Machine learning reveals limited predictive value of clinical factors for asthma exacerbations: insights from a real-world study

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Gisby, Hanneke Coumou, Jeroen Hoogland, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6664347/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background While predictors of asthma exacerbation risk are generally well established, predictors of exacerbation severity remain largely undefined. Identifying robust clinical predictors of exacerbation severity is essential to support tailored management strategies and optimize resource allocation. This study leverages machine learning to evaluate the predictive value of clinical factors for exacerbation severity in a real-world emergency department setting. Methods A retrospective cohort study was performed using medical records of 367 adults (644 exacerbations) who presented to the Amsterdam UMC emergency department between 2013 and 2020. Five severity outcomes were investigated: hospital admission, ICU admission, length of stay, oxygenation efficiency (SpO₂/FiO₂), and National Early Warning Score (NEWS). Associations were assessed using linear mixed models (LMM), and predictive modelling employed a machine learning approach combining LMMs with 5-fold cross-validated least absolute shrinkage and selection operator (LASSO) regression. Results Exacerbation severity was most consistently associated with lung function, the presence of a radiographic chest infiltrate, C-reactive protein levels, blood neutrophil count and theophylline maintenance use. No significant associations were found for blood eosinophil count, age, comorbidities, symptom duration, triggers, allergic sensitization, ethnicity or exacerbation history within the preceding 12 months. Internally validated prediction models for hospital and intensive care admission achieved areas under the curve of 0.632 and 0.695, respectively. The strongest predictors explained 18.8% of variability in NEWS, 15.2% in oxygenation efficiency, and 9.0% in length of hospital stay. In these prediction models, a radiographic chest infiltrate, followed by theophylline maintenance use and blood neutrophil count, were most frequently associated across the five severity outcomes. Conclusions Lung function and markers of acute respiratory infection were mostly frequently associated with asthma exacerbation severity. However, clinical and demographic variables have only modest predictive value, highlighting the need to identify additional robust predictors. Health sciences/Diseases/Respiratory tract diseases Health sciences/Diseases/Respiratory tract diseases/Asthma Asthma exacerbations predictive modelling machine learning risk stratification treatable traits Figures Figure 1 Figure 2 Introduction Asthma is a chronic inflammatory airway disease typically characterized by episodes of respiratory distress, known as exacerbations. Exacerbations comprise acute or subacute worsening in asthma symptoms (coughing, wheezing, dyspnoea and chest tightness) and lung function, attributable to increased airway constriction and/or inflammation 1 . They occur frequently in patients with severe or uncontrolled asthma and contribute to accelerated loss of lung function and a lower quality of life 2 , 3 . In the last two decades, asthma management targeting disease-specific inflammatory pathways has been proven successful in improving lung function, tapering of systemic steroids and preventing subsequent exacerbations 4 – 9 . On the other hand, asthma exacerbations are typically managed based on symptom severity rather than by underlying inflammatory mechanisms 1 , 10 , 11 . Standard treatment of severe exacerbations mainly consists of the correction of hypoxemia with oxygen and reversal of airflow limitation with inhaled beta-2-agonists, inhaled corticosteroids and in severe cases systemic corticosteroids and intravenous magnesium sulphate 1 , 10 , 11 . Given the heterogeneous and complex inflammatory processes underlying asthma, there is a growing need to move from a generalized approach to a more phenotype-directed treatment of exacerbations to improve clinical outcomes 12 . Eosinophil-targeted therapy already showed promising results 13 . However, this transition necessitates more precise identification of risk factors and treatable traits associated with severity of exacerbations. Known risk factors for the development of asthma exacerbations include previous exacerbations, smoking, female sex, inadequate asthma management, elevated blood or sputum eosinophil levels, increased fractional exhaled nitric oxide (FeNO) levels, and several pre-existing comorbidities such as obesity, chronic sinus disease, obstructive sleep apnoea and gastroesophageal reflux 14 – 19 . However, it remains unclear whether these same factors are associated with the severity of exacerbations or adverse clinical outcomes. Identification of risk factors or patients at risk for severe exacerbations could help prevent delayed escalation of care and enable more patient-tailored therapeutic interventions and. To optimise predictive performance while minimizing overfitting, machine learning models that incorporate cross-validation could effectively handle high-dimensional data and account for collinearity 20 . Therefore, the aim of this study is to identify clinical factors associated with exacerbation severity and clinical outcomes and to determine their predictive value using advanced s machine learning modelling. Methods Study population and design Data from electronic health records were retrospectively collected for patients who presented to the emergency department at Amsterdam University Medical Center, location Academical Medical Center (AMC), between October 1, 2013, and March 1, 2020, with an asthma exacerbation. These patients were identified using asthma diagnosis treatment combinations, which are physician-made diagnoses that are generally followed by a sequence of medical activities and follows the International Statistical Classification of Diseases and Related Health Problems (ICD). Adult patients, 18 years or older at presentation, were considered eligible for study inclusion if the diagnosis of asthma was based on a history of confirmed variable expiratory airflow limitation and characteristic pulmonary symptoms, verified by at least one of the methods described by the latest report of the Global Initiative for Asthma 1 , and the exacerbation was characterized by acute or subacute worsening of asthma-related symptoms resulting in a physician reported diagnosis of asthma exacerbation or status asthmaticus. Once a patient was eligible for study inclusion by two independent physicians, chart data was pseudonymized. Ethical approval for study conduction was obtained from the ethical committee of the hospital's clinical research unit. All methods were performed in accordance with relevant guidelines and regulations. Written informed consent was waived by the ethical committee. Instead, patients were informed about the study through a letter and were subsequently given the opportunity to object to inclusion by either email or returning a reply envelope. Study assessments Study parameters include demographics, patient characteristics, pre-existent comorbid diseases, medication use, requirement for oxygen supplementation (eTable 2), and laboratory, microbiological and radiographic findings. Information on lung function was gathered using spirometry measurements outside of the exacerbation period. Peak expiratory flow data was collected from handheld peak-flow meters at presentation. The total dosage of inhalation corticosteroids was calculated as beclomethasone equivalent dosage (BED) using the conversion factors presented in supplementary eTable 3. A complete overview of all study parameters and their definitions is provided in supplementary eTable 1. Outcome measures The severity of asthma exacerbations was determined by the ratio of peripheral oxygen saturation to the inspired fraction of oxygen (SpO2/FiO2), NEWS at emergency presentation, need for hospital admission, length of hospital stay and admission to the intensive care unit (ICU). Statistical analyses All statistical analyses were performed using R, version 4.2.2 and RStudio version 2023.03.0 Cherry Blossom. Descriptive statistics were used to summarize patient and exacerbation characteristics. If a patient had multiple exacerbations during the study time period, the first visit was used to extract patient’s baseline characteristics. The baseline characteristics of patients stratified by either hospital admission or ICU admission status were compared using an unpaired T-test, the Mann-Whitney U test and the Chi-square test, for normally distributed numeric variables, non-normally distributed numeric variables and categorical variables respectively. Associations between patient and exacerbation characteristics and outcome measures were assessed using linear mixed models (LMMs), with random intercepts assigned to each patient to account for repeated measures. All other variables were included as fixed effects. LMMs were implemented using the lmer function from the lme4 package. 21 . For the linear mixed models, the Benjamini-Hochberg procedure was used to adjust for multiplicity with a false discovery rate (FDR) set at 0.05. To select the parameters with the highest predictive value, we modelled the data using Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done by using the glmmLasso function from the glmmLasso package 20 . This allowed for selection of a sequence of predictors with the highest independent predictive value. Random intercepts were given to each subject, all other study variables were included as fixed effects. Since missing data is not allowed for these prediction models, variables containing more than 15% missing values were excluded from LASSO regression analyses. For the variables CRP, duration of symptoms, body mass index (BMI), eosinophil, neutrophil and leukocyte blood count, missing values were imputed with the mean or median depending on the distribution. For categorical variables, an additional category “unknown” was added. The LASSO models were first fit during 5-fold cross validation on a train set of patients, composed of 80% of randomly selected patients. Then, these models were tested on the remaining 20% of patients, comprising the hold-out validation cohort. The final LASSO model was then fit on the full dataset. Finally, the selected variables were included in a final LMM model. For all statistical tests, a p-value of < 0.05 was considered statistically significant. Results Cohort Characteristics The medical records of 1201 patients were screened, of which 824 patients were excluded due to not meeting the inclusion criteria (Fig. 1 ). From the 377 eligible patients, 10 patients were further excluded because they objected to their data being used for this study. A total of 367 patients were included, which had presented to the emergency department with a total of 644 exacerbations. The proportion of missing data for each included variable is provided in eTable 4. The cohort was predominantly female (60%), with a mean age of 45.1 ± 17.8 years (Table 1 ). The majority of patients were Caucasian (55%), 47% had a history of smoking or were current smokers, and 68% had an allergy to aeroallergens. Additionally, 52 patients (14%) were concomitantly diagnosed with COPD. While most patients presented once during the study period, 117 (32%) experienced more than one exacerbation (Table 1 ). Exacerbations occurred most frequently during winter (31.1%; χ² = 11.77, p = 0.0003, Table 2 ). The median duration of symptoms before patients visited the emergency department was 4 days [IQR 2, 14]. Maintenance inhalation corticosteroids (ICS) were used before 482 (74.8%) of exacerbations, with a median dosage of 1000 micrograms BED [IQR 0, 2000]. During almost half (47.2%) of the exacerbations, patients reported