European Journal of Cardiovascular Medicine

Platelet indices have emerged as significant hematological markers in understanding the pathophysiology and severity of various diseases, including chronic obstructive pulmonary disease ^[1]. COPD, a progressive inflammatory disorder characterized by persistent airflow limitation, is associated with systemic inflammation, oxidative stress, and increased risk of comorbidities such as cardiovascular diseases, pulmonary hypertension, and metabolic disorders. While traditional diagnostic methods such as spirometry and imaging remain central to assessing disease progression, the role of inflammatory markers, particularly platelet indices, has gained attention as a potential indicator of disease severity.^[1,2]

Platelets, primarily known for their role in haemostasis, also contribute to inflammation and immune regulation. Various platelet indices, including platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and

platelet lymphocyte ratio (PLR), have been studied ^[2]. MPV, which reflects platelet size, is indicative of platelet activation and has been linked to prothrombotic conditions and inflammatory diseases. Similarly, PDW, a measure of platelet size variability, provides insight into platelet heterogeneity and activation status. Elevated or decreased values of these indices have been observed in COPD patients, suggesting their potential role in monitoring disease severity and predicting exacerbations. ^[2,3]

The systemic inflammation in COPD is driven by a complex interplay of immune cells, cytokines, and oxidative stress. Chronic exposure to noxious particles,  particularly cigarette smoke and environmental pollutants, triggers an inflammatory cascade involving neutrophils, macrophages, and lymphocytes ^[4]. This inflammatory response extends beyond the lungs, contributing to systemic vascular dysfunction and a hypercoagulable state. Platelet activation, characterized by increased aggregation and degranulation, further amplifies the inflammatory process, leading to endothelial dysfunction and cardiovascular complications. ^[4,5]

The relationship between platelet indices and COPD severity has been the focus of several studies, with findings suggesting that patients with severe COPD exhibit significant alterations in platelet parameters. Elevated MPV ,PLR and PDW have been associated with heightened inflammatory response and platelet activation, whereas decreased platelet counts may indicate a consumptive process or bone marrow suppression due to chronic inflammation ^[5,6]. The variations in these indices have also been linked to acute exacerbations of COPD, a critical phase marked by worsened respiratory symptoms, increased hospitalizations, and higher mortality rates. Understanding these changes can provide valuable prognostic information, aiding in risk stratification and personalized disease management. ^[6]

Beyond their diagnostic and prognostic implications, platelet indices also hold therapeutic relevance in COPD. Given the role of platelets in vascular complications and thrombosis, antiplatelet therapy has been explored as a potential adjunct in COPD management. Studies have suggested that patients receiving antiplatelet agents, such as aspirin or P2Y12 inhibitors, may experience a reduced risk of cardiovascular events, which are a major cause of morbidity and mortality in COPD. The interplay between platelets, inflammation, and coagulation underscores the need for a multidimensional approach in managing COPD, incorporating both respiratory and cardiovascular considerations. ^[5-7]

Despite the growing evidence supporting the use of platelet indices as markers of disease severity in COPD, several challenges remain. The heterogeneity of COPD phenotypes, variations in comorbidities, and differences in study methodologies contribute to inconsistencies in reported findings ^[7]. Additionally, factors such as medications, lifestyle habits, and underlying hematological conditions can influence platelet parameters, necessitating careful interpretation of results. Further large-scale, longitudinal studies are required to establish standardized reference ranges and optimize the clinical utility of platelet indices in COPD management. ^[7,8]

The Silchar Medical College and Hospital which is the sole custodian of the entire health care system of Barak Valley, is the only referral hospital that offers excellent health care services and is situated in the southern part of Assam. Due to the features of strategic geographical location, it is also rendering systematic health care facilities to the ailing group of people in the neighbouring States like Mizoram, North Tripura, West Manipur and South Meghalaya. A major population of this catchment region is presented to the OPD and Emergency Department with COPD.

The aim of the study was to evaluate platelet indices as a marker of disease severity in patients with chronic obstructive pulmonary disease.

