High systemic immune inflammation index values are associated with prolonged length of hospital stay in patients with acute exacerbation of chronic obstructive pulmonary disease: a retrospective cohort study

Objectives: This study aimed to investigate the association between the Systemic Immune Inflammation Index (SII) and the prolonged length of hospital stay (PLOS) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

Methods: A retrospective analysis was conducted involving 986 patients aged ≥ 40 years with AECOPD admitted to Kailuan General Hospital between January 2018 and December 2024. PLOS was defined as a stay exceeding 7 days. Complete blood counts were collected within 24 h of admission to calculate the SII, which was the log-transformed and denoted as In-SII. Logistic regression analysis was employed to compare the predictive value of In-SII and In-NLR (neutrophil-to-lymphocyte ratio) for PLOS in patients with AECOPD. Additionally, restricted cubic splines (RCS) and decision curves analysis (DCA) were utilized to explore the nonlinear relationship and clinical net benefit between In-SII and PLOS in patients with AECOPD.

Results: In-SII was an independent risk factor for PLOS in patients with AECOPD (odds ratios for Model 1, Model 2, and Model 3 were 1.527, 1.294, and 1.496, respectively; p < 0.05). Its predictive performance is superior to In-NLR. According to RCS curves, there was a linear association between In-SII and PLOS in patients with AECOPD (Model 1: p for nonlinear = 0.664; Model 2: p for nonlinear = 0.663; Model 3: p for nonlinear = 0.571). Additionally, DCA indicated a significant net clinical benefit when the In-SII threshold ranged from 0.41 to 0.80.

Conclusion: High SII serves as an independent risk factor for PLOS in patients with AECOPD. This indicated that patients with AECOPD exhibiting high SII levels have poorer outcomes, necessitating earlier implementation of more robust intervention measures and close monitoring of disease progression.

Introduction

Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disorder characterized by persistent airflow limitation, posing a significant threat to global health (1). As the third leading cause of death worldwide, COPD accounts for approximately three million deaths annually (2). Acute exacerbations of COPD (AECOPD) are common events in the disease course, with about half of COPD patients experiencing at least one exacerbation per year (3, 4). AECOPD not only accelerates disease progression and reduces quality of life but also imposes substantial economic burdens on patients and healthcare systems (5). Such episodes typically require medical intervention for resolution, placing immense strain on already limited healthcare resources (6).

Length of hospital stay (LOS) serves as a key indicator for assessing resource consumption and economic burden in AECOPD patients, while also closely correlating with disease severity and clinical prognosis (7, 8). Prolonged LOS (PLOS) translates to higher medical costs and poorer clinical outcomes (9). Consequently, identifying biomarkers capable of effectively predicting PLOS is critically important, holding significant value for early intervention and improving patient prognosis. Currently, traditional inflammatory markers such as C-reactive protein (CRP) and Leukocyte count, along with novel inflammatory indicators like the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), have been employed to assess AECOPD prognosis (10–13). However, their predictive value remains limited.

The Systemic Immune Inflammation Index (SII) is a novel peripheral blood biomarker integrating platelet count, neutrophil count, and lymphocyte count to reflect both local immune responses and systemic inflammatory states. This index was first proposed by Hu et al. in 2014 (14). Since then, SII has been extensively validated as a robust prognostic indicator in gastric cancer, non-small cell lung cancer, and urothelial carcinoma (15–17). However, relevant research in the field of AECOPD remains relatively limited. Therefore, this study aims to explore the association between SII and PLOS through a retrospective analysis of 986 AECOPD patients, providing evidence for early risk identification and optimization of treatment strategies.

Materials and methods

Study participants

This retrospective study included hospitalized patients with AECOPD treated at Kailuan General Hospital from January 2018 to December 2024. The study was approved by the Medical Ethics Committee of the Kailuan General Hospital (No. 2024033).

