The CD4⁺/CD8⁺ ratio is independently associated with severe acute lung injury

The CD4⁺/CD8⁺ ratio is independently associated with severe acute lung injury | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The CD4⁺/CD8⁺ ratio is independently associated with severe acute lung injury Na Shen, Xiangjun Chen, Qingqing Huang, Cheng Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8426596/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 Objective: To investigate the association between the CD4⁺/CD8⁺ T-cell ratio and severe acute lung injury (ALI). Methods: Following the 2012 Berlin Definition of severe ALI, 31 patients with severe ALI admitted to the Department of Critical Care Medicine at the First Affiliated Hospital of Chongqing Medical University from June 2022 to October 2023 were enrolled as the study subjects (severe ALI group). while 147 non-severe ALI patients served as the control group during the same period. Clinical data were collected, and the serum CD4⁺/CD8⁺ T-cell ratio were analyzed using flow cytometry. The CD4⁺/CD8⁺ T-cell ratio levels were compared between the severe and the non-severe ALI groups. Linear relationships between the CD4⁺/CD8⁺ T-cell ratio and severe ALI were established through restricted cubic spline modeling, and multivariate logistic regression was employed to analyze their association. Results: Patients with severe ALI had significantly higher APACHE II scores, SOFA scores, serum lactate levels, and CD4⁺/CD8⁺ T-cell ratios than the non-severe group, while the absolute lymphocyte count was significantly lower (all P < 0.05). The proportion of patients receiving invasive mechanical ventilation and ECMO was significantly higher in the severe group. Multivariate logistic regression showed that, in addition to the SOFA score (OR = 1.21, P = 0.015) and oxygenation mode (OR = 1.54, P = 0.045), the CD4⁺/CD8⁺ ratio was independently associated with severe ALI (OR = 1.40, P = 0.019). Restricted cubic spline analysis confirmed a linear relationship between the CD4⁺/CD8⁺ ratio and disease severity (non-linear P = 0.757). After further correction of the use of vasopressors and corticosteroids, the increase in the CD4⁺/CD8⁺ ratio increased the risk of severe lung injury by 1.45 times (OR = 1.45, 95% CI: 1.07–1.94, P adj = 0.015). Conclusion: The CD4⁺/CD8⁺ T-cell ratio is an independent risk factor for severe ALI. CD4⁺/CD8⁺ ratio ALI ARDS Berlin Diagnostic Criteria Figures Figure 1 1. Introduction Acute lung injury (ALI) is an acute inflammatory process characterized by inflammatory cell infiltration, leading to severe damage of the alveolar epithelium and alveolar-capillary membrane, increased vascular permeability, and pulmonary interstitial/alveolar edema. It often presents as acute onset respiratory distress, refractory hypoxemia, and bilateral pulmonary infiltrates [1,2] . The incidence of ALI in intensive care unit patients is approximately 10% [3] . Among these, about 15%–25% of patients suffer from severe ALI (PaO₂/FiO₂ ≤ 100 mmHg), which is associated with a significantly higher risk of multi-organ failure, a greatly increased need for invasive mechanical ventilation, and a mortality rate as high as 46.1% compared to non-severe ALI [3–5] . The core of ALI progression lies in the imbalance of inflammatory responses exacerbating pulmonary epithelial and endothelial injury [5] . The inflammatory process driving lung injury is complex. Beyond the role of neutrophils, the balance between pro-inflammatory and pro-resolving T-cell phenotypes is involved in the pathophysiology of ALI [6,7] . Studies have shown that an increased proportion of the pro-inflammatory T-cell subset Th17 is associated with a higher risk of poor outcomes in acute respiratory distress syndrome (ARDS) [8] . Regulatory T cells(Tregs), generally known as a CD4⁺ T-cell subset that suppresses inflammatory responses, have also been observed to be elevated in the peripheral blood of ARDS patients in some studies [9] , and are associated with poor prognosis in ARDS [10] . CD8⁺ T cells are crucial for protective immunity against intracellular pathogens, and some research suggests that a reduced CD8⁺ T-cell count is linked to worse outcomes in ARDS [11] . The peripheral blood CD4⁺/CD8⁺ T-cell ratio, which combines the counts of both CD4 and CD8 cells, is a readily available clinical indicator reflecting T-cell homeostasis. However, whether the CD4⁺/CD8⁺ T-cell ratio can reflect the progression of ALI and serve as an indicator for assessing its severity has not yet been reported. Based on this, our study aims to enroll ALI patients to clarify the quantitative relationship between the CD4⁺/CD8⁺ T-cell ratio and the severity of acute lung injury. The findings will provide a new basis for early risk stratification and lay a theoretical foundation for the development of immune-targeted therapeutic strategies. 2. Methods 2.1 Study Population This retrospective cohort study utilized anonymously extracted data from patients admitted to the department of Critical Care Medicine who were diagnosed with ALI at the First Affiliated Hospital of Chongqing Medical University between June 2022 and October 2023. The Inclusion criteria were as follows: Patients met the 2012 Berlin Diagnostic Criteria for ARDS (2012 Berlin Diagnostic Criteria: presence of ARDS risk factors; new or worsening respiratory symptoms within 1 week of onset; bilateral diffuse pulmonary infiltrates unexplained by pleural effusion, lobar/lung collapse, or nodules; respiratory failure not fully attributable to heart failure or fluid overload; PaO₂/FiO₂ ≤ 300 mmHg with PEEP ≥ 5 cmH₂O) [12] . The diagnosis of ALI was screened by experienced intensive care physicians and radiologists based on the Berlin Diagnostic Criteria. Exclusion criteria comprised: (1) Age under 18 years, (2) unavailability of CD4⁺/CD8⁺ T-cell ratio records, or (3) history of confirmed primary/acquired immunosuppression. Based on the inclusion and exclusion criteria, a total of 178 subjects were included in this study. As this was a retrospective study, the informed consent was waived by the Institutional Review Board of the First Affiliated Hospital of Chongqing Medical University. 2.2 CD4⁺/CD8⁺ T-cell Ratio The percentages of CD4⁺ and CD8⁺ T-cells were measured using a BD Canto II flow cytometer with lymphocyte subset detection reagents produced by Beijing Kuangbo Biotechnology Co., Ltd. The CD4⁺/CD8⁺ ratio was calculated based on these measurements. 2.3 Outcomes Severity grading was performed according to the Berlin criteria [12] . Severe acute lung injury is defined as PaO₂/FiO₂ ≤ 100 mmHg, while non-severe acute lung injury is defined as 100 mmHg < PaO₂/FiO₂ ≤ 300 mmHg. 2.4 Covariates The clinical characteristics of all participants were meticulously documented by two independent investigators. Data collected included demographic information (age, sex, smoking history), clinical course parameters (length of hospitalization, ICU stay, and duration of mechanical ventilation), and illness severity scores (APACHE II and SOFA). Vital signs included systolic and diastolic blood pressure measurements. Laboratory analyses comprised complete blood count, hepatic and renal function panels, coagulation profile, BNP, and inflammatory biomarkers (procalcitonin, C-reactive protein, interleukin-6, and interleukin-8). Documented comorbidities included impaired consciousness, chronic pulmonary disease, cardiovascular disease, malignancy, diabetes mellitus, chronic liver disease, chronic kidney disease, neurological disorders, and connective tissue disease. Treatments of interest included respiratory support, vasopressor use, sedative agents, and corticosteroid administration. 