Treatable Traits in Long COVID: Inhaled corticosteroids and long-acting bronchodilators for small airway dysfunction among symptomatic Long COVID patients without known Asthma

Treatable Traits in Long COVID: Inhaled corticosteroids and long-acting bronchodilators for small airway dysfunction among symptomatic Long COVID patients without known Asthma | 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 Treatable Traits in Long COVID: Inhaled corticosteroids and long-acting bronchodilators for small airway dysfunction among symptomatic Long COVID patients without known Asthma U. E Abouelhassan, M. Plit, S. Faux, B. Jo, D. Ishak, S. Shahid, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7541120/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background and objective Long COVID is a chronic multi-system condition with limited treatment options. Small airway dysfunction (SAD), detectable by oscillometry or spirometry, may respond to inhaled corticosteroid/long-acting bronchodilator (ICS/LABA) therapy. We aimed to determine the prevalence and predictors of SAD in non-asthmatic long COVID patients and assess the impact of ICS/LABA. Methods Retrospective study of adults with WHO-defined long COVID at St Vincent’s, Sydney (June 2022–October 2024). Patients with prior asthma or ICS/LABA use were excluded. Demographics, co-morbidities, spirometry, forced oscillation techniques (FOT), and C19-YRS were assessed at baseline, 6, 12, and 18 months. SAD was defined using Chiu et al. criteria. Outcomes were compared between SAD and non-SAD patients and by ICS/LABA use (p < 0.05 significant). Logistic regression identified predictors of SAD. Results Of 251 patients screened, 163 met inclusion and 123 (75.5%) had SAD. Compared to non-SAD, SAD patients were older (52.9 ± 14.9 vs. 42.2 ± 15.6 years, p < 0.001), with higher BMI (29.1 ± 6.5 vs. 24.4 ± 4.0, p < 0.001), and more hypertension, obesity, and ischaemic heart disease. FOT showed significantly higher resistance (R5, R19, R5–19), increased AX and Fres, and more negative X5 (all p < 0.05), while spirometry only detected lower FEV1. Among 79 SAD patients prescribed ICS/LABA, resistance, reactance, and symptoms improved significantly: dyspnoea (83.5%→17.7%), fatigue (96.2%→15.2%), cough (43%→11.4%). Conclusion SAD appears highly prevalent in long COVID patients without known asthma and is best detected by oscillometry. Treatment with ICS/LABA was associated with improved symptoms and FOT indices, however randomised trials are needed to confirm efficacy. Long COVID Small airway dysfunction Oscillometry Inhaled corticosteroid therapy Treatable traits Forced oscillation technique Dyspnoea Fatigue. Figures Figure 1 INTRODUCTION Long COVID or post-acute Coronavirus Disease 2019 (COVID-19) sequelae is a complex, chronic health condition involving multiple organ systems and has limited treatment options. This makes it very challenging to manage. However, personalised and precision medicine approaches can identify and treat clinically relevant characteristics or traits, to improve quality of life and reduce symptom burden [1]. This “Treatable Traits” paradigm was initially developed for chronic respiratory diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD) and demonstrates improved patient-centred outcomes by detecting and treating clinically relevant traits [2]. A treatable trait (TT) is a measurable and clinically relevant characteristic that can be targeted with specific treatment that is known to provide benefit for that specific characteristic [1]. Multiple TTs often co-exist in one patient, and addressing each specifically maximises clinical benefit. Traits without current treatments highlight priorities for future research and trials [3]. Increasing evidence of improved clinically relevant outcomes exist for other complex and multi-system chronic conditions [2–4]. A recent systematic review found the treatable traits approach relevant to Long COVID, grouping 34 symptoms into eight clusters, although there was no specific mention of small airway dysfunction [1]. Persistent symptoms after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral infection, commonly called long COVID, is defined by the WHO as symptoms lasting > 3 months without another cause [5]. SARS-CoV-2 virus binds to angiotensin-converting enzyme 2 (ACE-2) receptors on respiratory cells, causing a spectrum of disease from asymptomatic or mild acute infection to a severe acute respiratory distress syndrome (ARDS) with bilateral pneumonitis that causes death [6]. SARS-CoV-2 can also lead to small airway dysfunction (SAD) characterised by airflow obstruction, spasm and gas trapping. This may persist for months following initial infection, as has been seen with other respiratory viral infections [6–8]. Histopathological features of SAD include bronchiolitis, reduced airway calibre, and bronchiolar hyper-responsiveness, with SARS-CoV-2 particles observed in distal airways by electron microscopy in acute COVID-19 [9]. SAD may occur without pre-existing asthma. We hypothesised that Inhaled Corticosteroid (ICS) and Long-Acting Beta-2 Agonist (LABA) may improve symptoms and lung function by targeting bronchiolitis, bronchiolar hyper-responsiveness, and ongoing airway inflammation. Lung function by oscillometry, using either impulse oscillometry (IOS) or the forced oscillation technique (FOT), is a sensitive, non-invasive test for detecting peripheral airway abnormalities. Prior studies in asthma and COPD, show oscillometry may identify small airway dysfunction before spirometry becomes abnormal and even prior to symptoms onset [10,11]. However, very limited research of SAD in long COVID exist, apart from one that utilised oscillometry, but failed to exclude known asthma patients [12]. Identifying SAD within a treatable traits (TT) paradigm may provide a therapeutic option for patients with an ‘asthma-like’ post COVID syndrome. This study, aimed to: (1) determine the prevalence and predictors of SAD in symptomatic long COVID patients ≥ 3 months post infection, and (2) assess respiratory function and symptom outcomes over 18 months in patients (newly) prescribed ICS/LABA. METHODS This retrospective cohort study included adult patients over 18 years from the St Vincent’s Long COVID outpatient service in Sydney, Australia. All met the WHO criteria for Long COVID with Polymerase Chain Reaction (PCR) confirmed infection (WHO, 2021, accessed 3 March 2023) between June 2022 and October 2024 [5]. Patients had persistent symptoms of at least 3 months duration following initial SARS-CoV-2 viral infection. Patients with a history of confirmed asthma or have a history of possible asthma or those already prescribed inhaled corticosteroids or other inhaled asthma medications were excluded. Local ethics approval was obtained (REGIS 2024/PID02621). Baseline data, blood tests, symptom scores, and lung function were obtained from electronic medical records at the initial visit and at 6, 12, and 18 months. Demographics included age, sex, BMI, COVID-19 history (dates, severity, frequency), vaccination status, co-morbidities (including asthma, obesity), smoking history, current medications (e.g., inhaled corticosteroids, bronchodilators), symptoms, and COVID-19 Yorkshire Rehabilitation Scale (C-19 YRS)[13]. Blood tests included IgG subclasses, vitamin D, thyroid function, COVID-19 serology, and viral markers (CMV, EBV, HSV), plus T-cell subsets. Lung function was assessed at rest using FOT and spirometry pre- and post-bronchodilator (400 mcg salbutamol). FOT (tremoflo® C-100, Thorasys, Montreal, Canada) was performed before spirometry at each visit, following ERS Task Force guidelines [14]. Parameters included R5, R19, R5–19, AX, X5, and Fres. SAD was defined per Chiu et al. ( 2020 ) [15] as R5–19 > 0.07 kPa·s·L⁻¹, X5 14.14 Hz, and AX > 0.44 kPa·L⁻¹. Resistance/reactance values were converted to cmH₂O units using standard calculations [14]. Spirometry was conducted using the ndd EasyOne® system (ndd Medical Technologies, Zurich, Switzerland) in accordance with ATS/ERS standards, measuring post-bronchodilator (BD) FVC, FEV₁, FEV₁/FVC, and FEF₂₅–₇₅%. SAD was defined as FEF₂₅–₇₅% <70% per ERS recommendations [16]. Tests were performed following a (minimum) 5-minute rest with FOT first. SAD patients were stratified by ICS/LABA use for outcome comparisons. Detailed FOT and spirometry protocols are provided in Appendix S1 in the Supporting Information. ICS/LABA initiation for SAD was at the discretion of the treating respiratory physician, guided by symptoms (dyspnoea, cough, wheeze) and lung function (pre/post-bronchodilator spirometry and oscillometry). Common regimens included budesonide/formoterol 200/6 µg Rapihaler with spacer (two puffs BID) and fluticasone propionate/salmeterol 250/25 µg pMDI with spacer (one puff BID). Statistical analyses were performed using SPSS Version 28 (IBM, Armonk, NY). Continuous variables were summarised as mean ± SD or median (IQR) and compared using independent t-tests or Mann–Whitney tests; paired data were analysed with Wilcoxon signed-rank tests. Repeated measures ANOVA or Friedman tests assessed changes over more than two time points. Categorical variables were presented as frequencies (%) and compared with Chi-square or exact tests; Cochran’s Q test evaluated categorical trends over time. Logistic regression identified factors associated with SAD. Statistical significance was set at P < 0.05 (two-tailed). RESULTS There were 251 symptomatic adult long COVID patients screened for eligibility for inclusion. Following the exclusion of 51 patients with a known history of asthma and a further 37 patients due to insufficient longitudinal data, 163 symptomatic adults with Long COVID and no history of asthma were included in the analysis (see CONSORT flow diagram in Fig. 1 ). The prevalence of small airways dysfunction based on pre-specified criteria was 75.5% (123/163). Table 1 Baseline demographic, clinical characteristics, C19 survey scores and symptom characteristics of patients with and without small airway dysfunction (SAD) Variable SAD present (n = 123) SAD absent (n = 40) P-value* Demographic Data Age (mean ± SD) 52.93 ± 14.88 42.18 ± 15.56 < 0.001 Sex (Female, %) 76 (61.8%) 20 (50%) 0.188 BMI (mean ± SD) 29.07 ± 6.5 24.44 ± 3.98 < 0.001 Comorbid Conditions (%) Hypertension 40 (32.5%) 4 (10%) 0.005 DM 18 (14.6%) 2 (5%) 0.107 Depression 21 (17.1%) 11 (27.5%) 0.149 Anxiety 23 (18.7%) 10 (25%) 0.389 GORD 13 (10.6%) 4 (10%) 0.919 Obesity 27 (22%) 3 (7.5%) 0.040 IHD 13 (10.6%) 0 (0%) 0.032 Sleep apnoea 22 (17.9%) 3 (7.5%) 0.113 Dyslipidaemia 11 (8.9%) 1 (2.5%) 0.175 Smoking 7 (5.7%) 0 (0%) 0.123 Time between first infection and first visit ( median (IQR) ) 305 (184, 474) 334.5 (225.5, 498.75) 0.784 C-19 YRS (median (IQR)) Pre-COVID symptoms score 11 (5, 22.25) 8 (3, 15.75) 0.014 Symptoms score 42.5 (24.75, 55.25) 36 (27, 53.5) 0.096 Pre-COVID functional score 0.5 (0, 4) 0 (0, 2) 0.333 Functional score 16.5 (4, 23) 18 (8, 25) 0.355 Pre-COVID overall score 8 (5, 9) 8 (7, 9) 0.411 Overall score 4 (3, 6.25) 4 (2, 5.75) 0.138 Symptoms (%) Neurological symptoms 66 (53.7%) 28 (70%) 0.069 Chest symptoms 108 (87.8%) 36 (90%) 0.707 Psychological symptoms 40 (32.5%) 12 (30%) 0.766 Sleep symptoms 43 (35%) 17 (42.5%) 0.390 Pain 35 (28.5%) 9 (22.5%) 0.461 Fatigue 115 (93.5%) 37 (92.5%) 0.827 BMI: body mass index; DM: diabetes mellitus; GORD: gastro-Oesophageal reflux disease. Statistical significance defined as P < 0.05. Long COVID patients with SAD without previously diagnosed asthma were significantly older (52.9 ± 14.9 vs. 42.2 ± 15.6 years, p < 0.001) and had a higher BMI (29.1 ± 6.5 vs. 24.4 ± 4.0, p < 0.001) than those without SAD. The prevalence of hypertension (32.5% vs. 10%, p = 0.005), obesity (22% vs. 7.5%, p = 0.040), and ischaemic heart disease (10.6% vs. 0%, p = 0.032) was also significantly higher in the SAD group. Only 7 of 123 patients with SAD (5.7%) were smokers, with no clinically significant difference compared to those without SAD. Based on symptoms, the only statistically significant difference (p < 0.05) observed between patients with and without SAD was the pre-COVID symptoms score (C-19 YRS), which was higher in the SAD group (median [IQR] 11 (5, 22.25) vs. 8 (3, 15.75),p = 0.014). No significant differences (p > 0.05) were found in overall functional symptom scores, overall score, or other symptom clusters (Table 1 ). At baseline (visit one) assessment, patients with SAD, expectedly, had significantly higher airway resistance (R5, R19, R5-19, and Fres), higher AX values, and more negative reactance at 5 Hz(X5) measurements compared to those without SAD (all p < 0.05). By spirometry, FEV1 was also significantly lower (p = 0.029) in the SAD group (Table 2 ). Table 2 FOT and spirometry parameters at baseline in patients with and without small airway dysfunction (SAD) Lung Function Parameters SAD No SAD P-value * FOT parameters (median (IQR)) R5 (cmH2O·s·L − 1 ) 3.6 (3.03, 4.77) 2.55 (2.21, 2.96) < 0.001 R19 (cmH2O·s·L − 1 ) 2.79 (2.34, 3.56) 2.45 (2.08, 2.84) 0.001 R5-19 (cmH2O·s·L − 1 ) 0.73 (0.5, 1.26) 0.12 (0, 0.32) < 0.001 AX (cmH2O·L − 1 ) 9.2 (6.39, 16.16) 2.85 (2.33, 3.42) < 0.001 X5 (cmH2O·s·L − 1 ) -1.56 (-2.19, -1.22) -0.87 (-1, -0.75) < 0.001 Fres 19.19 (15.74, 23.11) 11.49 (10.48, 12.53) < 0.001 Spirometry data (mean ± SD) FVC (L) 3.58 ± 0.99 3.88 ± 1.05 0.097 FEV1 (L) 2.83 ± 0.85 3.17 ± 0.85 0.029 FEV1/FVC (%) 0.8 ± 0.15 0.86 ± 0.37 0.137 FEF 25–75 (L/s) 2.78 ± 1.17 3.05 ± 1.11 0.193 SAD: small airway dysfunction; FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s; FEF 25–75: forced expiratory flow between 25% and 75% of vital capacity; Fres: resonant frequency; R5: resistance at 5 Hz; R19: resistance at 19 Hz; AX: Area under Reactance Curve; X5: reactance at 5 Hz. Statistical significance defined as P < 0.05. In univariate regression analysis, age and BMI were significant predictors of SAD among Long COVID patients at baseline (both p < 0.001), with each unit increase associated with 1.05-fold (95% CI: 1.02 to 1.08) and 1.17-fold (95% CI: 1.08 to 1.26) higher odds of SAD, respectively. Hypertension was also linked to increased odds of SAD (Odds Ratio (OR) = 4.34, 95% CI: 1.44 to 13.03, p = 0.009). However, after adjustment for relevant variables in multivariable analysis, no factors remained significantly associated with SAD (Table 3 ). Table 3 Univariate and multivariable logistic regression analysis of predictors of small airway dysfunction (SAD) Variable Univariate analysis Multivariable analysis OR (95%CI) P-value* OR (95%CI) P-value* Demographic Data Age 1.05 (1.02 to 1.08) < 0.001 1.01 (0.94 to 1.09) 0.791 Sex (female) 1.62 (0.79 to 3.32) 0.190 7.83 (0.79 to 77.31) 0.078 BMI 1.17 (1.08 to 1.26) < 0.001 1.27 (0.95 to 1.7) 0.105 Comorbid conditions Hypertension 4.34 (1.44 to 13.03) 0.009 1.26 (0.05 to 30.63) 0.885 Diabetes 3.26 (0.72 to 14.7) 0.125 Anxiety 0.69 (0.3 to 1.61) 0.391 Depression 0.54 (0.23 to 1.25) 0.153 Sleep apnoea 2.69 (0.76 to 9.51) 0.125 0.28 (0.01 to 10.46) 0.487 COVID history Number of infections 1.27 (0.69 to 2.33) 0.438 C-19 YRS survey Symptoms C-19 score 1.02 (0.99 to 1.05) 0.127 Functional C-19 score 1.02 (0.98 to 1.06) 0.345 Overall C-19 score 0.86 (0.69 to 1.06) 0.150 1.05 (0.59 to 1.86) 0.872 Lab Predictors Total Lymphocytes 1.65 (0.83 to 3.28) 0.153 CD4 Count 1 (1 to 1) 0.353 CD8 Count 1 (1 to 1.01) 0.243 CD4/CD8 ratio 0.98 (0.95 to 1.01) 0.165 0.98 (0.94 to 1.02) 0.281 IgG subclasses IgG1 subclasses 1.32 (0.8 to 2.2) 0.279 IgG2 subclasses 1.05 (0.67 to 1.65) 0.836 IgG3 subclasses 0.67 (0.07 to 6.16) 0.723 IgG4 subclasses 0.12 (0 to 3.58) 0.224 CMV Serology (positive) 1.44 (0.28 to 7.5) 0.667 Vitamin D 0.98 (0.93 to 1.03) 0.368 CMV cytomegalovirus, OR: odds ratio; CI: confidence interval. * Statistical significance defined as P < 0.05. Among Long COVID patients with SAD (without prior history of asthma) who received ICS/LABA therapy (79/123), there was a significant change in airway resistance and reactance over time. Median R5 values were 4.02 cmH2O·s·L − 1 (3.27, 5.36), 4.15 (3.12, 5.46), 3.47 (2.85, 5.27), and 3.61 (2.97, 3.93) across visits 1 to 4, respectively (p = 0.002). Similarly, R19 declined from 3.05 cmH2O·s·L − 1 (2.48, 3.78) to 2.78 (2.21, 3.22) (P = 0.005), AX decreased from 12.41 cmH2O·L − (7.28, 26.88) to 7.72 (4.91, 12.9) (P = 0.019), and X5 improved from − 1.78 cmH2O·s·L − 1 ; (-2.95, -1.3) to -1.23 (-1.72, -1.05) (P = 0.040). Symptomatically, the incidence of dyspnoea decreased from 83.5% at visit one to 17.7% at visit four (p < 0.001), fatigue from 96.2–15.2% (p < 0.001), and cough from 43–11.4% (p = 0.037). For the C-19 YRS questionnaire, symptom score also showed significant improvement across visits from 43 (28.75, 56) at baseline to 39 (22.25, 50) at visit 4, p = 0.016) (Table 4 ). Comparative data for IgG subclasses, T-cell subsets, and vitamin D levels between patients with and without SAD are provided Table S1 Supporting Information. Table 4 Longitudinal FOT, spirometry, symptoms, and patient-reported outcomes in SAD patients with and without ICS/LABA therapy Parameter ICS/LABA P-value* Non-ICS/LABA P-value* Visit 1 Visit 2 Visit 3 Visit 4 Visit 1 Visit 2 Visit 3 Visit 4 FOT parameters (Median (IQR)) R5 (cmH2O·s·L − 1 ) 4.02 (3.27, 5.36) 4.15 (3.12, 5.46) 3.47 (2.85, 5.27) 3.61 (2.97, 3.93) 0.002 3.1 (2.74, 3.64) 3.22 (2.41, 3.65) 2.81 (2.41, 3.42) 2.91 (2.36, 3.26) 0.086 R19 (cmH2O·s·L − 1 ) 3.05 (2.48, 3.78) 3.24 (2.4, 3.91) 2.89 (2.25, 3.96) 2.78 (2.21, 3.22) 0.005 2.51 (2.17, 2.95) 2.57 (2.06, 2.89) 2.13 (2.07, 3.11) 2.66 (1.81, 2.86) 0.815 R5-19 (cmH2O·s·L − 1 ) 0.89 (0.59, 1.66) 1.02 (0.58, 1.65) 0.69 (0.52, 1.38) 0.75 (0.55, 1.04) 0.825 0.55 (0.37, 0.74) 0.54 (0.24, 0.74) 0.54 (0.33, 0.65) 0.36 (0.24, 0.61) 0.297 AX (cmH2O·L − ) 12.41 (7.28, 26.88) 12.59 (5.41, 21.68) 11.51 (6.21, 16.2) 7.72 (4.91, 12.9) 0.019 7.38 (4.85, 11.29) 7.25 (4.53, 8.63) 5.41 (3.37, 10.4) 6.87 (3.92, 8.27) 0.856 X5 (cmH2O·s·L − 1 ) -1.78 (-2.95, -1.3) -1.6 (-2.64, -1.16) -1.64 (-1.96, -1.18) -1.23 (-1.72, -1.05) 0.040 -1.29 (-1.68, -1.02) -1.24 (-1.57, -0.95) -1.35 (-1.85, -0.96) -1.16 (-1.58, -1.03) 0.615 Fres 20.97 (16.89, 25.28) 20.12 (16.32, 24.36) 20.79 (16.7, 23.09) 17.45 (14.57, 19.43) 0.445 16.82 (14.54, 19.93) 17.2 (15.04, 19.64) 17.08 (13.15, 19.44) 17.25 (12.12, 18.63) 0.543 Spirometry data (mean ± SD) FVC (L) 3.65 ± 1.1 3.68 ± 1.27 3.96 ± 1.11 4 ± 1.12 0.178 3.45 ± 0.77 3.49 ± 0.99 3.41 ± 1.04 2.98 ± 0.6 0.385 FEV1 (L) 2.87 ± 0.92 2.88 ± 1.07 3.13 ± 0.98 3.12 ± 1.09 0.702 2.76 ± 0.7 2.79 ± 0.83 2.59 ± 0.89 2.2 ± 0.44 0.774 FEV1/FVC (%) 0.8 ± 0.17 0.78 ± 0.12 0.78 ± 0.11 0.76 ± 0.16 0.492 0.8 ± 0.11 0.8 ± 0.07 0.76 ± 0.1 0.74 ± 0.08 0.444 FEF 25–75 (L/s) 2.82 ± 1.16 2.91 ± 1.3 3.07 ± 1.13 2.98 ± 1.24 0.892 2.7 ± 1.2 2.7 ± 1.18 2.35 ± 1.03 1.85 ± 0.7 0.548 Symptoms (%) Dyspnoea 66 (83.5%) 52 (65.8%) 17 (21.5%) 14 (17.7%) < 0.001 31 (70.5%) 24 (54.5%) 9 (20.5%) 6 (13.6%) 0.536 Fatigue 76 (96.2%) 50 (63.3%) 30 (38%) 12 (15.2%) < 0.001 39 (88.6%) 31 (70.5%) 20 (45.5%) 9 (20.5%) 0.008 Cough 34 (43%) 30 (38%) 11 (13.9%) 9 (11.4%) 0.037 16 (36.4%) 15 (34.1%) 5 (11.4%) 5 (11.4%) 0.634 C-19 YRS Scores (median (IQR)) Symptoms Score 43 (28.75, 56) 43 (30, 53.5) 34 (25.25, 46.5) 39 (22.25, 50) 0.016 42.5 (29.75, 53) 39.5 (28.5, 57.25) 38 (29.5, 49.5) 28 (20.75, 39.5) 0.107 Functional Score 15 (4.75, 23.25) 15 (5.5, 22) 12 (5.5, 18) 13.5 (3.75, 21) 0.099 20 (9.5, 28.75) 15 (9.25, 22) 18.5 (6.5, 25.25) 16 (9.5, 20.5) 0.175 Overall Score 4 (3, 6) 5 (3.5, 6.5) 5.5 (4, 7.75) 4.5 (3, 5.75) 0.107 3.5 (2, 5) 4 (3, 5) 4 (3, 5.75) 4.5 (2.75, 5.25) 0.668 SAD: small airway dysfunction; ICS: inhaled corticosteroids; FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s; FEF 25–75: forced expiratory flow between 25% and 75% of vital capacity; Fres: resonant frequency; R5: resistance at 5 Hz; R19: resistance at 19 Hz; AX: Area under Reactance Curve X5: reactance at 5 Hz. Statistical significance defined as P < 0.05. Discussion Our study is the first to describe changes of SAD in long COVID over time and the first to suggest a potential benefit of ICS/LABA in symptomatic long COVID patients with SAD but no known asthma. We demonstrated the prevalence of SAD by oscillometry to be very high (75%) in our population that mostly experienced mild initial COVID-19 disease. Adults with SAD were more likely older, with higher BMI, and more cardiovascular risk factors (hypertension, ischaemic heart disease, and obesity), compared to those without SAD. At baseline, long COVID patients with SAD and no history of asthma but demonstrated significantly higher airway resistance (R5, R19, R5–19, and Fres), higher AX values, and more negative X5 measurements, along with lower FEV1 on spirometry. Over prolonged follow-up (18 months), adults with SAD commenced on ICS/LABA therapy demonstrated significant improvements to both airway resistance and reactance, as well as clinically meaningful reductions in dyspnoea, fatigue, cough, and C19-YRS symptom scores, compared to those who did not start ICS/LABA therapy. Since 1986, SAD was recognised as a major contributor to airway resistance in obstructive lung diseases including asthma and COPD. In healthy lungs, the small peripheral airways (< 2 mm in diameter) contribute less than 20% of total airway resistance [16]. However, when peripheral airways become narrowed, obstructed, or obliterated by mucus, inflammation, or mucosal thickening, airway resistance increases significantly. This leads to gas trapping and expiratory flow limitation, causing shortness of breath, reduced exercise capacity and other debilitating respiratory symptoms [17]. For COPD, a higher prevalence of SAD is linked to reduced quality of life and greater symptom burden. In asthma, people with SAD have worse symptom control, increased activity limitations, greater disease severity, and more pronounced type 2 inflammation [18–21]. Post-COVID-19, dyspnoea and exercise intolerance are common, however to date, lung function abnormalities did not correlate to symptoms or were only present in those with more severe acute COVID-19 disease (requiring hospitalisation). This is both puzzling and frustrating for clinicians managing long COVID patients. Rinaldo et al. ( 2021 ) reported that COVID-19 survivors experienced persistent dyspnoea and reduced exercise capacity three months after hospital discharge, despite having normal pulmonary function tests. Moreover, peak oxygen consumption (VO₂ max) did not correlate with FEV1 or FVC [22]. Darley et al. reported that among ambulatory COVID-19 survivors with predominantly mild disease, the most common resting lung function abnormality was reduced DLCO (11%), occurring more often in those with more severe illness [23]. Lung function abnormalities in hospitalised patients are less relevant when evaluating patients with mild, non-hospitalised illness [23]. Our finding of SAD by oscillometry in this group is significant, particularly as it appears very common and is associated with persistent and significant symptom burden (the C-19 scores are quite high for this cohort). SAD is also associated with static hyperinflation and may occur months after COVID-19 infection, to ongoing shortness of breath and fatigue persisting for up to eight months post-infection [24]. Our finding of SAD detected by oscillometry in Long COVID patients without asthma is novel. Compared with laboratory-based lung function testing, oscillometry is more