The Inclusion Criteria included the following: (1) Patients met the diagnostic criteria for AECOPD as defined by the “Expert Consensus on the Acute Exacerbation of Chronic Obstructive Pulmonary Disease in China (revision in 2023)”; (2) Age ≥40 years; (3) For patients with repeated hospitalizations, the most recent admission record was selected. Exclusion Criteria: (1) Incomplete medical records; (2) Patients who died during hospitalization; (3) Patients with acute infections outside the lungs; and (4) Patients with malignancies, hematological disorders, or immune system diseases.

Data collection

This study retrieved demographics, comorbidities, medical history, peripheral biomarkers, lung function test, and treatment interventions. The demographics included age, gender, LOS, Body Mass Index (BMI), smoking, and drinking history. The comorbidities included hypertension, diabetes, coronary heart disease, cerebrovascular disease, and pneumonia. The medical history included hospitalization due to AECOPD in the prior year. The peripheral biomarkers included leukocyte count, neutrophil count, monocyte count, lymphocyte count, eosinophil count, basophil count, erythrocyte count, hemoglobin count, platelet count, albumin, creatinine, and CRP, were all collected within 24 h of patient admission. The lung function test included GOLD categories. The treatment interventions included intravenous corticotherapy during hospitalization, antibiotics during hospitalization, the need for theophylline, diuretics, and oxygen therapy.

The SII was calculated using the following formula: SII = (platelet count × neutrophil count)/lymphocyte count.

The primary outcome was LOS, based on preliminary research findings (average hospitalization duration for AECOPD patients is 7 days) (18–21). Patients were divided into two groups for comparative analysis: the normal LOS (NLOS) group (LOS ≤ 7 days) and the PLOS group (LOS > 7 days).

Statistical analysis

SPSS 22.0 and the R language were used for data analysis. The original SII values failed the normality test (Kolmogorov–Smirnov test) and were found to be non-normally distributed (p < 0.05). To satisfy the normality assumption for parametric tests, data underwent a log transformation (base e). Non-normally distributed continuous variables were described using median and interquartile range, and compared between groups using the Mann–Whitney U-test. Categorical variables were described using counts and percentages and compared between groups using chi-square tests. The variance inflation factor (VIF) test was used to test the collinearity of variables. Construct univariate and multivariate logistic regression models to evaluate the predictive ability of In-SII and In-NLR for PLOS with AECOPD. A restricted cubic spline (RCS) was used to explore the correlation between ln-SII and PLOS in AECOPD patients. The decision curve analysis (DCA) was used to examine the predictive value of ln-SII for PLOS. p < 0.05 is considered to be statistically significant.

Results

The information on patients with AECOPD is shown in Table 1. A total of 986 patients were divided into the NLOS group 379 (38.4%) and the PLOS group 607 (61.6%). The two groups differed in hypertension, hospitalization due to AECOPD in the prior year, leukocyte count, neutrophil count, monocyte count, lymphocyte count, albumin, CRP, In-SII, GOLD categories, intravenous corticotherapy during hospitalization, antibiotics during hospitalization, diuretics, and oxygen therapy. Patients in the PLOS group had a higher prevalence of hypertension and a greater proportion of hospitalization due to AECOPD in the prior year ≥1 time (p < 0.05). For peripheral biomarkers, elevated levels of leukocyte count, neutrophil count, monocyte count, albumin, CRP, and In-SII, but lower lymphocyte count levels were observed in the PLOS group (all p < 0.05). In addition, the PLOS group accounted for a higher proportion of GOLD categories in 3–4, intravenous corticotherapy during hospitalization, antibiotics during hospitalization, diuretics, and oxygen therapy. However, there was no difference in age, gender, BMI, smoking and drinking history, diabetes, coronary heart disease, cerebrovascular disease and pneumonia, eosinophil count, basophil count, erythrocyte count, hemoglobin, platelet count, creatinine, need for theophylline (all p > 0.05).

Table 1

Table 1. Comparison of main characteristics between the NLOS group and the PLOS group in AECOPD patients.