The study was conducted in the Department of General Medicine at Silchar Medical College and Hospital, Silchar, Assam, India after getting the approval by the Institutional Ethics Committee. The duration of the study was for one year, (1st November 2023 to 31st October 2024) .152 patients fulfilling the inclusion exclusion criteria were selected for the study. Inclusion Criteria Patients of either sex aged greater than 35 years, who were diagnosed clinically with “COPD” with respect to the “Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023” guidelines. Exclusion Criteria Patients refusing to participate, having a history of prior blood transfusion within the preceding three months, having acute or chronic pulmonary thromboembolism, diagnosed with atherosclerotic cardiovascular diseases or cardiac failure, suffered from hepatic or renal failure, with active pulmonary tuberculosis infection in the past, concurrent diagnosis of bronchial asthma or obstructive sleep apnea, known hematological disorders, connective tissue disorders or inflammatory bowel diseases, undergoing long-term oxygen therapy for any malignancy, if had received any anti-inflammatory medications, including systemic steroids or immunosuppressive drugs, in the preceding two months, or were immunosuppressed for any other reason were excluded from the study. Informed written consent was obtained from each participant after a detailed explanation of the study objectives, procedures, potential risks, and benefits in a language that was easily understood by the participants. Confidentiality of patient data was strictly maintained throughout the study A detailed clinical history was obtained from each patient, including information on respiratory symptoms, smoking habits, occupational exposures, and previous hospital admissions. Following the clinical evaluation, patients underwent a series of investigations. These included routine blood tests for the complete blood count, with special emphasis on platelet indices, and radiological assessments such as chest X-rays. In addition, spirometry was performed on every patient to confirm the diagnosis of COPD by demonstrating a post-bronchodilator FEV1/FVC ratio of less than 0.70. Arterial blood gas (ABG) analysis was done. In selected cases, where additional diagnostic clarification was required, further investigations such as computed tomography (CT) of the thorax. Throughout the study, all patients received standard-of-care treatment for COPD, and the timing of data collection was carefully coordinated to minimize any impact of acute exacerbations on the measured parameters. Data collection was conducted using a pre-designed proforma that had been developed to capture all pertinent information. Data were extracted from hospital records as well as obtained through direct interviews with the patients. The collected data were then entered and analyzed into Microsoft Excel using SPSS 28.0 version.

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

FIG. 1: BAR DIAGRAMS SHOWING AGE DISTRIBUTION OF PATIENTS

FIG.2 : BAR DIAGRAM SHOWING DISTRIBUTION OF PATIENTS ACCORDING TO SMOKING STATUS

FIG. 3: PIE CHART SHOWING DISTRIBUTION OF THE PATIENTS ACCORDING TO GOLD STAGING

TABLE 1: COMPARISON OF MPV, PDW, PLR BETWEEN COPD PATIENTS AND

CONTROL GROUP

FIG. 4: BAR DIAGRAM MEAN OF MPV, PDW&PLR BETWEEN PATIENTS AND CONTROL GROUP

TABLE 2: COMPARISON OF AGE, PACKYEARS, BASELINE FEV1 AND SOP2 IN CASE AND CONTROL GROUP

FIG.5: CORRELATION OF MPV WITH OTHER DISEASE SEVERITY MARKET

TABLE 3: CORRELATION OF MPV, PDW, PLR TO EXACERBATION FREQUENCY

TABLE 4: CORRELATION OF MPV, PDW, PLR TO NUMBER OF HOSPITALISATIONS

TABLE 5: CORRELATION OF MPV,PDW,PLR TO NUMBER OF PATIENTS IN NEED OF SUPPLEMENTAL OXYGEN

TABLE 6 : CORRELATION OF MPV, PDW, PLR WITH OTHER DISEASE SEVERITY MARKERS

*Note: ** and * denote significance levels as reported in the study.

FIG.6: CORRELATION OF PDW WITH OTHER DISEASE SEVERITY MARKERS

FIG.7 : CORRELATION OF PLR WITH OTHER DISEASE SEVERITY MARKERS

The aim of this study was to evaluate the potential of readily available hematological markers—specifically MPV, PDW and PLR—to serve as surrogate indicators of disease severity in patients with COPD. The study sought to correlate these platelet indices with established clinical parameters such as pulmonary function tests (e.g., FEV₁ and SpO₂), exacerbation frequency, and hospitalisation rates, thereby assessing their utility in risk stratification and prognostication.

In the case group, patients were predominantly older with the highest percentages in the 61–70 (36.18%) and >70 (30.26%) age groups, indicating that COPD is more common in later life This suggests that cumulative exposure to risk factors over time may contribute to COPD development.. Furthermore, 76.32% of COPD patients were smokers with an average of 16.47 pack years, whereas controls averaged only 3.20 pack years.

These findings echo previous work by Moniruzzaman et al. ^[4], who noted progressive hematological changes with increasing disease severity attributed to smoking.These findings support efforts in smoking cessation as a critical strategy in the prevention and management of COPD.

FEV1 was significantly lower in cases (36.99%) compared to controls (78.13%) (t = -39.31, p < 0.001). This finding confirms the marked airflow limitation in the case group, consistent with COPD diagnosis. The large difference in FEV1 between cases and controls highlights the severity of the disease in the case group.

