Complementing longitudinal spirometry with electrical impedance tomography: a novel strategy to evaluate biological efficacy in severe asthma

Complementing longitudinal spirometry with electrical impedance tomography: a novel strategy to evaluate biological efficacy in severe 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 Complementing longitudinal spirometry with electrical impedance tomography: a novel strategy to evaluate biological efficacy in severe asthma Shuoyao Qu, Hong Yan, Liang Han, Zhangqin Chen, Feng Fu, Ruijiang Shi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9138241/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective Standard spirometry often fails to capture regional functional heterogeneity in severe asthma. This study aimed to evaluate the differential effects of biological versus conventional therapy on small airway function and ventilation homogeneity, utilizing a combined strategy of longitudinal spirometry and cross-sectional electrical impedance tomography (EIT) assessment at the 6-month endpoint. Methods In this observational study, 111 patients with severe asthma were stratified into two groups based on their clinical treatment regimen: a conventional group (high-dose ICS/LABA, n = 79) and a biological group (add-on biologic therapy, n = 32). Spirometry was performed at baseline and after 6 months. Electrical impedance tomography (EIT) measurements were only conducted at the 6-month follow-up to quantify regional ventilation dynamics, utilizing parameters such as the regional expiratory time constant (τ), global inhomogeneity (GI) index, and regional obstruction ratio (rOR). Results Over the 6-month period, the biological group demonstrated superior improvements compared to the conventional group. Significant Time×Group interactions were observed for all large airway parameters, including FEV1( P Interaction =0.01), FVC( P Interaction =0.047) and the FEV1/FVC ratio ( P Interaction =0.02) and FEV1% pred ( P Interaction =0.01). Small airway function also improved significantly more in the Biological group (MEF50, P Interaction =0.02; MEF25, P Interaction =0.04). EIT assessment at the endpoint confirmed these physiological benefits, showing significantly greater ventilation homogeneity (lower GI T75 , P = 0.01), faster lung emptying (shorter τ, P < 0.01), and reduced regional obstruction (lower rOR, P = 0.01) in the biological group. Conclusion Biological treatments demonstrated superior efficacy in restoring large and small airway function and improving ventilation homogeneity, compared to conventional therapy. While global spirometry tracks overall airflow trends, EIT provides critical insights into regional lung mechanics. Integrating both modalities offers a more comprehensive approach to uncovering the potential physiological improvements in severe asthma management. Figures Figure 1 Figure 2 Figure 3 1. Introduction Severe asthma is characterized by significant heterogeneity and complex pathophysiology. It manifests as poor clinical control, recurrent exacerbations, and impaired quality of life, carrying a high risk of future adverse outcomes[ 1 ]. Although severe asthma accounts for only 5–10% of the total asthma population, it represents a substantial portion of the overall disease burden[ 2 , 3 ]. The latest Global Initiative for Asthma (GINA) guidelines define severe asthma as asthma that remains uncontrolled despite optimized treatment with high-dose inhaled corticosteroids (ICS) and long-acting beta-agonists (LABA), or that requires high-dose therapy to prevent loss of control[ 4 , 5 ]. A major challenge in managing this condition lies in its heterogeneity, necessitating more precise treatment strategies and refined assessment methods[ 6 , 7 ]. Currently, two main therapies are used to control severe asthma: conventional therapy containing high-dose ICS and LABAs, and biological therapy targeting specific inflammatory pathways. Both aim to improve symptoms and lung function[ 8 – 10 ]. However, comprehensively evaluating and comparing the real-world efficacy of these therapies remains a challenge[ 11 , 12 ]. Overall asthma control is typically assessed through day/night symptoms, exacerbation frequency, rescue medication use, and questionnaires (e.g., asthma control test, ACT). However, the "gold standard" for assessing ventilation remains spirometry[ 13 ]. Forced expiratory volume in 1 second (FEV1) reliably reflects global airway conditions. Furthermore, in severe asthma, airway inflammation and remodeling are often unevenly distributed[ 14 , 15 ]; thus, advanced tools like electrical impedance tomography (EIT) should be included for assessing the treatment effect. EIT is a non-invasive, radiation-free bedside monitoring tool ideally suited for this purpose. It provides real-time, dynamic visualization of gas distribution within the lung[ 16 ]. Importantly, EIT allows for the calculation of regional mechanical parameters, such as the respiratory time constant (τ)[ 17 , 18 ]. This parameter measures the speed of regional lung filling and emptying and serves as an index of ventilation efficiency[ 19 ]. A more efficient lung region empties and fills faster, resulting in a shorter τ. This study aimed to compare the effects of two therapeutic strategies using both global (spirometry) and regional (EIT) assessment methods. While standard spirometry was performed longitudinally to track global changes from baseline, EIT was specifically employed at the 6-month follow-up as a cross-sectional tool. This design allowed us to quantify the 'final state' of regional ventilation and small airway function at the study endpoint, revealing pathophysiological differences that might be missed by routine longitudinal monitoring 2. Materials and Methods 2.1 Study Design and Subjects This was a two-arm, mixed-design observational clinical study. The objective was to quantify the longitudinal effects of treatment via conventional spirometry and to compare regional lung function heterogeneity cross-sectionally using EIT. The study was conducted at the Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of the Fourth Military Medical University. All procedures complied with the Declaration of Helsinki and were approved by the hospital’s Ethics Committee (Approval No. KY20252420-F-1). All participants provided written informed consent (Fig. 1 ). Adult patients diagnosed with moderate-to-severe asthma according to GINA guidelines were recruited consecutively from outpatient and inpatient units. Upon enrollment, patients were assigned to one of two cohorts based on their prescribed clinical treatment: Conventional Group Received standardized high-dose ICS/LABA combination therapy. Some patients also used leukotriene receptor antagonists (LTRA) or long-acting muscarinic antagonists (LAMA). Biological Group Patients who remained uncontrolled despite low-medium dose ICS/LABA (plus LAMA in some cases) and received add-on biologic therapy. Specific biological (Omalizumab, Dupilumab, or Mepolizumab) were selected based on the patient's type 2 inflammation profile. 2.2 Inclusion and Exclusion Criteria Inclusion criteria 1) Age 18–75 years; 2) Confirmed diagnosis of bronchial asthma, classified as severe (GINA Steps 4–5) due to poor control or high treatment intensity; 3) Receiving or initiating one of the defined study treatments; 4) Ability to understand and perform lung function tests and EIT procedures. Exclusion criteria 1) Current smokers or those who quit < 1 year ago (to minimize confounding from smoking-related remodeling); 2) Diagnosis of Asthma-COPD Overlap (ACO), non-cystic fibrosis bronchiectasis, interstitial lung disease, or other chronic lung conditions; 3) Use of systemic immunosuppressants or biological for other conditions (e.g., autoimmune disease); 4) Chest wall skin infections, large scars, or implants preventing electrode contact; 5) Implanted active electronic devices (e.g., pacemakers, ICDs); 6) Pregnancy or lactation. 2.3 Study Workflow Participants completed two visits over a 6-month period. Baseline Visit (Day 0) Performed at the initiation of the stable treatment regimen. Data collected included demographics, BMI, smoking history, allergy/rhinitis history, Asthma Control Test (ACT) scores, and baseline spirometry. Follow-up Visit (Month 6 ± 2 weeks) Performed after 6 months of treatment. Procedures included ACT assessment, repeat spirometry, and EIT measurement. EIT was performed during this visit to ensure temporal consistency with the lung function data. 2.4 Lung Function and Clinical Assessment Spirometry was performed using a standard device (Jaeger, CareFusion GmbH, Germany) following ATS/ERS guidelines. Bronchodilators were withheld prior to testing. Patients performed at least three acceptable and reproducible forced expiratory maneuvers. Key parameters included FVC, FEV1, FEV1/FVC ratio, and Peak Expiratory Flow (PEF). Pre-bronchodilator values were used for analysis. 