 16.7 ± 7.1 0.003 SOFA score 4.6 ± 3.9 8.9 ± 3.8 < 0.001 WBC (10 3 /µL) 11.8 ± 5.7 12.1 ± 10.2 0.799 Neutrophil (%) 78.3 ± 18.2 82.8 ± 17.6 0.083 Lymphocyte (%) 8.9 (5.2–14.4) 5.7 (2.9–9.5) 0.012 Neutrophil/lymphocyte ratio 8.7 (5.5–16.2) 15.3 (8–32.1) 0.007 Hemoglobin (g/dL) 10.5 ± 2.4 9.9 ± 2.1 0.062 Platelets (10 3 /µL) 204.5 ± 113.8 162.1 ± 108.9 0.008 Creatinine (mg/dL) 0.7 (0.5–1.2) 1.1 (0.5–2.1) 0.006 Bilirubin (total) (mg/dL) 0.7 (0.4–1.1) 0.5 (0.3–0.8) 0.627 Albumin (g/dL) 3 ± 0.5 2.7 ± 0.4 < 0.001 Lactate (mg/dL) 11 (7–14.7) 13 (9.5–19.5) 0.849 CRP (mg/L) 78.8 (22.5–133.1) 133.4 (59.2–200.4) 0.001 IL-6 (pg/mL) a 18.1 (4.7–48.4) 39.6 (12.7–171) 0.001 PaO 2 /FiO 2 (mm Hg) 347.6 ± 88.5 177.2 ± 86.6 < 0.001 HDL-C (mg/dL) 31.4 ± 17.7 21 ± 12.6 < 0.001 LDL-C (mg/dL) 71.4 ± 40.5 58.2 ± 38.1 0.018 Total cholesterol (mg/dL) 133.4 ± 46.8 114.6 ± 40.6 0.002 Triglyceride (mg/dL) 128.1 ± 80.2 163.3 ± 93.8 0.008 Statin medication 0 17 (7.6%) Mechanical ventilator use 47 (75.8%) 223 (100%) < 0.001 Hospital mortality 12 (19.4%) 96 (43%) 0.001 Data are presented as mean ± standard deviation, count (%) or median (interquartile range). APACHE Acute Physiology and Chronic Health Evaluation, ARDS acute respiratory distress syndrome, CRP C-reactive protein, FiO 2 fraction of inspired oxygen, HDL-C high-density lipoprotein cholesterol, ICU intensive care unit, IL interleukin, LDL-C low-density lipoprotein cholesterol, PaO 2 partial pressure of oxygen in arterial blood, SOFA Sequential Organ Failure Assessment, WBC white blood cells a Available for 46 ICU control patients and 159 ARDS patients Both APACHE II and SOFA scores were significantly higher in the ARDS group than in the non-ARDS group (both p < 0.05). Inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and IL-6, were also significantly elevated in patients with ARDS (all p < 0.05). Among lipid profile parameters, levels of HDL-C, LDL-C, and total cholesterol were significantly lower in the ARDS group compared with the non-ARDS group (all p < 0.05), whereas triglyceride levels were significantly higher in the ARDS group ( p = 0.008). Statin use was observed in 7.6% of patients with ARDS, whereas no patients without ARDS had received statin therapy. All-cause in-hospital mortality was significantly higher in the ARDS group than in the non-ARDS group (43.0% vs. 19.4%, p = 0.001). Comparisons between critically ill survivors and non-survivors As shown in Table 2 , no significant differences in age or gender distribution were observed between survivors and non-survivors. BMI was significantly higher among survivors. The proportion of patients with chronic lung disease was significantly higher among survivors, whereas immunocompromised status was more prevalent among non-survivors (both p < 0.05). Table 2 Baseline characteristics and clinical variables: Survivors versus non-survivors Variables Survivors Non-survivors p value ( n = 177) ( n = 108) Age (years) 63.9 ± 14.7 64.9 ± 11.3 0.543 Male (gender) 125 (70.6%) 80 (74.1%) 0.529 Body mass index (kg/m 2 ) 24.3 ± 4.7 22.4 ± 3.7 < 0.001 Hypertension 86 (48.6%) 45 (41.7%) 0.255 Diabetes mellitus 69 (39%) 37 (34.3%) 0.423 Chronic heart disease 24 (13.6%) 14 (13%) 0.886 Chronic lung disease 52 (29.4%) 18 (16.7%) 0.016 Chronic liver disease 21 (11.9%) 10 (9.3%) 0.493 Chronic kidney disease 26 (14.7%) 19 (17.6%) 0.514 Immunocompromised status 69 (39%) 67 (62%) < 0.001 APACHE II score 14.5 ± 6.4 18.9 ± 6.6 < 0.001 SOFA score 6.5 ± 3.9 10.3 ± 3.5 < 0.001 WBC (10 3 /µL) 11.3 ± 5.8 12.4 ± 10.4 0.329 Neutrophil (%) 81 ± 15.8 83.1 ± 20.7 0.353 Lymphocyte (%) 7.3 (4–11.6) 4.4 (1.9–8.7) 0.044 Neutrophil/lymphocyte ratio 11.3 (6.7–21.4) 20 (9–47.5) < 0.001 Hemoglobin (g/dL) 10.4 ± 2.3 9.4 ± 1.8 < 0.001 Platelets (10 3 /µL) 196.3 ± 109 130.4 ± 102.6 < 0.001 Serum creatinine (mg/dL) 0.9 (0.5–1.4) 1.1 (0.5–2.7) 0.027 Bilirubin (total) (mg/dL) 0.5 (0.3–0.8) 0.6 (0.3–1) 0.735 Albumin (g/dL) 2.8 ± 0.5 2.6 ± 0.4 0.002 Lactate (mg/dL) 12 (8.9–16.2) 14.4 (10–21.4) 0.029 CRP (mg/L) 123.7 (44.8–191.9) 124.3 (57.1–197.7) 0.617 IL-6 (pg/mL) a 28 (8.2–99.5) 54.2 (17–176) 0.878 PaO 2 /FiO 2 (mm Hg) 219.2 ± 112.7 185.8 ± 96.8 0.010 HDL-C (mg/dL) 25.2 ± 15.2 20.2 ± 12.7 0.005 LDL-C (mg/dL) 63.8 ± 36.6 56.7 ± 42.3 0.139 Total cholesterol (mg/dL) 123.7 ± 45.1 110.5 ± 37.2 0.012 Triglyceride (mg/dL) 153.6 ± 94.4 159.2 ± 88.3 0.623 Statin medication 12 (6.8%) 5 (4.6%) 0.457 Mechanical ventilator use 162 (91.5%) 108 (100%) 0.001 ARDS 127 (71.8%) 96 (88.9%) 0.001 Data are presented as mean ± standard deviation, count (%) or median (interquartile range). APACHE Acute Physiology and Chronic Health Evaluation, ARDS acute respiratory distress syndrome, CRP C-reactive protein, FiO 2 fraction of inspired oxygen, HDL-C high-density lipoprotein cholesterol, IL interleukin, LDL-C low-density lipoprotein cholesterol, PaO 2 partial pressure of oxygen in arterial blood, SOFA Sequential Organ Failure Assessment, WBC white blood cells a Available for 205 critically ill patients Both APACHE II and SOFA scores were significantly higher in non-survivors than in survivors (both p < 0.05). Inflammatory markers, including NLR, CRP, and IL-6, were elevated in non-survivors, with NLR reaching statistical significance ( p < 0.001). Non-survivors also exhibited more severe hypoxemia, as reflected by lower PaO 2 /FiO 2 ratios ( p = 0.010). Regarding lipid profile parameters, HDL-C and total cholesterol levels were significantly lower in non-survivors than in survivors (all p < 0.05). The values of lower LDL-C and higher triglyceride was detected in non-survivors; however, these differences did not reach statistical significance. Statin use did not differ significantly between the two groups. The proportions of mechanical ventilation use and ARDS occurrence were both significantly higher among non-survivors. Comparisons between critically ill patients with high and low plasma HDL-C levels Participants were stratified into a high HDL-C group (n = 123, 43.2%) and a low HDL-C group (n = 162, 56.8%) based on the optimal cutoff value of 22.5 mg/dL at ICU admission, determined using the maximum Youden index (Table 3 ). Table 3 Clinical variables of critically ill patients stratified by HDL-C at ICU admission Variables HDL-C at ICU admission High ( n = 123) (> 22.5 mg/dL) Low ( n = 162) (≤ 22.5 mg/dL) p value Age (years) 65.4 ± 14.1 63.4 ± 13.1 0.205 Male (gender) 79 (64.2%) 126 (77.8%) 0.012 Body mass index (kg/m 2 ) 23.5 ± 4.6 23.7 ± 4.4 0.782 APACHE II score at day 1 13.9 ± 5.9 17.8 ± 7.1 < 0.001 SOFA score at day 1 6.1 ± 3.7 9.3 ± 4 < 0.001 WBC (10 3 /µl) 11.5 ± 6.3 11.9 ± 8.8 0.721 Neutrophil (%) 83.5 ± 13 81 ± 19.7 0.213 Lymphocyte (%) 7 (3.7–11.6) 5.7 (2.9–9.8) 0.594 Neutrophil/lymphocyte ratio 12.1 (6.7–24.6) 15.2 (7.6–31.4) 0.919 Hemoglobin (g/dL) 10.4 ± 1.9 9.8 ± 2.3 0.015 Platelets (10 3 /µL) 198.1 ± 107.1 151 ± 110.2 < 0.001 Serum creatinine (mg/dL) 0.7 (0.5–1.3) 1.1 (0.6–2.2) 0.041 Bilirubin (total) (mg/dL) 0.4 (0.3–0.6) 0.6 (0.3–1.1) 0.002 Albumin (g/dL) 3 ± 0.4 2.5 ± 0.4 < 0.001 Lactate (mg/dl) 10.3 (7.6–14.3) 14.4 (10.4–22.8) < 0.001 CRP (mg/l) 70 (23.7–141.3) 158.1 (96.8–219.7) < 0.001 IL-6 (pg/mL) a 16.7 (4.6–73.3) 54.2 (18.7–171) 0.021 PaO 2 /FiO 2 (mm Hg) 229.6 ± 107 188.7 ± 105.2 0.002 HDL-C (mg/dL) 35.3 ± 13.5 14.1 ± 6 < 0.001 LDL-C (mg/dL) 75.1 ± 37.9 50.4 ± 36.4 < 0.001 Total cholesterol (mg/dL) 140.4 ± 43 102.3 ± 34.4 < 0.001 Triglyceride (mg/dL) 134 ± 79 174.4 ± 104.9 < 0.001 Statin medication 6 (4.9%) 11 (6.8%) 0.500 Data are presented as mean ± standard deviation, count or median (interquartile range) APACHE Acute Physiology and Chronic Health Evaluation, ARDS acute respiratory distress syndrome, CRP C-reactive protein, FiO 2 fraction of inspired oxygen, HDL-C high-density lipoprotein cholesterol, ICU intensive care unit, IL interleukin, LDL-C low-density lipoprotein cholesterol, PaO 2 partial pressure of oxygen in arterial blood, SOFA Sequential Organ Failure Assessment, WBC white blood cells a Available for 205 critically ill patients There were no significant differences between the two groups in terms of age, gender, or BMI. APACHE II and SOFA scores were significantly higher in the low HDL-C group than in the high HDL-C group (both p < 0.05). The low HDL-C group also exhibited significantly higher levels of inflammatory markers, including CRP and IL-6, and more severe hypoxemia, as reflected by lower PaO 2 /FiO 2 ratios (all p < 0.05). Regarding lipid profile parameters, lower levels of HDL-C, LDL-C, and total cholesterol, along with higher triglyceride levels, were observed in the low HDL-C group compared with the high HDL-C group, with all differences reaching statistical significance (all p < 0.05). Clinical outcomes of critically ill patients stratified by plasma HDL-C level The development of ARDS and 28-, 60-, and 90-day mortality, as well as all-cause in-hospital mortality, were all significantly higher in the low HDL-C group (all p < 0.05). Patients in the low HDL-C group also had significantly higher rates of shock, use of inotropic agents, and acute kidney injury (all p < 0.05), whereas the requirement for renal replacement therapy did not differ significantly between the two groups. No significant differences were observed between the groups in the duration of mechanical ventilation, ICU length of stay, or total hospital length of stay (Table 4 ). Table 4 Clinical outcomes as a function of HDL-C at ICU admission in critically ill patients Outcomes HDL-C at ICU admission High ( n = 123) (> 22.5 mg/dL) Low ( n = 162) (≤ 22.5 mg/dL) p value ARDS development 81 (65.9%) 142 (87.7%) < 0.001 Mortality 28-day hospital mortality 16 (13%) 53 (32.7%) < 0.001 60-day hospital mortality 31 (25.2%) 68 (42%) 0.003 90-day hospital mortality 34 (27.6%) 73 (45.1%) 0.003 All cause hospital mortality 35 (28.5%) 73 (45.1%) 0.006 Shock status 70 (56.9%) 132 (81.5%) < 0.001 Inotropic agents use 59 (48%) 129 (79.6%) < 0.001 Acute kidney injury 29 (23.6%) 68 (42%) 0.001 Renal replacement therapy a 15 (12.2%) 28 (17.3%) 0.235 Duration of mechanical ventilator (days) 12 (6–24) 15 (9–27) 0.299 Length of ICU stay (days) 15 (8–26) 17 (10–31) 0.153 Length of hospital stay (days) 32 (17–51) 32 (18–52) 0.676 Data are presented as mean ± standard deviation, count or median (interquartile range) ARDS acute respiratory distress syndrome, HDL-C high-density lipoprotein cholesterol, ICU intensive care unit a Excluded participants with end-stage renal disease requiring maintenance hemodialysis Factors associated with ARDS development and hospital mortality After adjusting for potential confounders, multivariable logistic regression analyses demonstrated that higher NLR, lower PaO 2 /FiO 2 , and lower plasma HDL-C levels were independently associated with the development of ARDS (Table 5 ). In a separate multivariable model, immunocompromised status, higher NLR and lower plasma HDL-C levels were independently associated with in-hospital mortality (Table 6 ). A significant inverse association was observed between plasma HDL-C levels and the risk of ARDS development (adjusted OR 0.947 [95% CI 0.920–0.974], p < 0.001), as well as in-hospital mortality (adjusted OR 0.963 [95% CI 0.939–0.987], p = 0.003). Table 5 Multivariable logistic regression analysis of factors associated with ARDS development