SpO2 was also significantly lower in cases (83.55%) compared to controls (97.62%) (t = -18.40, p < 0.001). This reflects the impaired gas exchange and hypoxemia in COPD patients, which is a clinical hallmark of advanced disease and a predictor of poorer outcomes.

The independent t-test was conducted to compare the MPV, PDW, PLR, age, FEV1, SpO2, and pack years between the case and control groups. The results reveal statistically significant differences across all parameters (p < 0.001), indicating meaningful variations between the two group.

The above study found significant positive correlations between platelet indices and hospitalisation rates in COPD patients, highlighting their potential role in predicting clinical outcomes .Mean platelet volume (MPV) was moderately correlated with the number of hospitalisations (r = 0.3741, p < 0.00001), while platelet distribution width (PDW) (r = 0.3145, p < 0.05) and platelet-to-lymphocyte ratio (PLR) (r = 0.3252, p < 0.00052) were also significantly associated. These correlations indicate that patients with higher platelet activation tend to have more frequent hospital admissions, suggesting a more severe or unstable disease course.

Erden et al. ^[9] reported that exacerbations—often necessitating hospital care—were accompanied by increased MPV, reinforcing our findings. Similarly, Moniruzzaman et al. ^[4] observed that worsening COPD severity, reflected in increasing hematological indices, was associated with higher rates of hospitalisation. The underlying mechanisms likely involve the pro-thrombotic and pro-inflammatory state induced by chronic platelet activation, which not only contributes to airway inflammation but may also predispose patients to cardiovascular events and other complications that require hospital care.

We observed moderate positive correlations between exacerbation frequency and key platelet indices. Specifically, mean platelet volume (MPV) showed a correlation coefficient of 0.4758 (p < 0.00001), while platelet distribution width (PDW) and platelet-to-lymphocyte ratio (PLR) demonstrated correlations of 0.472 and 0.4666 respectively (both p < 0.00001). Erden et al. ^[9] similarly reported that MPV increased during acute exacerbations, reinforcing the dynamic nature of platelet activation in response to inflammatory stress. Additionally, Kumar et al. ^[10] highlighted the complex interplay between PLR and disease exacerbations, indicating that while PLR is elevated in COPD, its correlation with exacerbation frequency may vary depending on additional inflammatory factors.

In above study, a striking 88.88% of patients require supplemental oxygen, whereas only 11.18% do not. This high dependency underscores the severity of hypoxemia and impaired gas exchange prevalent in advanced COPD. The data highlight that the majority of patients are at an advanced stage of the disease, necessitating continuous oxygen therapy to maintain adequate tissue oxygenation. Such a high reliance on supplemental oxygen “not only affects quality of life but also signals the need for intensified treatment strategies and close clinical monitoring.

The strength of the study is it’s ability to link routine, cost- effective blood parameters with advanced clinical indices represents an innovative approach that may facilitate early diagnosis and improved risk stratification in COPD. Moreover, by integrating objective measures of pulmonary function with biochemical markers of systemic inflammation, the study provides insights into the interplay between chronic inflammation and respiratory impairment.

The findings from this study have significant implications for both clinical practice and public health management of COPD. By demonstrating that routinely available hematological markers—such as mean platelet volume, platelet distribution width, and platelet-to-lymphocyte ratio—are significantly elevated in COPD patients and” correlate with key clinical outcomes, the study suggests that these parameters can serve as effective surrogate markers of systemic inflammation and disease severity.

 Ultimately, the implications of this study extend beyond individual patient care to inform broader public health strategies aimed at early detection, improved disease monitoring, and cost-effective management of COPD at a population level.

Despite its strengths, this study has several limitations such as, it limits the ability to establish causality or assess longitudinal changes in the measured parameters. The sample size is insufficient to capture the full heterogeneity of the COPD population, particularly across different stages of disease severity. Also, the variability in measurement techniques for platelet indices across different laboratories could also contribute to discrepancies in results, thus affecting the reproducibility of the findings. The lack of data on other inflammatory biomarkers limits the ability to compare the prognostic value of platelet indices with that of other established markers.

The study highlights the importance of a multidimensional approach in the evaluation of COPD, incorporating demographic factors, smoking history, pulmonary function tests, and hematological parameters to provide a comprehensive assessment of disease severity. These findings have significant implications for early diagnosis, risk stratification, and personalized management strategies, which are essential for improving patient outcomes and reducing the overall healthcare burden associated with COPD.

By leveraging readily available laboratory tests, clinicians can monitor disease progression more effectively, initiate timely interventions, and potentially mitigate the impact of exacerbations and hospitalisations, thereby enhancing the quality of life for patients with COPD.

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