Neutrophil-to-Lymphocyte Ratio in Bronchiectasis and it’s Association with Disease Severity – A Single Centre Retrospective Study

Neutrophil-to-Lymphocyte Ratio in Bronchiectasis and it’s Association with Disease Severity – A Single Centre Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Neutrophil-to-Lymphocyte Ratio in Bronchiectasis and it’s Association with Disease Severity – A Single Centre Retrospective Study Irfan Shafiq, Ali Saeed Wahla, Mateen Haider Uzbeck, Zaid Zoumot, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6691002/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has shown promise in assessing disease severity in chronic respiratory conditions. However, its utility in bronchiectasis remains underexplored. This study evaluated the association between NLR and clinical markers of bronchiectasis severity, including lung function and the Bronchiectasis Severity Index (BSI). Methods In this retrospective cohort study, electronic health records of 213 adults with radiologically confirmed bronchiectasis (2014–2021) were analyzed. NLR was derived from non-exacerbation complete blood counts. Correlations between NLR and spirometry (FEV1% predicted, FVC%, FEV1/FVC), BSI, and microbiological data were assessed using Pearson’s correlation and multivariate regression. Results Mean NLR was 7.05 ± 13.84. NLR correlated negatively with FEV1% PREDICTED (r = − 0.321, *p* < 0.001) and FVC% (r = − 0.342, *p* < 0.001), but not with BSI (*p* = 0.095). Multivariate analysis confirmed NLR as an independent predictor of FEV1% predicted (F = 21.058, *p* < 0.001) and eosinophil count (*p* = 0.019). Ordinal regression linked higher NLR to severe BSI categories (OR = 0.58, 95% CI 0.02–1.13, *p* = 0.041), though effect sizes were modest (Nagelkerke R² = 0.024). Pseudomonas aeruginosa (33.8%) was the most frequent pathogen. Conclusions Elevated NLR reflects worse lung function and may aid in stratifying bronchiectasis severity, particularly as a surrogate for neutrophilic inflammation. However, its inability to correlate strongly with BSI underscores the need for multimodal assessment. NLR is a practical, low-cost biomarker but should complement—not replace—existing tools like BSI. Prospective studies are needed to validate its prognostic utility. Bronchiectasis neutrophil-to-lymphocyte ratio disease severity lung function systemic inflammation Figures Figure 1 Figure 2 Figure 3 Introduction Bronchiectasis is a chronic respiratory condition characterized by irreversible bronchial dilation, persistent airway inflammation, and recurrent infections. Its global prevalence has been increasing, particularly among older adults and women. According to a recent meta-analysis, the pooled prevalence of bronchiectasis in adults is approximately 680 per 100,000 individuals( 1 ).​ The clinical course of bronchiectasis is variable, with patients experiencing different degrees of symptom severity, frequency of exacerbations, and disease progression. To aid in prognostication and management, multidimensional scoring systems such as the Bronchiectasis Severity Index (BSI) and the FACED score have been developed( 2 , 3 ). These tools incorporate clinical, radiological, and microbiological parameters to stratify patients based on disease severity and predict outcomes like mortality and hospitalization. However, their application in routine clinical practice can be limited due to the need for comprehensive data collection and complex calculations( 4 ). There remains a need for a simple, cost-effective biomarkers that can reliably reflect disease severity and assist in prognostication. The neutrophil-to-lymphocyte ratio (NLR), derived from routine complete blood counts, has emerged as a potential candidate. NLR serves as an indicator of systemic inflammation and has been associated with disease severity and outcomes in various chronic conditions, including chronic obstructive pulmonary disease (COPD) and cardiovascular diseases( 5 – 7 ). In the context of bronchiectasis, recent studies have explored the utility of NLR as a marker of disease severity. A study analysing data from the Spanish Bronchiectasis Registry found that higher NLR values correlated with increased disease severity as measured by BSI, FACED, and E-FACED scores. Furthermore, elevated NLR was associated with a higher frequency of exacerbations, greater colonization by pathogenic microorganisms, and poorer quality of life. These findings suggest that NLR could serve as a practical and accessible biomarker for assessing disease severity and predicting clinical outcomes in bronchiectasis patients( 5 ). ​ Building upon this evidence, our study aims to evaluate the relationship between NLR and BSI as well as lung function parameters, specifically forced expiratory volume in one second (FEV1% predicted), in a cohort of patients with bronchiectasis. By investigating this association, we seek to determine the potential of NLR as a surrogate marker for pulmonary function impairment and its role in the clinical assessment of bronchiectasis severity.​ Methods Population: Electronic Health Records (EHR) from our tertiary care center were queried using the ICD-10-CM code for bronchiectasis (J47) to identify eligible patients between April 2014 and December 2021. Inclusion criteria required a radiologically confirmed diagnosis of bronchiectasis on high-resolution computed tomography (HRCT) of the chest and a minimum of three follow-up visits to the pulmonary clinic, indicating established outpatient care. Patients were excluded if they were younger than 18 years, had a confirmed diagnosis of cystic fibrosis (CF), or were diagnosed with CF during the course of investigations (Fig. 1). Following application of inclusion and exclusion criteria, 564 patients were initially identified, with 213 retained for final analysis after complete data review. The study was approved by the Cleveland Clinic Abu Dhabi Research Ethics Committee, and all procedures were conducted in accordance with the Declaration of Helsinki. Due to retrospective observational nature of study, waiver of consent was applied for and approved by REC. Study Variables Demographic data—including age, sex, and body mass index (BMI)—were collected for all participants. Clinical parameters included spirometric measures such as FEV1% predicted, forced vital capacity (FVC%), and the FEV1/FVC ratio. Microbiological data were obtained from sputum cultures, and bronchiectasis severity was assessed using the Bronchiectasis Severity Index (BSI). Peripheral blood counts were reviewed to extract absolute neutrophil and lymphocyte counts. The neutrophil-to-lymphocyte ratio (NLR) was calculated as a marker of systemic inflammation and included. The complete blood count (CBC) test that was chosen to determine the NLR value, was the first available blood test done when the patient was not exacerbating. Statistical Methods Continuous variables are presented as mean ± standard deviation, while categorical data are reported as frequencies and percentages. Given the non-normal distribution of the neutrophil-to-lymphocyte ratio (NLR), the variable was log-transformed (logNLR) to achieve normality for parametric analysis. Pearson's correlation coefficient was used to examine the relationships between NLR and key clinical parameters, including FEV1% predicted, FVC%, FEV1/FVC ratio, and Bronchiectasis Severity Index (BSI) score. A Multivariate General Linear Model was constructed to evaluate the predictive value of logNLR on Bronchiectasis Severity Index (BSI) score, FEV1% predicted, FVC%, FEV1/FVC ratio, and eosinophil count, with Type III sum of squares