Since neutrophil count, lymphocyte count, and platelet count were included in the calculation of SII, the three variables were excluded from subsequent analyses to avoid collinearity. We further tested the collinearity of the significant baseline variables. With a VIF value >2 as a threshold, Leukocyte (VIF = 2.226) was eliminated, and the variables (hypertension, hospitalization due to AECOPD in the prior year, monocyte count, albumin, CRP, In-SII, GOLD categories, intravenous corticotherapy during hospitalization, antibiotics during hospitalization, diuretics, oxygen therapy) remained. As shown in Table 2.

Table 2

Table 2. Collinearity analysis.

Comparative predictive value of In-SII and In-NLR for the risk of PLOS in Patients with AECOPD. As shown in Table 3, in the univariate model (Model 1), both In-SII and In-NLR were significantly associated with PLOS (OR = 1.527 and OR = 1.576, respectively, both p < 0.05). After adjusting for confounders with VIF < 2 (Model 2), In-SII remained significantly independent (OR = 1.294, p = 0.018), while the association of In-NLR became non-significant (OR = 1.356, p = 0.073). This preliminary finding suggests that SII may possess more robust predictive capability than NLR when accounting for the same confounders. To directly validate this, Model 3 included both In-SII and In-NLR. Results showed In-SII retained significant predictive value (OR = 1.496, p = 0.008), whereas In-NLR was no longer significant. Subgroup analyses across three groups yielded consistent conclusions. In summary, SII is not only an independent predictor of PLOS but also demonstrates superior predictive value compared to NLR.

Table 3

Table 3. Comparative predictive value of In-SII and In-NLR for the risk of PLOS in patients with AECOPD: based on continuous and categorical variables.

Figure 1 displays three RCS regression models. In the following three models, a significant linear correlation exists between In-SII and the risk of PLOS in AECOPD patients (Model 1: overall p < 0.001, p for nonlinear = 0.664; Model 2: overall p = 0.048, p for nonlinear = 0.663; Model 3: overall p = 0.028, p for nonlinear = 0.571).

Figure 1

Figure 1. Restricted cubic splines of the association between the In-SII and the risk of PLOS in patients with AECOPD. Model 1 without adjustment; Model 2 adjusting Hypertension, Hospitalization due to AECOPD in the prior year, Monocyte count, Albumin, C-reactive protein, GOLD categories, Intravenous corticotherapy during hospitalization, Antibiotics during hospitalization, Diuretics, Oxgen therapy ; Model 3 adjusting Model 2 + In-NLR + In-SII. SII, Systemic Immune-Inflammatory Index; PLOS, prolonged length of hospital stay; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; GOLD, the Global Initiative for Chronic Obstructive Lung Disease; OR (95% CI), odds ratio (95% confidence interval).

As shown in Figure 2. In order to study the clinical value of In-SII, we explored its predictive value by DCA analysis. When the threshold was about 0.41–0.80, the clinical net benefit of ln-SII was higher than the treat none model and the treat all model. These suggested that ln-SII had good clinical value.

Figure 2

Figure 2. The predictive value of ln-SII for PLOS in patients with AECOPD. SII, Systemic Immune-Inflammatory Index; PLOS, prolonged length of hospital stay; AECOPD, acute exacerbation of chronic obstructive pulmonary disease.

Discussion

This study is a single-center retrospective investigation that collected laboratory indicators of In-SII to determine its association with PLOS in patients with AECOPD. Multivariate logistic regression analysis revealed that In-SII is an independent risk factor for predicting PLOS in patients with AECOPD, with significantly superior predictive performance compared to In-NLR. This association remained statistically significant even after adjusting for other potential confounding factors. These findings suggest clinicians may utilize SII as a prognostic indicator for AECOPD patients, aiding in the early identification of those at risk for PLOS and enabling enhanced intervention strategies.

Inflammation is a key mechanism in the pathogenesis of AECOPD (1), often triggered by exogenous stimuli such as viral infections, bacterial infections, and air pollution (22). These stimuli lead to massive activation of inflammatory cells within the airways, releasing multiple inflammatory mediators that collectively contribute to and exacerbate persistent damage to airway and alveolar structures (23). This results in significant airflow limitation and airway hyper responsiveness (23). Studies have shown that 70% of COPD patients exhibit elevated levels of at least one inflammatory marker (24). Serum CRP, NLR, fibrinogen, and TNF-α have been extensively studied as inflammatory biomarkers for AECOPD (25, 26). However, the search for more objective, scientifically validated, and readily accessible inflammatory markers, such as the SII derived from routine blood tests, has gained prominence. Recent research has revealed an association between elevated SII levels and increased COPD incidence (27), as well as a higher risk of all-cause mortality among COPD patients with elevated SII levels (28).