accessible, cost-effective, and feasible as a portable point-of-care tool in the clinic. If left undiagnosed and untreated, SAD may significantly impact long COVID patients, delaying their recovery as they are associated with concerning symptoms such as dyspnoea and reduced exercise capacity. Other studies suggest that undetected SAD in Long COVID may contribute to reduced exercise capacity and ongoing respiratory symptoms [25,26]. Importantly, SAD can be present despite normal spirometry, meaning symptomatic patients with dyspnoea may be falsely reassured. Such cases may still benefit from treatment, underscoring the need for prospective controlled trials. In our study, ~ 75% of long COVID patients had the treatable trait of SAD, consistent with Lopes et al. ( 2021 b) [27], who reported IOS abnormalities suggestive of SAD in ~ 70% of patients five months post–acute COVID-19. SAD patients had lower baseline FEV1, yet FEV1/FVC (> 75%) and FEF25–75 (> 70%) remained normal. This highlights a key limitation of spirometry: FEV1 primarily reflects large airways, while FEF25–75 is variable and FVC-dependent. When FEV1/FVC exceeds 75%, FEF25–75 often appears normal, reducing spirometry’s sensitivity for early SAD, as shown in prior studies [28–30]. Long COVID with respiratory symptoms may differ from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) of other post-viral origins. In non-COVID ME/CFS, respiratory symptoms and lung function abnormalities are less common, with generalised fatigue as the main complaint. By contrast, long COVID patients often report fatigue alongside persistent pulmonary function and, in some cases, radiological changes beyond one-year post-infection. This likely reflects SARS-CoV-2’s tropism for respiratory epithelium, supported by studies showing lung parenchymal changes and spirometric alterations in long COVID. In both post-COVID and non-COVID ME/CFS, reduced functional capacity and post-exertional malaise remain key features [31, 32]. We observed a clinically and statistically significant difference in the prevalence of hypertension between patients with SAD and those without. Univariate analysis showed that hypertensive patients had 4.34 times higher odds of developing SAD compared to those without hypertension. While the precise mechanism to explain this association is unclear and warrants further investigation, it is notable that hypertension is a recognised risk factor for severe COVID-19 and is linked to poorer outcomes [33]. Increased ACE-2 receptor expression in the lungs and heart of hypertensive individuals may provide more binding sites for SARS-CoV-2, potentially leading to greater vascular permeability and endothelial damage, particularly in the pulmonary capillaries. [33]. Co-existing cardiovascular comorbidities in hypertensive patients, such as diabetes mellitus and ischaemic heart disease, may amplify endothelial injury and systemic inflammation, further contributing to post-COVID small airway involvement [34]. We found that patients with SAD had a higher BMI than those without SAD. Univariate analysis demonstrated that for each unit increase in BMI was associated with 1.17-fold higher odds of developing SAD. This is consistent with other studies [21,35] and may be explained by several factors: obese individuals often have higher levels of systemic inflammation; obesity can alter lung mechanics by increasing heterogeneity of airway narrowing and promoting airway closure in the lung periphery. Additionally, increased chest wall stiffness, airway oedema and pulmonary congestion, are more common in obese patients than in non-obese individuals. These changes may predispose obese patients to SAD, which is reflected in altered airway resistance and reactance patterns observed on FOT/IOS [36,37]. SAD patients initiating ICS/LABA therapy showed greater improvement in C19 symptom scores over 18 months than those not treated, likely reflecting reductions in airway resistance and improved reactance. This improvement, detected by oscillometry but not spirometry, mirrors patterns seen in poorly controlled asthma following ICS/LABA initiation [38]. Other authors have demonstrated that oscillometry is more sensitive than spirometry in capturing treatment-related improvements in asthma and COPD [39–41]. A comparable scenario may exist in long COVID patients, where oscillometry could play an important role in monitoring and identifying response to therapy. A limitation in our study is that whilst patients with a history of asthma or recent use of ICS/LABA were excluded (the method used in other studies of asthma), it is possible that some patients prescribed inhaled pharmacotherapy in our clinic may have had undiagnosed asthma. It is also unclear if SAD associated with Long COVID is a type of post-viral asthma or something distinct. The exact mechanisms of SAD in long COVID and the benefits of ICS/LABA remain unclear. Further studies are needed to explore the underlying pathophysiology and molecular pathways, as well as how ICS/LABA improve lung function and symptoms. Limited evidence suggests this may involve improved ventilation homogeneity and reduced mechanical load [38]. It remains unclear as well whether improvement is due to ICS alone or the ICS + LABA combination. Our study did not assess whether ICS particle size affects SAD outcomes. Whether extra-fine particles ( 5 µm) particles remains unknown and warrants investigation in future randomised trials. Despite being a retrospective study without a control group, our study has key strengths. These include long (18-month) longitudinal follow-up, combined FOT and spirometry utilisation to detect SAD, exclusion of known asthma to reduce confounding, and consistent symptom assessment with the C-19 YRS. Further studies are needed to determine whether early detection of subclinical SAD in long COVID could offer a therapeutic window that prevents further deterioration, improves symptoms, and reduces overall health impact. Applying the treatable trait paradigm to long COVID patients with SAD offers a possible treatment option for many. We found SAD was detectable by oscillometry and was persistent and highly prevalent in symptomatic adults with long COVID, particularly in older, higher-BMI and those with cardiovascular risk factors. Patients with SAD who received ICS/LABA therapy showed significant improvements in validated symptom scores and oscillometry parameters. However, randomised controlled trials are needed to validate these findings and clarify the efficacy of ICS/LABA or ICS only in managing SAD in long COVID patients without known asthma. Abbreviations Full Term ACE-2 Angiotensin-Converting Enzyme 2 ANOVA Analysis Of Variance ARDS Acute Respiratory Distress Syndrome ATS American Thoracic Society AX Area Under Reactance Curve BD Bronchodilator BMI Body Mass Index C19-YRS COVID-19 Yorkshire Rehabilitation Scale CI Confidence Interval CFS Chronic Fatigue Syndrome CMV Cytomegalovirus COPD Chronic Obstructive Pulmonary Disease COVID-19 Coronavirus Disease 2019 DLCO Diffusion Capacity of Carbon Monoxide DM Diabetes Mellitus EBV Epstein Barr Virus ERS European Respiratory Society FEF25–75% Forced Expiratory Flow At 25–75% FEV1 Forced Expiratory Volume In 1st Second FOT Forced Oscillation Technique Fres Resonant Frequency FVC Forced Vital Capacity GORD Gastro-Oesophageal Reflux Disease HSV Herpes Simplex Virus IHD Ischaemic Heart Disease IQR Interquartile Range ICS Inhaled Corticosteroid ICS/LABA Inhaled Corticosteroid / Long-Acting Beta-Agonist IOS Impulse Oscillometry System ME Myalgic Encaphalomyelitis OR Odds Ratio PACS Post-Acute Covid Sequelae PCR Polymerase Chain Reaction. pMDI Pressurised Metered Dose Inhaler R5 / R19 Resistance At 5 Hz / 19 Hz R5–19 Resistance Between 5–19 Hz SAD Small Airway Dysfunction SD Standard Deviation SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2 SPSS Statistical Package for The Social Sciences TT Treatable Trait VO₂ max Peak Oxygen Consumption X5 Reactance At 5 Hz Declarations Ethics approval and consent to participate This study was approved by the St Vincent’s Hospital Human Research Ethics Committee (HREC) (Project ID: 2024/PID02621; Ethics Approval No: 2024/ETH02265; Governance Application No: 2024/STE04088). The study was conducted in accordance with the National Statement on Ethical Conduct in Human Research. A waiver of consent was granted as this is a retrospective analysis of routinely collected clinical data. Clinical trial number not applicable. Consent for publication 1. Not applicable. This manuscript does not contain any individual person’s data in any form (including individual details, images, or videos). Competing interests The authors declare that they have no competing interests. Funding No specific funding was received for this study. The Long COVID Fellowship at St Vincent’s Hospital Sydney (2024–2025), which supported clinical service and data collection for this project, was funded by the Matthew Handbury Foundation. The sponsor had no role in the design, conduct, analysis, or reporting of the research. Author Contribution U.E.A. performed data curation, analysis, investigation, visualization, and writing – original draft and review.A.B. contributed to conceptualization, methodology, resources, supervision, and writing – review and editing.M.P. contributed to conceptualization, methodology, and supervision.S.F. contributed to conceptualization, methodology, supervision, and resources.B.J. contributed to methodology, project administration, and writing – review and editing.S.S. contributed to analysis, validation, visualization, and writing – review.D.I. contributed to data curation, analysis, validation, and writing – review.E.A. contributed to investigation, resources, visualization, and writing – review.All authors read and approved the final manuscript. Acknowledgement 1.The authors and St Vincent’s Hospital Sydney acknowledge the support of the Matthew Handbury Foundation, which funded the Long COVID Fellowship at St Vincent’s Hospital Sydney for the 2024–2025 period. This fellowship enabled dedicated clinical service and data collection for patients attending the Long COVID Clinic. The Foundation had no role in the design, conduct, analysis, or reporting of this study.2.The authors also wish to thank Jane Wheatley, Clinical Psychologist St Vincent’s Hospital Sydney, for her invaluable contribution to the assessment of long COVID patients, and Amy Kennedy, former St Vincent’s Hospital Sydney Physiotherapist, for her dedicated role in the rehabilitation of patients with long COVID.3.The authors used OpenAI’s ChatGPT (GPT-5, August 2025 version) to assist in improving the clarity, grammar, and conciseness of the manuscript text. All content generated by the tool was reviewed, edited, and approved by the authors, who take full responsibility for the integrity, accuracy, and interpretation of the work. ChatGPT was not used for data analysis, interpretation of results, or generation of scientific conclusions. Data Availability The data that support the findings of this study will be available from the corresponding author after publication, for a period of 5 years. Data are stored in a secure workstation at the Heart Lung Clinic, Xavier Building, St Vincent’s Hospital, under the custodianship of Professor Anthony Byrne. Available materials include the study protocol, statistical analysis plan, and de-identified datasets (Excel and SPSS files). Access to these materials may be granted upon reasonable request by contacting the corresponding author, who will seek approval from the St Vincent’s Hospital Human Research Ethics Committee before release. References Lewthwaite H, Byrne A, Brew B, Gibson PG. Treatable traits for long COVID. Respirology. 