used to adjust for unbalanced data. Effect sizes were reported using partial eta-squared (η²), and statistical significance was defined as a two-tailed p-value less than 0.05. To examine whether the association between logNLR and the BSI scores varied by disease aetiology, a General Linear Model was created with BSI as the dependent variable, aetiology as a fixed factor, logNLR as a covariate, and their interaction term. Assumptions were assessed via Levene’s test for homogeneity of variance. The ordinal regression model was constructed to examine the association between logNLR and bronchiectasis severity index categories (mild, moderate, and severe), while adjusting for neutrophils, lymphocytes, age, and BMI. Model assumptions were verified through goodness-of-fit and a parallel lines test, supporting proportional odds. Effect sizes were interpreted using pseudo R². Data were analysed using IBM SPSS Statistics version 27. Results The study cohort consisted of 213 patients with bronchiectasis, including 99 males (46.5%) and 114 females (53.5%). The mean age was 53.9 ± 20.4 years, and the average body mass index (BMI) was 26.4 ± 6.7 kg/m². Lung function testing showed a mean FEV1% predicted of 62.4 ± 22.6, FVC% predicted of 66.7 ± 19.7, and a mean FEV1/FVC ratio of 76.7 ± 17.7, with mild sex differences noted (Table 1 ). Bronchiectasis Severity Index categorized 19 patients (8.9%) as having mild disease, 56 patients (26.3%) as moderate, and 138 patients (64.8%) as severe (Fig. 2 ). Sputum culture demonstrated colonization with Pseudomonas aeruginosa as the most common isolate, found in 33.8% of patients. Methicillin-sensitive Staphylococcus aureus (MSSA) was identified in 18.3%, methicillin-resistant Staphylococcus aureus (MRSA) in 4.7%, Haemophilus influenzae in 8.0%, and Stenotrophomonas maltophilia in 5.2% (Fig. 3 ). The mean NLR across the cohort was 7.05 ± 13.84. Correlation analysis revealed that NLR was significantly and negatively associated with FEV1% predicted (r = -0.321, p < 0.001) and FVC% (r = -0.342, p < 0.001), but not with FEV1/FVC ratio (r = -0.111, p = 0.135) or BSI score (r = 0.116, p = 0.095) (Table 2 ). A weak but statistically significant positive correlation was observed between NLR and eosinophil count (r = 0.399, p < 0.001). Multivariate analysis confirmed that logNLR was an independent predictor of FEV1% predicted (F = 21.058, p < 0.001, partial η² = 0.104), but not of FEV1/FVC ratio (p = 0.074) or BSI score (p = 0.132). It was also significantly associated with eosinophil count (F = 5.561, p = 0.019, partial η² = 0.030) (Table 3 ). The aetiology × logNLR interaction was nonsignificant (F[9,199] = 1.43, p = .177), indicating no evidence that logNLR’s association with BSI scores differed across aetiologies. The model explained minimal variance (R²=.06). Levene’s test indicated heteroscedasticity (p = .003), suggesting caution in interpretation. The ordinal regression model demonstrated that logNLR was significantly associated with bronchiectasis severity categories, mild, moderate and severe (estimate = 0.58, 95% CI 0.02–1.13, p = 0.041), with higher values predicting greater odds of severe disease. In contrast, absolute neutrophil count (p = 0.791), lymphocyte count (p = 0.252), age (p = 0.219), and BMI (p = 0.278) showed no statistically significant associations with BSI severity (Table 4 ). While logNLR emerged as the only significant predictor among the examined variables, its explanatory power was limited, as indicated by the small effect size (Nagelkerke R²=0.024). The model's overall fit was statistically significant (χ²=4.40, p = 0.036). Discussion Our study explored the relationship between the NLR and clinical indicators of disease severity in adult patients with bronchiectasis. The microbiological data indicated a predominance of Pseudomonas aeruginosa colonization and the Bronchiectasis Severity Index classification revealed a substantial proportion of participants in the severe category, probably because our hospital is the main referral tertiary centre in UAE. Our study uses the NLR, a readily accessible marker of systemic inflammation, is significantly associated with impaired lung function in patients with bronchiectasis, as reflected by its inverse correlations with FEV1% predicted. These findings underscore the pivotal role of neutrophilic inflammation in bronchiectasis pathogenesis, a phenomenon well-documented in prior studies ( 8 ). Neutrophils drive airway damage through the release of proteases and reactive oxygen species, leading to progressive bronchial dilation and parenchymal destruction ( 8 ). The strength of NLR’s association with lung function in our cohort suggests its potential utility as a surrogate marker for tracking disease progression, particularly in settings where frequent spirometry is impractical. However, the absence of a significant correlation between NLR and the Bronchiectasis Severity Index (BSI) score highlights the limitations of relying solely on inflammatory biomarkers to capture the multifactorial nature of bronchiectasis severity( 2 ). We used multivariate linear regression with NLR value as an independent variable to assess the dependence of various clinical variables including BSI and the spirometric parameters on it. The results showed an inverse relationship between the NLR and the FEV1% predicted but failed to show a significant link between the NLR and BSI. NLR’s relationship with BSI did not vary significantly by aetiology, However, low explanatory power (R²=.06) and heteroscedasticity limit conclusions. One problem with such analysis is that it treats BSI score as a continuous variable, however, since there is no established minimal clinically important difference (MCID) for BSI, it's hard to say what small changes in BSI mean on a clinical level ( 9 ). As we know that the grouping of BSI scores has definite clinical implications for patient care and prognosis, we utilized the ordinal regression used to see if BSI’s validated categories (mild/moderate/severe), have a better causal relationship with the NLR ( 3 ). The ordinal regression model demonstrated that log-transformed NLR was significantly associated with mild, moderate and severe bronchiectasis severity categories, hence NLR can provide some information about bronchiectasis severity though the effect size was small. A high NLR might suggest a greater likelihood of worse disease outcomes. Previously, Kwok et al. found that elevated baseline NLR predicted a higher risk of hospitalization for exacerbations over a four-year follow-up in a large Asian cohort( 10 ). Other researchers has reported the NLR levels to be higher during exacerbations and correlated with positive sputum cultures, which is understandable as acute disease exacerbations are predominantly due to neutrophilic inflammation( 11 ). Similarly, an large study by Martínez-García found that higher baseline NLR was linked to more severe disease, frequent flare-ups, and a poorer quality of life ( 5 ). However, not all researchers have found strong correlations between NLR and disease severity scores. Coban and Gungen reported that while NLR correlated with systemic inflammation markers, it was not independently associated with FACED or BSI scores among stable bronchiectasis patients( 12 ). The weak but statistically significant positive correlation between NLR and eosinophil count is an interesting observation as NLR is a marker of predominantly neutrophilic inflammation, our finding suggests a potential interplay between neutrophilic and eosinophilic inflammation in bronchiectasis. Moreover, several recent studies have