in critically ill patients Variables Univariable analysis Multivariable analysis model 1 Multivariable analysis model 2 OR (95% CI) p value Adjusted OR (95% CI) p value Adjusted OR (95% CI) p value Age (with each year increase) 1.011 (0.990–1.031) 0.312 Body mass index 1.070 (1.001–1.144) 0.045 Diabetes mellitus 0.600 (0.339–1.061) 0.079 Chronic lung disease 0.453 (0.247–0.831) 0.011 SOFA score 1.380 (1.250–1.522) < 0.001 Neutrophil/lymphocyte ratio 1.017 (1.003–1.032) 0.021 1.023 (1.001–1.045) 0.044 Hemoglobin 0.876 (0.771–0.996) 0.043 Platelets 0.997 (0.994–0.999) 0.009 Serum creatinine 1.220 (0.996–1.495) 0.055 Albumin 0.216 (0.094–0.493) < 0.001 Lactate 1.002 (0.981–1.024) 0.849 CRP 1.006 (1.002–1.009) 0.001 PaO 2 /FiO 2 0.984 (0.979–0.988) < 0.001 0.983 (0.978–0.988) < 0.001 0.984 (0.979–0.989) < 0.001 HDL-C 0.955 (0.936–0.974) < 0.001 0.947 (0.920–0.974) < 0.001 LDL-C 0.992 (0.985–0.999) 0.027 Total cholesterol 0.990 (0.984–0.997) 0.003 Triglyceride 1.005 (1.001–1.010) 0.009 HDL-C ≤ 22.5 mg/dL 3.681 (2.024–6.697) < 0.001 3.383 (1.278–8.952) 0.014 ARDS acute respiratory distress syndrome, CI confidence interval, CRP C-reactive protein, FiO 2 fraction of inspired oxygen, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, OR odds ratio, PaO 2 partial pressure of oxygen in arterial blood, SOFA Sequential Organ Failure Assessment For the continuous variables, the odds ratio indicates that the odds of ARDS development increases or decreases per unit increase of these variables Model 1: add HDL-C as a continuous variable Model 2: add HDL-C ≤ 22.5 mg/dL as a categorical variable Table 6 Multivariable logistic regression analysis of factors associated with hospital mortality in critically ill patients Variables Univariable analysis Multivariable analysis model 1 Multivariable analysis model 2 OR (95% CI) p value Adjusted OR (95% CI) p value Adjusted OR (95% CI) p value Age (with each year increase) 1.005 (0.987–1.023) 0.566 Body mass index 0.902 (0.851–0.957) < 0.001 Diabetes mellitus 0.816 (0.495–1.344) 0.424 Chronic lung disease 0.481 (0.264–0.877) 0.017 Immunocompromised status 2.558 (1.564–4.184) < 0.001 2.059 (1.164–3.641) 0.013 SOFA score 1.300 (1.205–1.402) < 0.001 Neutrophil/lymphocyte ratio 1.015 (1.007–1.023) < 0.001 1.016 (1.007–1.025) 0.001 1.036 (1.011–1.061) 0.004 Hemoglobin 0.781 (0.688–0.886) < 0.001 Platelets 0.994 (0.991–0.996) < 0.001 Serum creatinine 1.148 (1.021–1.290) 0.021 Albumin 0.364 (0.182–0.730) 0.004 Lactate 1.028 (1.005–1.052) 0.016 CRP 1.001 (0.998–1.003) 0.616 PaO 2 /FiO 2 0.997 (0.995–0.999) 0.014 HDL-C 0.973 (0.955–0.992) 0.006 0.963 (0.939–0.987) 0.003 LDL-C 0.995 (0.988–1.002) 0.143 Total cholesterol 0.992 (0.986–0.998) 0.013 Triglyceride 1.001 (0.998–1.003) 0.622 HDL-C ≤ 22.5 mg/dL 2.011 (1.221–3.314) 0.006 3.451 (1.194–9.975) 0.022 CI confidence interval, CRP C-reactive protein, FiO 2 fraction of inspired oxygen, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, OR odds ratio, PaO 2 partial pressure of oxygen in arterial blood, SOFA Sequential Organ Failure Assessment For the continuous variables, the odds ratio indicates that the odds of hospital mortality increases or decreases per unit increase of these variables Model 1: add HDL-C as a continuous variable Model 2: add HDL-C ≤ 22.5 mg/dL as a categorical variable Notably, a plasma HDL-C level ≤ 22.5 mg/dL demonstrated the highest predictive value among all studied variables and was independently associated with both ARDS development (adjusted OR 3.383 [95% CI 1.278–8.952], p = 0.014) and in-hospital mortality (adjusted OR 3.451 [95% CI 1.194–9.975], p = 0.022). Furthermore, the 90-day survival rate was significantly higher in the high HDL-C group (> 22.5 mg/dL) compared with the low HDL-C group (≤ 22.5 mg/dL) (72.4% vs. 54.9%, p = 0.003, log-rank test) (Fig. 2 ). Discussion The key finding of this prospective study is that plasma HDL-C demonstrated the greatest predictive value among all lipid profile parameters and was independently associated with both the development of ARDS and in-hospital mortality in critically ill patients. Our findings suggest that plasma HDL-C measured at ICU admission may serve as an easily accessible and valuable early prognostic biomarker in this population. Metabolic derangements in glucose, amino acid, and lipid metabolism occur during both acute and prolonged critical illness and are closely associated with disease severity and clinical outcomes [ 11 – 14 ]. Critical illnesses, particularly sepsis, are characterized by a hypermetabolic and hypercatabolic state that disrupts lipid homeostasis. This disruption is marked by an immediate and sustained decline in plasma HDL-C, LDL-C, and total cholesterol levels, the magnitude of which correlates with the intensity of inflammation, disease severity, and mortality, whereas hypertriglyceridemia appears to have limited prognostic value. Potential underlying mechanisms include reduced dietary intake and intestinal fat absorption, decreased lipid synthesis, impaired cholesterol transport, increased metabolic consumption, and enhanced toxin scavenging [ 6 – 9 ]. HDL-C exerts multiple biological functions, including promoting macrophage cholesterol efflux and reverse cholesterol transport from peripheral tissues to the liver, maintaining endothelial function by stimulating nitric oxide production, and providing antioxidative effects through the inhibition of LDL oxidation. In addition, HDL-C has anti-inflammatory, anti-apoptotic, antithrombotic, and immunomodulatory properties. Notably, HDL particles can bind and neutralize lipopolysaccharide and circulating cytokines via scavenger receptor class B type 1, the principal hepatic HDL receptor [ 8 , 15 – 24 ]. Plasma HDL-C levels have been shown to correlate with the severity of organ failure and clinical outcomes in patients with critical illness, including those with sepsis and septic shock [ 25 – 30 ]. However, the relationship between plasma lipid profiles and the development of ARDS, as well as associated clinical outcomes in critically ill patients, remains incompletely understood. In the present study, patients who developed ARDS exhibited significantly higher levels of inflammatory markers, including NLR, CRP, and IL-6, as well as greater disease severity, reflected by higher APACHE II and SOFA scores, compared with those without ARDS. Notably, these patients also demonstrated significantly lower levels of HDL-C, LDL-C, and total cholesterol (all p < 0.05). In multivariable regression analyses, NLR was independently associated with both ARDS development and in-hospital mortality. As an accessible and reliable biomarker reflecting the balance between innate inflammatory activation and adaptive immune response, NLR has been widely recognized as a predictor of disease severity and mortality across various clinical conditions [ 31 ]. These findings suggest that patients who develop ARDS experience profound dysregulation of inflammatory and immune responses, accompanied by progressive organ dysfunction during the acute phase of critical illness. These processes may lead to alterations in lipid metabolism, including increased lipoprotein consumption for endotoxin neutralization and enhanced utilization of cholesterol for steroidogenesis and other metabolic demands associated with the stress response to critical illness, thereby contributing to hypocholesterolemia. In addition, non-survivors among critically ill patients exhibited significantly higher levels of inflammatory markers, including NLR, more severe hypoxemia, greater organ failure severity, and lower levels of HDL-C and total cholesterol compared with survivors (all p < 0.05). These findings speculate that critically ill patients with more pronounced dysregulated inflammation and subsequent multiple organ failure, accompanied by hypocholesterolemia, are at an increased risk of mortality. Critically ill patients with low plasma HDL-C levels exhibited significantly higher inflammatory markers, including CRP and IL-6, more severe hypoxemia, greater organ failure severity, and lower levels of HDL-C, LDL-C, and total cholesterol compared with those with higher HDL-C levels. In addition, patients in the low HDL-C group experienced worse clinical outcomes, including a significantly higher risk of ARDS development, all-cause in-hospital mortality, shock, and acute kidney injury (all p < 0.05). These findings suppose that critically ill patients with more exaggerated inflammatory responses and multiple organ dysfunction may consume greater amounts of HDL particles due to their endotoxin-scavenging and anti-inflammatory properties, leading to reduced circulating HDL-C levels. This process may, in turn, contribute to poorer clinical outcomes. Low HDL-C levels are a common biomarker across a range of disease states; however, the precise mechanisms underlying the decline in HDL-C during critical illness remain incompletely understood. In our multivariable regression analyses, lower plasma HDL-C was the only lipid parameter independently associated with an increased risk of ARDS development and in-hospital mortality. These findings underscore the potentially central role of HDL-C in the pathophysiology of critical illness. Taken together, our results suggest that plasma HDL-C measured at ICU admission may serve as a simple, early, and clinically valuable prognostic biomarker. Furthermore, these findings raise the possibility that early therapeutic strategies targeting HDL metabolism or function may represent a novel approach to improve outcomes in critically ill patients. Although high HDL-C concentrations have traditionally been considered protective against cardiovascular and inflammatory diseases, their causal role remains uncertain, and extremely elevated levels may be detrimental to human health. Emerging evidence suggests a U-shaped relationship between HDL-C levels and clinical outcomes, with both low and excessively high HDL-C concentrations (> 80 mg/dL in men and > 100 mg/dL in women) associated with increased risk of adverse outcomes and all-cause mortality—a phenomenon referred to as the “HDL cholesterol paradox” [ 32 – 34 ]. The structure and function of HDL particles are highly heterogeneous and complex. Accordingly, the biological effects of HDL-C may depend not only on its circulating quantitative concentration but also on qualitative characteristics, including particle size and number, density, charge and shape, functional assay, apolipoprotein composition, and cholesterol efflux capacity [ 17 , 20 , 23 , 24 , 29 , 35 – 37 ]. These considerations highlight that HDL-C concentration alone may not fully capture its functional capacity, and further investigation into HDL functionality may provide deeper insights into its role in critical illness. This study has several limitations. First, this was a single-center observational cohort study conducted at a tertiary medical center in Taiwan, which may limit the generalizability of our findings to other populations and institutions. Second, as patients were enrolled during the acute phase of critical illness characterized by a hypercatabolic state, plasma lipid profiles were measured only at ICU admission; thus, dynamic changes over time were not assessed. Third, the study cohort included a relatively