2.5 EIT measurements EIT measurements were conducted only at the 6-month follow-up using a commercial system (VenTom-200, MidasMED, Suzhou, China). A 16-electrode silicone belt was placed around the thorax at the 4th–5th intercostal level. Patients sat comfortably. First, 5 minutes of tidal breathing were recorded after calibration. Then, while data were acquired at 50 Hz, patients performed at least three valid FVC maneuvers under instruction. Data were filtered to remove cardiac oscillations. A linear regression fitted the sum of impedance changes to spirometry volume to calculate a volume-impedance conversion coefficient. This allowed the conversion of dimensionless impedance values into volume (mL) for pixel-level analysis. Based on pixel-wise time-volume curves, eight functional parameters were derived[ 20 ]: Regional FVC Maximum impedance change per pixel during forced expiration. Regional FIVC Maximum capacity change during forced inspiration. Regional FEV1 Cumulative volume exhaled at the 1st second per pixel. Regional FEV1/FVC The ratio of Regional FEV1 to Regional FVC, identifying localized obstruction independent of absolute volume. Regional PEF Maximum instantaneous flow rate (first derivative of the volume curve) per pixel. Regional MEF25-75 Mean flow rate between 25% and 75% of regional FVC. This reflects small airway function. T75 Time required for a pixel to exhale 75% of its FVC. Prolonged T75 indicates delayed emptying and gas trapping. Regional Expiratory Time Constant ( τ ) Calculated by fitting a single exponential decay to the expiratory curve of each pixel where is the relative impedance changes of lung pixels at time point t . represents the impedance at the beginning of expiration. t represents the time interval from end-inspiration to end-expiration. c denotes the end-expiratory volume. τ is the regional time constant which is sensitive to changes in airway resistance and primarily reflects the functional status of peripheral small airways during the mid-to-late phase of expiration Spatial Dispersion (Delta, △):T he standard deviation of valid pixel values across the lung. Higher values indicate greater disparity between fast and slow-emptying units. Global Inhomogeneity Index (GI) The sum of absolute differences between each pixel value and the median, divided by the sum of all pixel values. Higher GI indicates severe ventilation inhomogeneity. Regional Obstructive Ratio (rOR) The percentage of total lung area occupied by pixels with a local FEV1/FVC < 0.7. Higher values imply more extensive airway obstruction. 2.6 Statistical Analysis Analysis was performed using MATLAB 2022b. Normality was assessed using the Shapiro-Wilk test. Data are presented as mean ± SD or median (interquartile range). P < 0.05 was considered statistically significant. Baseline characteristics were compared using independent t-tests, Mann-Whitney U tests, or Chi-square tests. Longitudinal spirometry data were analyzed using a Repeated Measures ANOVA (Group×Time). A significant interaction indicated a differential treatment response. For post-treatment EIT data, cross-sectional comparisons were made using the Mann-Whitney U test. Subgroup analysis (stratified by gender, smoking, rhinitis) used Two-way ANOVA to test for interactions between treatment effects and baseline characteristics. 3. Results 3.1 Recruitment and Grouping This study enrolled patients who visited the outpatient clinic of the Department of Pulmonary and Critical Care Medicine at Xijing Hospital, the Fourth Military Medical University, between November 22, 2024 and March 19, 2025. Of 135 screened patients, 111 met the eligibility criteria and were assigned to the conventional group (n = 79) or biological group (n = 32). All 111 patients completed the 6-month follow-up and were included in the final analysis (Fig. 2 ). 3.2 Baseline Characteristics Baseline demographics and clinical features were generally balanced (Table 1 ). There were no significant differences in gender, BMI, or smoking history. Although the biological group was slightly younger (46.4 vs. 53.0 years), this was not statistically significant ( P = 0.06). Notably, the prevalence of allergic rhinitis was significantly higher in the biological group (87.5% vs. 62.5%, P = 0.02). Table 1 Study participants’ characteristics Characteristics Conventional (n = 79) Biological (n = 32) P Gender (Male:Female) 33:46 13:19 0.91 Age, years 46.39 ± 15.44 46.44 ± 13.78 0.99 Height, cm 166.0(160.0-173.0) 165.5(159.5-172.5) 0.43 Weight, kg 68.34 ± 11.64 65.90 ± 15.00 0.36 BMI, kg/m² 24.00 (22.45–27.05) 22.85 (20.60-26.65) 0.25 Smoking History (Yes), n (%) 14 (17.72%) 5 (15.62%) 0.79 Rhinitis (Yes), n (%) 53 (67.09%) 28 (87.50%) 0.03 FEV1, L 2.51 ± 0.85 2.32 ± 0.77 0.28 FVC, L 3.39 ± 0.97 3.30 ± 0.97 0.66 FEV1/FVC,% 71.82 ± 9.76 68.67 ± 9.23 0.12 FEV1(% pred) 83.92 ± 18.05 79.36 ± 16.70 0.22 PEF, L/s 5.45(4.38–7.28) 5.71(4.12–6.79) 0.61 MEF75, L/s 4.65 ± 2.16 4.14 ± 1.71 0.24 MEF50, L/s 2.14(1.35–3.27) 2.07(1.23-3.00) 0.37 MEF25, L/s 0.67 (0.49–1.13) 0.65 (0.42–0.96) 0.17 MMEF, L/s 1.28(0.80–2.02) 1.15(0.90–1.85) 0.80 ACT 10.00 (9.00–12.00) 11.00 (9.00-12.50) 0.78 Acute Exacerbations 3.00 (2.00–4.00) 3.00 (3.00-4.50) 0.31 BMI: body mass index; FEV1: forced expiratory volume in the first second; FVC: forced vital capacity; pred: predicted; PEF: peak expiratory flow; MEF: maximal expiratory flow (at 75%, 50%, and 25% of FVC); MMEF: maximal mid-expiratory flow; ACT: asthma control test. Bold values indicate statistical significance (P < 0.05). Baseline lung function (both large and small airway indices) showed no significant differences between groups. Disease severity was comparable, with similar ACT scores and exacerbation histories (median 3.0 events/year), confirming a population with severe, uncontrolled asthma. 3.3 Spirometry Findings After 6 months of treatment, both groups showed improvements in most lung function parameters compared to baseline ( P Time <0.05). However, the biological group demonstrated superior efficacy in improving both large and small airway function compared to the conventional group. Regarding large airway function, repeated measures ANOVA revealed a significant Time×Group interaction, indicating greater benefits for the biological group. Specifically, the biological group showed significantly better improvements in FEV1 ( P Interaction =0.01), FVC ( P Interaction =0.047), and the FEV1/FVC ratio ( P Interaction =0.02). Notably, FEV1 (% pred) improved significantly more in the biological group compared to the conventional group ( P Interaction =0.01), rising from approximately 79% to 96%.Similarly, regarding small airway function, the biological group exhibited superior airflow recovery. Significant interactions were observed for MEF50( P Interaction =0.02) and MEF25( P Interaction =0.04), reflecting better air movement in the peripheral lungs. No significant between-group differences were found for PEF, MEF75, or MMEF ( P Interaction >0.05). Table 2 Pulmonary function outcomes at baseline and at 6-month follow-up Characteristics Conventional (n = 79) Biological (n = 32) ANOVA P -values Before After Before After P Time P Interaction FEV1, L 2.51 ± 0.85 2.54 ± 0.83 2.32 ± 0.77 2.54 ± 0.70 0.00 0.01 FVC, L 3.39 ± 0.97 3.41 ± 0.92 3.30 ± 0.97 3.46 ± 0.87 0.01 0.047 FEV1/FVC,% 71.82 ± 9.76 73.70 ± 9.97 68.67 ± 9.23 73.18 ± 8.16 0.00 0.02 FEV1(% pred) 83.92 ± 18.05 93.61 ± 20.49 79.36 ± 16.70 96.28 ± 17.15 0.00 0.01 PEF, L/s 5.45(4.38–7.28) 5.35(4.11–6.53) 5.71(4.12–6.79) 5.29(3.85–6.75) 0.06 0.24 MEF75, L/s 4.65 ± 2.16 4.73 ± 2.10 4.14 ± 1.71 4.55 ± 1.69 0.01 0.10 MEF50, L/s 2.14(1.35–3.27) 2.42(1.56–3.69) 2.07(1.23-3.00) 2.52(1.77–3.16) 0.00 0.02 MEF25, L/s 0.67 (0.49–1.13) 0.73 (0.56–1.28) 0.65 (0.42–0.96) 0.82 (0.57–1.03) 0.00 0.04 MMEF, L/s 1.28(0.80–2.02) 1.89 (1.31–2.83) 1.15(0.90–1.85) 2.04 (1.44–2.65) 0.00 0.64 Bold values indicate statistical significance (P < 0.05) 3.4 EIT outcomes Figure 3 Representative fEIT maps of two patients with severe asthma at 6-month. (a) A patient from the conventional treatment group showing heterogeneous ventilation distribution. The regional expiratory τ map (G) reveals elevated values in basal lung regions, and the corresponding histogram (H) displays a wide dispersion, indicating asynchronous lung emptying and persistent small airway obstruction. (b) A patient from the biologic treatment group demonstrating homogeneous ventilation. The τ map (G) shows uniformly low values, and the histogram (H) exhibits a narrow peak, suggesting synchronized lung emptying and effective resolution of air trapping. FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PEF, peak expiratory flow; MMEF, maximal mid-expiratory flow; τ, regional expiratory time constant. Table 3 showed that, the biological group had significantly lower GI T75 (0.14 ± 0.05 vs. 0.23 ± 0.14, P = 0.01) and △ T75 (0.06 ± 0.069 s vs. 0.13 ± 0.19 s, P = 0.04), indicating better temporal homogeneity, than the conventional group. rOR was significantly smaller in the biological group (18.47% vs. 44.57%, P = 0.01). Additionally, the biological group demonstrated significantly shorter median τ (0.54 s vs. 0.87 s, P < 0.01) and reduced spatial dispersion of τ ( P < 0.01), confirming faster and more uniform lung emptying. Table 3 Comparison of EIT-derived parameters between the conventional and biological treatment groups at 6-month EIT Parameters Conventional (n = 79) Biological (n = 32) P Improvement GI T75 0.23 ± 0.14 0.14 ± 0.05 0.01 39.17% △ T75 (s) 0.13 ± 0.19 0.06 ± 0.069 0.04 55.20% rOR(%) 44.57 ± 35.91 18.47 ± 29.13 0.01 58.65% τ med (s) 0.87 ± 0.34 0.54 ± 0.17 < 0.00 38.51% τ iqr (s) 0.24 ± 0.27 0.17 ± 0.07 < 0.00 52.21% Data are presented as mean ± standard deviation. GI, global inhomogeneity index; , change of spatial distribution; rOR, regional obstructive ratio; , median regional expiratory time constant; , interquartile range of regional expiratory time constant. 3.5 Subgroup and Interaction Analysis Subgroup analyses based on smoking history, rhinitis comorbidity, and gender revealed no significant interactions with treatment outcomes (all Interaction P > 0.05). This indicates that the observed treatment responses were not significantly modified by these baseline characteristics. 