2.5 Statistical Analysis Continuous variables with approximately normal distributions are presented as mean ± standard deviation, while those with non-normal distributions are described as median (quartile range). Categorical variables are summarized as frequency (percentage), and group comparisons were performed using the chi-square test. Differences in continuous variables between groups were assessed using Student’s t-test or the Mann–Whitney U test, as appropriate. Multivariable logistic regression was employed to examine the independent association between clinical indicators and severe ALI. The model included three core variables: age, gender, and biomarkers showing statistically significant differences between severe and non-severe ALI groups (P<0.1). To explore the potential linear relationship between the CD4⁺/CD8⁺ T-cell ratio and severe ALI, a four-knot restricted cubic spline (RCS) model was used. The likelihood ratio test was applied to compare the model fit under linear and nonlinear assumptions. In analyzing the association between the CD4⁺/CD8⁺ T-cell ratio and severe ALI, three models were constructed. Model 1 adjusted for age and gender. Model 2 further incorporated APACHE II and SOFA scores into Model 1. Model 3 expanded on Model 2 by adding systolic blood pressure, serum lactate level, lymphocyte absolute count, respiratory support modality, vasopressor use, and corticosteroid therapy as covariates. All statistical analyses were conducted using R version 4.0.3 (R Foundation for Statistical Computing, Vienna). The “rms” package was used for restricted cubic spline analysis, and the “glm” function was applied for multivariable logistic regression. P-values < 0.05 was considered statistically significant. 3. Results Compared to the non-severe ALI group, patients in the severe ALI group had significantly higher APACHE II and SOFA scores (P < 0.05). Additionally, there was a significant difference in respiratory support methods between the two groups, with the severe group demonstrating markedly higher rates of invasive mechanical ventilation and extracorporeal membrane oxygenation (ECMO). The severe group also showed trends toward longer hospital stays, a higher proportion of indirect lung injury, and lower systolic blood pressure (P < 0.1) (Table 1 ). Table 1 Clinical characteristic of included participants according to the severity of acute lung injury. Non-severe ALI Severe ALI P value N 147 31 Age (years) 64.5(17.6) 62.8(16.5) 0.622 Female (N, %) 45(30.6) 12(38.7) 0.505 Smoking (N, %) 25(17.5) 9(29.0) 0.222 Length of hospital stay (days) 18.9(26.4) 33.4(82.1) 0.080 ICU length of stay (days) 14.4(24.0) 14.7(20.1) 0.939 Duration of mechanical ventilation (days) 9.3(18.5) 11.9(15.0) 0.466 Systolic blood pressure (mmHg) 126.4(28.6) 117.2(25.4) 0.100 Diastolic blood pressure (mmHg) 71.7(17.9) 68.7(17.3) 0.399 Respiratory rate (breaths per minute) 25.1(8.1) 27.0(10.2) 0.276 APACHE II score 22.7(8.1) 27.4(8.7) 0.005 SOFA score 8.0(3.9) 12.1(4.5) < 0.001 Document etiology of direct pulmonary injury Direct pulmonary insult 110(74.8) 18(58.1) 0.095 Sepsis 16(10.9) 6(19.4) 0.316 Comorbidity Impaired consciousness (N, %) 86 (58.5) 18 (58.1) 1.000 History of chronic lung disease (N, %) 21 (14.3) 3 ( 9.7) 0.694 Cardiovascular disease (N, %) 77 (52.4) 18 (58.1) 0.705 Malignancy (N, %) 39 (26.5) 12 (38.7) 0.252 Diabetes mellitus (N, %) 32 (21.8) 5 (16.1) 0.646 Chronic liver disease (N, %) 24 (16.3) 4 (12.9) 0.838 Chronic kidney disease (N, %) 13 (8.8) 4 (12.9) 0.717 Neurological disorders (N, %) 26 (17.7) 2 ( 6.5) 0.197 Connective tissue disease (N, %) 4 ( 2.7) 0 ( 0.0) 0.793 Oxygen inhalation methods 0.018 Nasal cannula 28 (19.0) 0 ( 0.0) Face mask 17 (11.6) 5 (16.1) High-flow nasal cannula 11 ( 7.5) 0 ( 0.0) Non-invasive ventilation 26 (17.7) 4 (12.9) Invasive mechanical ventilation 64 (43.5) 21 (67.7) Extracorporeal membrane oxygenation 1 ( 0.7) 1 ( 3.2) Vasopressor (N, %) 48 (32.7) 15 (48.4) 0.145 Sedative therapy (N, %) 40 (27.2) 12 (38.7) 0.288 Corticosteroid therapy (N, %) 79 (53.7) 22 (71.0) 0.119 Regarding inflammatory markers, the absolute lymphocyte count was significantly lower in the severe group compared to the non-severe group (1.0 ± 1.1 vs. 0.6 ± 0.4, P = 0.045), while the serum CD4⁺/CD8⁺ T-cell ratio was significantly elevated (1.7 ± 1.2 vs. 2.5 ± 2.8, P = 0.006; Table 2 , Fig. 1 A). However no significant differences were observed in total white blood cell count, neutrophil percentage, CRP, procalcitonin, IL-6, or IL-8 levels. Among other laboratory parameters, lactate levels were significantly elevated in the severe group (Table 2 ). Table 2 Characteristics of inflammatory and laboratory indicators of included participants according to the severity of acute lung injury. Non-severe ALI Severe ALI P value Inflammatory indicators White blood cell count (×10⁹/L) 13.6(15.2) 11.3(8.4) 0.419 Absolute lymphocyte count (×10⁹/L) 1.0(1.1) 0.6(0.4) 0.045 Lymphocyte percentage (%) 10.1(10.0) 7.5(9.6) 0.211 Absolute neutrophil count (×10⁹/L) 11.6(10.4) 10.4(7.7) 0.565 Neutrophil percentage (%) 82.8(13.0) 86.5(11.1) 0.149 Procalcitonin (ng/mL) 13.8(27.4) 19.6(32.8) 0.304 C-Reactive Protein (mg/L) 129.2(108.2) 159.3(125.3) 0.181 Interleukin-6 (pg/mL) 1425.0(3572.6) 1911.6(2905.2) 0.488 Interleukin-8 (pg/mL) 1365.5(4435.1) 1769.2(2644.9) 0.654 CD4 + T lymphocyte/CD8 + T lymphocyte 1.7(1.2) 2.5(2.8) 0.006 Other laboratory indicators Hemoglobin (g/L) 99.8(27.7) 100.0(32.1) 0.978 Hematocrit (%) 31.1(8.5) 29.7(11.2) 0.416 Platelet Count (×10⁹/L) 163.2(118.7) 133.8(103.3) 0.202 Lactate (mmol/L) 3.5(3.5) 5.0(3.9) 0.038 Albumin (g/L) 28.7(7.5) 27.8(7.2) 0.538 Alanine aminotransferase (U/L) 90.4(212.7) 106.6(195.4) 0.695 Aspartate aminotransferase (U/L) 188.5(454.6) 302.6(628.6) 0.239 Lactate dehydrogenase (U/L) 742.7(1155.1) 1072.1(1390.3) 0.166 Creatinine (µmol/L) 179.1(206.8) 205.0(163.3) 0.515 Blood Urea Nitrogen (mmol/L) 14.0(12.4) 11.8(5.7) 0.340 Prothrombin time (seconds) 18.0(9.3) 16.4(4.1) 0.368 Activated partial thromboplastin time (seconds) 51.3(45.6) 52.0(48.4) 0.940 International normalized ratio 1.5(0.9) 1.4(0.4) 0.515 D-Dimer (mg/L FEU) 13.2(20.3) 13.7(16.2) 0.903 N-terminal pro-brain natriuretic peptide (pg/mL) 6212.6(8852.1) 8834.1(11700.6) 0.174 Continuous variables are presented as mean ± SD. Categorical variables are expressed as N (%) To identify independent risk factors for severe ALI, we incorporated into the regression model the previously observed indicators showing statistically significant differences between severe and non-severe ALI (P < 0.1) along with age and gender. The results indicated that, besides SOFA score (OR = 1.21, P = 0.015) and oxygen support modality (OR = 1.54, P = 0.045), the CD4⁺/CD8⁺ ratio was independently associated with severe ALI (OR = 1.40, P = 0.019). However, after adjusting for confounding factors, other indicators showed no statistically significant association with increased risk of severe ALI (Table 3 ). Table 3 The independent risk factor of severe acute lung injury Variables OR (95%CI) P value Age (years) 1.01 (0.98–1.04) 0.518 Female(N, %) 1.17 (0.41–3.31) 0.765 Length of hospital stay (days) 1.01 (1.00-1.02) 0.095 APACHE II score 0.99 (0.92–1.07) 0.791 SOFA score 1.21 (1.04–1.40) 0.015 Systolic blood pressure (mmHg) 0.99 (0.97–1.01) 0.449 Oxygen inhalation methods 1.54 (1.01–2.34) 0.045 Absolute lymphocyte count (×10⁹/L) 0.37 (0.13–1.08) 0.068 CD4 + T lymphocyte/CD8 + T lymphocyte 1.40 (1.06–1.86) 0.019 Lactate dehydrogenase (U/L) 1.03 (0.89–1.20) 0.663 To further investigate the linear relationship between the CD4⁺/CD8⁺ T-cell ratio and severe ALI, we employed a restricted cubic spline model. The results demonstrated a linear association between the CD4⁺/CD8⁺ T-cell ratio and the occurrence of severe ALI (nonlinearity test P = 0.757; Fig. 1 B). After adjusting for factors including age, gender, APACHE II score, SOFA score, systolic blood pressure, serum lactate level, absolute lymphocyte count, respiratory support modality, use of vasopressors, and use of corticosteroids, the CD4⁺/CD8⁺ T-cell ratio was associated with a 1.45-fold higher risk of severe ALI (95% CI: 1.07–1.94, P = 0.015) (Fig. 1 C). 