Introduction
Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory condition that can be categorized into a stable phase and an acute exacerbation phase (acute exacerbation of COPD, AECOPD) based on its clinical course. AECOPD is characterized by a sudden worsening of symptoms, requiring clinical intervention for improvement.1 However, the high incidence of AECOPD and limited medical resources pose a significant challenge to the global healthcare system.2–4 Given the low in-hospital mortality rate observed in this patient cohort, length of hospital stay (LHS) was selected as a more sensitive indicator of disease burden. Prolonged LHS for patients hospitalized for the first time with AECOPD correlates with higher medical costs, increased utilization of medical resources, and poorer prognosis.5–7 Therefore, identifying effective biomarkers for recognizing high-risk patients is of critical importance.
Several traditional biomarkers have been explored for their prognostic utility in AECOPD. For instance, C-reactive protein (CRP), fibrinogen, and white blood cell count (WBC) have been studied for their associations with disease severity and prognosis.8,9 These markers reflect various aspects of the inflammatory response and have been shown to correlate with clinical outcomes, although their predictive power may be limited in certain contexts. Additionally, the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have gained attention as potential prognostic indicators in COPD and other inflammatory diseases.10,11 These ratios integrate information from different immune cell populations and have demonstrated some utility in predicting disease progression and treatment response.
The Pan-Immune Inflammation Value (PIV) is a novel immunoinflammatory biomarker that provides a comprehensive reflection of the body’s immune and inflammatory response through integrated calculations based on the absolute counts of peripheral blood lymphocytes, neutrophils, monocytes, and platelets. In recent years, PIV has shown potential in the prognostic assessment of various diseases, including malignant tumors,12,13 autoimmune diseases,14 and cardiovascular diseases.15,16
To date, no studies have investigated the relationship between PIV and prolonged LHS in patients with AECOPD. Our study aimed to explore this association by retrospectively analyzing data from 5,810 patients hospitalized with AECOPD as the primary diagnosis, providing a scientific basis for clinical decision-making, optimizing treatment strategies, and improving patient outcomes.
Materials and Methods
Study Population
This study retrospectively analyzed the clinical data of patients admitted to the Affiliated Dongyang Hospital of Wenzhou Medical University with AECOPD as their primary diagnosis between January 2010 and March 2024. This study was approved by the Ethics Committee of Affiliated Dongyang Hospital of Wenzhou Medical University (No. 2024-YX-250). This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Because all extracted patient information was anonymous and unrelated to clinical care and treatment, patient informed consent was waived upon approval by the Ethics Committee.
COPD was diagnosed based on the ICD codes (J44.0/J44.1/J44.9). Patients with AECOPD were required to have respiratory deterioration, such as dyspnea and severe cough, within the previous 14 days.17 Inclusion criteria included adult patients (18 years or older) admitted to the hospital for the first time with AECOPD as their primary diagnosis. Exclusion criteria included: (1) patients who had been hospitalized for less than 24 hours; (2) patients who died in the hospital; (3) patients with malignant tumors, acquired immune deficiency syndrome, or autoimmune diseases; (4) patients with acute infections other than those of the respiratory system; and (5) patients with missing absolute counts of peripheral blood lymphocytes, neutrophils, monocytes, and platelets on the first day after admission.
Data Collection
Clinical information of the patients was retrospectively collected from the hospital database. Data collected included patient demographics, such as age, sex, smoking history, drinking history, hypertension, diabetes, and coronary artery disease. Additionally, the initial clinical and laboratory data within 24 hours of admission were also gathered, including pH, PO2, PCO2, oxygen saturation (SO2), blood lactate, WBC count, red blood cell (RBC) count, neutrophil count, lymphocyte count, monocyte count, eosinophil count, basophil count, red blood cell distribution width (RDW), hemoglobin level, platelet count, mean corpuscular volume, hematocrit, blood urea nitrogen (BUN), creatinine, albumin, glucose, and CRP. Owing to the presence of missing values of less than 15% for smoking history, drinking history, pH, PO2, PCO2, SO2, blood lactate, RDW, BUN, creatinine, albumin, glucose, and CRP, multiple imputations were used to fill in the missing values.18
PIV was calculated by multiplying the neutrophil (109/L), monocyte (109/L), and platelet counts (109/L) and dividing the result by the lymphocyte count (109/L), as follows:
The outcome of the study was prolonged LHS. Since there is currently no universally accepted definition for the appropriate LHS for patients with AECOPD, this study defined LHS as any stay exceeding the 75th percentile of all included patients, which was set as > 10 days.