4. Discussion Pathological changes and ventilatory dysfunction of the small airways represent the core pathological features of asthma. Currently, the clinical assessment of asthma control primarily relies on symptom scores, pulmonary function tests, and fractional exhaled nitric oxide (FeNO) measurements[ 21 ]. However, these conventional evaluation modalities exhibit inherent limitations in accurately assessing small airway ventilation status. Historically, asthma management has predominantly depended on ICS and LABA. In recent years, biological targeted therapies targeting type 2 inflammation have brought new prospects for the treatment of asthma, especially severe cases[ 22 ]. Nevertheless, a precise detection approach is still lacking to evaluate the improvements in small airway ventilatory function induced by both high-dose ICS therapy and biological targeted treatments. Our research team previously employed EIT to examine small airways in patients with chronic respiratory diseases, and demonstrated that EIT holds distinct advantages in monitoring small airway ventilatory function[ 17 , 19 , 20 , 23 , 24 ]. Building on the previous work, the present study focuses on investigating differences in global and regional ventilatory functions among asthmatic patients receiving different therapeutic interventions. This study combined global and regional lung function methods to evaluate the efficacy of biological and conventional therapies in severe asthma. Our results show that biological therapy demonstrated superior efficacy in both global airway metrics and regional ventilation distribution compared to conventional therapy. It is important to interpret these regional findings in the context of the study design. Although EIT data were acquired only at the post-treatment stage, the strict baseline matching of demographic and spirometry parameters between the two groups (Table 1 ) minimizes the likelihood of pre-existing regional disparities. Therefore, the superior ventilation homogeneity observed in the biological group at 6 months likely reflects the specific pharmacodynamic benefits of the treatment rather than intrinsic patient differences. EIT imaging revealed that patients treated with biological therapy achieved more homogeneous ventilation and lower dispersion of expiratory τ. Notably, the improvement was not limited to small airways. The Biological group showed significantly greater increases in FEV1 and FVC, indicating restored patency in large airways. This is supported by the EIT-derived rOR. Since rOR reflects airway obstruction across the whole lung, its significant reduction confirms that biological therapy effectively relieved blockage in both central (large) and peripheral (small) airways. The differential response may relate to drug delivery mechanisms and the pathophysiology of severe asthma. Conventional inhalation therapy relies on aerosol deposition. In severe asthma, proximal airflow turbulence and mucus plugging may create physical barriers. These barriers prevent inhaled drugs from effectively treating not only the distal lung but also the obstructed large airways [ 25 ]. The significantly lower improvement in FEV1 and MEF25 in the conventional group reflects this limitation. In contrast, biological medicines are administered systemically via blood circulation This allows the drug to reach the bronchial circulation, reducing inflammation and edema in the large airway walls. By systematically blocking Type 2 inflammatory pathways, biological treatment may reduce peripheral edema and mucus load, thereby improving both proximal and distal conditions[ 26 ]. Physiologically, the improvement in MEF25 (increased flow at low lung volume) and the shortening of EIT-derived T75 (reduced time to exhale 75% volume) are consistent. Both metrics reflect the "effort-independent" phase of expiration, determined largely by small airway caliber and elastic recoil. The coherence between these distinct modalities strengthens the conclusion that biological treatments may have a specific impact on reversing peripheral airway obstruction. It is also noteworthy that there was a significant baseline difference in the prevalence of allergic rhinitis between the two groups. According to the 'united airways' hypothesis, upper airway inflammation is inextricably linked to lower airway dysfunction, often sharing common pathological mechanisms such as type 2 inflammation[ 27 , 28 ]. The higher burden of rhinitis in the biological group theoretically presents a greater challenge for asthma control due to the potential for post-nasal drip or nasobronchial reflexes to aggravate bronchial constriction. However, our results showed superior airway improvement in these patients. This suggests that biological agents likely exert a comprehensive systemic anti-inflammatory effect, targeting the 'united airways' as a whole and mitigating the negative impact of upper airway pathology on lung mechanics, an advantage that local inhalation therapy may not fully achieve. This study also has several limitations. First, it was observational and non-randomized, which inherently introduces potential selection bias regarding treatment allocation. However, this real-world design provides high external validity, reflecting actual clinical decision-making and patient heterogeneity often excluded from strict randomized controlled trials. Second, EIT measurement was not conducted at the baseline. EIT was incorporated to provide additional dimensions of information at the endpoint of efficacy assessment that global tool cannot offer, rather than for evaluating individual longitudinal changes before and after treatment. While this precludes a longitudinal analysis of regional function changes within individuals, the cross-sectional comparison at the endpoint successfully highlighted the distinct physiological patterns achieved by different therapies. EIT thus served as a discriminatory tool to characterize the quality of functional improvement, complementing the quantitative magnitude provided by longitudinal spirometry. Therefore, the current findings were not diminished. Third, the analysis was not stratified by specific biological agents (e.g., anti-IgE vs. anti-IL-5/5R) due to the limited number of patients in each subgroup. Since different biologics target distinct inflammatory pathways that may differentially impact airway remodeling and small airway function, pooling these agents might mask drug-specific physiological signatures[ 29 ]. Consequently, we could only report the 'class effect' of biological therapy rather than the efficacy of individual molecules. Future prospective studies with larger cohorts are needed to validate these findings. 5. Conclusion In the management of severe asthma, biological therapy was more effective than conventional therapy in improving both large and small airway function. EIT played a crucial role in revealing these benefits. Unlike standard spirometry, which only measures total airflow, EIT visualized regional ventilation patterns. It confirmed that biological treatment leads to more uniform lung emptying and reduced obstruction. Combination of both methods provides more precise assessment and personalized treatment in severe asthma. Abbreviations EIT electrical impedance tomography τ regional expiratory time constant GI global inhomogeneity Declarations Ethics approval and consent to participate This study involved human participants and was approved by Medical Ethics Committee of the First Affiliated Hospital of Fourth Military Medical University (NO. KY20252420-F-1). Clinical trial number: not applicable. All participants enrolled in this study have signed informed consent based on the voluntary principle. This research presented here has been performed in accordance with the Declaration of Helsinki. All methods were carried out in accordance with relevant guidelines. Consent for publication Not applicable. All data included in this manuscript have been fully anonymized and de-identified, with no personal identifiable information of any enrolled participants. No individual consent for publication is required. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this study. Funding This work was supported by grants from the National Natural Science Foundation of China (NO. 82570041) (NO. 52277235); Shaanxi Province Health Research Innovation Capability Enhancement Plan Key Research and Development Projects(No. 2025YF-51);Xijing Hospital Promoting Project (No. XJZT25ZH03)༛Partner Laboratory of the Air Force Medical University (No. 2024HB009)༛Military Medical Science and Technology Breakthrough Project of Air Force Military Medical University(2025JSKY06). Author Contribution Conception and design: SY Qu, M Dai; (II) Administrative support: SY Qu, M Dai; (III) Provision of study materials or patients: SY Qu, Q Ju, XY Ti; (IV) Collection and assembly of data: RJ Shi, H Yan, L Han; (V) Data analysis and interpretation: ZQ Zhao, F Fu, L Yang; (VI) Manuscript writing:M Dai,SY Qu; (VII) Final approval of manuscript: All authors. Acknowledgement We thanked all participants enrolled in this cohort for their volunteered supports. Data Availability The datasets generated and analysed during the current study are not publicly available due to the considerably large file size of the original raw datasets and the requirement for dedicated specialized proprietary software to complete full reading, processing and analysis of the data, but are available from the corresponding author upon reasonable, academic non-commercial request. 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(2023) 102: e34749. 10.1097/MD.0000000000034749 Singh S, Dutta J, Ray A, Karmakar A, Mabalirajan U. Airway Epithelium: A Neglected but Crucial Cell Type in Asthma Pathobiology. Diagnostics (Basel) . (2023) 13: 10.3390/diagnostics13040808 Heines SJH, Becher TH, van der Horst ICC, Bergmans D. Clinical Applicability of Electrical Impedance Tomography in Patient-Tailored Ventilation: A Narrative Review. Tomography . (2023) 9: 1903–32. 10.3390/tomography9050150 Yang L, Zhao K, Gao Z, Fu F, Wang H, Wang C, Dai J, Liu Y, Qin Y, Dai M, Cao X, Zhao Z. The Influence of Breathing Exercises on Regional Ventilation in Healthy and Patients with Chronic Obstructive Pulmonary Disease. COPD . (2023) 20: 248–55. 10.1080/15412555.2023.2234992 Yang L, Gao Z, Cao X, Sun S, Wang C, Wang H, Dai J, Liu Y, Qin Y, Dai M, Guo W, Zhang B, Zhao K, Zhao Z. Electrical impedance tomography as a bedside assessment tool for COPD treatment during hospitalization. Front Physiol. 