4. Discussion ALI is a common clinical emergency and critical illness, with severe ALI exhibiting a mortality rate as high as 40%. The balance between pro-inflammatory and pro-resolving T-cell phenotypes plays a crucial role in the pathophysiology of ALI. This study found that the CD4⁺/CD8⁺ T-cell ratio—an indicator reflecting T-cell homeostasis—is an independent risk factor for ALI severity. To our knowledge, this finding has not been extensively reported either domestically or internationally and holds significant value for the assessment of ALI severity. Immunological dysregulation and pulmonary inflammation play key roles in the pathogenesis of ALI [13] . During acute lung injury, alveolar macrophages activate inflammatory cascades by recognizing pathogens or injury signals. This triggers the release of large amounts of pro-inflammatory cytokines and the recruitment of neutrophils, which generate neutrophil extracellular traps(NETs) and reactive oxygen species (ROS) to compromise the alveolar-capillary barrier. These neutrophils further activate NLRP3 inflammasomes, promoting the maturation and release of IL-1β to amplify the inflammatory response [14–16] . Concurrently, adaptive immune responses, including the balance between pro-inflammatory and pro-resolving T-cell phenotypes, are also involved in the pathophysiology of ALI [17] . These pathophysiological processes can be partially reflected in quantitative and qualitative changes in peripheral blood immune cell sub-populations. A study by Yu et al. demonstrated that pro-inflammatory effector T cells, such as Th17 cells, are associated with an increased risk of poor prognosis in ARDS [8] . Regulatory T cells (Tregs), generally considered the CD4⁺ T-cell subset that suppress inflammatory responses, have been experimentally shown to exacerbate inflammation and mortality when their lung tissue numbers decrease [18,19] . Clinical studies have found decreased Treg cell counts in alveolar tissue [9,10] but increased levels in peripheral blood [9] , a phenomenon likely linked to diminished Treg cell migration capacity to lung tissue. CD8⁺ T cells are crucial for protective immunity against intracellular pathogens. Research by Lei et al. Indicates that decreased peripheral blood CD8⁺ T-cell count correlate with worse outcomes in ARDS [11] . Current evidence suggests that during the progression of ALI, there may be an increase in peripheral blood CD4⁺ T cells accompanied by a reduction in CD8⁺ T cells. The CD4⁺/CD8⁺ T-cell ratio integrates dynamic balance information between CD4⁺ and CD8⁺ T cells and is currently used clinically as a key indicator of immune homeostasis. However, its role in evaluating the severity of ALI has not been thoroughly investigated. This study aimed to clarify the quantitative relationship between the CD4⁺/CD8⁺ T-cell ratio and the severity of ALI by enrolling patients with ALI. The results demonstrated that the peripheral blood CD4⁺/CD8⁺ T-cell ratio was significantly elevated in patients with severe ALI and exhibited a linear correlation with disease severity. After multivariate adjustment (including age, APACHE II score, corticosteroid use, etc.), the CD4⁺/CD8⁺ T-cell ratio remained independently associated with severe ALI. Our findings are consistent with previous observations showing an increased proportion of CD4⁺ T-cell subsets (Th17 and Treg cells) and a decreased level of CD8⁺ T cells in peripheral blood. Although the CD4⁺/CD8⁺ T-cell ratio does not provide precise subtyping of CD4⁺ or CD8⁺ T-cell subsets, it is easily accessible in clinical practice and may serve as an important factor for early risk stratification in ALI. This study has several limitations. First, the cross-sectional design of this study only allows analysis of correlations between variables, making it difficult to establish causal relationships between independent risk factors and outcomes. Future longitudinal studies are warranted to further explore these relationships. Second, the sample size was relatively limited, and the study was conducted at a single center. Expanding the sample size in future research would help validate the stability of the results. Third, although known confounding variables were adjusted for in the analysis, unmeasured or unaccounted factors (such as environmental or behavioral details) may still exist, which could influence the interpretation of the results. In summary, this study analyzed patients with ALI admitted to the intensive care unit, and found that the CD4⁺/CD8⁺ T-cell ratio, an indicator of T-cell homeostasis, was an independent risk factor for severe ALI. These findings suggest that T lymphocyte subsets may contribute to the assessment of ALI severity. Abbreviations ALI acute lung injury ARDS acute respiratory distress syndrome Tregs Regulatory T cells RCS restricted cubic spline ECMO extracorporeal membrane oxygenation NET sneutrophil extracellular traps ROS reactive oxygen species Declarations Ethics approval and consent to participate This study was approved by the First Affiliated Hospital of Chongqing Medical University. As this was a retrospective study, the informed consent was waived by the Institutional Review Board of the First Affiliated Hospital of Chongqing Medical University. This study was performed in accordance with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China [82501003]. Author contributions N.Shen,Q.Huang and C.Luo contributed to experimental data collection.N.Shen and X.Chen contributed to the data analysis and wrote the article.N.Shen and C.Luo designed the study,oversaw the data analysis,and interpreted the data.All authors read and approved the final manuscript. Acknowledgments The authors thank Jinbo Hu,MD,phD,for suggestions on study design and revisions. References Fan Eddy, Brodie Daniel, Slutsky Arthur S. Acute Respiratory Distress Syndrome: Advances in Diagnosis and Treatment. JAMA . 2018 Feb 20;319(7):698–710. Ellen A Gorman, Cecilia M O'Kane, Daniel F McAuley. 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Curr Opin Crit Care . 2018 Feb;24(1):1–9. Kapur Rick, Kim Michael, Aslam Rukhsana, et al. T regulatory cells and dendritic cells protect against transfusion-related acute lung injury via IL-10. Blood . 2017 May 4;129(18): 2557–2569. Li Ruiting, Zhang Jiancheng, Pan Shangwen ,et al. HMGB1 aggravates lipopolysaccharide- induced acute lung injury through suppressing the activity and function of Tregs. Cell Immunol . 2020 Oct:356:104192. Additional Declarations No competing interests reported. 