Statistical Analysis
Statistical analysis and data visualization were performed using IBM SPSS (version 26.0; IBM, Armonk, NY, USA) and R software (version 4.3.2; The R Foundation, Vienna, Austria). The original PIV values exhibited a right-skewed distribution (Shapiro–Wilk test, P < 0.001). To satisfy the normality assumption for parametric tests, we applied base-2 logarithmic transformation (log2-PIV) (Supplementary Figure 1). Patients were then divided into four groups based on the log2-PIV quartiles. Continuous variables following a normal distribution were expressed as mean ± standard deviation, while skewed data were presented as median (interquartile range). Continuous variables were compared between groups using the Student’s t-test or Mann–Whitney U-test. Count variables were expressed as numbers and percentages, and comparisons were made using the chi-squared or Fisher’s exact test. Logistic regression was employed to analyze the binary outcome of prolonged LHS. This method was selected owing to its suitability for binary outcomes, ability to provide clinically interpretable odds ratios for risk assessment, and flexibility in handling both continuous and categorical predictor variables while adjusting for potential confounders.Three unadjusted and multivariable-adjusted logistic regression models were constructed to assess the potential association between log2-PIV and the risk of prolonged LHS in patients with AECOPD. Model 1 was unadjusted, while Model 2 included age, sex, smoking history, drinking history, diabetes, hypertension, and coronary artery disease (CAD) as covariates. Model 3 incorporated clinical and laboratory variables based on Model 2 and established the optimal regression model using the stepwise regression method to reduce the influence of multicollinearity. Covariates included age, smoking history, diabetes, CAD, PaCO2, SO2, lactate, monocyte count, basophil count, platelet count, RDW, BUN, hematocrit, and albumin. Additionally, the relationship between log2-PIV and prolonged LHS in patients with AECOPD was explored using the restricted cubic spline (RCS) model with adjustments based on the same variables. Subgroup analyses were conducted for the outcome event, stratified by age (≤65 and >65 years), gender (female vs male), smoking history (yes vs no), drinking history (yes vs no), diabetes (yes vs no), hypertension (yes vs no), and CAD (yes vs no), to explore the consistency of the association between log2-PIV and the risk of prolonged LHS in the hospital in patients with AECOPD across different subgroups. To validate the robustness of our findings, we conducted sensitivity analyses by stratifying raw PIV values into quartiles (Q1–Q4) and refitting the regression models. Statistical significance was set at P < 0.05.
Results
Data from 7,629 patients with AECOPD as the primary diagnosis were retrospectively extracted from the hospital database. After applying the inclusion and exclusion criteria, 5,810 patients with AECOPD were analyzed in this study, including 4,198 males (72.3%) and 1.612 females (27.7%) (Figure 1). The baseline characteristics are summarized in Table 1. Among the 1,244 patients with prolonged LHS, older age was observed, and they had a lower proportion of smoking and drinking history but were more likely to have hypertension, diabetes, and cardiovascular diseases. Additionally, the log2-PIV of patients with prolonged LHS (9.18 [8.02; 10.4]) was significantly higher than that of patients with normal LHS (8.71 [7.65; 9.98]), and there were significant differences in multiple clinical and laboratory variables.
 |
Table 1 Baseline Demographics and Clinical Characteristics of Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease Stratified by Length of Hospital Stay
|
 |
Figure 1 Flowchart of Study Population Selection.
|
Association Between PIV and Prolonged LHS in Patients with AECOPD
As shown in Table 2, patients were divided into four groups based on log2-PIV quartiles (Q1: log2-PIV <7.71; Q2: 7.71 ≤ log2-PIV < 8.80; Q3: 8.80 ≤ log2-PIV < 10.08; Q4: log2-PIV ≥10.08). A significant increase in the proportion of patients with prolonged LHS was observed among those with higher log2-PIV values (P < 0.001).
 |
Table 2 Baseline Demographics and Clinical Characteristics of Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease Grouped by Log2-PIV Quartiles
|
Three logistic regression models were used to evaluate the association between log2-PIV and prolonged LHS in patients with AECOPD (Table 3). The study found that higher log2-PIV values were associated with an increased risk of prolonged LHS in patients with AECOPD (Model 1: Odds Ratio [OR] = 1.16, 95% Confidence Interval [CI] = 1.12–1.20, P < 0.001; Model 2: OR = 1.15, 95% CI = 1.11–1.19, P < 0.001; Model 3: OR = 1.15, 95% CI = 1.09–1.22, P < 0.001). Additionally, we analyzed the log2-PIV as a categorical variable based on quartiles. Compared to the Q1 group, patients with AECOPD in the higher log2-PIV group (Q4) exhibited a significantly increased risk of prolonged LHS (Model 1: OR = 1.83, 95% CI = 1.53–2.19, P < 0.001; Model 2: OR = 1.74, 95% CI = 1.45–2.10, P < 0.001; Model 3: OR = 1.57, 95% CI = 1.21–2.02, P = 0.001).