2023;28(11):1005–22. Agusti A, Bel E, Thomas M, Vogelmeier C, Brusselle G, Holgate S, et al. Treatable traits: toward precision medicine of chronic airway diseases. Eur Respir J. 2016;47:410–9. 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Supplementary Files Additionalfile1Table1.ComparativedataforIgGsubclassesTcellsubsetsandvitaminDlevelsbetweenpatientswith.docx Additionalfile2AppendixS1DetailedFOTandspirometryprotocols.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Nov, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 07 Oct, 2025 Editor assigned by journal 01 Oct, 2025 Editor invited by journal 10 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 09 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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01:48:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1537856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7541120/v1/9c2ba6ff-0531-44a6-9d8c-236eeaf597b5.pdf"},{"id":93977913,"identity":"b4cc086d-2703-47f5-bfae-e3eefe683eb9","added_by":"auto","created_at":"2025-10-21 01:32:50","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17867,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1Table1.ComparativedataforIgGsubclassesTcellsubsetsandvitaminDlevelsbetweenpatientswith.docx","url":"https://assets-eu.researchsquare.com/files/rs-7541120/v1/385871f2ef22c92840615969.docx"},{"id":93978328,"identity":"822baca1-8548-4f35-8aa0-245a5dfe86fd","added_by":"auto","created_at":"2025-10-21 01:40:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14410,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2AppendixS1DetailedFOTandspirometryprotocols.docx","url":"https://assets-eu.researchsquare.com/files/rs-7541120/v1/617a1f0b607ffdc515a6c648.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Treatable Traits in Long COVID: Inhaled corticosteroids and long-acting bronchodilators for small airway dysfunction among symptomatic Long COVID patients without known Asthma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLong COVID or post-acute Coronavirus Disease 2019 (COVID-19) sequelae is a complex, chronic health condition involving multiple organ systems and has limited treatment options. This makes it very challenging to manage. However, personalised and precision medicine approaches can identify and treat clinically relevant characteristics or traits, to improve quality of life and reduce symptom burden [1]. This \u0026ldquo;Treatable Traits\u0026rdquo; paradigm was initially developed for chronic respiratory diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD) and demonstrates improved patient-centred outcomes by detecting and treating clinically relevant traits [2]. A treatable trait (TT) is a measurable and clinically relevant characteristic that can be targeted with specific treatment that is known to provide benefit for that specific characteristic [1]. Multiple TTs often co-exist in one patient, and addressing each specifically maximises clinical benefit. Traits without current treatments highlight priorities for future research and trials [3]. Increasing evidence of improved clinically relevant outcomes exist for other complex and multi-system chronic conditions [2\u0026ndash;4]. A recent systematic review found the treatable traits approach relevant to Long COVID, grouping 34 symptoms into eight clusters, although there was no specific mention of small airway dysfunction [1].\u003c/p\u003e\u003cp\u003ePersistent symptoms after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral infection, commonly called long COVID, is defined by the WHO as symptoms lasting\u0026thinsp;\u0026gt;\u0026thinsp;3 months without another cause [5]. SARS-CoV-2 virus binds to angiotensin-converting enzyme 2 (ACE-2) receptors on respiratory cells, causing a spectrum of disease from asymptomatic or mild acute infection to a severe acute respiratory distress syndrome (ARDS) with bilateral pneumonitis that causes death [6]. SARS-CoV-2 can also lead to small airway dysfunction (SAD) characterised by airflow obstruction, spasm and gas trapping. This may persist for months following initial infection, as has been seen with other respiratory viral infections [6\u0026ndash;8]. Histopathological features of SAD include bronchiolitis, reduced airway calibre, and bronchiolar hyper-responsiveness, with SARS-CoV-2 particles observed in distal airways by electron microscopy in acute COVID-19 [9]. SAD may occur without pre-existing asthma. We hypothesised that Inhaled Corticosteroid (ICS) and Long-Acting Beta-2 Agonist (LABA) may improve symptoms and lung function by targeting bronchiolitis, bronchiolar hyper-responsiveness, and ongoing airway inflammation.\u003c/p\u003e\u003cp\u003eLung function by oscillometry, using either impulse oscillometry (IOS) or the forced oscillation technique (FOT), is a sensitive, non-invasive test for detecting peripheral airway abnormalities. Prior studies in asthma and COPD, show oscillometry may identify small airway dysfunction before spirometry becomes abnormal and even prior to symptoms onset [10,11]. However, very limited research of SAD in long COVID exist, apart from one that utilised oscillometry, but failed to exclude known asthma patients [12].\u003c/p\u003e\u003cp\u003eIdentifying SAD within a treatable traits (TT) paradigm may provide a therapeutic option for patients with an \u0026lsquo;asthma-like\u0026rsquo; post COVID syndrome. This study, aimed to: (1) determine the prevalence and predictors of SAD in symptomatic long COVID patients\u0026thinsp;\u0026ge;\u0026thinsp;3 months post infection, and (2) assess respiratory function and symptom outcomes over 18 months in patients (newly) prescribed ICS/LABA.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis retrospective cohort study included adult patients over 18 years from the St Vincent\u0026rsquo;s Long COVID outpatient service in Sydney, Australia. All met the WHO criteria for Long COVID with Polymerase Chain Reaction (PCR) confirmed infection (WHO, 2021, accessed 3 March 2023) between June 2022 and October 2024 [5]. Patients had persistent symptoms of at least 3 months duration following initial SARS-CoV-2 viral infection. Patients with a history of confirmed asthma or have a history of possible asthma or those already prescribed inhaled corticosteroids or other inhaled asthma medications were excluded. Local ethics approval was obtained (REGIS 2024/PID02621).\u003c/p\u003e\u003cp\u003eBaseline data, blood tests, symptom scores, and lung function were obtained from electronic medical records at the initial visit and at 6, 12, and 18 months. Demographics included age, sex, BMI, COVID-19 history (dates, severity, frequency), vaccination status, co-morbidities (including asthma, obesity), smoking history, current medications (e.g., inhaled corticosteroids, bronchodilators), symptoms, and COVID-19 Yorkshire Rehabilitation Scale (C-19 YRS)[13]. Blood tests included IgG subclasses, vitamin D, thyroid function, COVID-19 serology, and viral markers (CMV, EBV, HSV), plus T-cell subsets. Lung function was assessed at rest using FOT and spirometry pre- and post-bronchodilator (400 mcg salbutamol). FOT (tremoflo\u0026reg; C-100, Thorasys, Montreal, Canada) was performed before spirometry at each visit, following ERS Task Force guidelines [14]. Parameters included R5, R19, R5\u0026ndash;19, AX, X5, and Fres. SAD was defined per Chiu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) [15] as R5\u0026ndash;19\u0026thinsp;\u0026gt;\u0026thinsp;0.07 kPa\u0026middot;s\u0026middot;L⁻\u0026sup1;, X5 \u0026lt; \u0026minus;\u0026thinsp;0.12 kPa\u0026middot;s\u0026middot;L⁻\u0026sup1;, Fres\u0026thinsp;\u0026gt;\u0026thinsp;14.14 Hz, and AX\u0026thinsp;\u0026gt;\u0026thinsp;0.44 kPa\u0026middot;L⁻\u0026sup1;. Resistance/reactance values were converted to cmH₂O units using standard calculations [14].\u003c/p\u003e\u003cp\u003e Spirometry was conducted using the ndd EasyOne\u0026reg; system (ndd Medical Technologies, Zurich, Switzerland) in accordance with ATS/ERS standards, measuring post-bronchodilator (BD) FVC, FEV₁, FEV₁/FVC, and FEF₂₅\u0026ndash;₇₅%. SAD was defined as FEF₂₅\u0026ndash;₇₅% \u0026lt;70% per ERS recommendations [16]. Tests were performed following a (minimum) 5-minute rest with FOT first. SAD patients were stratified by ICS/LABA use for outcome comparisons. Detailed FOT and spirometry protocols are provided in Appendix S1 in the Supporting Information.\u003c/p\u003e\u003cp\u003eICS/LABA initiation for SAD was at the discretion of the treating respiratory physician, guided by symptoms (dyspnoea, cough, wheeze) and lung function (pre/post-bronchodilator spirometry and oscillometry). Common regimens included budesonide/formoterol 200/6 \u0026micro;g Rapihaler with spacer (two puffs BID) and fluticasone propionate/salmeterol 250/25 \u0026micro;g pMDI with spacer (one puff BID).\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using SPSS Version 28 (IBM, Armonk, NY). Continuous variables were summarised as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) and compared using independent t-tests or Mann\u0026ndash;Whitney tests; paired data were analysed with Wilcoxon signed-rank tests. Repeated measures ANOVA or Friedman tests assessed changes over more than two time points. Categorical variables were presented as frequencies (%) and compared with Chi-square or exact tests; Cochran\u0026rsquo;s Q test evaluated categorical trends over time. Logistic regression identified factors associated with SAD. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThere were 251 symptomatic adult long COVID patients screened for eligibility for inclusion. Following the exclusion of 51 patients with a known history of asthma and a further 37 patients due to insufficient longitudinal data, 163 symptomatic adults with Long COVID and no history of asthma were included in the analysis (see CONSORT flow diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The prevalence of small airways dysfunction based on pre-specified criteria was 75.5% (123/163).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eBaseline demographic, clinical characteristics, C19 survey scores and symptom characteristics of patients with and without small airway dysfunction (SAD)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAD present (n\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSAD absent\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic Data\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.93\u0026thinsp;\u0026plusmn;\u0026thinsp;14.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.18\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (Female, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (61.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.07\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbid Conditions (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (14.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGORD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep apnoea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidaemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime between first infection and first visit\u003c/b\u003e (\u003cb\u003emedian (IQR)\u003c/b\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e305 (184, 474)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e334.5 (225.5, 498.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC-19 YRS (median (IQR))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-COVID symptoms score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (5, 22.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (3, 15.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptoms score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.5 (24.75, 55.