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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-8426596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587683887,"identity":"b25ee83d-0edd-4ac6-9782-ff0242221109","order_by":0,"name":"Na Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Shen","suffix":""},{"id":587683889,"identity":"7bae22b0-df99-447c-85b5-716ec5960685","order_by":1,"name":"Xiangjun Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangjun","middleName":"","lastName":"Chen","suffix":""},{"id":587683896,"identity":"6511219c-5ba9-40f2-bd04-6bdf1e9b0da2","order_by":2,"name":"Qingqing Huang","email":"","orcid":"","institution":"Chongqing Western Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qingqing","middleName":"","lastName":"Huang","suffix":""},{"id":587683897,"identity":"a01448ce-7016-4f6e-84c1-f9283f27fc71","order_by":3,"name":"Cheng Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGUlEQVRIie3RsUrEMBjA8a8Eekts1xyBu1fIUbhFvb5KS6BThU7iIFgoZHWNbyEIzkrgXA7n6N3S5V6g6yEmrXVq9cYb8qcNIe2PkhTA5TrBAnMjOwmJHVm30vUyTPyeTGVLWLtyHGEaus/8Twh/bYqb3eppW6mmKA5zH1D9+SFgFujEa4ohknEqN3v+vFtnVDK2EOBH51cCoqlOEJVDJF+iM6H4UpsJZswTgH1qSPqoEx/hMfKleCTzqDEk7snd36RUK0ZyRg1Je5KwMYL3nOK1SojO7F4iLpDZS/5OFg+buqIDJJyYE8O3Kg4lNyd2mF3eT6p6m19fzIM3+2j4oG1pCT8/yI6kvcArxwFADL8EuvddLpfL1fcNURlYgkPJswUAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-12-22 15:39:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8426596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8426596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102239217,"identity":"d8d156d5-e3b0-4bc6-9600-f5c17dbe85e2","added_by":"auto","created_at":"2026-02-09 16:43:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":337583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between CD4+ T lymphocyte/CD8+ T lymphocyte and severe acute lung injury.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Plasma CD4+ T lymphocyte/CD8+ T lymphocyte among participants non-severe severe acute lung injury and severe acute lung injury, with DM alone, with CKD alone, and with DM and CKD at baseline. \u003cstrong\u003eB\u003c/strong\u003e. Hazard ratio analysis using a restricted cubic spline (4 knots) for participants, with non-linearity tested via likelihood ratio test comparing the spline model to a linear model.. \u003cstrong\u003eC\u003c/strong\u003e. The association of plasma CD4+ T lymphocyte/CD8+ T lymphocyte with severe acute lung injury.\u003c/p\u003e\n\u003cp\u003eModel 1: adjust for age and sex.\u003c/p\u003e\n\u003cp\u003eModel 2: further adjust for APACHE II score and SOFA score.\u003c/p\u003e\n\u003cp\u003eModel 3: further adjust for Length of hospital stay, SBP, absolute lymphocyte count, serum lactate, oxygen inhalation methods, vasopressor and corticosteroid therapy.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8426596/v1/349af5a254f8d641a87f3751.jpeg"},{"id":102404061,"identity":"992c8a7a-12c3-4682-8b16-e1348dc2175d","added_by":"auto","created_at":"2026-02-11 10:58:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1256193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8426596/v1/ee22b7c5-58ce-4e5a-b981-c5b860f1e05b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The CD4⁺/CD8⁺ ratio is independently associated with severe acute lung injury","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute lung injury (ALI) is an acute inflammatory process characterized by inflammatory cell infiltration, leading to severe damage of the alveolar epithelium and alveolar-capillary membrane, increased vascular permeability, and pulmonary interstitial/alveolar edema. It often presents as acute onset respiratory distress, refractory hypoxemia, and bilateral pulmonary infiltrates \u003csup\u003e[1,2]\u003c/sup\u003e. The incidence of ALI in intensive care unit patients is approximately 10% \u003csup\u003e[3]\u003c/sup\u003e. Among these, about 15%\u0026ndash;25% of patients suffer from severe ALI (PaO₂/FiO₂ \u0026le; 100 mmHg), which is associated with a significantly higher risk of multi-organ failure, a greatly increased need for invasive mechanical ventilation, and a mortality rate as high as 46.1% compared to non-severe ALI \u003csup\u003e[3\u0026ndash;5]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe core of ALI progression lies in the imbalance of inflammatory responses exacerbating pulmonary epithelial and endothelial injury \u003csup\u003e[5]\u003c/sup\u003e. The inflammatory process driving lung injury is complex. Beyond the role of neutrophils, the balance between pro-inflammatory and pro-resolving T-cell phenotypes is involved in the pathophysiology of ALI \u003csup\u003e[6,7]\u003c/sup\u003e. Studies have shown that an increased proportion of the pro-inflammatory T-cell subset Th17 is associated with a higher risk of poor outcomes in acute respiratory distress syndrome (ARDS) \u003csup\u003e[8]\u003c/sup\u003e. Regulatory T cells(Tregs), generally known as a CD4⁺ T-cell subset that suppresses inflammatory responses, have also been observed to be elevated in the peripheral blood of ARDS patients in some studies \u003csup\u003e[9]\u003c/sup\u003e, and are associated with poor prognosis in ARDS \u003csup\u003e[10]\u003c/sup\u003e. CD8⁺ T cells are crucial for protective immunity against intracellular pathogens, and some research suggests that a reduced CD8⁺ T-cell count is linked to worse outcomes in ARDS \u003csup\u003e[11]\u003c/sup\u003e. The peripheral blood CD4⁺/CD8⁺ T-cell ratio, which combines the counts of both CD4 and CD8 cells, is a readily available clinical indicator reflecting T-cell homeostasis. However, whether the CD4⁺/CD8⁺ T-cell ratio can reflect the progression of ALI and serve as an indicator for assessing its severity has not yet been reported.\u003c/p\u003e \u003cp\u003eBased on this, our study aims to enroll ALI patients to clarify the quantitative relationship between the CD4⁺/CD8⁺ T-cell ratio and the severity of acute lung injury. The findings will provide a new basis for early risk stratification and lay a theoretical foundation for the development of immune-targeted therapeutic strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003e This retrospective cohort study utilized anonymously extracted data from patients admitted to the department of Critical Care Medicine who were diagnosed with ALI at the First Affiliated Hospital of Chongqing Medical University between June 2022 and October 2023. The Inclusion criteria were as follows: Patients met the 2012 Berlin Diagnostic Criteria for ARDS (2012 Berlin Diagnostic Criteria: presence of ARDS risk factors; new or worsening respiratory symptoms within 1 week of onset; bilateral diffuse pulmonary infiltrates unexplained by pleural effusion, lobar/lung collapse, or nodules; respiratory failure not fully attributable to heart failure or fluid overload; PaO₂/FiO₂ \u0026le; 300 mmHg with PEEP\u0026thinsp;\u0026ge;\u0026thinsp;5 cmH₂O) \u003csup\u003e[12]\u003c/sup\u003e. The diagnosis of ALI was screened by experienced intensive care physicians and radiologists based on the Berlin Diagnostic Criteria. Exclusion criteria comprised: (1) Age under 18 years, (2) unavailability of CD4⁺/CD8⁺ T-cell ratio records, or (3) history of confirmed primary/acquired immunosuppression. Based on the inclusion and exclusion criteria, a total of 178 subjects were included in this study. As this was a retrospective study, the informed consent was waived by the Institutional Review Board of the First Affiliated Hospital of Chongqing Medical University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 CD4⁺/CD8⁺ T-cell Ratio\u003c/h2\u003e \u003cp\u003eThe percentages of CD4⁺ and CD8⁺ T-cells were measured using a BD Canto II flow cytometer with lymphocyte subset detection reagents produced by Beijing Kuangbo Biotechnology Co., Ltd. The CD4⁺/CD8⁺ ratio was calculated based on these measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcomes\u003c/h2\u003e \u003cp\u003eSeverity grading was performed according to the Berlin criteria\u003csup\u003e[12]\u003c/sup\u003e. Severe acute lung injury is defined as PaO₂/FiO₂ \u0026le; 100 mmHg, while non-severe acute lung injury is defined as 100 mmHg\u0026thinsp;\u0026lt;\u0026thinsp;PaO₂/FiO₂ \u0026le; 300 mmHg.