This study found that In-SII serves as an independent and superior biomarker to In-NLR for predicting PLOS in patients with AECOPD. NLR primarily reflects the imbalance between neutrophils and lymphocytes. In-SII, however, further integrates the critical dimension of platelets. Elevated SII, characterized by increased platelet and neutrophil counts accompanied by decreased lymphocyte counts, suggests a proinflammatory hematologic state. The potential mechanisms linking this condition to PLOS may be as follows: activated neutrophils release neutrophil extracellular traps (NETs) (29), which inevitably exacerbate lung tissue damage during their functional activity (30). Concurrently, the release of substantial proinflammatory mediators intensifies systemic inflammation (31), leading to PLOS. Second, lymphocytes contribute to tissue injury by secreting inflammatory cytokines, including IL-4, IL-5, and IFN-γ (32). However, persistent inflammation itself induces increased lymphocyte apoptosis and reduced numbers, diminishing pathogen clearance capacity and increasing susceptibility to secondary infections (33), thereby significantly PLOS. Finally, platelets play crucial roles not only in hemostasis and thrombosis but also in inflammatory and immune responses (34, 35). During AECOPD, activated platelets interact with neutrophils, monocytes, and endothelial cells to form microthrombi. This impairs microcirculation, causing tissue ischemia and hypoxia, ultimately damaging organ function (36). Therefore, as an emerging composite indicator, SII can capture the complex pathological network leading to PLOS more comprehensively than NLR. Inflammation also contributes to numerous complications, including infections, sepsis, and organ failure, all of which contribute to PLOS.

Currently, there is no unified criterion for determining the PLOS in patients with AECOPD. Different studies have employed varying standards (9, 37, 38). Given that Mushlin's (39) research indicated approximately 90% of AECOPD patients no longer experience complications or require close monitoring after 6 days of hospitalization, they recommended a necessary hospitalization duration of 6–9 days based on patient clinical characteristics. Additionally, Crisafulli et al. (9) found that LOS >7 days correlates with disease severity. Therefore, this study selected 7 days as the threshold for PLOS in AECOPD patients.

This study has several limitations. First, although we collected data from 986 patients, the single-center retrospective design may not reflect broader populations. Second, this study analyzed only the SII values at admission, lacking dynamic monitoring of SII during hospitalization. This limitation restricted our ability to assess the association between the evolution of the inflammatory response and clinical outcomes. Future prospective multicenter cohort studies should be conducted to further validate the prognostic value of SII. Continuous monitoring of SII dynamics during hospitalization should be pursued to develop a more precise risk prediction model based on inflammatory kinetics, thereby guiding clinical treatment decisions.

Conclusion

High SII levels were independently associated with a higher risk of PLOS in patients with AECOPD. Clinicians should closely monitor elevated SII values and initiate more aggressive anti-inflammatory or antimicrobial treatment regimens at an early stage to reduce the risk of prolonged hospitalization. This approach improves patient outcomes, enhances quality of life, and conserves healthcare resources.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Medical Ethics Committee of the Kailuan General Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin in accordance with the national legislation and institutional requirements.

Author contributions

JX: Conceptualization, Formal analysis, Supervision, Writing – original draft, Writing – review & editing. YL: Conceptualization, Data curation, Supervision, Writing – review & editing. LZ: Data curation, Writing – review & editing. QY: Data curation, Writing – review & editing. XH: Formal analysis, Writing – review & editing. YZ: Formal analysis, Writing – review & editing. DC: Data curation, Writing – review & editing. BF: Formal analysis, Writing – review & editing. LW: Conceptualization, Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was supported by Hebei Province 2024 Medical Science Research Project Plan (20241766).

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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