higher proportion of patients with ARDS compared with those without ARDS, which may introduce selection bias and warrants cautious interpretation of the results. Finally, given the observational design, our study was intended to evaluate associations between plasma lipid profiles at ICU admission and clinical outcomes, and causality cannot be inferred. In addition, the biological function of HDL cannot be fully elucidated by measuring absolute plasma HDL-C concentrations alone. Both acute and chronic systemic inflammation, as well as oxidative stress, may induce structural and conformational modifications of HDL particles, leading to potentially impaired antioxidant and anti-inflammatory functions—commonly referred to as dysfunctional HDL—which may further exacerbate inflammation, oxidative stress, and endothelial injury [ 23 , 24 ]. We did not assess genetic factors or the complex heterogeneity of HDL particle composition and function, nor did we investigate the precise cellular and molecular mechanisms linking lipid metabolism to the pathophysiology of ARDS development. Future studies incorporating functional assays and mechanistic approaches are warranted to better clarify these relationships. Conclusions Our findings demonstrate that plasma HDL-C levels at ICU admission are independently associated with the development of ARDS and in-hospital mortality in critically ill patients, suggesting that HDL-C may serve as a simple, inexpensive, and clinically valuable early prognosticator. Further studies are warranted to determine whether the functional heterogeneity of HDL particles or specific HDL subclasses are causally linked to the pathophysiology of ARDS development and may serve as early prognostic indicators or potential therapeutic targets. Abbreviations APACHE Acute Physiology and Chronic Health Evaluation ARDS acute respiratory distress syndrome BMI body mass index CI confidence interval CRP C-reactive protein HDL-C high-density lipoprotein cholesterol ICU intensive care unit IL interleukin LDL-C low-density lipoprotein cholesterol NLR neutrophil-to-lymphocyte ratio OR odds ratio SOFA sequential organ failure assessment. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki after receiving approval from the Institutional Review Board for Human Research of Chang Gung Memorial Hospital (CGMH IRB No. 202000760A3, 202100595A3, 202201833A3, and 202300897A3), and informed consent from all subjects involved in the study. Consent for publication Not applicable. Competing interests On behalf of all authors, the corresponding author states that there are no conflicts of interest. Funding This study was supported by grants from Chang Gung Memorial Hospital (CMRPG3L0821, CMRPG3L0822, CORPG3M0331, and CMRPG3N1121), the Taiwan Ministry of Science and Technology (MOST 111-2314-B-182A-148), and the Taiwan National Science and Technology Council (NSTC114-2314-B-182-065-). Author Contribution HHL and LCC assumed responsibility for the accuracy of the data analysis and drafting of the manuscript. HHL, TMC, HWK, and LCC performed the study design and data acquisition. HHL, CSL, PCH, SCHK, HCH, and LCC were responsible for statistical analysis of data. HHL, TMC, HWK, and LCC performed interpretation of the results. All authors contributed to the completion of the manuscript and have approved the final version. Acknowledgement The authors would like to express their appreciation for the patients and staffs at the ICUs of Chang Gung Memorial Hospital. Data Availability The datasets used or analyzed in the study are available from the corresponding author on reasonable request. References Matthay MA, Zemans RL, Zimmerman GA, Arabi YM, Beitler JR, Mercat A, et al. Acute respiratory distress syndrome. Nat Rev Dis Primers. 2019;5(1):18. Bos LDJ, Ware LB. Acute respiratory distress syndrome: causes, pathophysiology, and phenotypes. Lancet. 2022;400(10358):1145–1156. Al-Husinat L, Azzam S, Al Sharie S, Araydah M, Battaglini D, Abushehab S, et al. A narrative review on the future of ARDS: evolving definitions, pathophysiology, and tailored management. Crit Care. 2025;29(1):88. Ma W, Tang S, Yao P, Zhou T, Niu Q, Liu P, et al. Advances in acute respiratory distress syndrome: focusing on heterogeneity, pathophysiology, and therapeutic strategies. Signal Transduct Target Ther. 2025;10(1):75. Qadir N, Sahetya S, Munshi L, Summers C, Abrams D, Beitler J, et al. An Update on Management of Adult Patients with Acute Respiratory Distress Syndrome: An Official American Thoracic Society Clinical Practice Guideline. Am J Respir Crit Care Med. 2024;209(1):24–36. Lauwers C, De Bruyn L, Langouche L. Impact of critical illness on cholesterol and fatty acids: insights into pathophysiology and therapeutic targets. Intensive Care Med Exp. 2023;11(1):84. Hofmaenner DA, Arina P, Kleyman A, Page Black L, Salomao R, Tanaka S, et al. Association Between Hypocholesterolemia and Mortality in Critically Ill Patients With Sepsis: A Systematic Review and Meta-Analysis. Crit Care Explor. 2023;5(2):e0860. Hofmaenner DA, Kleyman A, Press A, Bauer M, Singer M. The Many Roles of Cholesterol in Sepsis: A Review. Am J Respir Crit Care Med. 2022;205(4):388–396. Golucci APBS, Marson FAL, Ribeiro AF, Nogueira RJN. Lipid profile associated with the systemic inflammatory response syndrome and sepsis in critically ill patients. Nutrition. 2018;55–56:7–14. ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307:2526–2533. Chiu LC, Tang HY, Fan CM, Lo CJ, Hu HC, Kao KC, et al. Kynurenine Pathway of Tryptophan Metabolism Is Associated with Hospital Mortality in Patients with Acute Respiratory Distress Syndrome: A Prospective Cohort Study. Antioxidants (Basel). 2022;11(10):1884. Chiu LC, Lo CJ, Fan CM, Tang HY, Chan TM, Ko HW, et al. Plasma phenylalanine is associated with hospital mortality in acute respiratory distress syndrome: a prospective metabolic profiling cohort study. Eur J Med Res. 2025;30(1):833. Siddiqui MA, Pandey S, Azim A, Sinha N, Siddiqui MH. Metabolomics: An emerging potential approach to decipher critical illnesses. Biophys Chem. 2020;267:106462. Mottale R, Dupuis C, Szklarzewska S, Preiser JC. The metabolic response to stress in critical illness: updated review on the pathophysiological mechanisms, consequences, and therapeutic implications. Ann Intensive Care. 2025;15(1):174. Meilhac O, Tanaka S, Couret D. High-Density Lipoproteins Are Bug Scavengers. Biomolecules. 2020;10(4):598. Shen WJ, Azhar S, Kraemer FB. SR-B1: A Unique Multifunctional Receptor for Cholesterol Influx and Efflux. Annu Rev Physiol. 2018;80:95–116. Razavi AC, Jain V, Grandhi GR, Patel P, Karagiannis A, Patel N, et al. Does Elevated High-Density Lipoprotein Cholesterol Protect Against Cardiovascular Disease? J Clin Endocrinol Metab. 2024;109(2):321–332. Deng H, Liang WY, Chen L, Huang K, Mccallum R, Rensen PCN, et al. High-density lipoprotein attenuates lipopolysaccharide-induced IL-1β activation via scavenger receptor class B type 1. J Lipid Res. 2025;66(8):100858. Bonacina F, Pirillo A, Catapano AL, Norata GD. HDL in Immune-Inflammatory Responses: Implications beyond Cardiovascular Diseases. Cells. 2021;10(5):1061. Brunham LR. The role of high-density lipoproteins in sepsis. J Lipid Res. 2025;66(1):100728. Parolini C. Sepsis and high-density lipoproteins: Pathophysiology and potential new therapeutic targets. Biochim Biophys Acta Mol Basis Dis. 2025;1871(5):167761. von Eckardstein A, Nordestgaard BG, Remaley AT, Catapano AL. High-density lipoprotein revisited: biological functions and clinical relevance. Eur Heart J. 2023;44(16):1394–1407. Chiesa ST, Charakida M. High-Density Lipoprotein Function and Dysfunction in Health and Disease. Cardiovasc Drugs Ther. 2019;33(2):207–219. Ouyang FW, Chiang HH, Hsu WL, Tsai MH, Huang CY, Remaley AT, et al. Dysfunctional high-density lipoprotein: an updated review. Front Cardiovasc Med. 2025;12:1713387. Cirstea M, Walley KR, Russell JA, Brunham LR, Genga KR, Boyd JH. Decreased high-density lipoprotein cholesterol level is an early prognostic marker for organ dysfunction and death in patients with suspected sepsis. J Crit Care. 2017;38:289–294. Tanaka S, Labreuche J, Drumez E, Harrois A, Hamada S, Vigué B, et al. Low HDL levels in sepsis versus trauma patients in intensive care unit. Ann Intensive Care. 2017;7(1):60. Guirgis FW, Dodani S, Leeuwenburgh C, Moldawer L, Bowman J, Kalynych C, et al. HDL inflammatory index correlates with and predicts severity of organ failure in patients with sepsis and septic shock. PLoS One. 2018;13(9):e0203813. Liu G, Jiang L, Kerchberger VE, Oeser A, Ihegword A, Dickson AL, Daniel LL, Shaffer C, Linton MF, Cox N, Chung CP, Wei WQ, Stein CM, Feng Q. The relationship between high density lipoprotein cholesterol and sepsis: A clinical and genetic approach. Clin Transl Sci. 2023;16(3):489–501. Tanaka S, Diallo D, Delbosc S, Genève C, Zappella N, Yong-Sang J, et al. High-density lipoprotein (HDL) particle size and concentration changes in septic shock patients. Ann Intensive Care. 2019;9(1):68. Taylor R, Zhang C, George D, Kotecha S, Abdelghaffar M, Forster T, et al. Low circulatory levels of total cholesterol, HDL-C and LDL-C are associated with death of patients with sepsis and critical illness: systematic review, meta-analysis, and perspective of observational studies. EBioMedicine. 2024;100:104981. Buonacera A, Stancanelli B, Colaci M, Malatino L. Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int J Mol Sci. 2022;23(7):3636. Madsen CM, Varbo A, Nordestgaard BG. Extreme high high-density lipoprotein cholesterol is paradoxically associated with high mortality in men and women: two prospective cohort studies. Eur Heart J. 2017;38(32):2478–2486. Liu C, Dhindsa D, Almuwaqqat Z, Ko YA, Mehta A, Alkhoder AA, et al. Association Between High-Density Lipoprotein Cholesterol Levels and Adverse Cardiovascular Outcomes in High-risk Populations. JAMA Cardiol. 2022;7(7):672–680. Ryu HE, Jung DH, Heo SJ, Park B, Lee YJ. Extremely high HDL cholesterol paradoxically increases the risk of all-cause mortality in non-diabetic males from the Korean population: Korean genome and epidemiology study-health examinees (KoGES-HEXA) cohorts. Front Med (Lausanne). 2025;12:1534524. Xiang AS, Kingwell BA. Rethinking good cholesterol: a clinicians' guide to understanding HDL. Lancet Diabetes Endocrinol. 2019;7(7):575–582. Hussain SM, McNeil JJ. An HDL Cholesterol paradox. Cardiovasc Drugs Ther. 2026;40(1):1–5. Hamilton F, Pedersen KM, Ghazal P, Nordestgaard BG, Smith GD. Low levels of small HDL particles predict but do not influence risk of sepsis. Crit Care. 2023;27(1):389. Additional Declarations No competing interests reported. Supplementary Files informedconsent1.pdf informedconsent2.pdf informedconsent3.pdf informedconsent4.