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eThe clinical characteristics of all participants were meticulously documented by two independent investigators. Data collected included demographic information (age, sex, smoking history), clinical course parameters (length of hospitalization, ICU stay, and duration of mechanical ventilation), and illness severity scores (APACHE II and SOFA). Vital signs included systolic and diastolic blood pressure measurements. Laboratory analyses comprised complete blood count, hepatic and renal function panels, coagulation profile, BNP, and inflammatory biomarkers (procalcitonin, C-reactive protein, interleukin-6, and interleukin-8). Documented comorbidities included impaired consciousness, chronic pulmonary disease, cardiovascular disease, malignancy, diabetes mellitus, chronic liver disease, chronic kidney disease, neurological disorders, and connective tissue disease. Treatments of interest included respiratory support, vasopressor use, sedative agents, and corticosteroid administration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables with approximately normal distributions are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while those with non-normal distributions are described as median (quartile range). Categorical variables are summarized as frequency (percentage), and group comparisons were performed using the chi-square test. Differences in continuous variables between groups were assessed using Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test, as appropriate.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression was employed to examine the independent association between clinical indicators and severe ALI. The model included three core variables: age, gender, and biomarkers showing statistically significant differences between severe and non-severe ALI groups (P\u0026lt;0.1). To explore the potential linear relationship between the CD4⁺/CD8⁺ T-cell ratio and severe ALI, a four-knot restricted cubic spline (RCS) model was used. The likelihood ratio test was applied to compare the model fit under linear and nonlinear assumptions. In analyzing the association between the CD4⁺/CD8⁺ T-cell ratio and severe ALI, three models were constructed. Model 1 adjusted for age and gender. Model 2 further incorporated APACHE II and SOFA scores into Model 1. Model 3 expanded on Model 2 by adding systolic blood pressure, serum lactate level, lymphocyte absolute count, respiratory support modality, vasopressor use, and corticosteroid therapy as covariates.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R version 4.0.3 (R Foundation for Statistical Computing, Vienna). The \u0026ldquo;rms\u0026rdquo; package was used for restricted cubic spline analysis, and the \u0026ldquo;glm\u0026rdquo; function was applied for multivariable logistic regression. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eCompared to the non-severe ALI group, patients in the severe ALI group had significantly higher APACHE II and SOFA scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, there was a significant difference in respiratory support methods between the two groups, with the severe group demonstrating markedly higher rates of invasive mechanical ventilation and extracorporeal membrane oxygenation (ECMO). The severe group also showed trends toward longer hospital stays, a higher proportion of indirect lung injury, and lower systolic blood pressure (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristic of included participants according to the severity of acute lung injury.\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-severe ALI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere ALI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\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\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.5(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.8(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale (N, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking (N, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of hospital stay (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9(26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.4(82.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU length of stay (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4(24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.7(20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of mechanical ventilation (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3(18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.4(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.2(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic blood pressure (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.7(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.7(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRespiratory rate (breaths per minute)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.1(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPACHE II score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4(8.7)\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\u003e\u003cb\u003eSOFA score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1(4.5)\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\u003e\u003cb\u003eDocument etiology of direct pulmonary injury\u003c/b\u003e\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\u003eDirect pulmonary insult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110(74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\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\u003eImpaired consciousness (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic lung disease (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 ( 9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological disorders (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 ( 6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnective tissue disease (N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 ( 2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxygen inhalation methods\u003c/b\u003e\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=\"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\u003eNasal cannula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ( 0.0)\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\u003eFace mask\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (16.1)\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\u003eHigh-flow nasal cannula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 ( 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 ( 0.0)\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\u003eNon-invasive ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9)\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\u003eInvasive mechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (67.7)\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\u003eExtracorporeal membrane oxygenation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 ( 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 ( 3.2)\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\u003e\u003cb\u003eVasopressor (N, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSedative therapy (N, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorticosteroid therapy (N, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding inflammatory markers, the absolute lymphocyte count was significantly lower in the severe group compared to the non-severe group (1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 vs. 