 |
Table 3 Logistic Regression Analysis of Log2-PIV in Relation to the Risk of Prolonged Hospital Stay in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease
|
RCS Regression Model
Figure 2 illustrates three RCS regression models, demonstrating a significant and linear correlation between log2-PIV and the risk of prolonged LHS in patients with AECOPD in both unadjusted and multivariable-adjusted models. (Model 1: overall P < 0.001, P for nonlinearity = 0.734; Model 2: overall P < 0.001, P for nonlinearity = 0.557; Model 3: overall P = 0.022, P for nonlinearity = 0.180).
 |
Figure 2 Association between Log2-PIV and the Risk of Prolonged Hospital Stay in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease Fitted by restricted cubic spline curves. (A) Model 1 was unadjusted; (B) Model 2 was adjusted for age, sex, smoking history, drinking history, diabetes, hypertension, and CAD. (C) Model 3 was adjusted for age, smoking history, diabetes, CAD, PaCO2, SO2, lactate, monocyte count, basophil count, platelet count, red blood cell distribution width, blood urea nitrogen, hematocrit, and albumin. Abbreviations: CAD, coronary artery disease; PIV, Pan-Immune Inflammation Value; SO2, oxygen saturation.
|
Subgroup Analysis
Subgroup analyses were performed based on age, sex, smoking history, drinking history, diabetes, hypertension, and CAD. Risk stratification analysis was conducted between the log2-PIV and the risk of prolonged LHS in patients with AECOPD (Figure 3). In all subgroups, there was a consistent relationship between increased log2-PIV and prolonged LHS in patients with AECOPD (all P-values < 0.05). Moreover, in the subgroup analysis, no interaction was observed between log2-PIV and the risk of prolonged LHS in patients with AECOPD (all P-values for interaction > 0.05).
 |
Figure 3 Subgroup Analysis of the Association between Log2-PIV and the Risk of Prolonged Hospital Stay in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease. Abbreviation: PIV, Pan-Immune Inflammation Value.
|
Sensitivity Analysis
Patients were stratified by PIV quartiles (Q1: PIV<210.1; Q2: 210.1≤ PIV < 447.5; Q3: 447.5≤ PIV < 1086.2; Q4: PIV ≥1086.2). Significance trends across the groups remained consistent (Supplementary Table 1), and logistic regression confirmed PIV’s association with prolonged hospitalization, aligning with the log2-PIV model (Supplementary Table 2).
Discussion
COPD is a common respiratory disease that often requires hospitalization. Prolonged LHS not only increases the consumption of medical resources but also impacts patient recovery and quality of life. Timely identification of the risk factors associated with prolonged LHS is crucial for optimizing clinical management. This study aimed to explore the role of PIV in prolonged LHS among patients with AECOPD through a retrospective analysis of 5,810 patients hospitalized for the first time with AECOPD as the primary diagnosis. This is the first systematic assessment of the relationship between PIV and prolonged LHS in patients with AECOPD. Logistic regression analysis revealed that patients with AECOPD with higher PIV at admission had a significantly increased risk of prolonged LHS. Even after adjusting for other potential confounding factors, the correlation between high PIV and prolonged LHS remained significant. This finding provides clinicians with a potential biomarker and a basis for a more accurate assessment of patient risk in clinical practice.
AECOPD is strongly associated with heightened inflammatory responses, which can be triggered by multiple factors, including viral and bacterial infections, environmental pollution, smoking, and other factors.19–21 Inflammation plays a multifaceted role in AECOPD. It is central to the pathophysiology of COPD, causing continuous damage to the airways and alveoli, which leads to airflow limitation and airway hyperreactivity.22 Moreover, inflammation is associated with systemic complications in patients with COPD, such as cardiovascular diseases, skeletal muscle dysfunction, and metabolic syndrome.23–25 These complications may affect the severity of AECOPD and influence the patient’s recovery process. It should be particularly emphasized that, although PIV demonstrates independent predictive value, advanced age and comorbid conditions may exert synergistic effects with PIV by exacerbating systemic inflammatory responses or compromising organ compensatory capacity. Future investigations should conduct stratified analyses to evaluate the predictive performance of PIV across different age groups and specific comorbidity profiles, thereby providing more precise guidance for individualized clinical applications.