2024;15. 10.3389/fphys.2024.1352391 . Qu S, Feng E, Dong D, Yang L, Dai M, Frerichs I, Liu S, Gao Y, Zheng J, Song L, Zhao Z. Early screening of lung function by electrical impedance tomography in people with normal spirometry reveals unrecognized pathological features. Nat Commun. 2025;16:622. 10.1038/s41467-024-55505-2 . Zhang J, Liu T, Chang Z, Dai M, Song L, Yang L, Ti X, Qu S, Zhao Z. Ventilation heterogeneity across A-B-E phenotypes in COPD: insights from spirometry and electrical impedance tomography. Front Med. 2025;12. 10.3389/fmed.2025.1731427 . Chong W, Li H, Wang J. Therapeutic efficacy of omalizumab in children with moderate-to-severe allergic asthma combined with chronic sinusitis. Front Allergy. 2023;4:1236798. 10.3389/falgy.2023.1236798 . Chinese Education Association of Chronic Airway D, and, China Asthma A. [Chinese expert consensus on the diagnosis and management of severe asthma (2024 edition)]. Zhonghua Yi Xue Za Zhi . (2024) 104: 1759–89. 10.3760/cma.j.cn112137-20231117-01120 Ma H, Dai M, Wu S, Zhao Z, Zhang Y, Zhao F, Yang L, Ti X, Qu S. Pulmonary rehabilitation ameliorates regional lung function in chronic obstructive pulmonary disease: a prospective single-arm clinical trial. Annals Translational Med. 2022;10:891–891. 10.21037/atm-22-3597 . Yang L, Gao Z, Cao X, Wang C, Wang H, Dai J, Liu Y, Qin Y, Dai M, Zhang B, Zhao K, Zhao Z. Visualizing pursed lips breathing of patients with chronic obstructive pulmonary disease through evaluation of global and regional ventilation using electrical impedance tomography. Physiol Meas. 2024;45. 10.1088/1361-6579/ad33a1 . Usmani OS. Small airways dysfunction in asthma: evaluation and management to improve asthma control. Allergy Asthma Immunol Res. 2014;6:376–88. 10.4168/aair.2014.6.5.376 . Thamboo AV, Lee M, Bhutani M, Chan C, Chan Y, Chapman KR, Chin CJ, Connors L, Dorscheid D, Ellis AK, Gall RM, Godbout K, Janjua A, Javer A, Kilty S, Kim H, Kirkpatrick G, Lee JM, Leigh R, Lemiere C, Monteiro E, Neighbour H, Keith PK, Philteos G, Quirt J, Rotenberg B, Ruiz JC, Scott JR, Sommer DD, Sowerby L, Tewfik M, Waserman S, Witterick I, Wright ED, Yamashita C, Desrosiers M. Canadian multidisciplinary expert consensus on the use of biologics in upper airways: a Delphi study. J Otolaryngol Head Neck Surg. 2023;52:30. 10.1186/s40463-023-00626-9 . Tao M, Zhang Y, Ding L, Peng D. Risk factors of sleep-disordered breathing and poor asthma control in children with asthma. BMC Pediatr. 2024;24:288. 10.1186/s12887-024-04762-7 . De Corso E, Bilo MB, Matucci A, Seccia V, Braido F, Gelardi M, Heffler E, Latorre M, Malvezzi L, Pelaia G, Senna G, Castelnuovo P, Canonica GW. Personalized Management of Patients with Chronic Rhinosinusitis with Nasal Polyps in Clinical Practice: A Multidisciplinary Consensus Statement. J Pers Med. 2022;12. 10.3390/jpm12050846 . Lombardi C, Cottini M, Berti A, Comberiati P. Monoclonal antibodies targeting small airways: a new perspective for biological therapies in severe asthma. Asthma Res Pract. 2022;8:6. 10.1186/s40733-022-00088-2 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 04 Apr, 2026 Editor assigned by journal 27 Mar, 2026 Editor invited by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 24 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9138241","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618969123,"identity":"3e642c23-313f-4149-91f3-899f25ab1e9a","order_by":0,"name":"Shuoyao Qu","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuoyao","middleName":"","lastName":"Qu","suffix":""},{"id":618969124,"identity":"b251ff52-6151-49c4-9de9-036ccba0d1e0","order_by":1,"name":"Hong Yan","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Yan","suffix":""},{"id":618969125,"identity":"65273e0e-7590-43ff-ab95-3cc851bfabea","order_by":2,"name":"Liang Han","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Han","suffix":""},{"id":618969126,"identity":"ba40f515-483f-4ca9-9c4b-6c34537a575f","order_by":3,"name":"Zhangqin Chen","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care of Medicine, YangLing Demonstration Zone Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhangqin","middleName":"","lastName":"Chen","suffix":""},{"id":618969127,"identity":"1a84643e-05ec-47a9-8442-817d5d7e30fe","order_by":4,"name":"Feng Fu","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Fu","suffix":""},{"id":618969128,"identity":"1dcb921f-2a85-4a10-a802-44699122eb78","order_by":5,"name":"Ruijiang Shi","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruijiang","middleName":"","lastName":"Shi","suffix":""},{"id":618969129,"identity":"ed4e7cf0-3797-4b5c-89f6-baf86f8924b3","order_by":6,"name":"Qing Ju","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Ju","suffix":""},{"id":618969130,"identity":"3643757e-b348-4a3e-b1c6-b58b0182308e","order_by":7,"name":"Xinyu Ti","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Ti","suffix":""},{"id":618969131,"identity":"14d7d6df-a40f-42c5-bd87-e4ba8c7439c5","order_by":8,"name":"Zhanqi Zhao","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhanqi","middleName":"","lastName":"Zhao","suffix":""},{"id":618969132,"identity":"98b5ed84-6a48-42f5-8393-cd81005118a9","order_by":9,"name":"Lin Yang","email":"","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Yang","suffix":""},{"id":618969133,"identity":"802ac184-22c0-4d73-965e-32d51e774a25","order_by":10,"name":"Meng Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDCCA4wNIEqOHUwxMBOvxZjnAPFaIFRiD9Fa+I4fbt3wcUdteo/Y6TQJhgrrxAb2swfwapE8k9h2c+aZ47k90rnbJBjOpCc28OQl4NVicCCx7TZv27Hc/SAtjG2HExskeAzwazn/sO3237Zj6TxgLf+I0XIDaAtjW00CREsDEVokbzxsu9nbdsAQ6JfNFgnH0o3beHLwa+E7n/7sxs+2OnmgLRtvfKixlu1nP4NfCxQchlAJQMxGjHogqCNS3SgYBaNgFIxIAABd/EzQuqEP8AAAAABJRU5ErkJggg==","orcid":"","institution":"The Fourth Military Medical University","correspondingAuthor":true,"prefix":"","firstName":"Meng","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2026-03-16 13:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9138241/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9138241/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106835869,"identity":"a9eb202b-b851-4289-9fb4-146fef189de5","added_by":"auto","created_at":"2026-04-14 02:03:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179251,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic illustration of the study design. Eligible patients with severe asthma were screened and enrolled after providing informed consent. Participants received either conventional therapy (inhalers) or biological agents (injections); treatment allocation was determined based on patients' clinical conditions and real-world medical prescriptions rather than randomization. Outcome assessments included global lung function evaluation via standard spirometry and regional functional assessment using electrical impedance tomography. \u003cem\u003eFVC, forced vital capacity; BD, Bronchodilator; EIT, electrical impedance tomography.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9138241/v1/e61f442d3ba0d807c5978218.jpg"},{"id":106960716,"identity":"174931d2-7bb3-42fb-9a27-618b683e540a","added_by":"auto","created_at":"2026-04-15 09:22:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187815,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flow Diagram. A total of 135 patients with severe asthma were screened for eligibility. 24 patients were excluded based on the inclusion and exclusion criteria. The remaining 111 patients were enrolled and classified into two cohorts based on their clinical treatment: the Conventional Therapy Group (n = 79), consisting of patients treated with standard high-dose ICS/LABA, and the Biological Therapy Group (n = 32), consisting of patients receiving add-on targeted biologic therapy due to uncontrolled asthma. All participants completed the 6-month follow-up with PFT and EIT measurements. All enrolled patients were included in the final analysis.\u003cem\u003e: ICS, inhaled corticosteroids; LABA, long-acting beta-agonists; LAMA, long-acting muscarinic antagonists; LTRA, leukotriene receptor antagonists; ACO, Asthma-COPD Overlap; ILD, interstitial lung disease; EIT, electrical impedance tomography.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9138241/v1/f0ecab7c3f94c0c37314847c.jpg"},{"id":106835871,"identity":"49d5bcd4-504e-4f7d-bd99-7ac634ff59a0","added_by":"auto","created_at":"2026-04-14 02:03:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163298,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative fEIT maps of two patients with severe asthma at 6-month. (a) A patient from the conventional treatment group showing heterogeneous ventilation distribution. The regional expiratory \u003cem\u003eτ\u003c/em\u003e map (G) reveals elevated values in basal lung regions, and the corresponding histogram (H) displays a wide dispersion, indicating asynchronous lung emptying and persistent small airway obstruction. (b) A patient from the biologic treatment group demonstrating homogeneous ventilation. The \u003cem\u003eτ\u003c/em\u003e map (G) shows uniformly low values, and the histogram (H) exhibits a narrow peak, suggesting synchronized lung emptying and effective resolution of air trapping.\u003cem\u003eFEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PEF, peak expiratory flow; MMEF, maximal mid-expiratory flow; τ, regional expiratory time constant.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9138241/v1/acb04de606ed2d659088f9e1.jpg"},{"id":106963172,"identity":"c064f543-fd32-4885-9b70-877c870eb22a","added_by":"auto","created_at":"2026-04-15 09:42:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1692429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9138241/v1/ff9eacdb-881b-44cd-99d1-6ffb09517b84.