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4, P\u0026thinsp;=\u0026thinsp;0.045), while the serum CD4⁺/CD8⁺ T-cell ratio was significantly elevated (1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 vs. 2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8, P\u0026thinsp;=\u0026thinsp;0.006; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). However no significant differences were observed in total white blood cell count, neutrophil percentage, CRP, procalcitonin, IL-6, or IL-8 levels. Among other laboratory parameters, lactate levels were significantly elevated in the severe group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of inflammatory and laboratory indicators of included participants according to the severity of acute lung injury.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-severe ALI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere ALI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.6(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.3(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute lymphocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0(1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.1(10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.5(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute neutrophil count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.6(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.4(7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.8(13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.5(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.8(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.6(32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Reactive Protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.2(108.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159.3(125.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-6 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1425.0(3572.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1911.6(2905.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-8 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1365.5(4435.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1769.2(2644.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T lymphocyte/CD8\u0026thinsp;+\u0026thinsp;T lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.7(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5(2.8)\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\u003e\u003cb\u003eOther laboratory indicators\u003c/b\u003e\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\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.8(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0(32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.1(8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.7(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163.2(118.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133.8(103.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.7(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.8(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.4(212.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106.6(195.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.5(454.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302.6(628.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e742.7(1155.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1072.1(1390.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179.1(206.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205.0(163.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Urea Nitrogen (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.0(12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin time (seconds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.0(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.4(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivated partial thromboplastin time (seconds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.3(45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.0(48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational normalized ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Dimer (mg/L FEU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.2(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.7(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-brain natriuretic peptide (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6212.6(8852.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8834.1(11700.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Categorical variables are expressed as N (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify independent risk factors for severe ALI, we incorporated into the regression model the previously observed indicators showing statistically significant differences between severe and non-severe ALI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1) along with age and gender. The results indicated that, besides SOFA score (OR\u0026thinsp;=\u0026thinsp;1.21, P\u0026thinsp;=\u0026thinsp;0.015) and oxygen support modality (OR\u0026thinsp;=\u0026thinsp;1.54, P\u0026thinsp;=\u0026thinsp;0.045), the CD4⁺/CD8⁺ ratio was independently associated with severe ALI (OR\u0026thinsp;=\u0026thinsp;1.40, P\u0026thinsp;=\u0026thinsp;0.019). However, after adjusting for confounding factors, other indicators showed no statistically significant association with increased risk of severe ALI (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\u003eThe independent risk factor of severe acute lung injury\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\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 \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale(N, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (0.41\u0026ndash;3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.765\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.92\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.791\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21 (1.04\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen inhalation methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.54 (1.01\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute lymphocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.13\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T lymphocyte/CD8\u0026thinsp;+\u0026thinsp;T lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.06\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.89\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the linear relationship between the CD4⁺/CD8⁺ T-cell ratio and severe ALI, we employed a restricted cubic spline model. The results demonstrated a linear association between the CD4⁺/CD8⁺ T-cell ratio and the occurrence of severe ALI (nonlinearity test P\u0026thinsp;=\u0026thinsp;0.757; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). After adjusting for factors including age, gender, APACHE II score, SOFA score, systolic blood pressure, serum lactate level, absolute lymphocyte count, respiratory support modality, use of vasopressors, and use of corticosteroids, the CD4⁺/CD8⁺ T-cell ratio was associated with a 1.45-fold higher risk of severe ALI (95% CI: 1.07\u0026ndash;1.94, P\u0026thinsp;=\u0026thinsp;0.015) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eALI is a common clinical emergency and critical illness, with severe ALI exhibiting a mortality rate as high as 40%. The balance between pro-inflammatory and pro-resolving T-cell phenotypes plays a crucial role in the pathophysiology of ALI. This study found that the CD4⁺/CD8⁺ T-cell ratio\u0026mdash;an indicator reflecting T-cell homeostasis\u0026mdash;is an independent risk factor for ALI severity. To our knowledge, this finding has not been extensively reported either domestically or internationally and holds significant value for the assessment of ALI severity.