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. 2025 Mar 1:18:1163-1172.
doi: 10.2147/IJGM.S501252. eCollection 2025.

Dynamic Immune Indicator Changes as Predictors of ARDS in ICU Patients with Sepsis: A Retrospective Study

Xiaochi Lu  1 , Yi Chen  1 , Gongping Zhang  1 , Xu Zeng  1 , Linjie Lai  1 , Chaojun Qu  2
Affiliations

Dynamic Immune Indicator Changes as Predictors of ARDS in ICU Patients with Sepsis: A Retrospective Study

Xiaochi Lu et al. Int J Gen Med. .

Abstract

Background: Understanding the dynamic changes in immune indicators during sepsis and their predictive value for Acute respiratory distress syndrome (ARDS) is crucial for improving patient outcomes.

Methods: This single-center, observational retrospective study was conducted at Lishui Central Hospital, Zhejiang Province. Patients diagnosed with Sepsis-3 were categorized into non-ARDS and ARDS groups based on ARDS development. Data collection included demographics, clinical data, and immune parameters. Immune parameters were collected on days 1, 3, and 7 post-admission. Multivariate logistic regression analysis identified independent risk factors for ARDS, and a nomogram model was constructed. The predictive ability of the model was evaluated using ROC curves.

Results: Multivariate analysis identified key factors for the nomogram, including CD4, CD8, Treg, lymphocyte, IgG, and IgA levels on Days 3 and 7. On Day 3, CD8 (P < 0.001), Tregs (P = 0.021), IgG (P < 0.001), and IgA (P < 0.001) showed significant negative correlations with ARDS development. On Day 7, CD4 (P < 0.001), CD8 (P < 0.001), lymphocyte count (P < 0.001), and IgA (P < 0.001) similarly demonstrated significant negative correlations with ARDS risk. The nomogram model had an AUC of 0.998 (95% CI: 0.997-0.999), indicating high predictive ability.

Conclusion: Early dynamic changes in immune indicators, including CD8, CD4, Treg, IgA, IgG, and Lymphocyte, predict ARDS development in ICU sepsis patients.

Keywords: acute respiratory distress syndrome; immune indicator dynamics; intensive care unit; nomograph; sepsis.

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Conflict of interest statement

The authors have no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Study flow chart.
Figure 2
Figure 2
Box plot of immune-related indicators. (A) CD4+cells. (B) CD8+cells. (C) Lymphocytes. (D) Neutrophils. (E) Treg cells. (F) IgA. (G) IgG. The hollow rectangles are the averages, and the solid diamonds are the outliers. ***: p < 0.001.
Figure 3
Figure 3
Nomogram comparison and evaluation. (A) Forest plot. The dashed line (OR=1) is an invalid line. Squares represent OR values for each feature, with horizontal lines showing 95% CI. Left of the invalid line indicates a negative relationship; Otherwise, the relationship is positive. (B) Nomograph. Each variable corresponds to a scale that represents its contribution to the total score. By summing the scores of each variable, the total score is obtained. (C) Calibration curve. The curve shows predicted vs observed probabilities. An ideal curve is close to the 45-degree line, indicating high prediction accuracy. (D) ROC curve of the scoring model. AUC close to 1 indicates strong discriminative ability of the model. (E) DCA curve. Model 1: Nomogram. Model 2: APACHEII. Model 3: SOFA. The X-axis represents the threshold predicted by the model, and the Y-axis represents the net benefit. The higher the net benefit, the better the utility performance of the prediction model.
None
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References

    1. Salomão R, Ferreira B, Salomão M, et al. Sepsis: evolving concepts and challenges. Braz J Med Biol Res. 2019;52:e8595. doi:10.1590/1414-431X20198595 - DOI - PMC - PubMed
    1. Watkins RR, Bonomo RA, Rello J. Managing sepsis in the era of precision medicine: challenges and opportunities. Expert Rev Anti Infect Ther 2022;20(6):871–880. - PubMed
    1. Ahmed MM, Zaki A, Alhazmi A, et al. Identification and validation of pathogenic genes in sepsis and associated diseases by integrated bioinformatics approach. Genes. 2022;13(2):209. doi:10.3390/genes13020209 - DOI - PMC - PubMed
    1. Sygitowicz G, Sitkiewicz D. Organ damage in sepsis: molecular mechanisms. Infect Sepsis Develop. 2021:263. - PMC - PubMed
    1. Gong H, Chen Y, Chen M, et al. Advanced development and mechanism of sepsis-related acute respiratory distress syndrome. Front Med. 2022;9:1043859. doi:10.3389/fmed.2022.1043859 - DOI - PMC - PubMed

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