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (27, 53.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-COVID functional score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5 (0, 4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0, 2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.5 (4, 23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (8, 25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-COVID overall score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (5, 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (7, 9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (3, 6.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2, 5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSymptoms (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurological symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (53.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChest symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (87.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (42.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (28.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (93.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (92.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI: body mass index; DM: diabetes mellitus; GORD: gastro-Oesophageal reflux disease. Statistical significance defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLong COVID patients with SAD without previously diagnosed asthma were significantly older (52.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9 vs. 42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a higher BMI (29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 vs. 24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those without SAD. The prevalence of hypertension (32.5% vs. 10%, p\u0026thinsp;=\u0026thinsp;0.005), obesity (22% vs. 7.5%, p\u0026thinsp;=\u0026thinsp;0.040), and ischaemic heart disease (10.6% vs. 0%, p\u0026thinsp;=\u0026thinsp;0.032) was also significantly higher in the SAD group. Only 7 of 123 patients with SAD (5.7%) were smokers, with no clinically significant difference compared to those without SAD.\u003c/p\u003e\u003cp\u003eBased on symptoms, the only statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) observed between patients with and without SAD was the pre-COVID symptoms score (C-19 YRS), which was higher in the SAD group (median [IQR] 11 (5, 22.25) vs. 8 (3, 15.75),p\u0026thinsp;=\u0026thinsp;0.014). No significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were found in overall functional symptom scores, overall score, or other symptom clusters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt baseline (visit one) assessment, patients with SAD, expectedly, had significantly higher airway resistance (R5, R19, R5-19, and Fres), higher AX values, and more negative reactance at 5 Hz(X5) measurements compared to those without SAD (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By spirometry, FEV1 was also significantly lower (p\u0026thinsp;=\u0026thinsp;0.029) in the SAD group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFOT and spirometry parameters at baseline in patients with and without small airway dysfunction (SAD)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung Function Parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo SAD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value *\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFOT parameters (median (IQR))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR5 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.6 (3.03, 4.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.55 (2.21, 2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR19 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.79 (2.34, 3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.45 (2.08, 2.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR5-19 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73 (0.5, 1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12 (0, 0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAX (cmH2O\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.2 (6.39, 16.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.85 (2.33, 3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX5 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.56 (-2.19, -1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.87 (-1, -0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFres\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.19 (15.74, 23.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.49 (10.48, 12.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpirometry data (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1 (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEF 25\u0026ndash;75 (L/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSAD: small airway dysfunction; FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s; FEF 25\u0026ndash;75: forced expiratory flow between 25% and 75% of vital capacity; Fres: resonant frequency; R5: resistance at 5 Hz; R19: resistance at 19 Hz; AX: Area under Reactance Curve; X5: reactance at 5 Hz. Statistical significance defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn univariate regression analysis, age and BMI were significant predictors of SAD among Long COVID patients at baseline (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with each unit increase associated with 1.05-fold (95% CI: 1.02 to 1.08) and 1.17-fold (95% CI: 1.08 to 1.26) higher odds of SAD, respectively. Hypertension was also linked to increased odds of SAD (Odds Ratio (OR)\u0026thinsp;=\u0026thinsp;4.34, 95% CI: 1.44 to 13.03, p\u0026thinsp;=\u0026thinsp;0.009). However, after adjustment for relevant variables in multivariable analysis, no factors remained significantly associated with SAD (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariable logistic regression analysis of predictors of small airway dysfunction (SAD)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariable analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic Data\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05 (1.02 to 1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.94 to 1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.62 (0.79 to 3.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.83 (0.79 to 77.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (1.08 to 1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27 (0.95 to 1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbid conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.34 (1.44 to 13.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.26 (0.05 to 30.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.26 (0.72 to 14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.69 (0.3 to 1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.54 (0.23 to 1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep apnoea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.69 (0.76 to 9.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28 (0.01 to 10.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOVID history\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of infections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.27 (0.69 to 2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC-19 YRS survey\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptoms C-19 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02 (0.99 to 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional C-19 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02 (0.98 to 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall C-19 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.86 (0.69 to 1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.05 (0.59 to 1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLab Predictors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Lymphocytes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.65 (0.83 to 3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4 Count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1 to 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD8 Count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1 to 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4/CD8 ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98 (0.95 to 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.94 to 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIgG subclasses\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgG1 subclasses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32 (0.8 to 2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgG2 subclasses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05 (0.67 to 1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgG3 subclasses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67 (0.07 to 6.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgG4 subclasses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12 (0 to 3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCMV Serology (positive)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.44 (0.28 to 7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98 (0.93 to 1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCMV cytomegalovirus, OR: odds ratio; CI: confidence interval. \u003cb\u003e*\u003c/b\u003e Statistical significance defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong Long COVID patients with SAD (without prior history of asthma) who received ICS/LABA therapy (79/123), there was a significant change in airway resistance and reactance over time. Median R5 values were 4.02 cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (3.27, 5.36), 4.15 (3.12, 5.46), 3.47 (2.85, 5.27), and 3.61 (2.97, 3.93) across visits 1 to 4, respectively (p\u0026thinsp;=\u0026thinsp;0.002). Similarly, R19 declined from 3.05 cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (2.48, 3.78) to 2.78 (2.21, 3.22) (P\u0026thinsp;=\u0026thinsp;0.005), AX decreased from 12.41 cmH2O\u0026middot;L\u003csup\u003e\u0026minus;\u003c/sup\u003e (7.28, 26.88) to 7.72 (4.91, 12.9) (P\u0026thinsp;=\u0026thinsp;0.019), and X5 improved from \u0026minus;\u0026thinsp;1.78 cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; (-2.95, -1.3) to -1.23 (-1.72, -1.05) (P\u0026thinsp;=\u0026thinsp;0.040).\u003c/p\u003e\u003cp\u003eSymptomatically, the incidence of dyspnoea decreased from 83.5% at visit one to 17.7% at visit four (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fatigue from 96.2\u0026ndash;15.2% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and cough from 43\u0026ndash;11.4% (p\u0026thinsp;=\u0026thinsp;0.037). For the C-19 YRS questionnaire, symptom score also showed significant improvement across visits from 43 (28.75, 56) at baseline to 39 (22.25, 50) at visit 4, p\u0026thinsp;=\u0026thinsp;0.016) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Comparative data for IgG subclasses, T-cell subsets, and vitamin D levels between patients with and without SAD are provided \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e Supporting Information.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLongitudinal FOT, spirometry, symptoms, and patient-reported outcomes in SAD patients with and without ICS/LABA therapy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eICS/LABA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003eNon-ICS/LABA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisit 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVisit 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVisit 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVisit 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVisit 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVisit 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVisit 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eVisit 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eFOT parameters (Median (IQR))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR5 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.02 (3.27, 5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.15 (3.12, 5.