flu-like symptoms. In 135 exacerbations, a nasal and/or throat swab was performed, which tested positive for a respiratory virus in 84 exacerbations (62.2%). A bacterial infection was cultured in 23 out of the 141 (16.3%) sputum samples. A radiographic infiltrate was observed in 65 (11.3%) exacerbations. Mean blood eosinophil count was 0.29 (SD 0.37) x 10^9/L and median CRP level was 5.9 [2.2, 16.5] mg/L. From all 644 exacerbations, 279 (43.3%) required hospital admission, 29 exacerbations (4.5%) led to ICU admission and 7 (1.1%) patients required invasive ventilation (Table 2 and eTable 5). None of the patients died due to the asthma exacerbation or its complications. Table 1 Patient characteristics. Patient characteristics N = 367 Age (years) 45.1 ± 17.8 Female Sex 220 (60%) Weight (kg) N = 266 82.8 ± 18.0 Height (cm) N = 279 168 ± 10 Body Mass Index (kg/m2) N = 264 29.4 ± 6.7 Ethnicity N = 339 Caucasian 185 (55%) Black 52 (15%) Asian or pacific islander 23 (7%) Hispanic 9 (3%) Mixed and other 69 (19%) Smoking N = 352 Never smoked 188 (53%) Past smoker 80 (23%) Current smoker 84 (24%) Comorbidities GERD 56 (15%) COPD 52 (14%) Rhinitis 122 (33%) CRSwNP 37 (10%) CRSsNP 35 (10%) bronchiectasis 21 (6%) OSAS 34 (9%) Urticaria 18 (5%) Allergies Aeroallergens N = 363 246 (68%) Aspergillus Fumigatus N = 148 36 (24%) Multiple (≥ 2) exacerbations 117 (32%) The total number of patients used to describe proportions is 367, unless stated otherwise. Data is presented as proportion (percentage) or mean ± SD. COPD = Chronic Obstructive Pulmonary Disease, CRSsNP = Chronic rhinosinusitis without nasal polyps, CRSwNP = Chronic rhinosinusitis with nasal polyps, GERD = Gastroesophageal Reflux Disease, OSAS = Obstructive Sleep Apnea Syndrome. Table 2 Exacerbation characteristics at emergency presentation. Characteristic All exacerbations N = 644 Exacerbation history Time since previous emergency department visit due to an asthma exacerbation (days) N = 227 84 [32, 208] Time since previous hospital admission (days) N = 146 102 [37, 209] Duration of symptoms (days) N = 634 4 [2, 14] Season Spring 145 (22.5%) Summer 149 (23.1%) Autumn 150 (23.3%) Winter 200 (31.1%) Daily maintenance medication use Inhalation Corticosteroid 486 (75.5%) Inhalation Corticosteroid dosage (in mcg BED) 1000 [0, 2000] Long-Acting Muscarinic Antagonist 176 (27.3%) Leukotriene Receptor Antagonists 98 (15.2%) Theophylline 14 (2.2%) Corticosteroid maintenance therapy 159 (24.7%) Inhalation Corticosteroid category 0-200 mcg BED 162 (25.2%) 201–500 mcg BED 53 (8.2%) 501–1000 mcg BED 119 (18.5%) > 1000 mcg BED 310 (48.1%) Use of any biological 64 (9.9%) Mepolizumab 35 (5.4%) Omalizumab 22 (3.4%) Reslizumab 6 (0.9%) Benralizumab 0 (0%) Dupilumab 1 (0.2%) Blood markers Eosinophil (x 10^9/L) N = 558 0.15 [0.04, 0.39] Neutrophil (x 10^9/L) N = 545 7.02 ± 3.53 Leukocyte (x 10^9/L) N = 585 10.2 ± 3.9 CRP (mg/L) N = 574 5.9 [2.2, 16.5] Patient reported triggers Flu-like symptoms 339 (52.6) Allergen exposure 51 (7.9%) Non-specific trigger 61 (9.5%) Occupational exposure 2 (0.3%) Lung function at presentation Peak expiratory flow (L/min) N = 355 241 ± 127 Peak expiratory flow % of predicted N = 355 45.0 ± 21.1 Radiologic findings Infiltrate on X-ray or computed tomography-scan N = 576 65 (11.3%) Viral and bacterial infections Any viral infection N = 135 84 (62.2%) Any bacterial infection N = 140 23 (16.3%) Arterial blood gas and oxygenation pH in arterial blood N = 379 7.46 ± 0.08 PaCO2 in arterial blood (kPa) N = 379 4.62 ± 1.44 PaO2 in arterial blood (kPa) N = 379 11.8 ± 4.7 Oxygen saturation (%) in arterial blood N = 379 94.2 ± 3.2 SpO2, % N = 638 96 [93, 98] Fraction of inspired oxygen (FiO2, %) 20 [20, 20] SpO2/FiO2 N = 638 475 [460, 490] PaO2/FiO2 (mmHg) N = 265 399 ± (141) Clinical outcome and disease severity National Early Warning Score (NEWS) N = 637 3 [1, 5] Admission 279 (43.3%) Length of hospital stay in admitted patients (days) N = 264 3 [2, 6] Intensive care admission 29 (4.5%) Intubation 7 (1.1%) Mortality 0 (0%) The total number of exacerbations used for analysis was 644, unless stated otherwise. Data is presented as proportion (percentage), mean ± SD or median [IQR]. BED = Beclomethasone Equivalent Dose, CRP = C-reactive protein, FiO2 = Fraction of inspired oxygen, PaCO2 = Partial pressure of carbon dioxide, PaO2 = Partial pressure of Oxygen, SpO2 = Saturation of Peripheral Oxygen. Characteristics associated with hospital admission First, we identified the characteristics of exacerbations that required hospital admission. A summary is provided in Table 3 , a complete overview is provided in eTable 6. Patients who were admitted had significantly more emergency department visits (p = .007) and hospital admissions (p = .001) in the 12 months prior to presentation. There were no significant differences in differential leukocyte counts, nor in CRP levels between the two groups. The exacerbations that required admission were characterized by more severe obstruction, as indicated by the lower percentage of predicted peak expiratory flow as assessed by a peak flow meter (p = .042). Their lung function, assessed outside of the exacerbation period, showed a significantly lower percentage of predicted FEV1 (p = .025) and FEV1/FVC (p = .001). A pulmonary infiltrate, detected either on a chest X-ray or CT-scan, was more often present in exacerbations that required admission (p = .001). However, admissions were not more often associated with viral or bacterial infections. Table 3 Stratification of patients by admission status. Not admitted Admitted P-value N = 365 N = 279 Patient characteristics Age (years) 44.0 ± 17.5 45.4 ± 17.2 0.298 Body Mass index (kg/m2), N = 519 29.1 ± 6.0 29.8 ± 6.5 0.268 Female 221 (60.5%) 190 (68.1%) 0.058 Smoking status, N = 626 0.371 Never smoked 197 (56.1%) 146 (53.0%) Past smoker 86 (24.5%) 81 (29.5%) Current smoker 68 (19.4%) 48 (17.5%) Allergy Aeroallergens 235 (68.1%) 187 (69.5%) 0.777 Exacerbation characteristics Number of asthma exacerbations 12 months prior 0.71 ± 1.46 1.03 ± 1.83 0.014 Number of emergency department visits 12 months prior 0.7 ± 1.3 1.0 ± 1.8 0.007 Number of hospital admissions 12 months prior 0.3 ± 0.8 0.6 ± 1.2 0.001 Time since last hospital admission (days), N = 146 118 [73, 245] 92 [31, 191] 0.017 Medication use Oral steroid maintenance 70 (19.2%) 89 (31.9%) < 0.001 Long-Acting Muscarinic Antagonist 79 (21.6%) 97 (34.8%) < 0.001 Blood counts CRP (mg/l), N = 574 5.40 [1.85, 13.75] 6.50 [2.75, 18.95] 0.075 Eosinophil (x10^9/L), N = 558 0.17 [0.07, 0.39] 0.14 [0.02, 0.39] 0.106 Neutrophil (x 10^9/L), N = 545 6.74 (3.28) 7.32 (3.76) 0.056 Leukocyte (x 10^9/L), N = 585 9.94 (3.60) 10.10 [7.43, 12.65] 0.100 Lung function Peak Expiratory Flow (% of predicted), N = 140 48.2 ± 19.2 40.9 ± 22.9 0.042 FEV1% of predicted, N = 526 78.6 ± 20.4 74.5 ± 21.4 0.025 FEV1/FVC index, N = 528 0.70 ± 0.13 0.66 ± 0.13 0.002 Infiltrate on X-ray or computed tomography-scan (%) N = 576 22 (7.1%) 43 (16.1%) 0.001 Infection Any virus, N = 135 30 (61.2%) 54 (62.8%) 1.000 Parainfluenza, N = 135 4 (3.0%) 0 (0.0%) 0.031 Any bacteria, N = 141 9 (6.4) 14 (9.9) 0.993 Clinical outcomes and disease severity SpO2 (pulse oximetry), N = 638 96.0 [94.0, 98.0] 94.0 [92.0, 97.0] < 0.001 pH, N = 379 7.45 [7.43, 7.49] 7.45 [7.42, 7.49] 0.434 PaO2 (kPa), N = 379 10.8 [9.4, 12.5] 9.9 [8.7, 13.4] 0.124 PaCO2 (kPa), N = 379 4.6 [4.1, 5.0] 4.6 [4.1, 5.2] 0.211 Oxygen saturation (%), N = 379 95.0 [93.4, 96.2] 94.4 [91.8, 96.8] 0.410 Hypercapnia, N = 379 1 (0.6) 14 (6.3) 0.011 FiO2, % 20.2 (1.1) 23.1 (9.1) < 0.001 National Early Warning Score (NEWS), N = 622 2 [1, 4] 4 [2, 6] < 0.001 SpO2/FiO2, N = 638 476.47 (25.72) 434.07 (82.28) < 0.001 The total number of exacerbations used for analysis was N = 644, unless stated otherwise. Data is presented as proportion (percentage), mean ± SD or median [IQR]. FEV1 = Forced Expiratory Volume in 1 second, FVC = Forced Vital Capacity, PaO2 = Partial pressure of Oxygen, SpO2 = Saturation of Peripheral Oxygen. Characteristics associated with intensive care admission Second, we examined the characteristics of exacerbations that required admission to the ICU (eTable 7), the variables that were statistically significant are provided in Table 4 . Exacerbations that led to ICU admission were characterized by higher blood neutrophil (p < .001) and leukocyte counts (p = 0.004). These patients experienced a shorter duration of symptoms before they presented to the emergency department (p < 0.001). A rhinovirus infection occurred more often in patients who were admitted to the ICU (20% versus 60%, p = 0.002), the incidence of bacterial infections was not different. ICU admissions were characterized by lower SpO2 (p = 0.001), lower pH in arterial blood gas (p = 0.001), higher FiO2 (p < 0.001), higher PaCO2 (p = 0.007) and higher NEWS (p < 0.001; Table 4 and eTable 7). Table 4 Stratification of patients by admission to the intensive care unit Not admitted to ICU N = 615 Admitted to ICU N = 29 p-value Medication Theophylline 9 (1.5%) 5 (17.2%) < 0.001 Exacerbation characteristics Duration of symptoms (days) 4.00 [2.00, 14.00] 1.00 [0.00, 3.00] < 0.001 Season (%) 0.007 Autumn 136 (22.1%) 14 (48.3%) Spring 143 (23.3%) 2 (6.9%) Summer 144 (23.4%) 5 (17.2%) Winter 192 (31.2%) 8 (27.6%) Non-specific trigger 54 (8.8%) 7 (24.1%) 0.015 Rhinovirus infections 24 (20.0%) 9 (60.0%) 0.002 Blood counts Neutrophil (x 10^9/L), N = 545 6.20 [4.47, 8.62] 9.94 [6.52, 12.47] < 0.001 Leukocyte (x 10^9/L), N = 585 9.60 [7.50, 12.03] 12.30 [9.20, 16.00] 0.004 Clinical outcomes and disease severity SpO2, N = 638 96.00 [93.00, 98.00] 92.00 [89.00, 97.00] 0.001 FiO2 (%) 20.00 [20.00, 20.00] 20.00 [20.00, 32.00] < 0.001 SpO2/FiO2, N = 638 462.97 ± 51.12 360.50 ± 132.18 < 0.001 pH N = 379 7.45 [7.43, 7.49] 7.41 [7.36, 7.47] 0.001 Hypercapnia, N = 379 8 (2.3%) 7 (24.1%) < 0.001 PaCO2 (kPa), N = 379 4.60 [4.10, 5.00] 5.10 [4.40, 6.00] 0.007 NEWS, N = 622 3.00 [1.00, 5.00] 7.00 [4.75, 8.25] < 0.001 The total number of exacerbations used for analysis was N = 644, unless stated otherwise. Data is presented as proportion (percentage), mean ± SD or median [IQR]. FiO2 = Fraction of inspired oxygen, ICU = Intensive Care Unit, NEWS = National Early Warning Score, PaCO2 = Partial pressure of carbon dioxide, SpO2 = Saturation of Peripheral Oxygen , Associations with five exacerbation severity outcomes Third, we examined the associations between patient and exacerbation characteristics and five key outcomes indicative of exacerbation severity: hospital admission, length of hospital stay, admission to the intensive care unit (ICU), NEWS score, and the SpO2/FiO2 ratio (Table 5 ). In contrast with the previous regression models, these models accounted for within-patient variability. Due to the wide range of viral and bacterial infections, only those with significant associations with one or more outcomes are reported. Comorbidities, seasonal categories, biological factors, ethnicity, and exacerbation history—including emergency presentations, hospitalizations, and steroid courses within the prior 12 months—are not presented here as none demonstrated significant associations. Full details are provided in eTable 8. Table 5 Associations between study characteristics and disease severity and clinical outcomes Admission Intensive care admission Length of hospital stay Oxygenation efficiency (SpO2/FiO2) NEWS Parameter Estimate (95% CI) Adjusted p-value Estimate (95% CI) Adjusted p-value Estimate (95% CI) Adjusted p-value Estimate (95% CI) Adjusted p-value Estimate (95% CI) Adjusted p-value Patient characteristics Age 0.002 (0.001) 0.369 -0.001 (0.001) 0.580 0.02 (0.01) 0.137 -0.18 (0.17) 0.830 0.01 (0.01) 0.305 Smoking status NA 0.769 NA 0.950 NA 0.351 NA 0.019 NA 0.292 Past smoker 0.069 (0.054) NA 0.008 (0.023) NA 0.59 (0.38) NA -12.14 (7.05) NA 0.51 (0.28) NA Current smoker 0.017 (0.058) NA 0.009 (0.024) NA -0.36 (0.41) NA -27.36 (7.45) NA 0.54 (0.29) NA Body mass index (kg/m 2 ) 0.004 (0.004) 0.690 0.003 (0.002) 0.580 0.09 (0.03) 0.022 0.10 (0.52) 0.987 0.02 (0.02) 0.670 Ethnicity NA 0.862 NA 0.817 NA 0.801 NA 0.987 NA 0.739 Sex (female) 0.059 (0.046) 0.588 -0.002 (0.020) 0.950 0.40 (0.33) 0.548 19.05 (5.98) 0.020 -0.21 (0.24) 0.670 Allergic sensitization to aeroallergens -0.005 (0.048) 0.958 -0.018 (0.020) 0.817 -0.78 (0.34) 0.137 6.03 (6.37) 0.917 -0.25 (0.25) 0.638 Allergic sensitization to Aspergillus -0.140 (0.077) 0.329 -0.067 (0.043) 0.580 -0.77 (0.62) 0.548 2.10 (10.02) 0.987 0.28 (0.41) 0.720 Exacerbation characteristics Peak flow % of predicted -0.004 (0.002) 0.216 -0.001 (0.001) 0.756 -0.01 (0.01) 0.591 0.31 (0.23) 0.614 -0.03 (0.01) 0.001 Season NA 0.864 NA 0.137 NA 0.827 NA 0.987 NA 0.957 Duration of symptoms (days) 0.000 (0.001) 0.910 0.000 (0.000) 0.620 0.00 (0.00) 0.727 0.13 (0.08) 0.376 -0.01 (0.00) 0.052 Flu-like symptoms 0.022 (0.039) 0.827 -0.002 (0.016) 0.950 0.13 (0.26) 0.801 -2.11 (4.70) 0.987 0.42 (0.19) 0.134 Non-specific trigger exposure -0.003 (0.067) 0.969 0.067 (0.028) 0.137 -0.19 (0.44) 0.827 13.07 (8.02) 0.403 -0.45 (0.32) 0.451 Allergen exposure -0.077 (0.072) 0.639 -0.020 (0.030) 0.819 -0.79 (0.49) 0.351 1.88 (8.78) 0.987 -0.55 (0.35) 0.346 Infiltrate on X-ray or CT -scan 0.235 (0.066) 0.024 0.030 (0.029) 0.756 1.51 (0.45) 0.022 -17.31 (8.38) 0.212 1.50 (0.32) 0.000 Lung function FEV1% of predicted -0.003 (0.001) 0.183 -0.001 (0.001) 0.756 -0.02 (0.01) 0.111 0.55 (0.15) 0.007 -0.02 (0.01) 0.003 FVC % of predicted 0.000 (0.001) 0.910 0.000 (0.001) 0.819 -0.01 (0.01) 0.351 0.31 (0.17) 0.307 -0.02 (0.01) 0.076 FEV1/FVC -0.503 (0.181) 0.086 -0.069 (0.083) 0.819 -2.20 (1.28) 0.351 103.50 (23.47) 0.001 -3.63 (0.93) 0.001 Use of maintenance medication ICS dosage in mcg BED x 1000 0.000 (0.000) 0.933 0.000 (0.000) 0.756 0.00 (0.00) 0.866 0.01 (0.00) 0.022 0.00 (0.00) 0.305 Using theophylline 0.220 (0.143) 0.460 0.275 (0.059) 0.000 1.05 (0.99) 0.637 -41.46 (18.88) 0.188 2.76 (0.74) 0.002 Using inhaled LAMA 0.124 (0.050) 0.138 -0.027 (0.022) 0.714 1.07 (0.35) 0.040 2.07 (6.54) 0.987 0.48 (0.26) 0.240 Using LTRA -0.004 (0.061) 0.967 -0.011 (0.026) 0.853 -0.08 (0.43) 0.911 11.36 (7.88) 0.549 -0.82 (0.31) 0.052 Using oral steroids 0.142 (0.051) 0.086 0.007 (0.022) 0.906 0.71 (0.36) 0.251 0.24 (6.64) 0.987 0.19 (0.26) 0.720 Using a biological -0.075 (0.070) 0.639 -0.041 (0.029) 0.620 -0.25 (0.47) 0.801 0.35 (8.66) 0.987 0.06 (0.35) 0.957 Blood values Leukocyte blood count 0.010 (0.005) 0.276 0.009 (0.002) 0.002 0.06 (0.04) 0.384 -1.71 (0.66) 0.078 0.12 (0.03) 0.000 Eosinophil blood count 0.035 (0.059) 0.827 0.003 (0.026) 0.950 -0.02 (0.42) 0.973 -6.44 (7.71) 0.987 -0.28 (0.30) 0.670 Neutrophil blood count 0.013 (0.006) 0.216 0.013 (0.003) 0.000 0.09 (0.04) 0.182 -2.63 (0.75) 0.009 0.15 (0.03) 0.000 C-reactive protein (CRP) 0.002 (0.001) 0.030 0.000 (0.000) 0.819 0.00 (0.00) 0.717 -0.03 (0.08) 0.987 0.01 (0.00) 0.001 Infections Any viral infection 0.019 (0.086) 0.910 0.021 (0.054) 0.855 -0.24 (0.83) 0.866 -5.03 (11.84) 0.987 0.28 (0.49) 0.752 Rhinovirus infection 0.079 (0.097) 0.769 0.191 (0.060) 0.023 0.11 (0.96) 0.951 -6.80 (13.28) 0.987 0.44 (0.55) 0.720 Enterovirus infection 0.368 (0.344) 0.639 0.645 (0.185) 0.015 2.04 (3.26) 0.801 15.02 (45.32) 0.987 -2.56 (1.94) 0.466 Any bacterial infection -0.061 (0.108) 0.827 -0.036 (0.073) 0.819 0.99 (0.96) 0.650 3.11 (17.63) 0.987 -0.69 (0.61) 0.570 Pseudomonas spp infection 0.359 (0.319) 0.639 -0.108 (0.227) 0.819 9.46 (2.81) 0.022 -25.40 (52.97) 0.987 -0.28 (1.85) 0.957 BED = Beclomethasone Equivalent Dose, CT = Computed Tomography, FiO2 = Fraction of inspired oxygen, FEV1 = Forced Expiratory Volume in 1 second, FVC = Forced Vital Capacity, ICS = Inhalation Corticosteroid, LAMA = Long-Acting Muscarinic Antagonist, LTRA = Leukotriene Receptor Antagonists, NEWS = National Early Warning Score, SpO2 = Saturation of Peripheral Oxygen. Predictive Modelling The presence of an infiltrate on a chest X-ray or CT-scan was associated with higher NEWS scores, more frequent hospital admissions, and longer hospital stays (Table 5 ). Worse pulmonary function, specifically a lower FEV1 and FEV1/FVC, was associated with higher NEWS and worse oxygenation efficiency. CRP levels were positively associated with NEWS and hospital admissions. Blood neutrophil count and total leukocyte count were associated with higher NEWS and ICU admission although blood eosinophil counts showed no significant association with any outcomes. Overall, the presence of viral or bacterial infections was not associated with the studied outcomes. However, rhinovirus and enterovirus infections were specifically linked to ICU admission, and pseudomonas infection or colonization was associated with prolonged hospital stays (Table 5 ) Lastly, we aimed to identify parameters that could predict our clinical outcomes of interest. To achieve this, we applied LASSO regression to select the most important predictors from the linear mixed models. A detailed overview of the variables selected by each model, along with their test characteristics, is provided in eTables S9–S13. Table 6 contains the variables that achieved statistical significance. The presence of an infiltrate on chest X-ray or CT scan emerged as an independent and significant predictor for all outcomes except ICU admission, for which a trend with p = 0.050 was observed (eTable 10). Blood neutrophil count was selected for the prediction of ICU admission and oxygenation efficiency, whilst eosinophil count only emerged as a significant predictor for oxygenation efficiency. CRP levels were a significant predictor for hospital admission only. Table 6 P-values of the variables selected in the LASSO models to predict the severity and outcomes of asthma exacerbations. Variable hospital admission ICU admission NEWS Oxygenation efficiency (SpO2/FiO2) Length of hospital stay Infiltrate on X-ray or CT-scan 0,000 ns 0,000 0,002 0,021 C-reactive protein (CRP) 0,012 ns ns ns ns Using oral corticosteroids 0,028 ns ns ns ns Using theophylline ns 0,000 0,000 0,005 ns Neutrophil blood count ns 0,001 ns 0,030 ns Season ns 0,018 ns ns ns Non-specific trigger exposure ns 0,022 ns ns ns Using leukotriene receptor antagonists ns ns 0,000 ns ns Duration of symptoms ns ns 0,002 ns ns Eosinophil blood count ns ns ns 0,035 ns Smoking status ns ns ns 0,046 ns Dosage of inhalation corticosteroids ns ns ns ns 0,039 CT = Computed Tomography, FiO2 = Fraction of inspired oxygen, ICU = Intensive Care Unit, NEWS = National Early Warning Score, ns = non-significant, SpO2 = Saturation of Peripheral Oxygen. For the two binary outcomes, hospital admission and ICU admission, receiver operating characteristic (ROC) curves were generated (Fig. 2 ). In a train cohort comprising 80% of randomly selected patients, the model yielded an area under the curve (AUC) of 0.692 for predicting hospital admission (Fig. 2 A). On the hold-out validation cohort, the model achieved an AUC of 0.632. Although the AUC for predicting ICU admission was higher at 0.917 in the training cohort, the performance on the test cohort was substantially lower with an AUC of 0.695 (Fig. 2 B). The proportion of variance explained by the selected variables in the test cohort, the fixed effects (R²m), was 18.8% for NEWS, 15.2% for oxygenation efficiency and 9.0% for length of hospital stay. The proportion of variance explained by both fixed and random (patient-specific variability) effects (R²c) was 36.3% for NEWS, 39.5% for oxygenation efficiency and 36.5% for length of hospital stay. Discussion This retrospective study of adults presenting with asthma exacerbations to an emergency department of a large university hospital aimed to identify clinical predictors of exacerbation severity. While numerous studies have explored risk factors for developing subsequent exacerbations, research specifically focused on predictors of exacerbation severity remains limited 22 . Understanding these severity predictors is essential to enhance tailored clinical decision-making and optimize the allocation of healthcare resources. We determined the strongest predictors and their predictive value using cross-validated machine learning models applied to high-dimensional, real-world data. Our findings show that impaired baseline lung function and markers of acute infection-specifically, radiographic infiltrate, elevated CRP, and increased blood neutrophils-were most frequently associated with severe exacerbations. In contrast, factors such as blood eosinophil count, age, comorbidities, symptom duration, and exacerbation history were not significant predictors. Notably, our predictive models demonstrated only moderate accuracy. These findings highlight the complex interplay of factors influencing asthma exacerbation severity and the need to identify additional (bio)markers and patient-specific traits to improve risk stratification. In our study, exacerbations requiring hospital admission frequently occurred in patients with a history of prior exacerbations, emergency department visits, or hospitalizations within the preceding 12 months. These findings align with previous studies identifying prior exacerbations as one of the strongest risk factors for recurrent exacerbations, often referred to as the ‘frequent exacerbator phenotype’ 14 – 16 , 23 , 24 . However, in our study, these features were not associated with more severe exacerbations or worse clinical outcomes. These findings suggest that prior exacerbations, emergency visits, and hospitalizations are not independent risk factors for exacerbation severity, but instead reflect a distinct phenotype of patients prone to recurrent exacerbations or poor asthma control 25 . Blood eosinophil count at hospital presentation was not associated with exacerbation severity in our study. Eosinophilic inflammation, with blood eosinophilia as a surrogate marker, is a well-established risk factor for future severe exacerbations and hospitalizations 26 , and even for the severity of concurrent exacerbations 27 . While this could be due to the high proportion of patients using inhaled corticosteroids (75.5%) or oral maintenance steroids (24.7%), our findings suggests that type 2 (T2) inflammation may not play a dominant role during exacerbations. This discrepancy may reflect the evolving understanding of asthma phenotypes, where neutrophilic and non–type 2 inflammatory pathways increasingly appear to drive severe exacerbations. This aligns with a previous study demonstrating that the degree of T2 inflammation at the start of a 48-week follow-up, assessed by FeNO or blood eosinophil count, was not predictive of T2 inflammation during exacerbations 28 . In contrast, we found that blood neutrophil count, CRP levels and the presence of an infiltrate on chest radiography were among the most important predictors of exacerbation severity. Neutrophilic inflammation, in both blood and tissue, is frequently associated with asthma exacerbations triggered by viral infections and has been linked to exacerbation severity 29 , 30 . These observations collectively suggest that pathogen-driven and non-eosinophilic pathways, such as inflammasome activation, interferon response, or Th-17 pathway upregulation play a more prominent role in determining exacerbation severity 31 . Exploring these mechanisms, could help develop targeted interventions. Due to the retrospective design of our study, specific inflammatory pathways were not directly investigated. Furthermore, microbiological testing, including throat swabs and sputum cultures, was performed only in a subset of exacerbations. Nevertheless, we observed that rhinovirus and enterovirus infections were associated with ICU admissions. While rhinovirus infections typically cause mild symptoms in healthy individuals, it is known to induce more severe and prolonged episodes of airway dysfunction in asthma patients 32 , 33 . The role of enteroviruses in adult asthma exacerbations is less well characterized, but similar to rhinoviruses, they belong to the Picornaviridae family and have