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Complementing longitudinal spirometry with electrical impedance tomography: a novel strategy to evaluate biological efficacy in severe asthma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSevere asthma is characterized by significant heterogeneity and complex pathophysiology. It manifests as poor clinical control, recurrent exacerbations, and impaired quality of life, carrying a high risk of future adverse outcomes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although severe asthma accounts for only 5\u0026ndash;10% of the total asthma population, it represents a substantial portion of the overall disease burden[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The latest Global Initiative for Asthma (GINA) guidelines define severe asthma as asthma that remains uncontrolled despite optimized treatment with high-dose inhaled corticosteroids (ICS) and long-acting beta-agonists (LABA), or that requires high-dose therapy to prevent loss of control[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A major challenge in managing this condition lies in its heterogeneity, necessitating more precise treatment strategies and refined assessment methods[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, two main therapies are used to control severe asthma: conventional therapy containing high-dose ICS and LABAs, and biological therapy targeting specific inflammatory pathways. Both aim to improve symptoms and lung function[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, comprehensively evaluating and comparing the real-world efficacy of these therapies remains a challenge[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall asthma control is typically assessed through day/night symptoms, exacerbation frequency, rescue medication use, and questionnaires (e.g., asthma control test, ACT). However, the \"gold standard\" for assessing ventilation remains spirometry[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Forced expiratory volume in 1 second (FEV1) reliably reflects global airway conditions. Furthermore, in severe asthma, airway inflammation and remodeling are often unevenly distributed[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; thus, advanced tools like electrical impedance tomography (EIT) should be included for assessing the treatment effect. EIT is a non-invasive, radiation-free bedside monitoring tool ideally suited for this purpose. It provides real-time, dynamic visualization of gas distribution within the lung[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Importantly, EIT allows for the calculation of regional mechanical parameters, such as the respiratory time constant (τ)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This parameter measures the speed of regional lung filling and emptying and serves as an index of ventilation efficiency[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A more efficient lung region empties and fills faster, resulting in a shorter τ.\u003c/p\u003e \u003cp\u003eThis study aimed to compare the effects of two therapeutic strategies using both global (spirometry) and regional (EIT) assessment methods. While standard spirometry was performed longitudinally to track global changes from baseline, EIT was specifically employed at the 6-month follow-up as a cross-sectional tool. This design allowed us to quantify the 'final state' of regional ventilation and small airway function at the study endpoint, revealing pathophysiological differences that might be missed by routine longitudinal monitoring\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Subjects\u003c/h2\u003e \u003cp\u003eThis was a two-arm, mixed-design observational clinical study. The objective was to quantify the longitudinal effects of treatment via conventional spirometry and to compare regional lung function heterogeneity cross-sectionally using EIT.\u003c/p\u003e \u003cp\u003eThe study was conducted at the Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of the Fourth Military Medical University. All procedures complied with the Declaration of Helsinki and were approved by the hospital\u0026rsquo;s Ethics Committee (Approval No. KY20252420-F-1). All participants provided written informed consent (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Adult patients diagnosed with moderate-to-severe asthma according to GINA guidelines were recruited consecutively from outpatient and inpatient units. Upon enrollment, patients were assigned to one of two cohorts based on their prescribed clinical treatment:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConventional Group\u003c/strong\u003e \u003cp\u003eReceived standardized high-dose ICS/LABA combination therapy. Some patients also used leukotriene receptor antagonists (LTRA) or long-acting muscarinic antagonists (LAMA).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBiological Group\u003c/strong\u003e \u003cp\u003ePatients who remained uncontrolled despite low-medium dose ICS/LABA (plus LAMA in some cases) and received add-on biologic therapy. Specific biological (Omalizumab, Dupilumab, or Mepolizumab) were selected based on the patient's type 2 inflammation profile.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eInclusion criteria\u003c/strong\u003e \u003cp\u003e1) Age 18\u0026ndash;75 years; 2) Confirmed diagnosis of bronchial asthma, classified as severe (GINA Steps 4\u0026ndash;5) due to poor control or high treatment intensity; 3) Receiving or initiating one of the defined study treatments; 4) Ability to understand and perform lung function tests and EIT procedures.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion criteria\u003c/strong\u003e \u003cp\u003e1) Current smokers or those who quit\u0026thinsp;\u0026lt;\u0026thinsp;1 year ago (to minimize confounding from smoking-related remodeling); 2) Diagnosis of Asthma-COPD Overlap (ACO), non-cystic fibrosis bronchiectasis, interstitial lung disease, or other chronic lung conditions; 3) Use of systemic immunosuppressants or biological for other conditions (e.g., autoimmune disease); 4) Chest wall skin infections, large scars, or implants preventing electrode contact; 5) Implanted active electronic devices (e.g., pacemakers, ICDs); 6) Pregnancy or lactation.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Study Workflow\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eParticipants completed two visits over a 6-month period.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBaseline Visit (Day 0)\u003c/strong\u003e \u003cp\u003ePerformed at the initiation of the stable treatment regimen. Data collected included demographics, BMI, smoking history, allergy/rhinitis history, Asthma Control Test (ACT) scores, and baseline spirometry.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFollow-up Visit (Month 6\u0026thinsp;\u0026plusmn;\u0026thinsp;2 weeks)\u003c/strong\u003e \u003cp\u003ePerformed after 6 months of treatment. Procedures included ACT assessment, repeat spirometry, and EIT measurement. EIT was performed during this visit to ensure temporal consistency with the lung function data.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Lung Function and Clinical Assessment\u003c/h2\u003e \u003cp\u003e Spirometry was performed using a standard device (Jaeger, CareFusion GmbH, Germany) following ATS/ERS guidelines. Bronchodilators were withheld prior to testing. Patients performed at least three acceptable and reproducible forced expiratory maneuvers. Key parameters included FVC, FEV1, FEV1/FVC ratio, and Peak Expiratory Flow (PEF). Pre-bronchodilator values were used for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 EIT measurements\u003c/h2\u003e \u003cp\u003eEIT measurements were conducted only at the 6-month follow-up using a commercial system (VenTom-200, MidasMED, Suzhou, China). A 16-electrode silicone belt was placed around the thorax at the 4th\u0026ndash;5th intercostal level. Patients sat comfortably. First, 5 minutes of tidal breathing were recorded after calibration. Then, while data were acquired at 50 Hz, patients performed at least three valid FVC maneuvers under instruction.\u003c/p\u003e \u003cp\u003eData were filtered to remove cardiac oscillations. A linear regression fitted the sum of impedance changes to spirometry volume to calculate a volume-impedance conversion coefficient. This allowed the conversion of dimensionless impedance values into volume (mL) for pixel-level analysis. Based on pixel-wise time-volume curves, eight functional parameters were derived[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional FVC\u003c/strong\u003e \u003cp\u003eMaximum impedance change per pixel during forced expiration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional FIVC\u003c/strong\u003e \u003cp\u003eMaximum capacity change during forced inspiration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional FEV1\u003c/strong\u003e \u003cp\u003eCumulative volume exhaled at the 1st second per pixel.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional FEV1/FVC\u003c/strong\u003e \u003cp\u003eThe ratio of Regional FEV1 to Regional FVC, identifying localized obstruction independent of absolute volume.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional PEF\u003c/strong\u003e \u003cp\u003eMaximum instantaneous flow rate (first derivative of the volume curve) per pixel.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional MEF25-75\u003c/strong\u003e \u003cp\u003eMean flow rate between 25% and 75% of regional FVC. This reflects small airway function.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eT75\u003c/strong\u003e \u003cp\u003eTime required for a pixel to exhale 75% of its FVC. Prolonged T75 indicates delayed emptying and gas trapping.