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.47 (2.85, 5.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.61 (2.97, 3.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.1 (2.74, 3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.22 (2.41, 3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.81 (2.41, 3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.91 (2.36, 3.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR19 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.05 (2.48, 3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.24 (2.4, 3.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.89 (2.25, 3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.78 (2.21, 3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.51 (2.17, 2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.57 (2.06, 2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.13 (2.07, 3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.66 (1.81, 2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR5-19 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.89 (0.59, 1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02 (0.58, 1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 (0.52, 1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75 (0.55, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.55 (0.37, 0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54 (0.24, 0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.54 (0.33, 0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.36 (0.24, 0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAX (cmH2O\u0026middot;L\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.41 (7.28, 26.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.59 (5.41, 21.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.51 (6.21, 16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.72 (4.91, 12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.38 (4.85, 11.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.25 (4.53, 8.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.41 (3.37, 10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.87 (3.92, 8.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX5 (cmH2O\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.78 (-2.95, -1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.6 (-2.64, -1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.64 (-1.96, -1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.23 (-1.72, -1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.29 (-1.68, -1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.24 (-1.57, -0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-1.35 (-1.85, -0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.16 (-1.58, -1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFres\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.97 (16.89, 25.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.12 (16.32, 24.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.79 (16.7, 23.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.45 (14.57, 19.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.82 (14.54, 19.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.2 (15.04, 19.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e17.08 (13.15, 19.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.25 (12.12, 18.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpirometry data (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1 (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEF 25\u0026ndash;75 (L/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSymptoms (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyspnoea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (83.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (65.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31 (70.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24 (54.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (96.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (63.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39 (88.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31 (70.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e20 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCough\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15 (34.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC-19 YRS Scores (median (IQR))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSymptoms Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (28.75, 56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (30, 53.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (25.25, 46.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39 (22.25, 50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.5 (29.75, 53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e39.5 (28.5, 57.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e38 (29.5, 49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28 (20.75, 39.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFunctional Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (4.75, 23.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (5.5, 22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (5.5, 18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.5 (3.75, 21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20 (9.5, 28.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15 (9.25, 22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e18.5 (6.5, 25.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16 (9.5, 20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (3, 6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (3.5, 6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.5 (4, 7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.5 (3, 5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.5 (2, 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 (3, 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4 (3, 5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.5 (2.75, 5.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eSAD: small airway dysfunction; ICS: inhaled corticosteroids; FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s; FEF 25\u0026ndash;75: forced expiratory flow between 25% and 75% of vital capacity; Fres: resonant frequency; R5: resistance at 5 Hz; R19: resistance at 19 Hz; AX: Area under Reactance Curve X5: reactance at 5 Hz. Statistical significance defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study is the first to describe changes of SAD in long COVID over time and the first to suggest a potential benefit of ICS/LABA in symptomatic long COVID patients with SAD but no known asthma. We demonstrated the prevalence of SAD by oscillometry to be very high (75%) in our population that mostly experienced mild initial COVID-19 disease. Adults with SAD were more likely older, with higher BMI, and more cardiovascular risk factors (hypertension, ischaemic heart disease, and obesity), compared to those without SAD. At baseline, long COVID patients with SAD and no history of asthma but demonstrated significantly higher airway resistance (R5, R19, R5\u0026ndash;19, and Fres), higher AX values, and more negative X5 measurements, along with lower FEV1 on spirometry. Over prolonged follow-up (18 months), adults with SAD commenced on ICS/LABA therapy demonstrated significant improvements to both airway resistance and reactance, as well as clinically meaningful reductions in dyspnoea, fatigue, cough, and C19-YRS symptom scores, compared to those who did not start ICS/LABA therapy.\u003c/p\u003e\u003cp\u003eSince 1986, SAD was recognised as a major contributor to airway resistance in obstructive lung diseases including asthma and COPD. In healthy lungs, the small peripheral airways (\u0026lt;\u0026thinsp;2 mm in diameter) contribute less than 20% of total airway resistance [16]. However, when peripheral airways become narrowed, obstructed, or obliterated by mucus, inflammation, or mucosal thickening, airway resistance increases significantly. This leads to gas trapping and expiratory flow limitation, causing shortness of breath, reduced exercise capacity and other debilitating respiratory symptoms [17]. For COPD, a higher prevalence of SAD is linked to reduced quality of life and greater symptom burden. In asthma, people with SAD have worse symptom control, increased activity limitations, greater disease severity, and more pronounced type 2 inflammation [18\u0026ndash;21].\u003c/p\u003e\u003cp\u003ePost-COVID-19, dyspnoea and exercise intolerance are common, however to date, lung function abnormalities did not correlate to symptoms or were only present in those with more severe acute COVID-19 disease (requiring hospitalisation). This is both puzzling and frustrating for clinicians managing long COVID patients. Rinaldo et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that COVID-19 survivors experienced persistent dyspnoea and reduced exercise capacity three months after hospital discharge, despite having normal pulmonary function tests. Moreover, peak oxygen consumption (VO₂ max) did not correlate with FEV1 or FVC [22]. Darley et al. reported that among ambulatory COVID-19 survivors with predominantly mild disease, the most common resting lung function abnormality was reduced DLCO (11%), occurring more often in those with more severe illness [23]. Lung function abnormalities in hospitalised patients are less relevant when evaluating patients with mild, non-hospitalised illness [23]. Our finding of SAD by oscillometry in this group is significant, particularly as it appears very common and is associated with persistent and significant symptom burden (the C-19 scores are quite high for this cohort). SAD is also associated with static hyperinflation and may occur months after COVID-19 infection, to ongoing shortness of breath and fatigue persisting for up to eight months post-infection [24]. Our finding of SAD detected by oscillometry in Long COVID patients without asthma is novel. Compared with laboratory-based lung function testing, oscillometry is more accessible, cost-effective, and feasible as a portable point-of-care tool in the clinic.\u003c/p\u003e\u003cp\u003eIf left undiagnosed and untreated, SAD may significantly impact long COVID patients, delaying their recovery as they are associated with concerning symptoms such as dyspnoea and reduced exercise capacity. Other studies suggest that undetected SAD in Long COVID may contribute to reduced exercise capacity and ongoing respiratory symptoms [25,26]. Importantly, SAD can be present despite normal spirometry, meaning symptomatic patients with dyspnoea may be falsely reassured. Such cases may still benefit from treatment, underscoring the need for prospective controlled trials.\u003c/p\u003e\u003cp\u003eIn our study, ~\u0026thinsp;75% of long COVID patients had the treatable trait of SAD, consistent with Lopes et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003eb) [27], who reported IOS abnormalities suggestive of SAD in ~\u0026thinsp;70% of patients five months post\u0026ndash;acute COVID-19. SAD patients had lower baseline FEV1, yet FEV1/FVC (\u0026gt;\u0026thinsp;75%) and FEF25\u0026ndash;75 (\u0026gt;\u0026thinsp;70%) remained normal. This highlights a key limitation of spirometry: FEV1 primarily reflects large airways, while FEF25\u0026ndash;75 is variable and FVC-dependent. When FEV1/FVC exceeds 75%, FEF25\u0026ndash;75 often appears normal, reducing spirometry\u0026rsquo;s sensitivity for early SAD, as shown in prior studies [28\u0026ndash;30].