described a sub-type of patients with eosinophilic bronchiectasis with a specific phenotype and have shown eosinophilic subtypes of bronchiectasis to be associated with milder disease ( 13 , 14 ). In our previously published work, patients exhibiting peripheral eosinophilia appeared to have more severe disease, as indicated by their BSI scores. This observation may be attributed to the high prevalence of atopy and allergic disease in this region of the world and peripheral eosinophilia among allergic bronchopulmonary aspergillosis (ABPA) and asthma patients in our cohort( 15 ). The study had several limitations, including its retrospective, single-centre design, which introduces the potential for selection and information bias. Furthermore, systemic inflammation markers like NLR can be affected by various comorbidities or subclinical infections that were not adequately controlled for in this study. Finally, temporal changes in NLR and exacerbation episodes were not assessed. Our retrospective, single-centre observational study underscores the clinical significance of the neutrophil-to-lymphocyte ratio in individuals with bronchiectasis. While an inverse relationship between NLR and FEV1% predicted and a positive link between BSI categories appear to exist, currently available data are not robust enough for NLR to replace BSI in the prognostication of bronchiectasis patients. Therefore, NLR should be used alongside existing clinical tools rather than as a standalone measure. Abbreviations Abbreviation Full Form / Explanation NLR Neutrophil-to-Lymphocyte Ratio BSI Bronchiectasis Severity Index FEV1% predicted Forced Expiratory Volume in One Second, expressed as a percentage of the predicted value FVC% Forced Vital Capacity, expressed as a percentage of the predicted value FEV1/FVC Ratio of Forced Expiratory Volume in One Second to Forced Vital Capacity OR Odds Ratio CI Confidence Interval BMI Body Mass Index HRCT High-Resolution Computed Tomography EHR Electronic Health Record ICD-10-CM International Classification of Diseases, 10th Revision, Clinical Modification CF Cystic Fibrosis CBC Complete Blood Count logNLR Log-transformed Neutrophil-to-Lymphocyte Ratio η² (eta-squared) Effect size used in ANOVA or regression analysis MSSA Methicillin-Sensitive Staphylococcus aureus MRSA Methicillin-Resistant Staphylococcus aureus ABPA Allergic Bronchopulmonary Aspergillosis MCID Minimal Clinically Important Difference SPSS Statistical Package for the Social Sciences (IBM software for data analysis) COPD Chronic Obstructive Pulmonary Disease UK United Kingdom UAE United Arab Emirates Declarations Ethics approval and consent to participate: The study was approved by the Cleveland Clinic Abu Dhabi Research Ethics Committee (REC). Due to the retrospective, anonymised nature of the study, a waiver of consent was applied for and agreed upon by the REC. The study was conducted in accordance with the principles outlined in the Helsinki Declaration (https://www.wma.net/policies-post/wma-declaration-of-helsinki/). Consent for publication: Not Applicable Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests: Authors have no real or perceived competing interests in relation to this publication. Funding : Not Applicable Authors' contributions: -Irfan Shafiq: Study conceptualisation and supervision, Data analysis, manuscript writing -Ali Saeed Wahla: Data collection, Critical review of manuscript -Mateen Haider Uzbeck: Data collection, Critical review of the manuscript -Zaid Zoumot: Data collection, Critical review of manuscript -Mohamed Abuzakouk: Study conceptualisation and design, Data analysis -Shuayb Elkhalifa: Data collection, Critical review of manuscript -Jahnavi Bodi: Data collection, Critical review of manuscript -Said Isse: Study conceptualisation and supervision, Data analysis, Drafting manuscript Acknowledgements: Not Applicable References Wang L, Wang J, Zhao G, Li J. Prevalence of bronchiectasis in adults: a meta-analysis. BMC Public Health. 2024;24(1):2675. Chalmers JD, Goeminne P, Aliberti S, McDonnell MJ, Lonni S, Davidson J, et al. The bronchiectasis severity index. An international derivation and validation study. Am J Respir Crit Care Med. 2014;189(5):576–85. Martínez-García MÁ, de Gracia J, Vendrell Relat M, Girón RM, Máiz Carro L. de la Rosa Carrillo D, Multidimensional approach to non-cystic fibrosis bronchiectasis: the FACED score. Eur Respir J. 2014;43(5):1357–67. He M, Zhu M, Wang C, Wu Z, Xiong X, Wu H, et al. Prognostic performance of the FACED score and bronchiectasis severity index in bronchiectasis: a systematic review and meta-analysis. Biosci Rep. 2020;40(10):BSR20194514. Martinez-García MÁ, Olveira C, Girón R, García-Clemente M, Máiz-Carro L, Sibila O, et al. Peripheral Neutrophil-to-Lymphocyte Ratio in Bronchiectasis: A Marker of Disease Severity. Biomolecules. 2022;12(10):1399. Ye Z, Ai X, Liao Z, You C, Cheng Y. The prognostic values of neutrophil to lymphocyte ratio for outcomes in chronic obstructive pulmonary disease. Med (Baltim). 2019;98(28):e16371. Wang Y, Zhuang Y, Lin C, Hong H, Chen F, Ke J. The neutrophil-to-lymphocyte ratio is associated with coronary heart disease risk in adults: A population-based study. PLoS ONE. 2024;19(2):e0296838. Chalmers JD, Metersky M, Aliberti S, Morgan L, Fucile S, Lauterio M, et al. Neutrophilic inflammation in bronchiectasis. Eur Respir Rev Off J Eur Respir Soc. 2025;34(176):240179. Chalmers JD, Goeminne P, Aliberti S, McDonnell MJ, Lonni S, Davidson J, et al. The bronchiectasis severity index. An international derivation and validation study. Am J Respir Crit Care Med. 2014;189(5):576–85. Kwok WC, Ho JCM, Lam DCL, Ip MSM, Tam TCC. Baseline neutrophil-to-lymphocyte ratio as a predictor of response to hospitalized bronchiectasis exacerbation risks. Eur Clin Respir J. 2024;11(1):2372901. Georgakopoulou VE, Trakas N, Damaskos C, Garmpis N, Karakou E, Chatzikyriakou R, et al. Neutrophils to Lymphocyte Ratio as a Biomarker in Bronchiectasis Exacerbation: A Retrospective Study. Cureus. 2020;12(8):e9728. Coban H, Gungen AC. Is There a Correlation between New Scoring Systems and Systemic Inflammation in Stable Bronchiectasis? Can Respir J. 2017;2017(1):9874068. Shoemark A, Shteinberg M, De Soyza A, Haworth CS, Richardson H, Gao Y, et al. Characterization of Eosinophilic Bronchiectasis: A European Multicohort Study. Am J Respir Crit Care Med. 2022;205(8):894–902. Chen W, Ran S, Li C, Li Z, Wei N, Li J, et al. Elevated Eosinophil Counts in Acute Exacerbations of Bronchiectasis: Unveiling a Distinct Clinical Phenotype. Lung. 2024;202(1):53–61. Shafiq I, Wahla AS, Uzbeck MH, Zoumot Z, Abuzakouk M, Elkhalifa S, et al. Etiology and clinical characteristics of a non-cystic fibrosis bronchiectasis cohort in a middle eastern population. BMC Pulm Med. 2023;23(1):250. Tables Table 1: Baseline Characteristics of Study Participants (N=213) Characteristic Overall (n=213) Male (n=99) Female (n=114) Age (years), mean ± SD 53.9 ± 20.4 54.2 ± 19.8 53.7 ± 21.0 BMI (kg/m²), mean ± SD 26.4 ± 6.7 25.8 ± 5.9 26.9 ± 7.3 Lung Function FEV1% predicted, mean ± SD 62.4 ± 22.6 60.1 ± 23.2 64.3 ± 22.0 FVC% predicted, mean ± SD 66.7 ± 19.7 64.5 ± 20.1 68.6 ± 19.2 FEV1/FVC ratio, mean ± SD 76.7 ± 17.7 74.9 ± 18.3 78.2 ± 17.0 Table 2: Correlation Analysis Between NLR and Clinical Parameters Variable Pearson's r p-value FEV1% predicted -0.33 <0.001 FVC% -0.36 <0.001 FEV1/FVC ratio -0.13 0.135 BSI score 0.15 0.095 Eosinophils 0.4 <0.001 Table 3: Multivariate Analysis of log_NLR Associations Dependent Variable F-value p-value Partial η² FEV1% predicted 21.058 <0.001 0.104 FEV1/FVC ratio 3.238 0.074 0.018 