PIV is an emerging inflammatory marker that provides a novel perspective for assessing immune responses in the study of various diseases.16,26 During AECOPD, the inflammatory response intensifies, with various inflammatory immune cells, such as neutrophils, monocytes/macrophages, and lymphocytes, participating in the immune response through both non-specific and specific immune reactions.27–29 In inflammatory reactions, platelets can be activated by pathogens or inflammatory factors, engaging in the inflammatory process through various mechanisms, such as interaction with white blood cells and the release of inflammatory mediators. These are crucial for maintaining vascular integrity and regulating the inflammatory process.30,31 Therefore, in the context of AECOPD, PIV may reflect an imbalance between inflammatory and anti-inflammatory immune responses, which may be associated with prolonged LHS.
Previous studies on inflammatory indicators and prognostic predictions in patients with COPD have largely focused on mortality.32,33 However, mortality rates among patients hospitalized for the first time due to AECOPD were extremely low, making prolonged LHS a more appropriate indicator for medical resource utilizationand the overall disease burden on patients. Compared to traditional inflammatory markers,34–36 the advantage of PIV lies in its ability to comprehensively reflect dynamic changes in various blood cells, offering clinicians a more comprehensive assessment tool. Additionally, PIV may support clinical decision-making by identifying high-risk patients early, facilitating tailored treatment strategies, optimizing resource allocation, and informing referrals for extended care, pending further validation.
Despite the strong evidence of the relationship between PIV and prolonged LHS in patients with AECOPD, this study has some limitations. First, as a retrospective study, there may have been selection and information biases. The inherent limitations of retrospective data collection can introduce variability in data quality and completeness, potentially affecting the accuracy of our findings. Second, our data were obtained from a single center, which may limit the generalizability of the findings. The specific characteristics of our patient population and institutional clinical practices may have influenced the observed associations. Future prospective multicenter studies are needed to validate these findings and further explore the role of PIV across diverse patient populations and clinical settings. Third, although our sample size was large, we did not divide the dataset into development and validation cohorts. Implementing such an approach could have strengthened validation and enhanced the robustness of our conclusions. Future studies should consider this methodology to confirm the predictive utility of PIV in broader clinical contexts.
Conclusion
This retrospective study identified a significant correlation between high PIV and prolonged LHS in patients hospitalized for the first time with AECOPD, supporting its potential role as a predictive biomarker. Future research should further explore the specific application of PIV in clinical decision-making and investigate how it can be integrated with other clinical parameters and biomarker information to optimize the management of patients with AECOPD. Furthermore, prospective multicenter studies are warranted to validate these findings and establish standardized PIV cutoff values for clinical implementation across diverse healthcare settings.
Abbreviations
AECOPD, acute exacerbation of chronic obstructive pulmonary disease; BUN, blood urea nitrogen; CAD, coronary artery disease; COPD, Chronic Obstructive Pulmonary Disease; LHS, length of hospital stay; PIV, Pan-Immune Inflammation Value; RBC, red blood cell; RCS, restricted cubic spline; RDW, red blood cell distribution width; SO2, oxygen saturation; WBC, white blood cell.
Data Sharing Statement
All data generated or analyzed during this study are included in this published article.
Ethics Approval and Informed Consent
This study was approved by the Ethics Committee of Affiliated Dongyang Hospital of Wenzhou Medical University (No. 2024-YX-250). As all extracted patient information was anonymous and unrelated to clinical care and treatment, informed consent from patients was waived upon approval by the Ethics Committee.
Acknowledgments
We are grateful to Professor Longyi Zhang for his expert guidance on statistical analysis. We also acknowledge Editage (www.editage.cn) for providing English language editing services.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Disclosure
The authors declare that there are no conflicts of interest related to this work.