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 06 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9329846","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619635038,"identity":"7e1537b4-c9bc-4286-afe4-842941dc597e","order_by":0,"name":"Hsin-Hsien Li","email":"","orcid":"","institution":"Linkou Chang Gung Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hsin-Hsien","middleName":"","lastName":"Li","suffix":""},{"id":619635039,"identity":"fd12817e-8e08-40c0-b303-2c294a2b4b21","order_by":1,"name":"Tien-Ming Chan","email":"","orcid":"","institution":"Linkou Chang Gung Memorial 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16:52:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":464327,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the enrollment of critical ill patients with or without ARDS. \u003cem\u003eARDS \u003c/em\u003eacute respiratory distress syndrome, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol,\u003cem\u003e ICU\u003c/em\u003e intensive care unit\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9329846/v1/107a17a5e1d60f9d451ccfaf.png"},{"id":106727417,"identity":"6e8bf7cc-eafe-46a6-acbd-3951f24e8051","added_by":"auto","created_at":"2026-04-12 18:38:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":529130,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier 90-day survival curves for critically ill patients stratified by plasma HDL-C values, using an optimal cutoff value of 22.5 mg/dL at ICU admission.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eICU\u003c/em\u003eintensive care unit\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9329846/v1/301536da290d26a5de9330fb.png"},{"id":107480461,"identity":"faffe8c2-47cb-4e2b-9c00-52747869db5d","added_by":"auto","created_at":"2026-04-22 02:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2044537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9329846/v1/f84fbb8d-e0c2-416f-a75b-8e1692ee7d3d.pdf"},{"id":106726953,"identity":"0ad7b89c-8027-40b4-bac7-1deeb2488108","added_by":"auto","created_at":"2026-04-12 18:37:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":441646,"visible":true,"origin":"","legend":"","description":"","filename":"informedconsent1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9329846/v1/1711360041fc9d4e9aa95796.pdf"},{"id":106727424,"identity":"0e9b8e75-e0af-40d4-9452-823d27182dbb","added_by":"auto","created_at":"2026-04-12 18:39:02","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2439208,"visible":true,"origin":"","legend":"","description":"","filename":"informedconsent2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9329846/v1/76ca30914ca87385b7c2f4af.pdf"},{"id":106959035,"identity":"ad320de1-53dd-47e1-bc7d-125269ee0eb4","added_by":"auto","created_at":"2026-04-15 08:44:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":914495,"visible":true,"origin":"","legend":"","description":"","filename":"informedconsent3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9329846/v1/a2b8ed6bd2670e22b4905477.pdf"},{"id":106636143,"identity":"4493dd87-87aa-4a18-90a8-f7b23b2ff722","added_by":"auto","created_at":"2026-04-10 16:52:42","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3791168,"visible":true,"origin":"","legend":"","description":"","filename":"informedconsent4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9329846/v1/f2a78599a467a02135ecafd5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HDL-C levels are associated with the development of acute respiratory distress syndrome and hospital mortality in critically ill patients: a prospective cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eCritical illness encompasses life-threatening conditions that require prompt intensive monitoring, support, and treatment of vital organ functions. Sepsis and acute respiratory distress syndrome (ARDS) represent two of the most common causes of critical illness among patients admitted to the intensive care unit (ICU) and are major contributors to multiple organ failure, with persistently high morbidity and mortality.\u003c/p\u003e \u003cp\u003eARDS is a life-threatening form of acute hypoxemic respiratory failure characterized by dysregulated airway and systemic inflammatory cascades and immune activation [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To date, no effective pharmacotherapies have been established, and lung-protective mechanical ventilation remains the cornerstone of evidence-based management for ARDS, with demonstrated benefits in improving clinical outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite significant advances in ARDS management, morbidity and mortality remain unacceptably high. Early identification of potential risk factors and prognostic determinants during the course of critical illness is therefore essential and may offer opportunities to reduce the burden of ARDS and improve patient outcomes.\u003c/p\u003e \u003cp\u003eLipid synthesis, transport, and metabolism are frequently disrupted during the acute phase of critical illness, thereby affecting immune function, hormone and vitamin production, as well as cell membrane integrity and signaling. Hypocholesterolemia commonly occurs during critical illness and has been associated with disease severity and adverse clinical outcomes. The underlying pathophysiological mechanisms are multifactorial, complex, and not yet fully elucidated. Sepsis is the primary predisposing factor for the development of ARDS, and prior studies have largely focused on the impact of lipid profiles on clinical outcomes in critically ill populations, particularly those with sepsis or septic shock [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the relationship between hypocholesterolemia and the development of ARDS, as well as its associated clinical outcomes in critically ill patients, remains incompletely understood and warrants further investigation.\u003c/p\u003e \u003cp\u003eTherefore, the objective of this study was to evaluate the association between plasma lipid profiles at ICU admission and the development of ARDS, as well as related clinical outcomes, in critically ill patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003eThis prospective observational study was conducted between October 2020 and July 2025 in the ICU of a tertiary referral center, Chang Gung Memorial Hospital (CGMH), Linkou branch, Taiwan. ARDS was defined according to the Berlin criteria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. All critically ill patients admitted to the ICU were screened, and those who met the Berlin criteria for ARDS (ARDS group) as well as those who did not (non-ARDS group) were enrolled. The exclusion criteria were as follows: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;20 years; (2) death within 7 days after ICU admission; and (3) inability to obtain informed consent from the patient or their legal representative. This study was conducted in accordance with the Declaration of Helsinki, and ethical approval was obtained from the Institutional Review Board of CGMH for Human Research (CGMH IRB No. 202000760A3, 202100595A3, 202201833A3, and 202300897A3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eDemographic characteristics and underlying comorbidities were recorded for all participants. Blood samples were collected at ICU admission in 6 mL plastic tubes containing K₂EDTA as an anticoagulant (BD, Franklin Lakes, NJ, USA), and analyses were performed in the institutional biochemistry laboratory. Clinical and laboratory variables were obtained, including interleukin-6 (IL-6); plasma lipid profiles, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol, and triglycerides; recent statin use within the preceding six months; Acute Physiology and Chronic Health Evaluation II (APACHE II) score; and Sequential Organ Failure Assessment (SOFA) score. The dates of hospital and ICU admission, mechanical ventilator initiation and liberation, the development of ARDS, ICU and hospital discharge, and time of death were recorded. In addition, the presence of shock, use of inotropic agents, and requirement for renal replacement therapy during the ICU stay were documented.\u003c/p\u003e\n\u003ch3\u003eOutcome measurements\u003c/h3\u003e\n\u003cp\u003eHospital mortality was defined as death from any cause during the index hospitalization. Patients who remained alive 90 days after hospital discharge were classified as survivors. Mortality outcomes included 28-day, 60-day, and 90-day mortality, as well as all-cause hospital mortality. Additional clinical outcomes included the incidence of shock, use of inotropic agents, development of acute kidney injury, requirement for renal replacement therapy, duration of mechanical ventilation, length of stay in the ICU, and total hospital length of stay.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eComparisons between groups were performed using analysis of variance or the Student\u0026rsquo;s t test for normally distributed variables, and the Kruskal\u0026ndash;Wallis test or Mann\u0026ndash;Whitney U test for non-normally distributed variables, as appropriate. All continuous variables were reported as mean (standard deviation) for normally distributed variables or median (interquartile range) for non-normally distributed variables. Categorical variables were reported as frequency (proportion) and were compared using the chi-square test for equal proportions or Fisher\u0026rsquo;s exact test. Receiver operating characteristic curves and the Youden index were conducted to determine the optimal cutoff values to dichotomize continuous variables. Univariate analysis was examined to identify the risk factors associated with the development of ARDS and hospital mortality in the first step, followed by the construction of multivariate logistic regression models with stepwise selection. The results were expressed using odds ratio (OR) and 95% confidence interval (CI). Kaplan\u0026ndash;Meier method was employed to estimate the cumulative incidence of hospital survival for each group and the difference in survival curve was assessed by log-rank test. All statistical analyses were performed using SPSS version 29.0 (IBM Inc., Armonk, NY), and a two-sided \u003cem\u003ep\u003c/em\u003e value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 6,238 critically ill patients admitted to the ICU were screened during the study period. Of these, 62 patients without ARDS and 223 patients with ARDS were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The overall all-cause in-hospital mortality among the study population was 37.9%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparisons between critically ill patients with and without ARDS\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no significant differences in age or gender distribution between patients with and without ARDS. Body mass index (BMI) was significantly higher in the ARDS group than in the non-ARDS group. Underlying comorbidities were generally comparable between the two groups, except for a higher prevalence of chronic lung disease among patients without ARDS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and clinical variables: patients without ARDS versus with ARDS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout ARDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith ARDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;223)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.7 \u0026plusmn; 14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.7 \u0026plusmn; 13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (gender)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.6 \u0026plusmn; 4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.9 \u0026plusmn; 4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunocompromised status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1 \u0026plusmn; 5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7 \u0026plusmn; 7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 \u0026plusmn; 3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9 \u0026plusmn; 3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8 \u0026plusmn; 5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1 \u0026plusmn; 10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.3 \u0026plusmn; 18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.8 \u0026plusmn; 17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9 (5.2\u0026ndash;14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (2.9\u0026ndash;9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil/lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7 (5.5\u0026ndash;16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.3 (8\u0026ndash;32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 \u0026plusmn; 2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9 \u0026plusmn; 2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204.5 \u0026plusmn; 113.