BSI score 2.287 0.132 0.012 Eosinophil count 5.561 0.019 0.030 Abbreviations: MSSA: Methicillin-sensitive Staphylococcus aureus MRSA: Methicillin-resistant Staphylococcus aureus Partial η²: Effect size (0.01=small, 0.06=medium, 0.14=large) Table 4: Ordinal Regression Analysis of Predictors for Bronchiectasis Severity (BSI Categories) Variable Estimate (SE) Wald χ² p-value 95% CI log_NLR 0.58 (0.28) 4.18 0.041* 0.02 to 1.13 Neutrophils 0.02 (0.07) 0.07 0.791 -0.11 to 0.15 Lymphocytes 0.34 (0.29) 1.31 0.252 -0.24 to 0.91 Age 0.01 (0.01) 1.51 0.219 -0.01 to 0.02 BMI -0.02 (0.02) 1.18 0.278 -0.06 to 0.02 Model Characteristics: Proportional odds assumption upheld (χ²=3.73, p=0.054), Nagelkerke pseudo R² = 0.024, Overall model χ²=4.40 (p=0.036) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6691002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476464179,"identity":"51c17999-18d2-47af-b635-481ccebb7ec3","order_by":0,"name":"Irfan 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09:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6691002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6691002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85519335,"identity":"7f2c2b42-3048-4641-be37-2de2f0069186","added_by":"auto","created_at":"2025-06-26 19:13:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBronchiectasis cohort flow diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6691002/v1/a5c45264b9ebb3df9dda985c.jpeg"},{"id":85519673,"identity":"2b22c999-9bcc-445f-bb00-be616baa141b","added_by":"auto","created_at":"2025-06-26 19:29:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBronchiectasis Severity Index (BSI) Distribution\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6691002/v1/9a23cf006a6f77b32c6fdb35.png"},{"id":85519407,"identity":"3a55e80a-6894-4ffa-9cea-c18e867b08a9","added_by":"auto","created_at":"2025-06-26 19:21:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobiological Isolates in Study Population\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6691002/v1/38d90e9bc3013ee7095aa38c.png"},{"id":100371208,"identity":"e285d553-f441-4db1-96c2-79f061c53a0c","added_by":"auto","created_at":"2026-01-16 08:09:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":955233,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6691002/v1/4cca9fe4-297f-456e-9cf6-86c8609e146a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neutrophil-to-Lymphocyte Ratio in Bronchiectasis and it’s Association with Disease Severity – A Single Centre Retrospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBronchiectasis is a chronic respiratory condition characterized by irreversible bronchial dilation, persistent airway inflammation, and recurrent infections. Its global prevalence has been increasing, particularly among older adults and women. According to a recent meta-analysis, the pooled prevalence of bronchiectasis in adults is approximately 680 per 100,000 individuals(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).​\u003c/p\u003e \u003cp\u003eThe clinical course of bronchiectasis is variable, with patients experiencing different degrees of symptom severity, frequency of exacerbations, and disease progression. To aid in prognostication and management, multidimensional scoring systems such as the Bronchiectasis Severity Index (BSI) and the FACED score have been developed(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These tools incorporate clinical, radiological, and microbiological parameters to stratify patients based on disease severity and predict outcomes like mortality and hospitalization. However, their application in routine clinical practice can be limited due to the need for comprehensive data collection and complex calculations(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere remains a need for a simple, cost-effective biomarkers that can reliably reflect disease severity and assist in prognostication. The neutrophil-to-lymphocyte ratio (NLR), derived from routine complete blood counts, has emerged as a potential candidate. NLR serves as an indicator of systemic inflammation and has been associated with disease severity and outcomes in various chronic conditions, including chronic obstructive pulmonary disease (COPD) and cardiovascular diseases(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of bronchiectasis, recent studies have explored the utility of NLR as a marker of disease severity. A study analysing data from the Spanish Bronchiectasis Registry found that higher NLR values correlated with increased disease severity as measured by BSI, FACED, and E-FACED scores. Furthermore, elevated NLR was associated with a higher frequency of exacerbations, greater colonization by pathogenic microorganisms, and poorer quality of life. These findings suggest that NLR could serve as a practical and accessible biomarker for assessing disease severity and predicting clinical outcomes in bronchiectasis patients(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). ​\u003c/p\u003e \u003cp\u003eBuilding upon this evidence, our study aims to evaluate the relationship between NLR and BSI as well as lung function parameters, specifically forced expiratory volume in one second (FEV1% predicted), in a cohort of patients with bronchiectasis. By investigating this association, we seek to determine the potential of NLR as a surrogate marker for pulmonary function impairment and its role in the clinical assessment of bronchiectasis severity.​\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation:\u003c/h2\u003e \u003cp\u003eElectronic Health Records (EHR) from our tertiary care center were queried using the ICD-10-CM code for bronchiectasis (J47) to identify eligible patients between April 2014 and December 2021. Inclusion criteria required a radiologically confirmed diagnosis of bronchiectasis on high-resolution computed tomography (HRCT) of the chest and a minimum of three follow-up visits to the pulmonary clinic, indicating established outpatient care.\u003c/p\u003e \u003cp\u003ePatients were excluded if they were younger than 18 years, had a confirmed diagnosis of cystic fibrosis (CF), or were diagnosed with CF during the course of investigations (Fig.\u0026nbsp;1). Following application of inclusion and exclusion criteria, 564 patients were initially identified, with 213 retained for final analysis after complete data review.\u003c/p\u003e \u003cp\u003e The study was approved by the Cleveland Clinic Abu Dhabi Research Ethics Committee, and all procedures were conducted in accordance with the Declaration of Helsinki. Due to retrospective observational nature of study, waiver of consent was applied for and approved by REC.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Variables\u003c/h3\u003e\n\u003cp\u003eDemographic data\u0026mdash;including age, sex, and body mass index (BMI)\u0026mdash;were collected for all participants. Clinical parameters included spirometric measures such as FEV1% predicted, forced vital capacity (FVC%), and the FEV1/FVC ratio. Microbiological data were obtained from sputum cultures, and bronchiectasis severity was assessed using the Bronchiectasis Severity Index (BSI). Peripheral blood counts were reviewed to extract absolute neutrophil and lymphocyte counts. The neutrophil-to-lymphocyte ratio (NLR) was calculated as a marker of systemic inflammation and included. The complete blood count (CBC) test that was chosen to determine the NLR value, was the first available blood test done when the patient was not exacerbating.