\u003c/p\u003e \u003cp\u003eImmunological dysregulation and pulmonary inflammation play key roles in the pathogenesis of ALI \u003csup\u003e[13]\u003c/sup\u003e. During acute lung injury, alveolar macrophages activate inflammatory cascades by recognizing pathogens or injury signals. This triggers the release of large amounts of pro-inflammatory cytokines and the recruitment of neutrophils, which generate neutrophil extracellular traps(NETs) and reactive oxygen species (ROS) to compromise the alveolar-capillary barrier. These neutrophils further activate NLRP3 inflammasomes, promoting the maturation and release of IL-1β to amplify the inflammatory response\u003csup\u003e[14\u0026ndash;16]\u003c/sup\u003e. Concurrently, adaptive immune responses, including the balance between pro-inflammatory and pro-resolving T-cell phenotypes, are also involved in the pathophysiology of ALI \u003csup\u003e[17]\u003c/sup\u003e. These pathophysiological processes can be partially reflected in quantitative and qualitative changes in peripheral blood immune cell sub-populations. A study by Yu et al. demonstrated that pro-inflammatory effector T cells, such as Th17 cells, are associated with an increased risk of poor prognosis in ARDS \u003csup\u003e[8]\u003c/sup\u003e. Regulatory T cells (Tregs), generally considered the CD4⁺ T-cell subset that suppress inflammatory responses, have been experimentally shown to exacerbate inflammation and mortality when their lung tissue numbers decrease\u003csup\u003e[18,19]\u003c/sup\u003e. Clinical studies have found decreased Treg cell counts in alveolar tissue \u003csup\u003e[9,10]\u003c/sup\u003e but increased levels in peripheral blood \u003csup\u003e[9]\u003c/sup\u003e, a phenomenon likely linked to diminished Treg cell migration capacity to lung tissue. CD8⁺ T cells are crucial for protective immunity against intracellular pathogens. Research by Lei et al. Indicates that decreased peripheral blood CD8⁺ T-cell count correlate with worse outcomes in ARDS \u003csup\u003e[11]\u003c/sup\u003e. Current evidence suggests that during the progression of ALI, there may be an increase in peripheral blood CD4⁺ T cells accompanied by a reduction in CD8⁺ T cells.\u003c/p\u003e \u003cp\u003eThe CD4⁺/CD8⁺ T-cell ratio integrates dynamic balance information between CD4⁺ and CD8⁺ T cells and is currently used clinically as a key indicator of immune homeostasis. However, its role in evaluating the severity of ALI has not been thoroughly investigated. This study aimed to clarify the quantitative relationship between the CD4⁺/CD8⁺ T-cell ratio and the severity of ALI by enrolling patients with ALI. The results demonstrated that the peripheral blood CD4⁺/CD8⁺ T-cell ratio was significantly elevated in patients with severe ALI and exhibited a linear correlation with disease severity. After multivariate adjustment (including age, APACHE II score, corticosteroid use, etc.), the CD4⁺/CD8⁺ T-cell ratio remained independently associated with severe ALI. Our findings are consistent with previous observations showing an increased proportion of CD4⁺ T-cell subsets (Th17 and Treg cells) and a decreased level of CD8⁺ T cells in peripheral blood. Although the CD4⁺/CD8⁺ T-cell ratio does not provide precise subtyping of CD4⁺ or CD8⁺ T-cell subsets, it is easily accessible in clinical practice and may serve as an important factor for early risk stratification in ALI.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the cross-sectional design of this study only allows analysis of correlations between variables, making it difficult to establish causal relationships between independent risk factors and outcomes. Future longitudinal studies are warranted to further explore these relationships. Second, the sample size was relatively limited, and the study was conducted at a single center. Expanding the sample size in future research would help validate the stability of the results. Third, although known confounding variables were adjusted for in the analysis, unmeasured or unaccounted factors (such as environmental or behavioral details) may still exist, which could influence the interpretation of the results.\u003c/p\u003e \u003cp\u003eIn summary, this study analyzed patients with ALI admitted to the intensive care unit, and found that the CD4⁺/CD8⁺ T-cell ratio, an indicator of T-cell homeostasis, was an independent risk factor for severe ALI. These findings suggest that T lymphocyte subsets may contribute to the assessment of ALI severity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eALI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eacute lung injury\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eacute\u0026nbsp;respiratory\u0026nbsp;distress\u0026nbsp;syndrome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTregs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegulatory T cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRCS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003erestricted cubic spline\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECMO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eextracorporeal membrane oxygenation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNET\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esneutrophil extracellular traps\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ereactive oxygen species\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the First Affiliated Hospital of Chongqing Medical University. As this was a retrospective study, the informed consent was waived by the Institutional Review Board of the First Affiliated Hospital of Chongqing Medical University. This study was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [82501003].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.Shen,Q.Huang and C.Luo contributed to experimental data collection.N.Shen and X.Chen contributed to the data analysis and wrote the article.N.Shen and C.Luo designed the study,oversaw the data analysis,and interpreted the data.All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Jinbo Hu,MD,phD,for suggestions on study design and revisions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Fan Eddy, Brodie Daniel, Slutsky Arthur S. Acute Respiratory Distress Syndrome: Advances in Diagnosis and Treatment.\u003cem\u003eJAMA\u003c/em\u003e. 2018 Feb 20;319(7):698\u0026ndash;710.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Ellen A Gorman, Cecilia M O'Kane, Daniel F McAuley. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. \u003cem\u003eLancet\u003c/em\u003e. 2022 Oct 1;400 (10358):1157\u0026ndash;1170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Bellani Giacomo, Laffey John G, Pham T\u0026agrave;i ,et al. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. \u003cem\u003eJAMA\u003c/em\u003e. 2016 Feb 23;315(8): 788\u0026ndash;800.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Rubenfeld Gordon D, Caldwell Ellen, Peabody Eve ,et al. Incidence and outcomes of acute lung injury. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2005 Oct 20;353(16):1685-93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Katherine D Wick, Lorraine B Ware, Michael A Matthay. Acute respiratory distress syndrome. \u003cem\u003eBMJ\u003c/em\u003e. 2024 Oct 28:387:e076612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Reiss Lucy K, Schuppert Andreas, Uhlig Stefan. Inflammatory processes during acute respiratory distress syndrome: a complex system. \u003cem\u003eCurr Opin Crit Care\u003c/em\u003e. 2018 Feb;24(1):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e S\u0026eacute;bastien Halter, Michelle Rosenzwajg, David Klatzmann. Regulatory T Cells in Acute Respiratory Distress Syndrome: Current Status and Potential for Future Immunotherapies. \u003cem\u003eAnesthesiology\u003c/em\u003e. 2024 Oct 1;141(4):755\u0026ndash;764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Yu Zhi-xin, Ji Mu-sen, Yan Jun, et al. The ratio of Th17/Treg cells as a risk indicator in early acute respiratory distress syndrome. \u003cem\u003eCrit Care\u003c/em\u003e. 2015 Mar 11;19(1):82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Halter Sebastien, Aimade Lucr\u0026egrave;ce, Barbi\u0026eacute; Mich\u0026egrave;le, et al. T regulatory cells activation and distribution are modified in critically ill patients with acute respiratory distress syndrome: A prospective single-centre observational study. \u003cem\u003eAnaesth Crit Care Pain Med\u003c/em\u003e. 2020 Feb;39(1):35\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Adamzik Michael, Broll Jasmin, Steinmann J\u0026ouml;rg, et al. An increased alveolar CD4\u0026thinsp;+\u0026thinsp;CD25\u0026thinsp;+\u0026thinsp;Foxp3\u0026thinsp;+\u0026thinsp;T-regulatory cell ratio in acute respiratory distress syndrome is associated with increased 30-day mortality. \u003cem\u003eIntensive Care Med\u003c/em\u003e. 2013 Oct;39(10):1743-51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Lei Yan, Yumei Chen, Yi Han ,et al. Role of CD8 T cell exhaustion in the progression and prognosis of acute respiratory distress syndrome induced by sepsis: a prospective observational study. \u003cem\u003eBMC Emerg Med\u003c/em\u003e. 2022 Nov 19;22(1):182.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Ranieri V Marco, Rubenfeld Gordon D, Thompson B Taylor ,et al. Acute respiratory distress syndrome: the Berlin Definition. \u003cem\u003eJAMA\u003c/em\u003e. 2012 Jun 20;307(23):2526-33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Matthay Michael A, Zemans Rachel L, Zimmerman Guy A ,et al. Acute respiratory distress syndrome. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e. 2019 Mar 14;5(1):18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Williams Andrew E, Chambers Rachel C. The mercurial nature of neutrophils: still an enigma in ARDS? \u003cem\u003eAm J Physiol Lung Cell Mol Physiol\u003c/em\u003e. 2014 Feb;306(3):L217-30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Lefran\u0026ccedil;ais Emma, Mallavia Be\u0026ntilde;at, Zhuo Hanjing ,et al. Maladaptive role of neutrophil extracellular traps in pathogen-induced lung injury. \u003cem\u003eJCI Insight\u003c/em\u003e. 2018 Feb 8;3(3):e98178.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Eun Yeong Lim, So-Young Lee, Hee Soon Shin ,et al. Reactive Oxygen Species and Strategies for Antioxidant Intervention in Acute Respiratory Distress Syndrome. \u003cem\u003eAntioxidants (Basel)\u003c/em\u003e. 2023 Nov 18;12(11):2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Reiss Lucy K, Schuppert Andreas, Uhlig Stefan. Inflammatory processes during acute respiratory distress syndrome: a complex system. \u003cem\u003eCurr Opin Crit Care\u003c/em\u003e. 2018 Feb;24(1):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Kapur Rick, Kim Michael, Aslam Rukhsana, et al. T regulatory cells and dendritic cells protect against transfusion-related acute lung injury via IL-10. \u003cem\u003eBlood\u003c/em\u003e. 2017 May 4;129(18): 2557\u0026ndash;2569.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Li Ruiting, Zhang Jiancheng, Pan Shangwen ,et al. HMGB1 aggravates lipopolysaccharide- induced acute lung injury through suppressing the activity and function of Tregs. \u003cem\u003eCell Immunol\u003c/em\u003e. 2020 Oct:356:104192.\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":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CD4⁺/CD8⁺ ratio, ALI, ARDS, Berlin Diagnostic Criteria","lastPublishedDoi":"10.21203/rs.3.rs-8426596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8426596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To investigate the association between the CD4⁺/CD8⁺ T-cell ratio and severe acute lung injury (ALI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Following the 2012 Berlin Definition of severe ALI, 31 patients with severe ALI admitted to the Department of Critical Care Medicine at the First Affiliated Hospital of Chongqing Medical University from June 2022 to October 2023 were enrolled as the study subjects (severe ALI group). while 147 non-severe ALI patients served as the control group during the same period. Clinical data were collected, and the serum CD4⁺/CD8⁺ T-cell ratio were analyzed using flow cytometry. The CD4⁺/CD8⁺ T-cell ratio levels were compared between the severe and the non-severe ALI groups. Linear relationships between the CD4⁺/CD8⁺ T-cell ratio and severe ALI were established through restricted cubic spline modeling, and multivariate logistic regression was employed to analyze their association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Patients with severe ALI had significantly higher APACHE II scores, SOFA scores, serum lactate levels, and CD4⁺/CD8⁺ T-cell ratios than the non-severe group, while the absolute lymphocyte count was significantly lower (all P \u0026lt; 0.05). The proportion of patients receiving invasive mechanical ventilation and ECMO was significantly higher in the severe group. Multivariate logistic regression showed that, in addition to the SOFA score (OR = 1.21, P = 0.015) and oxygenation mode (OR = 1.54, P = 0.045), the CD4⁺/CD8⁺ ratio was independently associated with severe ALI (OR = 1.40, P = 0.019). Restricted cubic spline analysis confirmed a linear relationship between the CD4⁺/CD8⁺ ratio and disease severity (non-linear P = 0.757). After further correction of the use of vasopressors and corticosteroids, the increase in the CD4⁺/CD8⁺ ratio increased the risk of severe lung injury by 1.45 times (OR = 1.45, 95% CI: 1.07–1.94, P\u003csub\u003eadj\u003c/sub\u003e = 0.015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The CD4⁺/CD8⁺ T-cell ratio is an independent risk factor for severe ALI.\u003c/p\u003e","manuscriptTitle":"The CD4⁺/CD8⁺ ratio is independently associated with severe acute lung injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 16:43:30","doi":"10.21203/rs.3.rs-8426596/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-18T09:01:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T03:43:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T18:46:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210795854842290785485671591707110908023","date":"2026-02-14T06:59:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T14:45:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85670066910657965473105346895980525167","date":"2026-02-08T10:52:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318487369510714471705844051248337414632","date":"2026-02-07T16:04:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208119719574978446193778360781191236518","date":"2026-02-07T09:42:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T04:00:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T08:06:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-12T11:24:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-10T08:31:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-01-10T08:25:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18b52603-1553-4c38-8605-e978ecfe7f64","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:43:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 16:43:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8426596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8426596","identity":"rs-8426596","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}