References
1. Jeyachandran V, Hurst JR. Advances in chronic obstructive pulmonary disease: management of exacerbations. Br J Hosp Med. 2022;83(7):1–7. doi:10.12968/hmed.2022.0275
2. Sun YC. Chronic obstructive pulmonary disease in primary healthcare institutions in China: challenges and solutions. Chronic Dis Transl Med. 2020;6(4):219–223. doi:10.1016/j.cdtm.2020.05.007
3. Katzenberg G, Deacon A, Aigbirior J, Vestbo J. Management of chronic obstructive pulmonary disease. Br J Hosp Med. 2021;82(7):1–10. doi:10.12968/hmed.2020.0561
4. Criner RN, Han MK. COPD Care in the 21st Century: a Public Health Priority. Respiratory Care. 2018;63(5):591–600. doi:10.4187/respcare.06276
5. Kılıç M, Yüzkat N, Soyalp C, Gülhaş N. Cost Analysis on Intensive Care Unit Costs Based on the Length of Stay. Turk J Anaesthesiol Reanim. 2019;47(2):142–145. doi:10.5152/TJAR.2019.80445
6. Ruparel M, López-Campos JL, Castro-Acosta A, Hartl S, Pozo-Rodriguez F, Roberts CM. Understanding variation in length of hospital stay for COPD exacerbation: european COPD audit. ERJ Pen Res. 2016;2(1):00034–2015. doi:10.1183/23120541.00034-2015
7. García-Sanz MT, González-Barcala FJ, Cánive-Gómez JC, García-Couceiro N, Alonso-Acuña S, Carreira JM. Prolonged stay predictors in patients admitted with chronic obstructive pulmonary disease acute exacerbation. Lung India. 2018;35(4):316–320. doi:10.4103/lungindia.lungindia_469_17
8. Ellingsen J, Janson C, Bröms K, et al. CRP, Fibrinogen, White Blood Cells, and Blood Cell Indices as Prognostic Biomarkers of Future COPD Exacerbation Frequency: the TIE Cohort Study. J Clin Med. 2024;13(13):3855. doi:10.3390/jcm13133855
9. Fermont JM, Masconi KL, Jensen MT, et al. Biomarkers and clinical outcomes in COPD: a systematic review and meta-analysis. Thorax. 2019;74(5):439–446. doi:10.1136/thoraxjnl-2018-211855
10. Zinellu A, Zinellu E, Mangoni AA, et al. Clinical significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in acute exacerbations of COPD: present and future. Eur Respirat Rev. 2022;31(166):220095. doi:10.1183/16000617.0095-2022
11. Cai C, Zeng W, Wang H, Ren S. Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Monocyte-to-Lymphocyte Ratio (MLR) as Biomarkers in Diagnosis Evaluation of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: a Retrospective, Observational Study. Int J Chronic Obstr. 2024;19:933–943. doi:10.2147/COPD.S452444
12. Kuang T, Qiu Z, Wang K, Zhang L, Dong K, Wang W. Pan-immune inflammation value as a prognostic biomarker for cancer patients treated with immune checkpoint inhibitors. Front Immunol. 2024;15:1326083. doi:10.3389/fimmu.2024.1326083
13. Palomino-Secca I, Peña-Tuya M, Quintana-García LA, et al. Pan-immune-inflammation value and survival in patients with breast cancer from a Peruvian reference hospital. Sci Rep. 2024;14(1):17132. doi:10.1038/s41598-024-68304-y
14. Başaran P, Dogan M. The relationship between disease activity with pan-immune-inflammatory value and systemic immune-inflammation index in rheumatoid arthritis. Medicine. 2024;103(9):e37230. doi:10.1097/MD.0000000000037230
15. Liu Y, Liu J, Liu L, et al. Association of Systemic Inflammatory Response Index and Pan-Immune-Inflammation-Value with Long-Term Adverse Cardiovascular Events in ST-Segment Elevation Myocardial Infarction Patients After Primary Percutaneous Coronary Intervention. J Inflamm Res. 2023;16:3437–3454. doi:10.2147/JIR.S421491
16. Murat B, Murat S, Ozgeyik M, Bilgin M. Comparison of pan-immune-inflammation value with other inflammation markers of long-term survival after ST-segment elevation myocardial infarction. Eur J Clin Invest. 2023;53(1):e13872. doi:10.1111/eci.13872
17. Agustí A, Celli BR, Criner GJ, et al. Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary. Eur Resp J. 2023;61(4):2300239. doi:10.1183/13993003.00239-2023