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional Expiratory Time Constant (\u003cem\u003eτ\u003c/em\u003e)\u003c/strong\u003e \u003cp\u003eCalculated by fitting a single exponential decay to the expiratory curve of each pixel\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e is the relative impedance changes of lung pixels at time point \u003cem\u003et\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003erepresents the impedance at the beginning of expiration. \u003cem\u003et\u003c/em\u003e represents the time interval from end-inspiration to end-expiration. \u003cem\u003ec\u003c/em\u003e denotes the end-expiratory volume. \u003cem\u003eτ\u003c/em\u003e is the regional time constant which is sensitive to changes in airway resistance and primarily reflects the functional status of peripheral small airways during the mid-to-late phase of expiration\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial Dispersion (Delta, △):T\u003c/b\u003ehe standard deviation of valid pixel values across the lung. Higher values indicate greater disparity between fast and slow-emptying units.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGlobal Inhomogeneity Index (GI)\u003c/strong\u003e \u003cp\u003eThe sum of absolute differences between each pixel value and the median, divided by the sum of all pixel values. Higher GI indicates severe ventilation inhomogeneity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegional Obstructive Ratio (rOR)\u003c/strong\u003e \u003cp\u003eThe percentage of total lung area occupied by pixels with a local FEV1/FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7. Higher values imply more extensive airway obstruction.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAnalysis was performed using MATLAB 2022b. Normality was assessed using the Shapiro-Wilk test. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (interquartile range). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eBaseline characteristics were compared using independent t-tests, Mann-Whitney U tests, or Chi-square tests. Longitudinal spirometry data were analyzed using a Repeated Measures ANOVA (Group\u0026times;Time). A significant interaction indicated a differential treatment response. For post-treatment EIT data, cross-sectional comparisons were made using the Mann-Whitney U test. Subgroup analysis (stratified by gender, smoking, rhinitis) used Two-way ANOVA to test for interactions between treatment effects and baseline characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Recruitment and Grouping\u003c/h2\u003e \u003cp\u003e This study enrolled patients who visited the outpatient clinic of the Department of Pulmonary and Critical Care Medicine at Xijing Hospital, the Fourth Military Medical University, between November 22, 2024 and March 19, 2025. Of 135 screened patients, 111 met the eligibility criteria and were assigned to the conventional group (n\u0026thinsp;=\u0026thinsp;79) or biological group (n\u0026thinsp;=\u0026thinsp;32). All 111 patients completed the 6-month follow-up and were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Baseline Characteristics\u003c/h2\u003e \u003cp\u003eBaseline demographics and clinical features were generally balanced (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were no significant differences in gender, BMI, or smoking history. Although the biological group was slightly younger (46.4 vs. 53.0 years), this was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06). Notably, the prevalence of allergic rhinitis was significantly higher in the biological group (87.5% vs. 62.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02).\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\u003eStudy participants\u0026rsquo; characteristics\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\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiological (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male:Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33:46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13:19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.39\u0026thinsp;\u0026plusmn;\u0026thinsp;15.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.44\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166.0(160.0-173.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165.5(159.5-172.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.34\u0026thinsp;\u0026plusmn;\u0026thinsp;11.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.90\u0026thinsp;\u0026plusmn;\u0026thinsp;15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.00 (22.45\u0026ndash;27.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.85 (20.60-26.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking History (Yes), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (17.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (15.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRhinitis (Yes), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e53 (67.09%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e28 (87.50%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\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\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \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\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\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\u003e71.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1(% pred)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.92\u0026thinsp;\u0026plusmn;\u0026thinsp;18.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.36\u0026thinsp;\u0026plusmn;\u0026thinsp;16.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEF, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.45(4.38\u0026ndash;7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71(4.12\u0026ndash;6.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEF75, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEF50, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14(1.35\u0026ndash;3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07(1.23-3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEF25, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.49\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.42\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMEF, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28(0.80\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15(0.90\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00 (9.00\u0026ndash;12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00 (9.00-12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Exacerbations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 (2.00\u0026ndash;4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (3.00-4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eBMI: body mass index; FEV1: forced expiratory volume in the first second; FVC: forced vital capacity; pred: predicted; PEF: peak expiratory flow; MEF: maximal expiratory flow (at 75%, 50%, and 25% of FVC); MMEF: maximal mid-expiratory flow; ACT: asthma control test. Bold values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBaseline lung function (both large and small airway indices) showed no significant differences between groups. Disease severity was comparable, with similar ACT scores and exacerbation histories (median 3.0 events/year), confirming a population with severe, uncontrolled asthma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spirometry Findings\u003c/h2\u003e \u003cp\u003eAfter 6 months of treatment, both groups showed improvements in most lung function parameters compared to baseline (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eTime\u003c/sub\u003e\u0026lt;0.05). However, the biological group demonstrated superior efficacy in improving both large and small airway function compared to the conventional group. Regarding large airway function, repeated measures ANOVA revealed a significant Time\u0026times;Group interaction, indicating greater benefits for the biological group. Specifically, the biological group showed significantly better improvements in FEV1 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e =0.01), FVC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.047), and the FEV1/FVC ratio (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.02). Notably, FEV1 (% pred) improved significantly more in the biological group compared to the conventional group (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.01), rising from approximately 79% to 96%.Similarly, regarding small airway function, the biological group exhibited superior airflow recovery. Significant interactions were observed for MEF50(\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.02) and MEF25(\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.04), reflecting better air movement in the peripheral lungs. No significant between-group differences were found for PEF, MEF75, or MMEF (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e \u0026gt;0.05).\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\u003ePulmonary function outcomes at baseline and at 6-month follow-up\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eConventional (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBiological (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eANOVA \u003cem\u003eP\u003c/em\u003e-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBefore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eTime\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1, L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC, L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1/FVC,%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e71.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e73.70\u0026thinsp;\u0026plusmn;\u0026thinsp;9.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e68.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e73.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFEV1(% pred)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e83.92\u0026thinsp;\u0026plusmn;\u0026thinsp;18.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e93.61\u0026thinsp;\u0026plusmn;\u0026thinsp;20.