been linked to near-fatal asthma episodes 34 . These findings underscore the importance of further investigating non-eosinophilic pathways and viral triggers in asthma exacerbations. Theophylline maintenance use was associated with increased exacerbation severity, including higher NEWS and ICU admission, and emerged as a key predictor of these measures and oxygenation efficiency. Patients on theophylline maintenance exhibited significantly higher NEWS (median 8, IQR 4–9 vs. 3, IQR 1–5, p < 0.001), worse oxygenation efficiency (median SpO 2 /FiO 2 460, IQR 362–480 vs. 475, IQR 460–490, p = 0.033), higher ICU admission rates (35.7% vs. 3.8%, p < 0.001), and greater need for mechanical ventilation (21.4% vs. 0.6%, p < 0.001) compared to non-users. However, despite a higher body mass index (BMI; 33.2 kg/m² vs. 29.2 kg/m², p = 0.023), the two groups were comparable in demographics, pulmonary function, and comorbidities. A review of the medical records revealed no evidence of cardiovascular, metabolic, or neurologic toxicity linked to theophylline. While theoretical concerns exist about theophylline causing muscle fatigue or impaired oxygenation efficiency due to enhanced respiratory drive and subtle ventilation-perfusion mismatch, these effects have not been consistently observed in clinical studies 35 . Therefore, it is most likely that theophylline users represent a subgroup with more severe or refractory disease. The key strength of this study comprises the use of high-dimensional data from a larger cohort of asthma patients in a hospital care setting. Moreover, the associations between a wide range of clinical factors and five key outcomes related to asthma exacerbation severity were systematically investigated. Another notable strength is the use of advanced prediction modelling, incorporating both linear mixed models and LASSO regression. This dual approach not only identified independent factors associated with exacerbation severity but also leveraged a machine learning technique to select the most effective predictors. LASSO regression in particular enables efficient identification of key clinical predictors while minimizing overfitting, thereby enhancing the reliability and interpretability of our predictive models. A final major strength is the inclusion of multiple presentations per patient this allowed us to capture within-patient variability and better reflect the real-world complexity of asthma exacerbations. Despite variations in the course of consecutive exacerbations between individuals, patient-specific factors demonstrated a stronger predictive value for NEWS, oxygenation efficiency, and length of hospital stay than all other selected variables combined. This is demonstrated by the substantial differences between the marginal R 2 , which reflects the variability explained by fixed predictors, and the conditional R 2 , which accounts for both fixed predictors and patient-specific random effects. These findings highlight the significance of patient-specific variability and support the inclusion of multiple study visits per patient in future research designs. This study suffers from a few limitations. Foremost, is the retrospective design which resulted in missing data for certain variables, which prevented their inclusion in the prediction models. Consequently, the predictive value of blood gas analyses, peak flow measurements during exacerbations, treatment adherence, exacerbation history, and detailed microbiological data could not be fully assessed. Notably, the missing data were not entirely random and were more common in cases of lower disease severity, potentially introducing bias. Additionally, the retrospective nature of the study limited the availability of potentially important data, particularly regarding inflammatory pathways and dynamic changes in biomarkers, which could have provided further mechanistic insights into exacerbation severity. We chose to limit data collection to the period before the national onset of the first wave of the COVID-19 pandemic to avoid potential confounding factors that could influence asthma exacerbation trends. However, this approach, combined with temporal shifts in treatment patterns, such as the increased adoption of biologic therapies (with 9.9% of patients already receiving biologics at the time of exacerbation) and the decreasing use of medications like theophylline, may impact the generalizability of our findings to contemporary clinical practice. These limitations should be considered when interpreting the study's findings. While our models incorporated a large number of variables, their predictive performance was moderate to poor, with a relatively low percentage of variability explained. The limited predictive accuracy for ICU admission may, in part, reflect the small number of ICU admissions in the dataset. Nevertheless, our findings do not support the routine clinical application of these prediction models. Instead, they underscore that the most important predictors of asthma exacerbation severity remain unidentified. We recommend that future research incorporate longitudinal monitoring of lung function and multi-compartment biomarker or omics-based approaches to better assess the dynamic nature of airway inflammation. Combining these data with advanced machine learning techniques could provide deeper insights into the complex mechanisms underlying exacerbation severity and ultimately lead to more personalized and effective management strategies for patients with asthma exacerbations. Conclusions The presence of an infiltrate on chest radiography, pre-existing impaired lung function, elevated CRP levels and increased blood neutrophil count are the most consistent variables associated with exacerbation severity. However, despite using advanced machine learning, the predictive value of clinical and demographic parameters is limited, indicating the need to identify additional biomarkers or patient-specific traits to further improve tailored exacerbation management strategies. Abbreviations AUC = Area Under the Curve BED = Beclomethasone dipropionate Equivalent Dosage BMI = Body Mass Index COPD = Chronic Obstructive Pulmonary Disease CRP = C-reactive protein CRSwNP = Chronic rhinosinusitis with nasal polyps CRSsNP = Chronic rhinosinusitis without nasal polyps CT = Computed Tomography ED = Emergency Department FDR = False Discovery Rate FeNO = Fractional exhaled Nitric Oxide FEV1= Forced Expiratory Volume in 1 second FiO2 = Fraction of inspired oxygen FVC = Forced Vital Capacity GERD = Gastroesophageal Reflux Disease GINA = Global Initiative for Asthma HMPV = Human Metapneumovirus ICD = International Classification of Diseases ICS = Inhalation Corticosteroids ICU = Intensive Care Unit IQR = Interquartile Range LAMA = Long-Acting Muscarinic Antagonist LASSO = Least Absolute Shrinkage and Selection operator LMM = Linear Mixed Models LTRA = leukotriene receptor antagonist MICE = Multivariate Imputation by Chained Equations NEWS = National Early Warning Score NRM = non-rebreathing mask OCS = Oral Corticosteroid OSAS = Obstructive Sleep Apnea Syndrome PaCO2= partial pressure of carbon dioxide PaO2= partial pressure of oxygen PEF = Peak Expiratory Flow ROC = Receiver Operating Characteristic RSV = Respiratory Syncytial Virus SpO2 = peripheral oxygen saturation Declarations Ethics approval and consent to participate Ethical approval for study conduction was obtained from the ethical committee of the hospital's clinical research unit. Written informed consent was waived by the ethical committee. Consent for publication Not applicable Availability of data and materials Pseudonymized patient data used in this study are available from the corresponding author ( [email protected] ) for other researchers when reuse conditions are met. Competing interests The authors declare that they have no competing interests Funding This study was conducted independently, without external funding or sponsorship Authors’ contributions E.D. is the guarantor of this manuscript and takes responsibility for the integrity of the data and accuracy of the data analyses. E.D, M.A. and E.W. designed the study. E.D., J.H and J.G. conducted the statistical analysis. All authors, E.D., J.G., H.C., J.H., B.H., A.S., M.A. and E.W. have contributed to the interpretation of the results, provided critical feedback and approved the final version of the manuscript. Acknowledgements Not applicable References Asthma GIf. 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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-6664347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483124958,"identity":"70c68d76-2653-4fb1-b33a-cad38b1b8d09","order_by":0,"name":"Erik Duijvelaar","email":"data:image/png;base64,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","orcid":"","institution":"Amsterdam University Medical Centers","correspondingAuthor":true,"prefix":"","firstName":"Erik","middleName":"","lastName":"Duijvelaar","suffix":""},{"id":483124959,"identity":"77f4bc2d-4abf-43dd-bd4e-96ce015733a8","order_by":1,"name":"Jack S. Gisby","email":"","orcid":"","institution":"William Harvey Research Institute, Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Jack","middleName":"S.","lastName":"Gisby","suffix":""},{"id":483124961,"identity":"43743b3c-6f44-4363-ba98-f886cfb51fdf","order_by":2,"name":"Hanneke Coumou","email":"","orcid":"","institution":"Amsterdam University Medical Centers","correspondingAuthor":false,"prefix":"","firstName":"Hanneke","middleName":"","lastName":"Coumou","suffix":""},{"id":483124962,"identity":"46f58b06-e4be-4047-9405-3b4a463fed6a","order_by":3,"name":"Jeroen Hoogland","email":"","orcid":"","institution":"Amsterdam UMC Location VUmc","correspondingAuthor":false,"prefix":"","firstName":"Jeroen","middleName":"","lastName":"Hoogland","suffix":""},{"id":483124963,"identity":"79afed8a-7dc2-4f19-84c3-cc58d35784ec","order_by":4,"name":"Bart Hilvering","email":"","orcid":"","institution":"Amsterdam University Medical Centers","correspondingAuthor":false,"prefix":"","firstName":"Bart","middleName":"","lastName":"Hilvering","suffix":""},{"id":483124965,"identity":"5b66146c-3e5a-4edf-aebd-091b5c5e1ce5","order_by":5,"name":"Anirban Sinha","email":"","orcid":"","institution":"AbbVie Cambridge Research Center","correspondingAuthor":false,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Sinha","suffix":""},{"id":483124970,"identity":"ca2cb245-d3cd-42e8-8ecf-4be6a5d508c6","order_by":6,"name":"Marijke Amelink","email":"","orcid":"","institution":"Spaarne Gasthuis","correspondingAuthor":false,"prefix":"","firstName":"Marijke","middleName":"","lastName":"Amelink","suffix":""},{"id":483124971,"identity":"e66a8930-f217-4997-a40c-68d298311262","order_by":7,"name":"Els JM Weersink","email":"","orcid":"","institution":"Amsterdam University Medical Centers","correspondingAuthor":false,"prefix":"","firstName":"Els","middleName":"JM","lastName":"Weersink","suffix":""}],"badges":[],"createdAt":"2025-05-14 12:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6664347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6664347/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-19056-w","type":"published","date":"2025-10-08T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86408508,"identity":"be7219b0-9096-4fae-bb11-839aed11c195","added_by":"auto","created_at":"2025-07-10 10:21:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow diagram of patient screening and inclusion.\u003c/p\u003e","description":"","filename":"Figure1.13APR2024.png","url":"https://assets-eu.researchsquare.com/files/rs-6664347/v1/73c29381b7ed796d17d303c7.png"},{"id":86409485,"identity":"757e65ca-a6f8-41b9-81fa-2fc21eaa8724","added_by":"auto","created_at":"2025-07-10 10:29:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerformance of the train (blue) and test (orange) prediction models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating curves showing the performance of the prediction models for hospital admission (A) and intensive care unit admission (B). \u0026nbsp;AUC = Area under the curve.