\u003c/p\u003e\n\u003ch3\u003eStatistical Methods\u003c/h3\u003e\n\u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical data are reported as frequencies and percentages. Given the non-normal distribution of the neutrophil-to-lymphocyte ratio (NLR), the variable was log-transformed (logNLR) to achieve normality for parametric analysis. Pearson's correlation coefficient was used to examine the relationships between NLR and key clinical parameters, including FEV1% predicted, FVC%, FEV1/FVC ratio, and Bronchiectasis Severity Index (BSI) score.\u003c/p\u003e \u003cp\u003eA Multivariate General Linear Model was constructed to evaluate the predictive value of logNLR on Bronchiectasis Severity Index (BSI) score, FEV1% predicted, FVC%, FEV1/FVC ratio, and eosinophil count, with Type III sum of squares used to adjust for unbalanced data. Effect sizes were reported using partial eta-squared (η\u0026sup2;), and statistical significance was defined as a two-tailed p-value less than 0.05. To examine whether the association between logNLR and the BSI scores varied by disease aetiology, a General Linear Model was created with BSI as the dependent variable, aetiology as a fixed factor, logNLR as a covariate, and their interaction term. Assumptions were assessed via Levene\u0026rsquo;s test for homogeneity of variance.\u003c/p\u003e \u003cp\u003eThe ordinal regression model was constructed to examine the association between logNLR and bronchiectasis severity index categories (mild, moderate, and severe), while adjusting for neutrophils, lymphocytes, age, and BMI. Model assumptions were verified through goodness-of-fit and a parallel lines test, supporting proportional odds. Effect sizes were interpreted using pseudo R\u0026sup2;. Data were analysed using IBM SPSS Statistics version 27.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study cohort consisted of 213 patients with bronchiectasis, including 99 males (46.5%) and 114 females (53.5%). The mean age was 53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4 years, and the average body mass index (BMI) was 26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 kg/m\u0026sup2;. Lung function testing showed a mean FEV1% predicted of 62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6, FVC% predicted of 66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7, and a mean FEV1/FVC ratio of 76.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7, with mild sex differences noted (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBronchiectasis Severity Index categorized 19 patients (8.9%) as having mild disease, 56 patients (26.3%) as moderate, and 138 patients (64.8%) as severe (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sputum culture demonstrated colonization with Pseudomonas aeruginosa as the most common isolate, found in 33.8% of patients. Methicillin-sensitive Staphylococcus aureus (MSSA) was identified in 18.3%, methicillin-resistant Staphylococcus aureus (MRSA) in 4.7%, Haemophilus influenzae in 8.0%, and Stenotrophomonas maltophilia in 5.2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mean NLR across the cohort was 7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;13.84. Correlation analysis revealed that NLR was significantly and negatively associated with FEV1% predicted (r = -0.321, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and FVC% (r = -0.342, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not with FEV1/FVC ratio (r = -0.111, p\u0026thinsp;=\u0026thinsp;0.135) or BSI score (r\u0026thinsp;=\u0026thinsp;0.116, p\u0026thinsp;=\u0026thinsp;0.095) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A weak but statistically significant positive correlation was observed between NLR and eosinophil count (r\u0026thinsp;=\u0026thinsp;0.399, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eMultivariate analysis confirmed that logNLR was an independent predictor of FEV1% predicted (F\u0026thinsp;=\u0026thinsp;21.058, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, partial η\u0026sup2; = 0.104), but not of FEV1/FVC ratio (p\u0026thinsp;=\u0026thinsp;0.074) or BSI score (p\u0026thinsp;=\u0026thinsp;0.132). It was also significantly associated with eosinophil count (F\u0026thinsp;=\u0026thinsp;5.561, p\u0026thinsp;=\u0026thinsp;0.019, partial η\u0026sup2; = 0.030) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aetiology \u0026times; logNLR interaction was nonsignificant (F[9,199]\u0026thinsp;=\u0026thinsp;1.43, p\u0026thinsp;=\u0026thinsp;.177), indicating no evidence that logNLR\u0026rsquo;s association with BSI scores differed across aetiologies. The model explained minimal variance (R\u0026sup2;=.06). Levene\u0026rsquo;s test indicated heteroscedasticity (p\u0026thinsp;=\u0026thinsp;.003), suggesting caution in interpretation.\u003c/p\u003e \u003cp\u003eThe ordinal regression model demonstrated that logNLR was significantly associated with bronchiectasis severity categories, mild, moderate and severe (estimate\u0026thinsp;=\u0026thinsp;0.58, 95% CI 0.02\u0026ndash;1.13, p\u0026thinsp;=\u0026thinsp;0.041), with higher values predicting greater odds of severe disease. In contrast, absolute neutrophil count (p\u0026thinsp;=\u0026thinsp;0.791), lymphocyte count (p\u0026thinsp;=\u0026thinsp;0.252), age (p\u0026thinsp;=\u0026thinsp;0.219), and BMI (p\u0026thinsp;=\u0026thinsp;0.278) showed no statistically significant associations with BSI severity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e). While logNLR emerged as the only significant predictor among the examined variables, its explanatory power was limited, as indicated by the small effect size (Nagelkerke R\u0026sup2;=0.024). The model's overall fit was statistically significant (χ\u0026sup2;=4.40, p\u0026thinsp;=\u0026thinsp;0.036).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eOur study explored the relationship between the NLR and clinical indicators of disease severity in adult patients with bronchiectasis. The microbiological data indicated a predominance of \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e colonization and the Bronchiectasis Severity Index classification revealed a substantial proportion of participants in the severe category, probably because our hospital is the main referral tertiary centre in UAE.\u003c/p\u003e \u003cp\u003eOur study uses the NLR, a readily accessible marker of systemic inflammation, is significantly associated with impaired lung function in patients with bronchiectasis, as reflected by its inverse correlations with FEV1% predicted. These findings underscore the pivotal role of neutrophilic inflammation in bronchiectasis pathogenesis, a phenomenon well-documented in prior studies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Neutrophils drive airway damage through the release of proteases and reactive oxygen species, leading to progressive bronchial dilation and parenchymal destruction (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The strength of NLR\u0026rsquo;s association with lung function in our cohort suggests its potential utility as a surrogate marker for tracking disease progression, particularly in settings where frequent spirometry is impractical. However, the absence of a significant correlation between NLR and the Bronchiectasis Severity Index (BSI) score highlights the limitations of relying solely on inflammatory biomarkers to capture the multifactorial nature of bronchiectasis severity(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used multivariate linear regression with NLR value as an independent variable to assess the dependence of various clinical variables including BSI and the spirometric parameters on it. The results showed an inverse relationship between the NLR and the