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e79.36\u0026thinsp;\u0026plusmn;\u0026thinsp;16.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e96.28\u0026thinsp;\u0026plusmn;\u0026thinsp;17.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEF, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.45(4.38\u0026ndash;7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.35(4.11\u0026ndash;6.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.71(4.12\u0026ndash;6.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29(3.85\u0026ndash;6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEF75, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEF50, L/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.14(1.35\u0026ndash;3.27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.42(1.56\u0026ndash;3.69)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.07(1.23-3.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.52(1.77\u0026ndash;3.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEF25, L/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.67 (0.49\u0026ndash;1.13)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.73 (0.56\u0026ndash;1.28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.65 (0.42\u0026ndash;0.96)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.82 (0.57\u0026ndash;1.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMEF, L/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28(0.80\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89 (1.31\u0026ndash;2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.90\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.04 (1.44\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eBold values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 EIT outcomes\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Representative fEIT maps of two patients with severe asthma at 6-month. (a) A patient from the conventional treatment group showing heterogeneous ventilation distribution. The regional expiratory \u003cem\u003eτ\u003c/em\u003e map (G) reveals elevated values in basal lung regions, and the corresponding histogram (H) displays a wide dispersion, indicating asynchronous lung emptying and persistent small airway obstruction. (b) A patient from the biologic treatment group demonstrating homogeneous ventilation. The \u003cem\u003eτ\u003c/em\u003e map (G) shows uniformly low values, and the histogram (H) exhibits a narrow peak, suggesting synchronized lung emptying and effective resolution of air trapping. \u003cem\u003eFEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; PEF, peak expiratory flow; MMEF, maximal mid-expiratory flow; τ, regional expiratory time constant.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed that, the biological group had significantly lower GI\u003csub\u003eT75\u003c/sub\u003e (0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 vs. 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and △\u003csub\u003eT75\u003c/sub\u003e (0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.069 s vs. 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 s, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), indicating better temporal homogeneity, than the conventional group. rOR was significantly smaller in the biological group (18.47% vs. 44.57%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Additionally, the biological group demonstrated significantly shorter median τ (0.54 s vs. 0.87 s, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and reduced spatial dispersion of τ (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming faster and more uniform lung emptying.\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\u003eComparison of EIT-derived parameters between the conventional and biological treatment groups at 6-month\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEIT Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiological (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImprovement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003csub\u003eT75\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e△\u003csub\u003eT75\u003c/sub\u003e(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erOR(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.57\u0026thinsp;\u0026plusmn;\u0026thinsp;35.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.47\u0026thinsp;\u0026plusmn;\u0026thinsp;29.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.65%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eτ\u003csub\u003emed\u003c/sub\u003e(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eτ\u003csub\u003eiqr\u003c/sub\u003e(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. GI, global inhomogeneity index;\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e, \u003cem\u003echange of spatial distribution; rOR, regional obstructive ratio;\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e, \u003cem\u003emedian regional expiratory time constant;\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e, \u003cem\u003einterquartile range of regional expiratory time constant.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup and Interaction Analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses based on smoking history, rhinitis comorbidity, and gender revealed no significant interactions with treatment outcomes (all Interaction \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This indicates that the observed treatment responses were not significantly modified by these baseline characteristics.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePathological changes and ventilatory dysfunction of the small airways represent the core pathological features of asthma. Currently, the clinical assessment of asthma control primarily relies on symptom scores, pulmonary function tests, and fractional exhaled nitric oxide (FeNO) measurements[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, these conventional evaluation modalities exhibit inherent limitations in accurately assessing small airway ventilation status. Historically, asthma management has predominantly depended on ICS and LABA. In recent years, biological targeted therapies targeting type 2 inflammation have brought new prospects for the treatment of asthma, especially severe cases[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, a precise detection approach is still lacking to evaluate the improvements in small airway ventilatory function induced by both high-dose ICS therapy and biological targeted treatments. Our research team previously employed EIT to examine small airways in patients with chronic respiratory diseases, and demonstrated that EIT holds distinct advantages in monitoring small airway ventilatory function[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Building on the previous work, the present study focuses on investigating differences in global and regional ventilatory functions among asthmatic patients receiving different therapeutic interventions.\u003c/p\u003e \u003cp\u003eThis study combined global and regional lung function methods to evaluate the efficacy of biological and conventional therapies in severe asthma. Our results show that biological therapy demonstrated superior efficacy in both global airway metrics and regional ventilation distribution compared to conventional therapy. It is important to interpret these regional findings in the context of the study design. Although EIT data were acquired only at the post-treatment stage, the strict baseline matching of demographic and spirometry parameters between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) minimizes the likelihood of pre-existing regional disparities. Therefore, the superior ventilation homogeneity observed in the biological group at 6 months likely reflects the specific pharmacodynamic benefits of the treatment rather than intrinsic patient differences. EIT imaging revealed that patients treated with biological therapy achieved more homogeneous ventilation and lower dispersion of expiratory τ. Notably, the improvement was not limited to small airways. The Biological group showed significantly greater increases in FEV1 and FVC, indicating restored patency in large airways. This is supported by the EIT-derived rOR. Since rOR reflects airway obstruction across the whole lung, its significant reduction confirms that biological therapy effectively relieved blockage in both central (large) and peripheral (small) airways.\u003c/p\u003e \u003cp\u003eThe differential response may relate to drug delivery mechanisms and the pathophysiology of severe asthma. Conventional inhalation therapy relies on aerosol deposition. In severe asthma, proximal airflow turbulence and mucus plugging may create physical barriers. These barriers prevent inhaled drugs from effectively treating not only the distal lung but also the obstructed large airways [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The significantly lower improvement in FEV1 and MEF25 in the conventional group reflects this limitation. In contrast, biological medicines are administered systemically via blood circulation This allows the drug to reach the bronchial circulation, reducing inflammation and edema in the large airway walls. By systematically blocking Type 2 inflammatory pathways, biological treatment may reduce peripheral edema and mucus load, thereby improving both proximal and distal conditions[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhysiologically, the improvement in MEF25 (increased flow at low lung volume) and the shortening of EIT-derived T75 (reduced time to exhale 75% volume) are consistent. Both metrics reflect the \"effort-independent\" phase of expiration, determined largely by small airway caliber and elastic recoil. The coherence between these distinct modalities strengthens the conclusion that biological treatments may have a specific impact on reversing peripheral airway obstruction.