\u003c/p\u003e\u003cp\u003eLong COVID with respiratory symptoms may differ from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) of other post-viral origins. In non-COVID ME/CFS, respiratory symptoms and lung function abnormalities are less common, with generalised fatigue as the main complaint. By contrast, long COVID patients often report fatigue alongside persistent pulmonary function and, in some cases, radiological changes beyond one-year post-infection. This likely reflects SARS-CoV-2\u0026rsquo;s tropism for respiratory epithelium, supported by studies showing lung parenchymal changes and spirometric alterations in long COVID. In both post-COVID and non-COVID ME/CFS, reduced functional capacity and post-exertional malaise remain key features [31, 32].\u003c/p\u003e\u003cp\u003eWe observed a clinically and statistically significant difference in the prevalence of hypertension between patients with SAD and those without. Univariate analysis showed that hypertensive patients had 4.34 times higher odds of developing SAD compared to those without hypertension. While the precise mechanism to explain this association is unclear and warrants further investigation, it is notable that hypertension is a recognised risk factor for severe COVID-19 and is linked to poorer outcomes [33]. Increased ACE-2 receptor expression in the lungs and heart of hypertensive individuals may provide more binding sites for SARS-CoV-2, potentially leading to greater vascular permeability and endothelial damage, particularly in the pulmonary capillaries. [33]. Co-existing cardiovascular comorbidities in hypertensive patients, such as diabetes mellitus and ischaemic heart disease, may amplify endothelial injury and systemic inflammation, further contributing to post-COVID small airway involvement [34].\u003c/p\u003e\u003cp\u003eWe found that patients with SAD had a higher BMI than those without SAD. Univariate analysis demonstrated that for each unit increase in BMI was associated with 1.17-fold higher odds of developing SAD. This is consistent with other studies [21,35] and may be explained by several factors: obese individuals often have higher levels of systemic inflammation; obesity can alter lung mechanics by increasing heterogeneity of airway narrowing and promoting airway closure in the lung periphery. Additionally, increased chest wall stiffness, airway oedema and pulmonary congestion, are more common in obese patients than in non-obese individuals. These changes may predispose obese patients to SAD, which is reflected in altered airway resistance and reactance patterns observed on FOT/IOS [36,37].\u003c/p\u003e\u003cp\u003eSAD patients initiating ICS/LABA therapy showed greater improvement in C19 symptom scores over 18 months than those not treated, likely reflecting reductions in airway resistance and improved reactance. This improvement, detected by oscillometry but not spirometry, mirrors patterns seen in poorly controlled asthma following ICS/LABA initiation [38]. Other authors have demonstrated that oscillometry is more sensitive than spirometry in capturing treatment-related improvements in asthma and COPD [39\u0026ndash;41]. A comparable scenario may exist in long COVID patients, where oscillometry could play an important role in monitoring and identifying response to therapy.\u003c/p\u003e\u003cp\u003eA limitation in our study is that whilst patients with a history of asthma or recent use of ICS/LABA were excluded (the method used in other studies of asthma), it is possible that some patients prescribed inhaled pharmacotherapy in our clinic may have had undiagnosed asthma. It is also unclear if SAD associated with Long COVID is a type of post-viral asthma or something distinct. The exact mechanisms of SAD in long COVID and the benefits of ICS/LABA remain unclear. Further studies are needed to explore the underlying pathophysiology and molecular pathways, as well as how ICS/LABA improve lung function and symptoms. Limited evidence suggests this may involve improved ventilation homogeneity and reduced mechanical load [38]. It remains unclear as well whether improvement is due to ICS alone or the ICS\u0026thinsp;+\u0026thinsp;LABA combination. Our study did not assess whether ICS particle size affects SAD outcomes. Whether extra-fine particles (\u0026lt;\u0026thinsp;2 \u0026micro;m) offer greater small airway benefits than medium (2\u0026ndash;5 \u0026micro;m) or coarse (\u0026gt;\u0026thinsp;5 \u0026micro;m) particles remains unknown and warrants investigation in future randomised trials.\u003c/p\u003e\u003cp\u003eDespite being a retrospective study without a control group, our study has key strengths. These include long (18-month) longitudinal follow-up, combined FOT and spirometry utilisation to detect SAD, exclusion of known asthma to reduce confounding, and consistent symptom assessment with the C-19 YRS. Further studies are needed to determine whether early detection of subclinical SAD in long COVID could offer a therapeutic window that prevents further deterioration, improves symptoms, and reduces overall health impact.\u003c/p\u003e\u003cp\u003eApplying the treatable trait paradigm to long COVID patients with SAD offers a possible treatment option for many. We found SAD was detectable by oscillometry and was persistent and highly prevalent in symptomatic adults with long COVID, particularly in older, higher-BMI and those with cardiovascular risk factors. Patients with SAD who received ICS/LABA therapy showed significant improvements in validated symptom scores and oscillometry parameters. However, randomised controlled trials are needed to validate these findings and clarify the efficacy of ICS/LABA or ICS only in managing SAD in long COVID patients without known asthma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"Description\"\u003e\u003cp\u003eFull Term\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACE-2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAngiotensin-Converting Enzyme 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnalysis Of Variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute Respiratory Distress Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eATS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Thoracic Society\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAX\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under Reactance Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBronchodilator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eC19-YRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCOVID-19 Yorkshire Rehabilitation Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic Fatigue Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCMV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCytomegalovirus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCoronavirus Disease 2019\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDLCO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiffusion Capacity of Carbon Monoxide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEBV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEpstein Barr Virus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eERS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEuropean Respiratory Society\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFEF25\u0026ndash;75%\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eForced Expiratory Flow At 25\u0026ndash;75%\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFEV1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eForced Expiratory Volume In 1st Second\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFOT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eForced Oscillation Technique\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFres\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eResonant Frequency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFVC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eForced Vital Capacity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGORD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGastro-Oesophageal Reflux Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHSV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHerpes Simplex Virus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIschaemic Heart Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile Range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInhaled Corticosteroid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICS/LABA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInhaled Corticosteroid / Long-Acting Beta-Agonist\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImpulse Oscillometry System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMyalgic Encaphalomyelitis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePACS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePost-Acute Covid Sequelae\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePolymerase Chain Reaction.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003epMDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePressurised Metered Dose Inhaler\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eR5 / R19\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eResistance At 5 Hz / 19 Hz\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eR5\u0026ndash;19\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eResistance Between 5\u0026ndash;19 Hz\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSmall Airway Dysfunction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSARS-CoV-2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSevere Acute Respiratory Syndrome Coronavirus 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for The Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTreatable Trait\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVO₂ max\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePeak Oxygen Consumption\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eX5\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReactance At 5 Hz\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study was approved by the St Vincent\u0026rsquo;s Hospital Human Research Ethics Committee (HREC) (Project ID: 2024/PID02621; Ethics Approval No: 2024/ETH02265; Governance Application No: 2024/STE04088). The study was conducted in accordance with the National Statement on Ethical Conduct in Human Research. A waiver of consent was granted as this is a retrospective analysis of routinely collected clinical data.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003e1. Not applicable. This manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo specific funding was received for this study. The Long COVID Fellowship at St Vincent\u0026rsquo;s Hospital Sydney (2024\u0026ndash;2025), which supported clinical service and data collection for this project, was funded by the Matthew Handbury Foundation. The sponsor had no role in the design, conduct, analysis, or reporting of the research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eU.E.A. performed data curation, analysis, investigation, visualization, and writing \u0026ndash; original draft and review.A.B. contributed to conceptualization, methodology, resources, supervision, and writing \u0026ndash; review and editing.M.P. contributed to conceptualization, methodology, and supervision.S.F. contributed to conceptualization, methodology, supervision, and resources.B.J. contributed to methodology, project administration, and writing \u0026ndash; review and editing.S.S. contributed to analysis, validation, visualization, and writing \u0026ndash; review.D.I. contributed to data curation, analysis, validation, and writing \u0026ndash; review.E.A. contributed to investigation, resources, visualization, and writing \u0026ndash; review.