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162.1 \u0026plusmn; 108.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.5\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.5\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (total) (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.4\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.3\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 \u0026plusmn; 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (7\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (9.5\u0026ndash;19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.8 (22.5\u0026ndash;133.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.4 (59.2\u0026ndash;200.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/mL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.1 (4.7\u0026ndash;48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.6 (12.7\u0026ndash;171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347.6 \u0026plusmn; 88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.2 \u0026plusmn; 86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.4 \u0026plusmn; 17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 \u0026plusmn; 12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.4 \u0026plusmn; 40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.2 \u0026plusmn; 38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.4 \u0026plusmn; 46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.6 \u0026plusmn; 40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.1 \u0026plusmn; 80.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.3 \u0026plusmn; 93.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilator use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, count (%) or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAPACHE\u003c/em\u003e Acute Physiology and Chronic Health Evaluation, \u003cem\u003eARDS\u003c/em\u003e acute respiratory distress syndrome, \u003cem\u003eCRP\u003c/em\u003e C-reactive protein, \u003cem\u003eFiO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e fraction of inspired oxygen, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eICU\u003c/em\u003e intensive care unit, \u003cem\u003eIL\u003c/em\u003e interleukin, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003ePaO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e partial pressure of oxygen in arterial blood, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment, \u003cem\u003eWBC\u003c/em\u003e white blood cells\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eAvailable for 46 ICU control patients and 159 ARDS patients\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBoth APACHE II and SOFA scores were significantly higher in the ARDS group than in the non-ARDS group (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and IL-6, were also significantly elevated in patients with ARDS (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong lipid profile parameters, levels of HDL-C, LDL-C, and total cholesterol were significantly lower in the ARDS group compared with the non-ARDS group (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas triglyceride levels were significantly higher in the ARDS group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Statin use was observed in 7.6% of patients with ARDS, whereas no patients without ARDS had received statin therapy. All-cause in-hospital mortality was significantly higher in the ARDS group than in the non-ARDS group (43.0% vs. 19.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComparisons between critically ill survivors and non-survivors\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, no significant differences in age or gender distribution were observed between survivors and non-survivors. BMI was significantly higher among survivors. The proportion of patients with chronic lung disease was significantly higher among survivors, whereas immunocompromised status was more prevalent among non-survivors (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and clinical variables: Survivors versus non-survivors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;177)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.9 \u0026plusmn; 14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.9 \u0026plusmn; 11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (gender)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.3 \u0026plusmn; 4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4 \u0026plusmn; 3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunocompromised status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.5 \u0026plusmn; 6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9 \u0026plusmn; 6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 \u0026plusmn; 3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3 \u0026plusmn; 3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3 \u0026plusmn; 5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4 \u0026plusmn; 10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 \u0026plusmn; 15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.1 \u0026plusmn; 20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 (4\u0026ndash;11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 (1.9\u0026ndash;8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil/lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3 (6.7\u0026ndash;21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (9\u0026ndash;47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.4 \u0026plusmn; 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4 \u0026plusmn; 1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196.3 \u0026plusmn; 109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.4 \u0026plusmn; 102.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.5\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.5\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (total) (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.3\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.3\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8 \u0026plusmn; 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (8.9\u0026ndash;16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4 (10\u0026ndash;21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.7 (44.8\u0026ndash;191.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.3 (57.1\u0026ndash;197.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/mL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (8.2\u0026ndash;99.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2 (17\u0026ndash;176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219.2 \u0026plusmn; 112.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185.8 \u0026plusmn; 96.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.2 \u0026plusmn; 15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.2 \u0026plusmn; 12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.8 \u0026plusmn; 36.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.7 \u0026plusmn; 42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.7 \u0026plusmn; 45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.5 \u0026plusmn; 37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153.6 \u0026plusmn; 94.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159.2 \u0026plusmn; 88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilator use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, count (%) or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAPACHE\u003c/em\u003e Acute Physiology and Chronic Health Evaluation, \u003cem\u003eARDS\u003c/em\u003e acute respiratory distress syndrome, \u003cem\u003eCRP\u003c/em\u003e C-reactive protein, \u003cem\u003eFiO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e fraction of inspired oxygen, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eIL\u003c/em\u003e interleukin, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003ePaO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e partial pressure of oxygen in arterial blood, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment, \u003cem\u003eWBC\u003c/em\u003e white blood cells\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eAvailable for 205 critically ill patients\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBoth APACHE II and SOFA scores were significantly higher in non-survivors than in survivors (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Inflammatory markers, including NLR, CRP, and IL-6, were elevated in non-survivors, with NLR reaching statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Non-survivors also exhibited more severe hypoxemia, as reflected by lower PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratios (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e \u003cp\u003eRegarding lipid profile parameters, HDL-C and total cholesterol levels were significantly lower in non-survivors than in survivors (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The values of lower LDL-C and higher triglyceride was detected in non-survivors; however, these differences did not reach statistical significance. Statin use did not differ significantly between the two groups. The proportions of mechanical ventilation use and ARDS occurrence were both significantly higher among non-survivors.