\u003c/p\u003e","description":"","filename":"Figure2.13APR2025.png","url":"https://assets-eu.researchsquare.com/files/rs-6664347/v1/f39767a3c6f72ed3c6d38e18.png"},{"id":93419473,"identity":"d2d6f153-508d-46da-b213-03d99ee27e8d","added_by":"auto","created_at":"2025-10-13 16:02:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2756741,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6664347/v1/4d61559c-8d3a-4160-af31-a849d60df103.pdf"},{"id":86408510,"identity":"314b0bc7-b834-4302-be2c-fddc425bd684","added_by":"auto","created_at":"2025-07-10 10:21:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":160627,"visible":true,"origin":"","legend":"","description":"","filename":"Esupplement14APR2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6664347/v1/3933789931507892ed71443f.docx"},{"id":86408518,"identity":"ccd94bf6-6395-4518-8dac-3382aee73896","added_by":"auto","created_at":"2025-07-10 10:21:47","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3130109,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract19MAR2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6664347/v1/3268d5a89108f231c3f3d993.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine learning reveals limited predictive value of clinical factors for asthma exacerbations: insights from a real-world study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAsthma is a chronic inflammatory airway disease typically characterized by episodes of respiratory distress, known as exacerbations. Exacerbations comprise acute or subacute worsening in asthma symptoms (coughing, wheezing, dyspnoea and chest tightness) and lung function, attributable to increased airway constriction and/or inflammation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. They occur frequently in patients with severe or uncontrolled asthma and contribute to accelerated loss of lung function and a lower quality of life\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the last two decades, asthma management targeting disease-specific inflammatory pathways has been proven successful in improving lung function, tapering of systemic steroids and preventing subsequent exacerbations\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. On the other hand, asthma exacerbations are typically managed based on symptom severity rather than by underlying inflammatory mechanisms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Standard treatment of severe exacerbations mainly consists of the correction of hypoxemia with oxygen and reversal of airflow limitation with inhaled beta-2-agonists, inhaled corticosteroids and in severe cases systemic corticosteroids and intravenous magnesium sulphate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Given the heterogeneous and complex inflammatory processes underlying asthma, there is a growing need to move from a generalized approach to a more phenotype-directed treatment of exacerbations to improve clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Eosinophil-targeted therapy already showed promising results\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, this transition necessitates more precise identification of risk factors and treatable traits associated with severity of exacerbations.\u003c/p\u003e\u003cp\u003eKnown risk factors for the development of asthma exacerbations include previous exacerbations, smoking, female sex, inadequate asthma management, elevated blood or sputum eosinophil levels, increased fractional exhaled nitric oxide (FeNO) levels, and several pre-existing comorbidities such as obesity, chronic sinus disease, obstructive sleep apnoea and gastroesophageal reflux\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, it remains unclear whether these same factors are associated with the severity of exacerbations or adverse clinical outcomes. Identification of risk factors or patients at risk for severe exacerbations could help prevent delayed escalation of care and enable more patient-tailored therapeutic interventions and. To optimise predictive performance while minimizing overfitting, machine learning models that incorporate cross-validation could effectively handle high-dimensional data and account for collinearity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Therefore, the aim of this study is to identify clinical factors associated with exacerbation severity and clinical outcomes and to determine their predictive value using advanced s machine learning modelling.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population and design\u003c/h2\u003e\u003cp\u003eData from electronic health records were retrospectively collected for patients who presented to the emergency department at Amsterdam University Medical Center, location Academical Medical Center (AMC), between October 1, 2013, and March 1, 2020, with an asthma exacerbation. These patients were identified using asthma diagnosis treatment combinations, which are physician-made diagnoses that are generally followed by a sequence of medical activities and follows the International Statistical Classification of Diseases and Related Health Problems (ICD). Adult patients, 18 years or older at presentation, were considered eligible for study inclusion if the diagnosis of asthma was based on a history of confirmed variable expiratory airflow limitation and characteristic pulmonary symptoms, verified by at least one of the methods described by the latest report of the Global Initiative for Asthma\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and the exacerbation was characterized by acute or subacute worsening of asthma-related symptoms resulting in a physician reported diagnosis of asthma exacerbation or status asthmaticus. Once a patient was eligible for study inclusion by two independent physicians, chart data was pseudonymized. Ethical approval for study conduction was obtained from the ethical committee of the hospital's clinical research unit. All methods were performed in accordance with relevant guidelines and regulations. Written informed consent was waived by the ethical committee. Instead, patients were informed about the study through a letter and were subsequently given the opportunity to object to inclusion by either email or returning a reply envelope.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy assessments\u003c/h3\u003e\n\u003cp\u003eStudy parameters include demographics, patient characteristics, pre-existent comorbid diseases, medication use, requirement for oxygen supplementation (eTable 2), and laboratory, microbiological and radiographic findings. Information on lung function was gathered using spirometry measurements outside of the exacerbation period. Peak expiratory flow data was collected from handheld peak-flow meters at presentation. The total dosage of inhalation corticosteroids was calculated as beclomethasone equivalent dosage (BED) using the conversion factors presented in supplementary eTable 3. A complete overview of all study parameters and their definitions is provided in supplementary eTable 1.\u003c/p\u003e\n\u003ch3\u003eOutcome measures\u003c/h3\u003e\n\u003cp\u003eThe severity of asthma exacerbations was determined by the ratio of peripheral oxygen saturation to the inspired fraction of oxygen (SpO2/FiO2), NEWS at emergency presentation, need for hospital admission, length of hospital stay and admission to the intensive care unit (ICU).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R, version 4.2.2 and RStudio version 2023.03.0 Cherry Blossom. Descriptive statistics were used to summarize patient and exacerbation characteristics. If a patient had multiple exacerbations during the study time period, the first visit was used to extract patient\u0026rsquo;s baseline characteristics. The baseline characteristics of patients stratified by either hospital admission or ICU admission status were compared using an unpaired T-test, the Mann-Whitney U test and the Chi-square test, for normally distributed numeric variables, non-normally distributed numeric variables and categorical variables respectively. Associations between patient and exacerbation characteristics and outcome measures were assessed using linear mixed models (LMMs), with random intercepts assigned to each patient to account for repeated measures. All other variables were included as fixed effects. LMMs were implemented using the lmer function from the lme4 package.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. For the linear mixed models, the Benjamini-Hochberg procedure was used to adjust for multiplicity with a false discovery rate (FDR) set at 0.05.\u003c/p\u003e\u003cp\u003eTo select the parameters with the highest predictive value, we modelled the data using Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done by using the glmmLasso function from the glmmLasso package\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This allowed for selection of a sequence of predictors with the highest independent predictive value. Random intercepts were given to each subject, all other study variables were included as fixed effects. Since missing data is not allowed for these prediction models, variables containing more than 15% missing values were excluded from LASSO regression analyses. For the variables CRP, duration of symptoms, body mass index (BMI), eosinophil, neutrophil and leukocyte blood count, missing values were imputed with the mean or median depending on the distribution. For categorical variables, an additional category \u0026ldquo;unknown\u0026rdquo; was added. The LASSO models were first fit during 5-fold cross validation on a train set of patients, composed of 80% of randomly selected patients. Then, these models were tested on the remaining 20% of patients, comprising the hold-out validation cohort. The final LASSO model was then fit on the full dataset. Finally, the selected variables were included in a final LMM model. For all statistical tests, a p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCohort Characteristics\u003c/h2\u003e\u003cp\u003eThe medical records of 1201 patients were screened, of which 824 patients were excluded due to not meeting the inclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). From the 377 eligible patients, 10 patients were further excluded because they objected to their data being used for this study. A total of 367 patients were included, which had presented to the emergency department with a total of 644 exacerbations. The proportion of missing data for each included variable is provided in eTable 4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe cohort was predominantly female (60%), with a mean age of 45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority of patients were Caucasian (55%), 47% had a history of smoking or were current smokers, and 68% had an allergy to aeroallergens. Additionally, 52 patients (14%) were concomitantly diagnosed with COPD. While most patients presented once during the study period, 117 (32%) experienced more than one exacerbation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Exacerbations occurred most frequently during winter (31.1%; χ\u0026sup2; = 11.77, p\u0026thinsp;=\u0026thinsp;0.0003, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The median duration of symptoms before patients visited the emergency department was 4 days [IQR 2, 14]. Maintenance inhalation corticosteroids (ICS) were used before 482 (74.8%) of exacerbations, with a median dosage of 1000 micrograms BED [IQR 0, 2000]. During almost half (47.2%) of the exacerbations, patients reported flu-like symptoms. In 135 exacerbations, a nasal and/or throat swab was performed, which tested positive for a respiratory virus in 84 exacerbations (62.2%). A bacterial infection was cultured in 23 out of the 141 (16.3%) sputum samples. A radiographic infiltrate was observed in 65 (11.3%) exacerbations. Mean blood eosinophil count was 0.29 (SD 0.37) x 10^9/L and median CRP level was 5.9 [2.2, 16.5] mg/L. From all 644 exacerbations, 279 (43.3%) required hospital admission, 29 exacerbations (4.5%) led to ICU admission and 7 (1.1%) patients required invasive ventilation (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and eTable 5). None of the patients died due to the asthma exacerbation or its complications.