FEV1% predicted but failed to show a significant link between the NLR and BSI. NLR\u0026rsquo;s relationship with BSI did not vary significantly by aetiology, However, low explanatory power (R\u0026sup2;=.06) and heteroscedasticity limit conclusions. One problem with such analysis is that it treats BSI score as a continuous variable, however, since there is no established minimal clinically important difference (MCID) for BSI, it's hard to say what small changes in BSI mean on a clinical level (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). As we know that the grouping of BSI scores has definite clinical implications for patient care and prognosis, we utilized the ordinal regression used to see if BSI\u0026rsquo;s validated categories (mild/moderate/severe), have a better causal relationship with the NLR (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The ordinal regression model demonstrated that log-transformed NLR was significantly associated with mild, moderate and severe bronchiectasis severity categories, hence NLR can provide some information about bronchiectasis severity though the effect size was small. A high NLR might suggest a greater likelihood of worse disease outcomes. Previously, Kwok et al. found that elevated baseline NLR predicted a higher risk of hospitalization for exacerbations over a four-year follow-up in a large Asian cohort(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Other researchers has reported the NLR levels to be higher during exacerbations and correlated with positive sputum cultures, which is understandable as acute disease exacerbations are predominantly due to neutrophilic inflammation(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, an large study by Mart\u0026iacute;nez-Garc\u0026iacute;a found that higher baseline NLR was linked to more severe disease, frequent flare-ups, and a poorer quality of life (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, not all researchers have found strong correlations between NLR and disease severity scores. Coban and Gungen reported that while NLR correlated with systemic inflammation markers, it was not independently associated with FACED or BSI scores among stable bronchiectasis patients(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe weak but statistically significant positive correlation between NLR and eosinophil count is an interesting observation as NLR is a marker of predominantly neutrophilic inflammation, our finding suggests a potential interplay between neutrophilic and eosinophilic inflammation in bronchiectasis. Moreover, several recent studies have described a sub-type of patients with eosinophilic bronchiectasis with a specific phenotype and have shown eosinophilic subtypes of bronchiectasis to be associated with milder disease (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In our previously published work, patients exhibiting peripheral eosinophilia appeared to have more severe disease, as indicated by their BSI scores. This observation may be attributed to the high prevalence of atopy and allergic disease in this region of the world and peripheral eosinophilia among allergic bronchopulmonary aspergillosis (ABPA) and asthma patients in our cohort(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study had several limitations, including its retrospective, single-centre design, which introduces the potential for selection and information bias. Furthermore, systemic inflammation markers like NLR can be affected by various comorbidities or subclinical infections that were not adequately controlled for in this study. Finally, temporal changes in NLR and exacerbation episodes were not assessed.\u003c/p\u003e \u003cp\u003eOur retrospective, single-centre observational study underscores the clinical significance of the neutrophil-to-lymphocyte ratio in individuals with bronchiectasis. While an inverse relationship between NLR and FEV1% predicted and a positive link between BSI categories appear to exist, currently available data are not robust enough for NLR to replace BSI in the prognostication of bronchiectasis patients. Therefore, NLR should be used alongside existing clinical tools rather than as a standalone measure.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFull Form / Explanation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNeutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBronchiectasis Severity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFEV1% predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eForced Expiratory Volume in One Second, expressed as a percentage of the predicted value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFVC%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eForced Vital Capacity, expressed as a percentage of the predicted value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFEV1/FVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRatio of Forced Expiratory Volume in One Second to Forced Vital Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHRCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-Resolution Computed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eElectronic Health Record\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eICD-10-CM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInternational Classification of Diseases, 10th Revision, Clinical Modification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCystic Fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComplete Blood Count\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elogNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLog-transformed Neutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eη² (eta-squared)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect size used in ANOVA or regression analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMethicillin-Sensitive \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMRSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMethicillin-Resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eABPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAllergic Bronchopulmonary Aspergillosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMCID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimal Clinically Important Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistical Package for the Social Sciences (IBM software for data analysis)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnited Arab Emirates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Cleveland Clinic Abu Dhabi Research Ethics Committee (REC). Due to the retrospective, anonymised nature of the study, a waiver of consent was applied for and agreed upon by the REC.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles outlined in the Helsinki Declaration (https://www.wma.net/policies-post/wma-declaration-of-helsinki/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003cbr\u003e \u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have no real or perceived competing interests in relation to this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-Irfan Shafiq: Study conceptualisation and supervision, Data analysis, manuscript writing\u003c/p\u003e\n\u003cp\u003e-Ali Saeed Wahla: Data collection, Critical review of manuscript\u003c/p\u003e\n\u003cp\u003e-Mateen Haider Uzbeck: Data collection, Critical review of the manuscript\u003c/p\u003e\n\u003cp\u003e-Zaid Zoumot: Data collection, Critical review of manuscript\u003c/p\u003e\n\u003cp\u003e-Mohamed Abuzakouk: Study conceptualisation and design, Data analysis\u003c/p\u003e\n\u003cp\u003e-Shuayb Elkhalifa:\u0026nbsp;Data collection, Critical review of manuscript\u003c/p\u003e\n\u003cp\u003e-Jahnavi\u0026nbsp;Bodi: Data collection, Critical review of manuscript\u003c/p\u003e\n\u003cp\u003e-Said Isse:\u0026nbsp;Study conceptualisation and supervision, Data analysis, Drafting manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang L, Wang J, Zhao G, Li J. Prevalence of bronchiectasis in adults: a meta-analysis. BMC Public Health. 