18. Beesley LJ, Taylor JMG. A stacked approach for chained equations multiple imputation incorporating the substantive model. Biometrics. 2021;77(4):1342–1354. doi:10.1111/biom.13372
19. Ko FW, Chan KP, Hui DS, et al. Acute exacerbation of COPD. Respirology. 2016;21(7):1152–1165. doi:10.1111/resp.12780
20. Ni L, Chuang CC, Zuo L. Fine particulate matter in acute exacerbation of COPD. Front Physiol. 2015;6:294. doi:10.3389/fphys.2015.00294
21. Panek I, Liczek M, Gabryelska A, Rakoczy I, Kuna P, Panek M. Inflammasome signalling pathway in the regulation of inflammation – its involvement in the development and exacerbation of asthma and chronic obstructive pulmonary disease. Postepy Dermatologii Alergologii. 2023;40(4):487–495. doi:10.5114/ada.2022.118077
22. Xu J, Zeng Q, Li S, Su Q, Fan H. Inflammation mechanism and research progress of COPD. Front Immunol. 2024;15:1404615. doi:10.3389/fimmu.2024.1404615
23. van Eeden SF, Sin DD. Chronic obstructive pulmonary disease: a chronic systemic inflammatory disease. Respiration Int Rev Thorac Dis. 2008;75(2):224–238. doi:10.1159/000111820
24. Gea J, Agustí A, Roca J. Pathophysiology of muscle dysfunction in COPD. J Appl Physiol. 2013;114(9):1222–1234. doi:10.1152/japplphysiol.00981.2012
25. Akpınar EE, Akpınar S, Ertek S, Sayın E, Gülhan M. Systemic inflammation and metabolic syndrome in stable COPD patients. Tuberkuloz Toraks. 2012;60(3):230–237. doi:10.5578/tt.4018
26. Guven DC, Sahin TK, Erul E, Kilickap S, Gambichler T, Aksoy S. The Association between the Pan-Immune-Inflammation Value and Cancer Prognosis: a Systematic Review and Meta-Analysis. Cancers. 2022;14(11):2675. doi:10.3390/cancers14112675
27. Norman KC, Freeman CM, Bidthanapally NS, et al. Inference of Cellular Immune Environments in Sputum and Peripheral Blood Associated with Acute Exacerbations of COPD. Cell Mol Bioeng. 2019;12(2):165–177. doi:10.1007/s12195-019-00567-2
28. Hong X, Xiao Z. Changes in peripheral blood TBNK lymphocyte subsets and their association with acute exacerbation of chronic obstructive pulmonary disease. J Int Med Res. 2023;51(6):3000605231182556. doi:10.1177/03000605231182556
29. Yang J, Qiao M, Li Y, et al. Expansion of a Population of Large Monocytes (Atypical Monocytes) in Peripheral Blood of Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Diseases. Mediators Inflamm. 2018;2018:9031452. doi:10.1155/2018/9031452
30. Fawzy A, Putcha N, Raju S, et al. Urine and Plasma Markers of Platelet Activation and Respiratory Symptoms in COPD. Chronic Obstruct Pulmonary Dis. 2023;10(1):22–32. doi:10.15326/jcopdf.2022.0326
31. Thomas MR, Storey RF. The role of platelets in inflammation. Thrombosis Haemostasis. 2015;114(3):449–458. doi:10.1160/TH14-12-1067
32. Zhang Y, Tan X, Hu S, Cui Z, Chen W. Relationship Between Systemic Immune-Inflammation Index and Risk of Respiratory Failure and Death in COPD: a Retrospective Cohort Study Based on the MIMIC-IV Database. Int J Chronic Obstr. 2024;19:459–473. doi:10.2147/COPD.S446364
33. Lan CC, Su WL, Yang MC, Chen SY, Wu YK. Predictive role of neutrophil-percentage-to-albumin, neutrophil-to-lymphocyte and eosinophil-to-lymphocyte ratios for mortality in patients with COPD: evidence from NHANES 2011–2018. Respirology. 2023;28(12):1136–1146. doi:10.1111/resp.14589
34. Gong C, Yang Y, Chen M, Xie Z. Effect of procalcitonin on the prognosis of patients with COPD. Biomed Rep. 2020;12(6):313–318. doi:10.3892/br.2020.1298
35. Liu S, Zhang H, Zhu P, Chen S, Lan Z. Predictive role of red blood cell distribution width and hemoglobin-to-red blood cell distribution width ratio for mortality in patients with COPD: evidence from NHANES 1999–2018. BMC Pulm Med. 2024;24(1):413. doi:10.1186/s12890-024-03229-w
36. El-Gazzar AG, Kamel MH, Elbahnasy OKM, El-Naggar ME. Prognostic value of platelet and neutrophil to lymphocyte ratio in COPD patients. Expert Rev Respiratory Med. 2020;14(1):111–116. doi:10.1080/17476348.2019.1675517