\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\u003ePatient characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePatient characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;367\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220 (60%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWeight (kg) N\u0026thinsp;=\u0026thinsp;266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHeight (cm) N\u0026thinsp;=\u0026thinsp;279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBody Mass Index (kg/m2) N\u0026thinsp;=\u0026thinsp;264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity N\u0026thinsp;=\u0026thinsp;339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaucasian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185 (55%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (15%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian or pacific islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed and other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (19%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSmoking N\u0026thinsp;=\u0026thinsp;352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188 (53%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePast smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (23%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGERD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (15%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (14%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRhinitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (33%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRSwNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRSsNP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebronchiectasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOSAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrticaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllergies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAeroallergens N\u0026thinsp;=\u0026thinsp;363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e246 (68%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAspergillus Fumigatus N\u0026thinsp;=\u0026thinsp;148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMultiple (\u0026ge;\u0026thinsp;2) exacerbations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (32%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eThe total number of patients used to describe proportions is 367, unless stated otherwise. Data is presented as proportion (percentage) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. COPD\u0026thinsp;=\u0026thinsp;Chronic Obstructive Pulmonary Disease, CRSsNP\u0026thinsp;=\u0026thinsp;Chronic rhinosinusitis without nasal polyps, CRSwNP\u0026thinsp;=\u0026thinsp;Chronic rhinosinusitis with nasal polyps, GERD\u0026thinsp;=\u0026thinsp;Gastroesophageal Reflux Disease, OSAS\u0026thinsp;=\u0026thinsp;Obstructive Sleep Apnea Syndrome.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExacerbation characteristics at emergency presentation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll exacerbations N\u0026thinsp;=\u0026thinsp;644\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eExacerbation history\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime since previous emergency department visit due to an asthma exacerbation (days) N\u0026thinsp;=\u0026thinsp;227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 [32, 208]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime since previous hospital admission (days) N\u0026thinsp;=\u0026thinsp;146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 [37, 209]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of symptoms (days) N\u0026thinsp;=\u0026thinsp;634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 [2, 14]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDaily maintenance medication use\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhalation Corticosteroid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e486 (75.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhalation Corticosteroid dosage (in mcg BED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000 [0, 2000]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong-Acting Muscarinic Antagonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukotriene Receptor Antagonists\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheophylline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorticosteroid maintenance therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e159 (24.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhalation Corticosteroid category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0-200 mcg BED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e201\u0026ndash;500 mcg BED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e501\u0026ndash;1000 mcg BED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1000 mcg BED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e310 (48.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of any biological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMepolizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmalizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReslizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenralizumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDupilumab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBlood markers\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil (x 10^9/L) N\u0026thinsp;=\u0026thinsp;558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15 [0.04, 0.39]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil (x 10^9/L) N\u0026thinsp;=\u0026thinsp;545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte (x 10^9/L) N\u0026thinsp;=\u0026thinsp;585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg/L) N\u0026thinsp;=\u0026thinsp;574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.9 [2.2, 16.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePatient reported triggers\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlu-like symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e339 (52.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllergen exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-specific trigger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLung function at presentation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak expiratory flow (L/min) N\u0026thinsp;=\u0026thinsp;355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e241\u0026thinsp;\u0026plusmn;\u0026thinsp;127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak expiratory flow % of predicted N\u0026thinsp;=\u0026thinsp;355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;21.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRadiologic findings\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfiltrate on X-ray or computed tomography-scan N\u0026thinsp;=\u0026thinsp;576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eViral and bacterial infections\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny viral infection N\u0026thinsp;=\u0026thinsp;135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (62.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny bacterial infection N\u0026thinsp;=\u0026thinsp;140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eArterial blood gas and oxygenation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH in arterial blood N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaCO2 in arterial blood (kPa) N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaO2 in arterial blood (kPa) N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygen saturation (%) in arterial blood N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO2, % N\u0026thinsp;=\u0026thinsp;638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 [93, 98]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFraction of inspired oxygen (FiO2, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 [20, 20]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO2/FiO2 N\u0026thinsp;=\u0026thinsp;638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e475 [460, 490]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaO2/FiO2 (mmHg) N\u0026thinsp;=\u0026thinsp;265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e399 \u0026plusmn; (141)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eClinical outcome and disease severity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNational Early Warning Score (NEWS) N\u0026thinsp;=\u0026thinsp;637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 [1, 5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e279 (43.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of hospital stay in admitted patients (days) N\u0026thinsp;=\u0026thinsp;264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 [2, 6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntensive care admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntubation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eThe total number of exacerbations used for analysis was 644, unless stated otherwise. Data is presented as proportion (percentage), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median [IQR]. BED\u0026thinsp;=\u0026thinsp;Beclomethasone Equivalent Dose, CRP\u0026thinsp;=\u0026thinsp;C-reactive protein, FiO2\u0026thinsp;=\u0026thinsp;Fraction of inspired oxygen, PaCO2\u0026thinsp;=\u0026thinsp;Partial pressure of carbon dioxide, PaO2\u0026thinsp;=\u0026thinsp;Partial pressure of Oxygen, SpO2\u0026thinsp;=\u0026thinsp;Saturation of Peripheral Oxygen.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCharacteristics associated with hospital admission\u003c/h3\u003e\n\u003cp\u003eFirst, we identified the characteristics of exacerbations that required hospital admission. A summary is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a complete overview is provided in eTable 6. Patients who were admitted had significantly more emergency department visits (p\u0026thinsp;=\u0026thinsp;.007) and hospital admissions (p\u0026thinsp;=\u0026thinsp;.001) in the 12 months prior to presentation. There were no significant differences in differential leukocyte counts, nor in CRP levels between the two groups. The exacerbations that required admission were characterized by more severe obstruction, as indicated by the lower percentage of predicted peak expiratory flow as assessed by a peak flow meter (p\u0026thinsp;=\u0026thinsp;.042). Their lung function, assessed outside of the exacerbation period, showed a significantly lower percentage of predicted FEV1 (p\u0026thinsp;=\u0026thinsp;.025) and FEV1/FVC (p\u0026thinsp;=\u0026thinsp;.001). A pulmonary infiltrate, detected either on a chest X-ray or CT-scan, was more often present in exacerbations that required admission (p\u0026thinsp;=\u0026thinsp;.001). However, admissions were not more often associated with viral or bacterial infections.\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\u003eStratification of patients by admission status.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot admitted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdmitted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass index (kg/m2), N\u0026thinsp;=\u0026thinsp;519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190 (68.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, N\u0026thinsp;=\u0026thinsp;626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197 (56.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (53.