2024;24(1):2675.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalmers JD, Goeminne P, Aliberti S, McDonnell MJ, Lonni S, Davidson J, et al. The bronchiectasis severity index. An international derivation and validation study. Am J Respir Crit Care Med. 2014;189(5):576\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Garc\u0026iacute;a M\u0026Aacute;, de Gracia J, Vendrell Relat M, Gir\u0026oacute;n RM, M\u0026aacute;iz Carro L. de la Rosa Carrillo D, Multidimensional approach to non-cystic fibrosis bronchiectasis: the FACED score. Eur Respir J. 2014;43(5):1357\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe M, Zhu M, Wang C, Wu Z, Xiong X, Wu H, et al. Prognostic performance of the FACED score and bronchiectasis severity index in bronchiectasis: a systematic review and meta-analysis. Biosci Rep. 2020;40(10):BSR20194514.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Garc\u0026iacute;a M\u0026Aacute;, Olveira C, Gir\u0026oacute;n R, Garc\u0026iacute;a-Clemente M, M\u0026aacute;iz-Carro L, Sibila O, et al. Peripheral Neutrophil-to-Lymphocyte Ratio in Bronchiectasis: A Marker of Disease Severity. Biomolecules. 2022;12(10):1399.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe Z, Ai X, Liao Z, You C, Cheng Y. The prognostic values of neutrophil to lymphocyte ratio for outcomes in chronic obstructive pulmonary disease. Med (Baltim). 2019;98(28):e16371.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Zhuang Y, Lin C, Hong H, Chen F, Ke J. The neutrophil-to-lymphocyte ratio is associated with coronary heart disease risk in adults: A population-based study. PLoS ONE. 2024;19(2):e0296838.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalmers JD, Metersky M, Aliberti S, Morgan L, Fucile S, Lauterio M, et al. Neutrophilic inflammation in bronchiectasis. Eur Respir Rev Off J Eur Respir Soc. 2025;34(176):240179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalmers JD, Goeminne P, Aliberti S, McDonnell MJ, Lonni S, Davidson J, et al. The bronchiectasis severity index. An international derivation and validation study. Am J Respir Crit Care Med. 2014;189(5):576\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwok WC, Ho JCM, Lam DCL, Ip MSM, Tam TCC. Baseline neutrophil-to-lymphocyte ratio as a predictor of response to hospitalized bronchiectasis exacerbation risks. Eur Clin Respir J. 2024;11(1):2372901.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgakopoulou VE, Trakas N, Damaskos C, Garmpis N, Karakou E, Chatzikyriakou R, et al. Neutrophils to Lymphocyte Ratio as a Biomarker in Bronchiectasis Exacerbation: A Retrospective Study. Cureus. 2020;12(8):e9728.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoban H, Gungen AC. Is There a Correlation between New Scoring Systems and Systemic Inflammation in Stable Bronchiectasis? Can Respir J. 2017;2017(1):9874068.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShoemark A, Shteinberg M, De Soyza A, Haworth CS, Richardson H, Gao Y, et al. Characterization of Eosinophilic Bronchiectasis: A European Multicohort Study. Am J Respir Crit Care Med. 2022;205(8):894\u0026ndash;902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Ran S, Li C, Li Z, Wei N, Li J, et al. Elevated Eosinophil Counts in Acute Exacerbations of Bronchiectasis: Unveiling a Distinct Clinical Phenotype. Lung. 2024;202(1):53\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShafiq I, Wahla AS, Uzbeck MH, Zoumot Z, Abuzakouk M, Elkhalifa S, et al. Etiology and clinical characteristics of a non-cystic fibrosis bronchiectasis cohort in a middle eastern population. BMC Pulm Med. 2023;23(1):250.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Baseline Characteristics of Study Participants (N=213)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n=213)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMale (n=99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (n=114)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.9 \u0026plusmn; 20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.2 \u0026plusmn; 19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.7 \u0026plusmn; 21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.4 \u0026plusmn; 6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.8 \u0026plusmn; 5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.9 \u0026plusmn; 7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLung Function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFEV1% predicted, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.4 \u0026plusmn; 22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.1 \u0026plusmn; 23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.3 \u0026plusmn; 22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFVC% predicted, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.7 \u0026plusmn; 19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.5 \u0026plusmn; 20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.6 \u0026plusmn; 19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFEV1/FVC ratio, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.7 \u0026plusmn; 17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.9 \u0026plusmn; 18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.2 \u0026plusmn; 17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Correlation Analysis Between NLR and Clinical Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border: none;width:239.0pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:110.9pt;border:solid #8EA9DB 1.0pt;border-right: none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:150%;font-family:\"Arial\",sans-serif;color:black;'\u003eVariable\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.85pt;border-top:solid #8EA9DB 1.0pt;border-left: none;border-bottom:solid #8EA9DB 1.0pt;border-right:none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height: 15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:150%;font-family:\"Arial\",sans-serif;color:black;'\u003ePearson\u0026apos;s r\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.25pt;border:solid #8EA9DB 1.0pt;border-left: none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cstrong\u003e\u003cspan style='font-size:16px;line-height:150%;font-family:\"Arial\",sans-serif;color:black;'\u003ep-value\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:110.9pt;border-top:none;border-left:solid #8EA9DB 1.0pt;border-bottom:solid #8EA9DB 1.0pt;border-right:none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003eFEV1% predicted\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.85pt;border:none;border-bottom:solid #8EA9DB 1.0pt;background:#F87C7E;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e-0.33\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.25pt;border-top:none;border-left:none;border-bottom:solid #8EA9DB 1.0pt;border-right:solid #8EA9DB 1.0pt;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:110.9pt;border-top:none;border-left:solid #8EA9DB 1.0pt;border-bottom:solid #8EA9DB 1.0pt;border-right:none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003eFVC%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.85pt;border:none;border-bottom:solid #8EA9DB 