\u003c/p\u003e\n\u003ch3\u003eComparisons between critically ill patients with high and low plasma HDL-C levels\u003c/h3\u003e\n\u003cp\u003eParticipants were stratified into a high HDL-C group (n\u0026thinsp;=\u0026thinsp;123, 43.2%) and a low HDL-C group (n\u0026thinsp;=\u0026thinsp;162, 56.8%) based on the optimal cutoff value of 22.5 mg/dL at ICU admission, determined using the maximum Youden index (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical variables of critically ill patients stratified by HDL-C at ICU admission\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHDL-C at ICU admission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;22.5 mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;162)\u003c/p\u003e \u003cp\u003e(\u0026le;\u0026thinsp;22.5 mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.4 \u0026plusmn; 14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.4 \u0026plusmn; 13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (gender)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5 \u0026plusmn; 4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7 \u0026plusmn; 4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II score at day 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.9 \u0026plusmn; 5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8 \u0026plusmn; 7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score at day 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1 \u0026plusmn; 3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3 \u0026plusmn; 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 \u0026plusmn; 6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9 \u0026plusmn; 8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.5 \u0026plusmn; 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 \u0026plusmn; 19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.7\u0026ndash;11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7 (2.9\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil/lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1 (6.7\u0026ndash;24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2 (7.6\u0026ndash;31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.4 \u0026plusmn; 1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8 \u0026plusmn; 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198.1 \u0026plusmn; 107.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 \u0026plusmn; 110.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.5\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.6\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (total) (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4 (0.3\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.3\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3 (7.6\u0026ndash;14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4 (10.4\u0026ndash;22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (23.7\u0026ndash;141.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.1 (96.8\u0026ndash;219.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/mL) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7 (4.6\u0026ndash;73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2 (18.7\u0026ndash;171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229.6 \u0026plusmn; 107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188.7 \u0026plusmn; 105.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.3 \u0026plusmn; 13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1 \u0026plusmn; 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.1 \u0026plusmn; 37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.4 \u0026plusmn; 36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.4 \u0026plusmn; 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.3 \u0026plusmn; 34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 \u0026plusmn; 79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174.4 \u0026plusmn; 104.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, count or median (interquartile range)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAPACHE\u003c/em\u003e Acute Physiology and Chronic Health Evaluation, \u003cem\u003eARDS\u003c/em\u003e acute respiratory distress syndrome, \u003cem\u003eCRP\u003c/em\u003e C-reactive protein, \u003cem\u003eFiO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e fraction of inspired oxygen, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eICU\u003c/em\u003e intensive care unit, \u003cem\u003eIL\u003c/em\u003e interleukin, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003ePaO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e partial pressure of oxygen in arterial blood, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment, \u003cem\u003eWBC\u003c/em\u003e white blood cells\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eAvailable for 205 critically ill patients\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were no significant differences between the two groups in terms of age, gender, or BMI. APACHE II and SOFA scores were significantly higher in the low HDL-C group than in the high HDL-C group (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The low HDL-C group also exhibited significantly higher levels of inflammatory markers, including CRP and IL-6, and more severe hypoxemia, as reflected by lower PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratios (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eRegarding lipid profile parameters, lower levels of HDL-C, LDL-C, and total cholesterol, along with higher triglyceride levels, were observed in the low HDL-C group compared with the high HDL-C group, with all differences reaching statistical significance (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical outcomes of critically ill patients stratified by plasma HDL-C level\u003c/h2\u003e \u003cp\u003eThe development of ARDS and 28-, 60-, and 90-day mortality, as well as all-cause in-hospital mortality, were all significantly higher in the low HDL-C group (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Patients in the low HDL-C group also had significantly higher rates of shock, use of inotropic agents, and acute kidney injury (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the requirement for renal replacement therapy did not differ significantly between the two groups. No significant differences were observed between the groups in the duration of mechanical ventilation, ICU length of stay, or total hospital length of stay (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical outcomes as a function of HDL-C at ICU admission in critically ill patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHDL-C at ICU admission\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;22.5 mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;162)\u003c/p\u003e \u003cp\u003e(\u0026le;\u0026thinsp;22.5 mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (65.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (87.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60-day hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll cause hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShock status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInotropic agents use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (79.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal replacement therapy \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of mechanical ventilator (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (6\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (9\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of ICU stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (8\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (10\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospital stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (17\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (18\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, count or median (interquartile range)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eARDS\u003c/em\u003e acute respiratory distress syndrome, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eICU\u003c/em\u003e intensive care unit\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e Excluded participants with end-stage renal disease requiring maintenance hemodialysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with ARDS development and hospital mortality\u003c/h2\u003e \u003cp\u003eAfter adjusting for potential confounders, multivariable logistic regression analyses demonstrated that higher NLR, lower PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e, and lower plasma HDL-C levels were independently associated with the development of ARDS (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In a separate multivariable model, immunocompromised status, higher NLR and lower plasma HDL-C levels were independently associated with in-hospital mortality (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A significant inverse association was observed between plasma HDL-C levels and the risk of ARDS development (adjusted OR 0.947 [95% CI 0.920\u0026ndash;0.974], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as in-hospital mortality (adjusted OR 0.963 [95% CI 0.939\u0026ndash;0.987], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression analysis of factors associated with ARDS development in critically ill patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable analysis model 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariable analysis model 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (with each year increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.011 (0.990\u0026ndash;1.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.070 (1.001\u0026ndash;1.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.600 (0.339\u0026ndash;1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.453 (0.247\u0026ndash;0.831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.380 (1.250\u0026ndash;1.522)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil/lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.017 (1.003\u0026ndash;1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.023 (1.001\u0026ndash;1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876 (0.771\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.997 (0.994\u0026ndash;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.220 (0.996\u0026ndash;1.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.216 (0.094\u0026ndash;0.493)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.002 (0.981\u0026ndash;1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.006 (1.002\u0026ndash;1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.984 (0.979\u0026ndash;0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983 (0.978\u0026ndash;0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984 (0.979\u0026ndash;0.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.955 (0.936\u0026ndash;0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.947 (0.920\u0026ndash;0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992 (0.985\u0026ndash;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.990 (0.984\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.005 (1.001\u0026ndash;1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u0026thinsp;\u0026le;\u0026thinsp;22.5 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.681 (2.024\u0026ndash;6.697)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.383 (1.278\u0026ndash;8.