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePast smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllergy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAeroallergens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235 (68.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187 (69.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExacerbation characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of asthma exacerbations 12 months prior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of emergency department visits 12 months prior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of hospital admissions 12 months prior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime since last hospital admission (days), N\u0026thinsp;=\u0026thinsp;146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 [73, 245]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 [31, 191]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedication use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOral steroid maintenance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong-Acting Muscarinic Antagonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood counts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg/l), N\u0026thinsp;=\u0026thinsp;574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.40 [1.85, 13.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.50 [2.75, 18.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil (x10^9/L), N\u0026thinsp;=\u0026thinsp;558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.17 [0.07, 0.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14 [0.02, 0.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil (x 10^9/L), N\u0026thinsp;=\u0026thinsp;545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.74 (3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.32 (3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte (x 10^9/L), N\u0026thinsp;=\u0026thinsp;585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.94 (3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.10 [7.43, 12.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung function\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak Expiratory Flow (% of predicted), N\u0026thinsp;=\u0026thinsp;140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.9\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1% of predicted, N\u0026thinsp;=\u0026thinsp;526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC index, N\u0026thinsp;=\u0026thinsp;528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfiltrate on X-ray or computed tomography-scan (%) N\u0026thinsp;=\u0026thinsp;576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny virus, N\u0026thinsp;=\u0026thinsp;135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (61.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (62.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParainfluenza, N\u0026thinsp;=\u0026thinsp;135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny bacteria, N\u0026thinsp;=\u0026thinsp;141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical outcomes and disease severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO2 (pulse oximetry), N\u0026thinsp;=\u0026thinsp;638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.0 [94.0, 98.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.0 [92.0, 97.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH, N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.45 [7.43, 7.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.45 [7.42, 7.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaO2 (kPa), N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.8 [9.4, 12.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.9 [8.7, 13.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaCO2 (kPa), N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.6 [4.1, 5.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.6 [4.1, 5.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygen saturation (%), N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.0 [93.4, 96.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.4 [91.8, 96.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercapnia, N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFiO2, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.2 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.1 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNational Early Warning Score (NEWS), N\u0026thinsp;=\u0026thinsp;622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 [1, 4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 [2, 6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO2/FiO2, N\u0026thinsp;=\u0026thinsp;638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e476.47 (25.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e434.07 (82.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eThe total number of exacerbations used for analysis was N\u0026thinsp;=\u0026thinsp;644, unless stated otherwise. Data is presented as proportion (percentage), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median [IQR]. FEV1\u0026thinsp;=\u0026thinsp;Forced Expiratory Volume in 1 second, FVC\u0026thinsp;=\u0026thinsp;Forced Vital Capacity, PaO2\u0026thinsp;=\u0026thinsp;Partial pressure of Oxygen, SpO2\u0026thinsp;=\u0026thinsp;Saturation of Peripheral Oxygen.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eCharacteristics associated with intensive care admission\u003c/h3\u003e\n\u003cp\u003eSecond, we examined the characteristics of exacerbations that required admission to the ICU (eTable 7), the variables that were statistically significant are provided in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Exacerbations that led to ICU admission were characterized by higher blood neutrophil (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and leukocyte counts (p\u0026thinsp;=\u0026thinsp;0.004). These patients experienced a shorter duration of symptoms before they presented to the emergency department (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A rhinovirus infection occurred more often in patients who were admitted to the ICU (20% versus 60%, p\u0026thinsp;=\u0026thinsp;0.002), the incidence of bacterial infections was not different. ICU admissions were characterized by lower SpO2 (p\u0026thinsp;=\u0026thinsp;0.001), lower pH in arterial blood gas (p\u0026thinsp;=\u0026thinsp;0.001), higher FiO2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher PaCO2 (p\u0026thinsp;=\u0026thinsp;0.007) and higher NEWS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and eTable 7).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStratification of patients by admission to the intensive care unit\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot admitted to ICU\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;615\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdmitted to ICU\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTheophylline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExacerbation characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of symptoms (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.00 [2.00, 14.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 [0.00, 3.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (31.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (27.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-specific trigger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRhinovirus infections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood counts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil (x 10^9/L), N\u0026thinsp;=\u0026thinsp;545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.20 [4.47, 8.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.94 [6.52, 12.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte (x 10^9/L), N\u0026thinsp;=\u0026thinsp;585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.60 [7.50, 12.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.30 [9.20, 16.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical outcomes and disease severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO2, N\u0026thinsp;=\u0026thinsp;638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.00 [93.00, 98.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.00 [89.00, 97.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFiO2 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.00 [20.00, 20.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.00 [20.00, 32.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO2/FiO2, N\u0026thinsp;=\u0026thinsp;638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e462.97\u0026thinsp;\u0026plusmn;\u0026thinsp;51.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e360.50\u0026thinsp;\u0026plusmn;\u0026thinsp;132.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.45 [7.43, 7.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.41 [7.36, 7.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercapnia, N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaCO2 (kPa), N\u0026thinsp;=\u0026thinsp;379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.60 [4.10, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.10 [4.40, 6.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEWS, N\u0026thinsp;=\u0026thinsp;622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 [1.00, 5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.00 [4.75, 8.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eThe total number of exacerbations used for analysis was N\u0026thinsp;=\u0026thinsp;644, unless stated otherwise. Data is presented as proportion (percentage), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median [IQR]. FiO2\u0026thinsp;=\u0026thinsp;Fraction of inspired oxygen, ICU\u0026thinsp;=\u0026thinsp;Intensive Care Unit, NEWS\u0026thinsp;=\u0026thinsp;National Early Warning Score, PaCO2\u0026thinsp;=\u0026thinsp;Partial pressure of carbon dioxide, SpO2\u0026thinsp;=\u0026thinsp;Saturation of Peripheral Oxygen\u003c/b\u003e,\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAssociations with five exacerbation severity outcomes\u003c/h2\u003e\u003cp\u003eThird, we examined the associations between patient and exacerbation characteristics and five key outcomes indicative of exacerbation severity: hospital admission, length of hospital stay, admission to the intensive care unit (ICU), NEWS score, and the SpO2/FiO2 ratio (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast with the previous regression models, these models accounted for within-patient variability. Due to the wide range of viral and bacterial infections, only those with significant associations with one or more outcomes are reported. Comorbidities, seasonal categories, biological factors, ethnicity, and exacerbation history\u0026mdash;including emergency presentations, hospitalizations, and steroid courses within the prior 12 months\u0026mdash;are not presented here as none demonstrated significant associations. Full details are provided in eTable 8.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between study characteristics and disease severity and clinical outcomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAdmission\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eIntensive care admission\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eLength of hospital stay\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eOxygenation efficiency (SpO2/FiO2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eNEWS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjusted p-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted p-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEstimate (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjusted p-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEstimate (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdjusted p-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eEstimate (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAdjusted p-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003ePatient characteristics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.18 (0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePast smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.069 (0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008 (0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.59 (0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-12.14 (7.