Study of inflammatory biomarkers in Chronic obstructive pulmonary disease (COPD) and their association with comorbidities

Aim

The primary aim of this study is to investigate inflammatory biomarkers in Chronic Obstructive Pulmonary Disease (COPD) and explore their associations with comorbidities.

Objectives

Study design

This study will employ a cross-sectional and observational design to investigate the relationship between inflammatory biomarkers and COPD and their association with comorbidities. The cross-sectional design allows data collection at a single point, providing a snapshot of the relationship between variables.

Study population

The study population will consist of patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD) who meet the inclusion criteria. Patients will be selected from the Respiratory Medicine department at AVBRH. Inclusion criteria encompass individuals diagnosed with COPD and those aged above 40 years. Exclusion criteria include patients unwilling to provide consent, non-cooperative patients, and those with connective tissue disorders such as rheumatoid arthritis or COPD asthma overlap.

Place of study

The study will be conducted at AVBRH, a tertiary care hospital attached to Jawaharlal Nehru Medical College. AVBRH is situated in the rural area of Sawangi, Meghe, Wardha, in central India. This location provides a diverse and representative sample of patients with COPD in a real-world clinical setting. The choice of a rural area enhances the study’s external validity, capturing a population that may have different characteristics compared to urban populations.

Inclusion criteria

Exclusion criteria

Bias

Data collection process

The data collection process for this study on inflammatory biomarkers in Chronic Obstructive Pulmonary Disease (COPD) and their association with comorbidities will be conducted with meticulous attention to detail and adherence to standardized procedures. Participant recruitment will involve identifying eligible individuals from the Respiratory Medicine department at AVBRH based on stringent inclusion and exclusion criteria. Informed consent will be sought from each participant, ensuring they fully understand the study’s purpose, procedures, and potential implications.

Upon obtaining consent, a baseline assessment will be conducted to gather demographic information and confirm the diagnosis of COPD through pulmonary function tests and chest X-rays. Diagnostic tests, including spirometry for lung function, chest X-rays for initial assessment, and HRCT of the thorax in cases of severe derangement, will be carried out to evaluate the respiratory status of participants comprehensively.

The biomarker testing phase will involve a series of laboratory tests. Blood samples will be collected for C-reactive protein (CRP) testing and the Westergren Katz tube test to measure Erythrocyte Sedimentation Rate (ESR). A complete blood count (CBC) will also be performed to assess leukocyte count and hemoglobin levels. Participants will undergo HbA1c testing to rule out diabetes. Cardiac evaluations, including echocardiogram and ECG, will be conducted to assess heart function. Furthermore, kidney function tests and measurements of inflammatory biomarkers such as Interleukin-6 (IL-6), Tumor Necrosis Factor-Alpha (TNF-ALFA), and fibrinogen will be performed.

Data management will be a critical aspect of the process, involving the design of a secure and structured database to store and manage collected data. Regular checks and validation procedures will ensure data integrity throughout the study. Statistical analysis will be conducted using the SPSS 27.0 version, employing appropriate methods such as chi-square tests and Pearson’s correlation coefficient to analyze the relationships between biomarkers, COPD severity, and comorbidities.

Sample size

Where;

Z is the level of significance at 5%, i.e., 95% confidence interval = 1.96

P = Prevalence of COPD = 7.4% (Reference study⁷)

80% of COPD patients had comorbidity: $\frac{7.4\ast 80}{100}=5.92\%$

d = Desired error of margin = 5% = 0.05

n = 90 COPD patients needed in the study.

Statistical method

The study on inflammatory biomarkers in Chronic Obstructive Pulmonary Disease (COPD) and their association with comorbidities will rigorously utilize various statistical methods to analyze the relationships among key variables. The initial phase involves descriptive statistics, which will summarize the study population’s demographic details, COPD severity, and prevalence of comorbidities. Measures such as mean, median, standard deviation, and percentages will provide an overview of the characteristics of the participants.

Chi-square tests will be employed to analyze categorical variables, particularly assessing the presence or absence of comorbidities across different COPD severity groups. This statistical method aids in understanding the associations between inflammatory biomarkers and the occurrence of specific comorbidities. Pearson’s correlation coefficient will be used to explore the linear relationship between continuous variables, such as the levels of inflammatory biomarkers and the severity of COPD. The strength and direction of these correlations will be assessed, providing valuable insights into potential associations.

Multivariate analysis, including multiple regression analysis, may be considered to simultaneously examine the independent contributions of various factors to the observed associations. This approach provides a more comprehensive understanding of the interplay between inflammatory biomarkers, COPD severity, and comorbidities. Comparative analysis will involve statistical tests, such as t-tests or analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables, to compare biomarker levels between different groups, such as varying COPD severity categories or the presence/absence of specific comorbidities.

Furthermore, logistic regression will be employed to model the likelihood of comorbidities based on factors such as inflammatory biomarker levels and COPD severity. This statistical method aids in identifying potential predictors of comorbidities within the study population. Subgroup analysis will be conducted to explore variations in relationships across different subgroups, such as age groups or gender, providing insights into potential effect modifiers.

Survival analysis techniques like Kaplan-Meier curves and Cox proportional hazards models may be considered in cases involving longitudinal data and time-to-event outcomes. These methods can provide valuable information on the occurrence of specific comorbidities over time. Sensitivity analysis will be conducted to assess the robustness of results, testing the impact of variations in key parameters or assumptions.

The statistical software chosen for these analyses is SPSS version 27.0 statistical tools,⁸ ensuring the accuracy and reliability of the results. Through the thoughtful application of these statistical methods, the study aims to unveil meaningful associations, correlations, and predictors related to inflammatory biomarkers, COPD severity, and comorbidities, contributing to a nuanced understanding of the intricate relationships within this complex medical landscape.

Ethical considerations

The Institutional Ethics Committee of Datta Meghe Institute of Higher Education and Research (DU) has granted its approval to the study protocol (Reference number: DMIHER (DU)/IEC/2022/119. Date: 21-07-2022). Before commencing the study, we will obtain written informed consent from all participants, providing them with a comprehensive explanation of the study’s objectives.

Limitations

Despite the careful planning and execution of the study on inflammatory biomarkers in Chronic Obstructive Pulmonary Disease (COPD) and their association with comorbidities, several limitations must be considered. While efficient for snapshot assessments, the cross-sectional design cannot establish causal relationships, warranting the need for longitudinal studies to explore temporal dynamics. Sampling bias is inherent due to the study’s reliance on a specific tertiary care hospital in a rural area, potentially limiting the generalizability of findings to diverse populations. The exclusion of individuals with connective tissue disorders and COPD asthma overlap might introduce selection bias, impacting the representation of specific subgroups within the COPD population. Additionally, self-reported data may introduce recall and social desirability biases, affecting the accuracy of information provided by participants. The focus on specific inflammatory biomarkers may limit the study’s comprehensiveness, as other relevant biomarkers may exist. Unmeasured confounders, external factors, and ethnic and cultural influences could further impact the study’s generalizability. Acknowledging these limitations is essential for a proper interpretation of results and highlight the need for cautious application to broader populations.

Dissemination

After the completion of the study, we will publish it in an indexed journal or conference.

Study status

The study has yet to start. After the publication of the protocol, we will start recruitment in the study.