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eARDS\u003c/em\u003e acute respiratory distress syndrome, \u003cem\u003eCI\u003c/em\u003e confidence interval, \u003cem\u003eCRP\u003c/em\u003e C-reactive protein, \u003cem\u003eFiO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e fraction of inspired oxygen, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003eOR\u003c/em\u003e odds ratio, \u003cem\u003ePaO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e partial pressure of oxygen in arterial blood, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFor the continuous variables, the odds ratio indicates that the odds of ARDS development increases or decreases per unit increase of these variables\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: add HDL-C as a continuous variable\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 2: add HDL-C\u0026thinsp;\u0026le;\u0026thinsp;22.5 mg/dL as a categorical variable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression analysis of factors associated with hospital mortality in critically ill patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable analysis model 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariable analysis model 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (with each year increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.005 (0.987\u0026ndash;1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.902 (0.851\u0026ndash;0.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.816 (0.495\u0026ndash;1.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.481 (0.264\u0026ndash;0.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunocompromised status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.558 (1.564\u0026ndash;4.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.059 (1.164\u0026ndash;3.641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.300 (1.205\u0026ndash;1.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil/lymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.015 (1.007\u0026ndash;1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.016 (1.007\u0026ndash;1.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.036 (1.011\u0026ndash;1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.781 (0.688\u0026ndash;0.886)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.994 (0.991\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.148 (1.021\u0026ndash;1.290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364 (0.182\u0026ndash;0.730)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028 (1.005\u0026ndash;1.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001 (0.998\u0026ndash;1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.997 (0.995\u0026ndash;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.973 (0.955\u0026ndash;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.963 (0.939\u0026ndash;0.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.995 (0.988\u0026ndash;1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992 (0.986\u0026ndash;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001 (0.998\u0026ndash;1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u0026thinsp;\u0026le;\u0026thinsp;22.5 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.011 (1.221\u0026ndash;3.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.451 (1.194\u0026ndash;9.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eCI\u003c/em\u003e confidence interval, \u003cem\u003eCRP\u003c/em\u003e C-reactive protein, \u003cem\u003eFiO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e fraction of inspired oxygen, \u003cem\u003eHDL-C\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eLDL-C\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003eOR\u003c/em\u003e odds ratio, \u003cem\u003ePaO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e partial pressure of oxygen in arterial blood, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFor the continuous variables, the odds ratio indicates that the odds of hospital mortality increases or decreases per unit increase of these variables\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: add HDL-C as a continuous variable\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 2: add HDL-C\u0026thinsp;\u0026le;\u0026thinsp;22.5 mg/dL as a categorical variable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotably, a plasma HDL-C level\u0026thinsp;\u0026le;\u0026thinsp;22.5 mg/dL demonstrated the highest predictive value among all studied variables and was independently associated with both ARDS development (adjusted OR 3.383 [95% CI 1.278\u0026ndash;8.952], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and in-hospital mortality (adjusted OR 3.451 [95% CI 1.194\u0026ndash;9.975], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). Furthermore, the 90-day survival rate was significantly higher in the high HDL-C group (\u0026gt;\u0026thinsp;22.5 mg/dL) compared with the low HDL-C group (\u0026le;\u0026thinsp;22.5 mg/dL) (72.4% vs. 54.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, log-rank test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe key finding of this prospective study is that plasma HDL-C demonstrated the greatest predictive value among all lipid profile parameters and was independently associated with both the development of ARDS and in-hospital mortality in critically ill patients. Our findings suggest that plasma HDL-C measured at ICU admission may serve as an easily accessible and valuable early prognostic biomarker in this population.\u003c/p\u003e \u003cp\u003eMetabolic derangements in glucose, amino acid, and lipid metabolism occur during both acute and prolonged critical illness and are closely associated with disease severity and clinical outcomes [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Critical illnesses, particularly sepsis, are characterized by a hypermetabolic and hypercatabolic state that disrupts lipid homeostasis. This disruption is marked by an immediate and sustained decline in plasma HDL-C, LDL-C, and total cholesterol levels, the magnitude of which correlates with the intensity of inflammation, disease severity, and mortality, whereas hypertriglyceridemia appears to have limited prognostic value. Potential underlying mechanisms include reduced dietary intake and intestinal fat absorption, decreased lipid synthesis, impaired cholesterol transport, increased metabolic consumption, and enhanced toxin scavenging [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHDL-C exerts multiple biological functions, including promoting macrophage cholesterol efflux and reverse cholesterol transport from peripheral tissues to the liver, maintaining endothelial function by stimulating nitric oxide production, and providing antioxidative effects through the inhibition of LDL oxidation. In addition, HDL-C has anti-inflammatory, anti-apoptotic, antithrombotic, and immunomodulatory properties. Notably, HDL particles can bind and neutralize lipopolysaccharide and circulating cytokines via scavenger receptor class B type 1, the principal hepatic HDL receptor [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlasma HDL-C levels have been shown to correlate with the severity of organ failure and clinical outcomes in patients with critical illness, including those with sepsis and septic shock [\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the relationship between plasma lipid profiles and the development of ARDS, as well as associated clinical outcomes in critically ill patients, remains incompletely understood. In the present study, patients who developed ARDS exhibited significantly higher levels of inflammatory markers, including NLR, CRP, and IL-6, as well as greater disease severity, reflected by higher APACHE II and SOFA scores, compared with those without ARDS. Notably, these patients also demonstrated significantly lower levels of HDL-C, LDL-C, and total cholesterol (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn multivariable regression analyses, NLR was independently associated with both ARDS development and in-hospital mortality. As an accessible and reliable biomarker reflecting the balance between innate inflammatory activation and adaptive immune response, NLR has been widely recognized as a predictor of disease severity and mortality across various clinical conditions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings suggest that patients who develop ARDS experience profound dysregulation of inflammatory and immune responses, accompanied by progressive organ dysfunction during the acute phase of critical illness. These processes may lead to alterations in lipid metabolism, including increased lipoprotein consumption for endotoxin neutralization and enhanced utilization of cholesterol for steroidogenesis and other metabolic demands associated with the stress response to critical illness, thereby contributing to hypocholesterolemia.\u003c/p\u003e \u003cp\u003eIn addition, non-survivors among critically ill patients exhibited significantly higher levels of inflammatory markers, including NLR, more severe hypoxemia, greater organ failure severity, and lower levels of HDL-C and total cholesterol compared with survivors (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings speculate that critically ill patients with more pronounced dysregulated inflammation and subsequent multiple organ failure, accompanied by hypocholesterolemia, are at an increased risk of mortality.\u003c/p\u003e \u003cp\u003eCritically ill patients with low plasma HDL-C levels exhibited significantly higher inflammatory markers, including CRP and IL-6, more severe hypoxemia, greater organ failure severity, and lower levels of HDL-C, LDL-C, and total cholesterol compared with those with higher HDL-C levels. In addition, patients in the low HDL-C group experienced worse clinical outcomes, including a significantly higher risk of ARDS development, all-cause in-hospital mortality, shock, and acute kidney injury (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suppose that critically ill patients with more exaggerated inflammatory responses and multiple organ dysfunction may consume greater amounts of HDL particles due to their endotoxin-scavenging and anti-inflammatory properties, leading to reduced circulating HDL-C levels. This process may, in turn, contribute to poorer clinical outcomes.\u003c/p\u003e \u003cp\u003eLow HDL-C levels are a common biomarker across a range of disease states; however, the precise mechanisms underlying the decline in HDL-C during critical illness remain incompletely understood. In our multivariable regression analyses, lower plasma HDL-C was the only lipid parameter independently associated with an increased risk of ARDS development and in-hospital mortality. These findings underscore the potentially central role of HDL-C in the pathophysiology of critical illness. Taken together, our results suggest that plasma HDL-C measured at ICU admission may serve as a simple, early, and clinically valuable prognostic biomarker. Furthermore, these findings raise the possibility that early therapeutic strategies targeting HDL metabolism or function may represent a novel approach to improve outcomes in critically ill patients.