\u003c/p\u003e \u003cp\u003eIt is also noteworthy that there was a significant baseline difference in the prevalence of allergic rhinitis between the two groups. According to the 'united airways' hypothesis, upper airway inflammation is inextricably linked to lower airway dysfunction, often sharing common pathological mechanisms such as type 2 inflammation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The higher burden of rhinitis in the biological group theoretically presents a greater challenge for asthma control due to the potential for post-nasal drip or nasobronchial reflexes to aggravate bronchial constriction. However, our results showed superior airway improvement in these patients. This suggests that biological agents likely exert a comprehensive systemic anti-inflammatory effect, targeting the 'united airways' as a whole and mitigating the negative impact of upper airway pathology on lung mechanics, an advantage that local inhalation therapy may not fully achieve.\u003c/p\u003e \u003cp\u003eThis study also has several limitations. First, it was observational and non-randomized, which inherently introduces potential selection bias regarding treatment allocation. However, this real-world design provides high external validity, reflecting actual clinical decision-making and patient heterogeneity often excluded from strict randomized controlled trials. Second, EIT measurement was not conducted at the baseline. EIT was incorporated to provide additional dimensions of information at the endpoint of efficacy assessment that global tool cannot offer, rather than for evaluating individual longitudinal changes before and after treatment. While this precludes a longitudinal analysis of regional function changes within individuals, the cross-sectional comparison at the endpoint successfully highlighted the distinct physiological patterns achieved by different therapies. EIT thus served as a discriminatory tool to characterize the quality of functional improvement, complementing the quantitative magnitude provided by longitudinal spirometry. Therefore, the current findings were not diminished. Third, the analysis was not stratified by specific biological agents (e.g., anti-IgE vs. anti-IL-5/5R) due to the limited number of patients in each subgroup. Since different biologics target distinct inflammatory pathways that may differentially impact airway remodeling and small airway function, pooling these agents might mask drug-specific physiological signatures[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consequently, we could only report the 'class effect' of biological therapy rather than the efficacy of individual molecules. Future prospective studies with larger cohorts are needed to validate these findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn the management of severe asthma, biological therapy was more effective than conventional therapy in improving both large and small airway function. EIT played a crucial role in revealing these benefits. Unlike standard spirometry, which only measures total airflow, EIT visualized regional ventilation patterns. It confirmed that biological treatment leads to more uniform lung emptying and reduced obstruction. Combination of both methods provides more precise assessment and personalized treatment in severe asthma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eelectrical impedance tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eτ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eregional expiratory time constant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglobal inhomogeneity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThis study involved human participants and was approved by Medical Ethics Committee of the First Affiliated Hospital of Fourth Military Medical University (NO. KY20252420-F-1). Clinical trial number: not applicable. All participants enrolled in this study have signed informed consent based on the voluntary principle. This research presented here has been performed in accordance with the Declaration of Helsinki. All methods were carried out in accordance with relevant guidelines.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable. All data included in this manuscript have been fully anonymized and de-identified, with no personal identifiable information of any enrolled participants. No individual consent for publication is required.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (NO. 82570041) (NO. 52277235); Shaanxi Province Health Research Innovation Capability Enhancement Plan Key Research and Development Projects(No. 2025YF-51);Xijing Hospital Promoting Project (No. XJZT25ZH03)༛Partner Laboratory of the Air Force Medical University (No. 2024HB009)༛Military Medical Science and Technology Breakthrough Project of Air Force Military Medical University(2025JSKY06).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design: SY Qu, M Dai; (II) Administrative support: SY Qu, M Dai; (III) Provision of study materials or patients: SY Qu, Q Ju, XY Ti; (IV) Collection and assembly of data: RJ Shi, H Yan, L Han; (V) Data analysis and interpretation: ZQ Zhao, F Fu, L Yang; (VI) Manuscript writing:M Dai,SY Qu; (VII) Final approval of manuscript: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thanked all participants enrolled in this cohort for their volunteered supports.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to the considerably large file size of the original raw datasets and the requirement for dedicated specialized proprietary software to complete full reading, processing and analysis of the data, but are available from the corresponding author upon reasonable, academic non-commercial request. 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Asthma Res Pract. 2022;8:6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40733-022-00088-2\u003c/span\u003e\u003cspan address=\"10.1186/s40733-022-00088-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"","lastPublishedDoi":"10.21203/rs.3.rs-9138241/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9138241/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStandard spirometry often fails to capture regional functional heterogeneity in severe asthma. This study aimed to evaluate the differential effects of biological versus conventional therapy on small airway function and ventilation homogeneity, utilizing a combined strategy of longitudinal spirometry and cross-sectional electrical impedance tomography (EIT) assessment at the 6-month endpoint.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this observational study, 111 patients with severe asthma were stratified into two groups based on their clinical treatment regimen: a conventional group (high-dose ICS/LABA, n\u0026thinsp;=\u0026thinsp;79) and a biological group (add-on biologic therapy, n\u0026thinsp;=\u0026thinsp;32). Spirometry was performed at baseline and after 6 months. Electrical impedance tomography (EIT) measurements were only conducted at the 6-month follow-up to quantify regional ventilation dynamics, utilizing parameters such as the regional expiratory time constant (τ), global inhomogeneity (GI) index, and regional obstruction ratio (rOR).\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOver the 6-month period, the biological group demonstrated superior improvements compared to the conventional group. Significant Time\u0026times;Group interactions were observed for all large airway parameters, including FEV1(\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.01), FVC(\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.047) and the FEV1/FVC ratio (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e =0.02) and FEV1% pred (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e =0.01). Small airway function also improved significantly more in the Biological group (MEF50, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e=0.02; MEF25, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eInteraction\u003c/sub\u003e =0.04). EIT assessment at the endpoint confirmed these physiological benefits, showing significantly greater ventilation homogeneity (lower GI\u003csub\u003eT75\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), faster lung emptying (shorter τ, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and reduced regional obstruction (lower rOR, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) in the biological group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBiological treatments demonstrated superior efficacy in restoring large and small airway function and improving ventilation homogeneity, compared to conventional therapy. While global spirometry tracks overall airflow trends, EIT provides critical insights into regional lung mechanics. Integrating both modalities offers a more comprehensive approach to uncovering the potential physiological improvements in severe asthma management.\u003c/p\u003e","manuscriptTitle":"Complementing longitudinal spirometry with electrical impedance tomography: a novel strategy to evaluate biological efficacy in severe asthma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 02:03:12","doi":"10.21203/rs.3.rs-9138241/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-05T03:18:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T01:46:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T17:12:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T12:05:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-03-24T11:57:42+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":"506f765e-bc6f-47ef-8bba-28e8f342ed4c","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T02:03:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 02:03:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9138241","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9138241","identity":"rs-9138241","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}