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e1.The authors and St Vincent\u0026rsquo;s Hospital Sydney acknowledge the support of the Matthew Handbury Foundation, which funded the Long COVID Fellowship at St Vincent\u0026rsquo;s Hospital Sydney for the 2024\u0026ndash;2025 period. This fellowship enabled dedicated clinical service and data collection for patients attending the Long COVID Clinic. The Foundation had no role in the design, conduct, analysis, or reporting of this study.2.The authors also wish to thank Jane Wheatley, Clinical Psychologist St Vincent\u0026rsquo;s Hospital Sydney, for her invaluable contribution to the assessment of long COVID patients, and Amy Kennedy, former St Vincent\u0026rsquo;s Hospital Sydney Physiotherapist, for her dedicated role in the rehabilitation of patients with long COVID.3.The authors used OpenAI\u0026rsquo;s ChatGPT (GPT-5, August 2025 version) to assist in improving the clarity, grammar, and conciseness of the manuscript text. All content generated by the tool was reviewed, edited, and approved by the authors, who take full responsibility for the integrity, accuracy, and interpretation of the work. ChatGPT was not used for data analysis, interpretation of results, or generation of scientific conclusions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study will be available from the corresponding author after publication, for a period of 5 years. Data are stored in a secure workstation at the Heart Lung Clinic, Xavier Building, St Vincent\u0026rsquo;s Hospital, under the custodianship of Professor Anthony Byrne. Available materials include the study protocol, statistical analysis plan, and de-identified datasets (Excel and SPSS files). Access to these materials may be granted upon reasonable request by contacting the corresponding author, who will seek approval from the St Vincent\u0026rsquo;s Hospital Human Research Ethics Committee before release.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLewthwaite H, Byrne A, Brew B, Gibson PG. Treatable traits for long COVID. Respirology. 2023;28(11):1005\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgusti A, Bel E, Thomas M, Vogelmeier C, Brusselle G, Holgate S, et al. Treatable traits: toward precision medicine of chronic airway diseases. Eur Respir J. 2016;47:410\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcDonald VM, Fingleton J, Agusti A, Hiles SA, Clark VL, Holland AE et al. Treatable traits: a new paradigm for 21st century management of chronic airway diseases: treatable traits down under international workshop report. Eur Respir J. 2019; 53: 1802058.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgust\u0026iacute; A, Bafadhel M, Beasley R, Bel EH, Faner R, Gibson PG, et al. Precision medicine in airway diseases: moving to clinical practice. 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BMJ. 1969;1(5636):73\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi X, Wang C, Kou S, Luo P, Zhao M, Yu K. Lung ventilation function characteristics of survivors from severe COVID-19: a prospective study. Crit Care. 2020;24:300.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbuquerque CG, Andrade FM, Rocha MA, Oliveira AF, Ladosky W, Victor EG, et al. Determining respiratory system resistance and reactance by impulse oscillometry in obese individuals. J Bras Pneumol. 2015;41:422\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalant SP, Komarow HD, Shin HW, Siddiqui S, Lipworth BJ. The case for impulse oscillometry in the management of asthma in children and adults. Ann Allergy Asthma Immunol. 2017;118:664\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLopes AJ, Litrento PF, Provenzano BC, Carneiro AS, Monnerat LB, da Cal MS, et al. 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Small airway dysfunction assessed by impulse oscillometry in symptomatic patients exhibiting preserved pulmonary function. J Allergy Clin Immunol Pract. 2020;8:229\u0026ndash;e2353.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHogg JC, Macklem PT, Thurlbeck WM. Site and nature of airway obstruction in chronic obstructive lung disease. N Engl J Med. 1968;278(25):1355\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUsmani OS, Dhand R, Lavorini F, Price D. Why we should target small airways disease in our management of chronic obstructive pulmonary disease. Mayo Clin Proc. 2021;96(9):2448\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePisi R, Aiello M, Zanini A, Tzani P, Paleari D, Marangio E, et al. Small airway dysfunction and flow and volume bronchodilator responsiveness in patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2015;10:1191\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraido F, Scichilone N, Lavorini F, Papi A, Pedersen S, Soriano JB, et al. Interasma Executive Board; WAO Board of Directors; ARIA; GA\u0026sup2;LEN. Manifesto on small airway involvement and management in asthma and chronic obstructive pulmonary disease: an Interasma (Global Asthma Association - GAA) and World Allergy Organization (WAO) document endorsed by Allergic Rhinitis and its Impact on Asthma (ARIA) and Global Allergy and Asthma European Network (GA\u0026sup2;LEN). Asthma Res Pract. 2016;2:12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan G, Gochicoa-Rangel L, Cottini M, Comberiati P, Gaillard EA, Ducharme FM et al. Ascertainment of small airway dysfunction using oscillometry to better define asthma control and future risk: are we ready to implement it in clinical practice? Chest. 2024; (in press).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdo M, Trinkmann F, Kirsten AM, Pedersen F, Herzmann C, Von Mutius E, et al. Small airway dysfunction links asthma severity with physical activity and symptom control. J Allergy Clin Immunol Pract. 2021;9:3359\u0026ndash;e33681.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRinaldo RF, Mondoni M, Parazzini EM, Pitari F, Brambilla E, Luraschi S, et al. Deconditioning as main mechanism of impaired exercise response in COVID-19 survivors. Eur Respir J. 2021;58:2100870.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDarley DR, Dore GJ, Cysique L, Wilhelm KA, Andresen D, Tonga K, et al. Persistent symptoms up to four months after community and hospital-managed SARS-CoV-2 infection. Med J Aust. 2021;214(6):279.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLo PC, Feng JY, Hsiao YH, Hsia TC, Lin CH, Wu YK, et al. 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Ventilation heterogeneity and oscillometry predict asthma control improvement following step-up inhaled therapy in uncontrolled asthma. Respirology. 2020;25:827\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLarsen G, Morgan W, Heldt G, Sahgal V, McElderry L, Navale S, et al. Impulse oscillometry versus spirometry in a long-term study of controller therapy for pediatric asthma. J Allergy Clin Immunol. 2009;123:861\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTimmins SC, Diba C, Schoeffel RE, Berend N, Salome CM, King GG. Changes in oscillatory impedance and nitrogen washout with combination fluticasone/salmeterol therapy in COPD. Respir Med. 2014;108:344\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eda Costa GM, Faria AC, Di Mango AM, Lopes AJ, Jansen JM, Lopes FF, et al. Respiratory impedance and response to salbutamol in healthy individuals and patients with COPD. Respiration. 2014;88:101\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Long COVID, Small airway dysfunction, Oscillometry, Inhaled corticosteroid therapy, Treatable traits, Forced oscillation technique, Dyspnoea, Fatigue.","lastPublishedDoi":"10.21203/rs.3.rs-7541120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7541120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and objective\u003c/p\u003e\u003cp\u003eLong COVID is a chronic multi-system condition with limited treatment options. Small airway dysfunction (SAD), detectable by oscillometry or spirometry, may respond to inhaled corticosteroid/long-acting bronchodilator (ICS/LABA) therapy. We aimed to determine the prevalence and predictors of SAD in non-asthmatic long COVID patients and assess the impact of ICS/LABA.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eRetrospective study of adults with WHO-defined long COVID at St Vincent\u0026rsquo;s, Sydney (June 2022\u0026ndash;October 2024). Patients with prior asthma or ICS/LABA use were excluded. Demographics, co-morbidities, spirometry, forced oscillation techniques (FOT), and C19-YRS were assessed at baseline, 6, 12, and 18 months. SAD was defined using Chiu et al. criteria. Outcomes were compared between SAD and non-SAD patients and by ICS/LABA use (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant). Logistic regression identified predictors of SAD.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eOf 251 patients screened, 163 met inclusion and 123 (75.5%) had SAD. Compared to non-SAD, SAD patients were older (52.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9 vs. 42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher BMI (29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 vs. 24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more hypertension, obesity, and ischaemic heart disease. FOT showed significantly higher resistance (R5, R19, R5\u0026ndash;19), increased AX and Fres, and more negative X5 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while spirometry only detected lower FEV1. Among 79 SAD patients prescribed ICS/LABA, resistance, reactance, and symptoms improved significantly: dyspnoea (83.5%\u0026rarr;17.7%), fatigue (96.2%\u0026rarr;15.2%), cough (43%\u0026rarr;11.4%).\u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003eSAD appears highly prevalent in long COVID patients without known asthma and is best detected by oscillometry. Treatment with ICS/LABA was associated with improved symptoms and FOT indices, however randomised trials are needed to confirm efficacy.\u003c/p\u003e","manuscriptTitle":"Treatable Traits in Long COVID: Inhaled corticosteroids and long-acting bronchodilators for small airway dysfunction among symptomatic Long COVID patients without known Asthma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 01:24:45","doi":"10.21203/rs.3.rs-7541120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-01T10:32:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2029457309308073141131358449743317925","date":"2025-10-23T12:20:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T15:42:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152474701806504463594254719926068504132","date":"2025-10-08T05:29:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-07T10:38:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T09:31:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T12:34:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T14:12:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-09-09T14:07:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ee2d4e7-adff-494d-9372-aa04a38b362e","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T01:24:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 01:24:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7541120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7541120","identity":"rs-7541120","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}