1.0pt;background:#F8696B;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e-0.36\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.25pt;border-top:none;border-left:none;border-bottom:solid #8EA9DB 1.0pt;border-right:solid #8EA9DB 1.0pt;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:15.5pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:110.9pt;border-top:none;border-left:solid #8EA9DB 1.0pt;border-bottom:solid #8EA9DB 1.0pt;border-right:none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:21.55pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003eFEV1/FVC ratio\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.85pt;border:none;border-bottom:solid #8EA9DB 1.0pt;background:#FCFCFF;padding:0cm 5.4pt 0cm 5.4pt;height:21.55pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e-0.13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.25pt;border-top:none;border-left:none;border-bottom:solid #8EA9DB 1.0pt;border-right:solid #8EA9DB 1.0pt;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:21.55pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e0.135\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:110.9pt;border-top:none;border-left:solid #8EA9DB 1.0pt;border-bottom:solid #8EA9DB 1.0pt;border-right:none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:29.0pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003eBSI score\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.85pt;border:none;border-bottom:solid #8EA9DB 1.0pt;background:#A7C0E1;padding:0cm 5.4pt 0cm 5.4pt;height:29.0pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e0.15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.25pt;border-top:none;border-left:none;border-bottom:solid #8EA9DB 1.0pt;border-right:solid #8EA9DB 1.0pt;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:29.0pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e0.095\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:110.9pt;border-top:none;border-left:solid #8EA9DB 1.0pt;border-bottom:solid #8EA9DB 1.0pt;border-right:none;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:25.6pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003eEosinophils\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.85pt;border:none;border-bottom:solid #8EA9DB 1.0pt;background:#5A8AC6;padding:0cm 5.4pt 0cm 5.4pt;height:25.6pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e0.4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.25pt;border-top:none;border-left:none;border-bottom:solid #8EA9DB 1.0pt;border-right:solid #8EA9DB 1.0pt;background:white;padding:0cm 5.4pt 0cm 5.4pt;height:25.6pt;\"\u003e\n \u003cp style='margin-top:0cm;margin-right:0cm;margin-bottom:0cm;margin-left:0cm;font-size:11.0pt;font-family:\"Calibri\",sans-serif;text-align:justify;line-height: 150%;'\u003e\u003cspan style='font-size:16px;line-height:150%;font-family: \"Arial\",sans-serif;color:black;'\u003e\u0026lt;0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Multivariate Analysis of log_NLR Associations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePartial \u0026eta;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFEV1% predicted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFEV1/FVC ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBSI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEosinophil count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eMSSA: Methicillin-sensitive \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eMRSA: Methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003ePartial \u0026eta;\u0026sup2;: Effect size (0.01=small, 0.06=medium, 0.14=large)\u003c/li\u003e\n \u003c/ul\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Ordinal Regression Analysis of Predictors for Bronchiectasis Severity (BSI Categories)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWald \u0026chi;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elog_NLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02 to 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNeutrophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.11 to 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.34 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.24 to 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01 to 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.02 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06 to 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel Characteristics:\u0026nbsp;\u003c/strong\u003eProportional odds assumption upheld (\u0026chi;\u0026sup2;=3.73, p=0.054), Nagelkerke pseudo R\u0026sup2; = 0.024, Overall model \u0026chi;\u0026sup2;=4.40 (p=0.036)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bronchiectasis, neutrophil-to-lymphocyte ratio, disease severity, lung function, systemic inflammation","lastPublishedDoi":"10.21203/rs.3.rs-6691002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6691002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has shown promise in assessing disease severity in chronic respiratory conditions. However, its utility in bronchiectasis remains underexplored. This study evaluated the association between NLR and clinical markers of bronchiectasis severity, including lung function and the Bronchiectasis Severity Index (BSI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective cohort study, electronic health records of 213 adults with radiologically confirmed bronchiectasis (2014\u0026ndash;2021) were analyzed. NLR was derived from non-exacerbation complete blood counts. Correlations between NLR and spirometry (FEV1% predicted, FVC%, FEV1/FVC), BSI, and microbiological data were assessed using Pearson\u0026rsquo;s correlation and multivariate regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMean NLR was 7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;13.84. NLR correlated negatively with FEV1% PREDICTED (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.321, *p* \u0026lt; 0.001) and FVC% (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.342, *p* \u0026lt; 0.001), but not with BSI (*p* = 0.095). Multivariate analysis confirmed NLR as an independent predictor of FEV1% predicted (F\u0026thinsp;=\u0026thinsp;21.058, *p* \u0026lt; 0.001) and eosinophil count (*p* = 0.019). Ordinal regression linked higher NLR to severe BSI categories (OR\u0026thinsp;=\u0026thinsp;0.58, 95% CI 0.02\u0026ndash;1.13, *p* = 0.041), though effect sizes were modest (Nagelkerke R\u0026sup2; = 0.024). \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (33.8%) was the most frequent pathogen.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated NLR reflects worse lung function and may aid in stratifying bronchiectasis severity, particularly as a surrogate for neutrophilic inflammation. However, its inability to correlate strongly with BSI underscores the need for multimodal assessment. NLR is a practical, low-cost biomarker but should complement\u0026mdash;not replace\u0026mdash;existing tools like BSI. Prospective studies are needed to validate its prognostic utility.\u003c/p\u003e","manuscriptTitle":"Neutrophil-to-Lymphocyte Ratio in Bronchiectasis and it’s Association with Disease Severity – A Single Centre Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 19:13:49","doi":"10.21203/rs.3.rs-6691002/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e81fccc-14fa-4eb8-b431-26c334eceeca","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T12:40:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-26 19:13:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6691002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6691002","identity":"rs-6691002","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}