\u003c/p\u003e \u003cp\u003eAlthough high HDL-C concentrations have traditionally been considered protective against cardiovascular and inflammatory diseases, their causal role remains uncertain, and extremely elevated levels may be detrimental to human health. Emerging evidence suggests a U-shaped relationship between HDL-C levels and clinical outcomes, with both low and excessively high HDL-C concentrations (\u0026gt;\u0026thinsp;80 mg/dL in men and \u0026gt;\u0026thinsp;100 mg/dL in women) associated with increased risk of adverse outcomes and all-cause mortality\u0026mdash;a phenomenon referred to as the \u0026ldquo;HDL cholesterol paradox\u0026rdquo; [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The structure and function of HDL particles are highly heterogeneous and complex. Accordingly, the biological effects of HDL-C may depend not only on its circulating quantitative concentration but also on qualitative characteristics, including particle size and number, density, charge and shape, functional assay, apolipoprotein composition, and cholesterol efflux capacity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These considerations highlight that HDL-C concentration alone may not fully capture its functional capacity, and further investigation into HDL functionality may provide deeper insights into its role in critical illness.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, this was a single-center observational cohort study conducted at a tertiary medical center in Taiwan, which may limit the generalizability of our findings to other populations and institutions. Second, as patients were enrolled during the acute phase of critical illness characterized by a hypercatabolic state, plasma lipid profiles were measured only at ICU admission; thus, dynamic changes over time were not assessed. Third, the study cohort included a relatively higher proportion of patients with ARDS compared with those without ARDS, which may introduce selection bias and warrants cautious interpretation of the results. Finally, given the observational design, our study was intended to evaluate associations between plasma lipid profiles at ICU admission and clinical outcomes, and causality cannot be inferred. In addition, the biological function of HDL cannot be fully elucidated by measuring absolute plasma HDL-C concentrations alone. Both acute and chronic systemic inflammation, as well as oxidative stress, may induce structural and conformational modifications of HDL particles, leading to potentially impaired antioxidant and anti-inflammatory functions\u0026mdash;commonly referred to as dysfunctional HDL\u0026mdash;which may further exacerbate inflammation, oxidative stress, and endothelial injury [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We did not assess genetic factors or the complex heterogeneity of HDL particle composition and function, nor did we investigate the precise cellular and molecular mechanisms linking lipid metabolism to the pathophysiology of ARDS development. Future studies incorporating functional assays and mechanistic approaches are warranted to better clarify these relationships.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings demonstrate that plasma HDL-C levels at ICU admission are independently associated with the development of ARDS and in-hospital mortality in critically ill patients, suggesting that HDL-C may serve as a simple, inexpensive, and clinically valuable early prognosticator. Further studies are warranted to determine whether the functional heterogeneity of HDL particles or specific HDL subclasses are causally linked to the pathophysiology of ARDS development and may serve as early prognostic indicators or potential therapeutic targets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPACHE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Physiology and Chronic Health Evaluation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute respiratory distress syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esequential organ failure assessment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki after receiving approval from the Institutional Review Board for Human Research of Chang Gung Memorial Hospital (CGMH IRB No. 202000760A3, 202100595A3, 202201833A3, and 202300897A3), and informed consent from all subjects involved in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eOn behalf of all authors, the corresponding author states that there are no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by grants from Chang Gung Memorial Hospital (CMRPG3L0821, CMRPG3L0822, CORPG3M0331, and CMRPG3N1121), the Taiwan Ministry of Science and Technology (MOST 111-2314-B-182A-148), and the\u003c/p\u003e \u003cp\u003eTaiwan National Science and Technology Council (NSTC114-2314-B-182-065-).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHHL and LCC assumed responsibility for the accuracy of the data analysis and drafting of the manuscript. HHL, TMC, HWK, and LCC performed the study design and data acquisition. HHL, CSL, PCH, SCHK, HCH, and LCC were responsible for statistical analysis of data. HHL, TMC, HWK, and LCC performed interpretation of the results. All authors contributed to the completion of the manuscript and have approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their appreciation for the patients and staffs at the ICUs of Chang Gung Memorial Hospital.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used or analyzed in the study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMatthay MA, Zemans RL, Zimmerman GA, Arabi YM, Beitler JR, Mercat A, et al. Acute respiratory distress syndrome. 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Association Between High-Density Lipoprotein Cholesterol Levels and Adverse Cardiovascular Outcomes in High-risk Populations. JAMA Cardiol. 2022;7(7):672\u0026ndash;680.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyu HE, Jung DH, Heo SJ, Park B, Lee YJ. Extremely high HDL cholesterol paradoxically increases the risk of all-cause mortality in non-diabetic males from the Korean population: Korean genome and epidemiology study-health examinees (KoGES-HEXA) cohorts. Front Med (Lausanne). 2025;12:1534524.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang AS, Kingwell BA. Rethinking good cholesterol: a clinicians' guide to understanding HDL. Lancet Diabetes Endocrinol. 2019;7(7):575\u0026ndash;582.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussain SM, McNeil JJ. An HDL Cholesterol paradox. Cardiovasc Drugs Ther. 2026;40(1):1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton F, Pedersen KM, Ghazal P, Nordestgaard BG, Smith GD. Low levels of small HDL particles predict but do not influence risk of sepsis. Crit Care. 2023;27(1):389.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"journal-of-intensive-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Intensive Care","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Critical illness, Acute respiratory distress syndrome, Lipid profiles, High-density lipoprotein cholesterol, Outcomes, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-9329846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9329846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic perturbations frequently occur during the acute phase of critical illness and may disrupt lipid homeostasis. Acute respiratory distress syndrome (ARDS) is a severe condition that contributes to multiple organ failure and is associated with high morbidity and mortality in critically ill patients. This study aimed to evaluate the association between plasma lipid profiles and the development of ARDS, as well as clinical outcomes in critically ill patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a prospective observational cohort study of critically ill patients in Taiwan between October 2020 and July 2025. Plasma lipid profiles and clinical variables were measured at intensive care unit (ICU) admission. Clinical outcomes were compared between patients stratified by plasma HDL-C levels.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 285 critically ill patients were included, comprising 62 without ARDS and 223 with ARDS. The all-cause in-hospital mortality rates were 19.4% in patients without ARDS and 43.0% in those with ARDS. Patients who developed ARDS had significantly higher neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and interleukin-6 (IL-6) levels, higher risk of organ failure (i.e., APACHE II and SOFA scores), and lower plasma HDL-C levels (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Non-survivors exhibited significantly higher NLR, greater organ failure severity, and lower HDL-C levels compared with survivors (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Patients with low HDL-C (\u0026le;\u0026thinsp;22.5 mg/dL; n\u0026thinsp;=\u0026thinsp;162, 56.8%) had significantly higher CRP and IL-6, greater organ failure severity, and increased 28-, 60-, 90-day, and all-cause hospital mortality, compared with those with high HDL-C (\u0026gt;\u0026thinsp;22.5 mg/dL; n\u0026thinsp;=\u0026thinsp;123, 43.2%) (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In multivariable logistic regression analyses, elevated NLR and low HDL-C were independently associated with ARDS development and in-hospital mortality. Notably, HDL-C\u0026thinsp;\u0026le;\u0026thinsp;22.5 mg/dL demonstrated the strongest predictive value among all variables (adjusted OR 3.383 [95% CI 1.278\u0026ndash;8.952], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; adjusted OR 3.451 [95% CI 1.194\u0026ndash;9.975], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022, respectively).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePlasma HDL-C levels at ICU admission are independently associated with ARDS development and in-hospital mortality in critically ill patients. HDL-C may serve as a simple, accessible, and valuable early prognostic biomarker in this population.\u003c/p\u003e","manuscriptTitle":"HDL-C levels are associated with the development of acute respiratory distress syndrome and hospital mortality in critically ill patients: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 16:52:38","doi":"10.21203/rs.3.rs-9329846/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-26T00:56:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T12:24:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T22:57:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T02:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155171913149766753746576225324129633954","date":"2026-04-12T20:17:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268127599827232193901597434118170429004","date":"2026-04-10T04:33:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203232405278749651042729376709044836793","date":"2026-04-08T11:56:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T10:07:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146397519001049541390648153526635521856","date":"2026-04-06T22:51:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T10:17:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T08:54:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T08:54:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Intensive Care","date":"2026-04-06T04:44:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"journal-of-intensive-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Intensive Care","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8c60032a-15b9-40d8-b1d7-587a4ac7c7b1","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T15:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 16:52:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9329846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9329846","identity":"rs-9329846","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}