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.51 (0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.017 (0.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009 (0.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.36 (0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-27.36 (7.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.54 (0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004 (0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003 (0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.02 (0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.059 (0.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002 (0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.40 (0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.05 (5.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.21 (0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllergic sensitization to aeroallergens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005 (0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.018 (0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.78 (0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.03 (6.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.25 (0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllergic sensitization to Aspergillus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.140 (0.077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.067 (0.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.77 (0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.10 (10.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.28 (0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eExacerbation characteristics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeak flow % of predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004 (0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.31 (0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.03 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of symptoms (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.01 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlu-like symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.022 (0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002 (0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.11 (4.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.42 (0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-specific trigger exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003 (0.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.067 (0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.19 (0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.07 (8.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.45 (0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllergen exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.077 (0.072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.020 (0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.79 (0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.88 (8.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.55 (0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfiltrate on X-ray or CT -scan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.235 (0.066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030 (0.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.51 (0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-17.31 (8.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.50 (0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eLung function\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1% of predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.02 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.55 (0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.02 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFVC % of predicted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.31 (0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.02 (0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.076\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\u003e-0.503 (0.181)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.069 (0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.20 (1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e103.50 (23.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-3.63 (0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eUse of maintenance medication\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICS dosage in mcg BED x 1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000 (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing theophylline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.220 (0.143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.275 (0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05 (0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-41.46 (18.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.76 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing inhaled LAMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.124 (0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.027 (0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.07 (0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.07 (6.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.48 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing LTRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004 (0.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.011 (0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.08 (0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.36 (7.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.82 (0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing oral steroids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.142 (0.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007 (0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.71 (0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.24 (6.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.19 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing a biological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.075 (0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.041 (0.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.25 (0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.35 (8.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.06 (0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eBlood values\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte blood count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.010 (0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009 (0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.71 (0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.12 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil blood count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.035 (0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003 (0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.02 (0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-6.44 (7.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.28 (0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil blood count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013 (0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013 (0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.63 (0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.15 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (CRP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002 (0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000 (0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.03 (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.01 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eInfections\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny viral infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.019 (0.086)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021 (0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.24 (0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-5.03 (11.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.28 (0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRhinovirus infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.079 (0.097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.191 (0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11 (0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-6.80 (13.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.44 (0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnterovirus infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.368 (0.344)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.645 (0.185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.04 (3.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.02 (45.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-2.56 (1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny bacterial infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.061 (0.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.036 (0.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.11 (17.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.69 (0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudomonas spp infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.359 (0.319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.108 (0.227)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.46 (2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-25.40 (52.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.28 (1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eBED\u0026thinsp;=\u0026thinsp;Beclomethasone Equivalent Dose, CT\u0026thinsp;=\u0026thinsp;Computed Tomography, FiO2\u0026thinsp;=\u0026thinsp;Fraction of inspired oxygen, FEV1\u0026thinsp;=\u0026thinsp;Forced Expiratory Volume in 1 second, FVC\u0026thinsp;=\u0026thinsp;Forced Vital Capacity, ICS\u0026thinsp;=\u0026thinsp;Inhalation Corticosteroid, LAMA\u0026thinsp;=\u0026thinsp;Long-Acting Muscarinic Antagonist, LTRA\u0026thinsp;=\u0026thinsp;Leukotriene Receptor Antagonists, NEWS\u0026thinsp;=\u0026thinsp;National Early Warning Score, SpO2\u0026thinsp;=\u0026thinsp;Saturation of Peripheral Oxygen.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePredictive Modelling\u003c/h2\u003e\u003cp\u003eThe presence of an infiltrate on a chest X-ray or CT-scan was associated with higher NEWS scores, more frequent hospital admissions, and longer hospital stays (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Worse pulmonary function, specifically a lower FEV1 and FEV1/FVC, was associated with higher NEWS and worse oxygenation efficiency. CRP levels were positively associated with NEWS and hospital admissions. Blood neutrophil count and total leukocyte count were associated with higher NEWS and ICU admission although blood eosinophil counts showed no significant association with any outcomes. Overall, the presence of viral or bacterial infections was not associated with the studied outcomes. However, rhinovirus and enterovirus infections were specifically linked to ICU admission, and pseudomonas infection or colonization was associated with prolonged hospital stays (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eLastly, we aimed to identify parameters that could predict our clinical outcomes of interest. To achieve this, we applied LASSO regression to select the most important predictors from the linear mixed models. A detailed overview of the variables selected by each model, along with their test characteristics, is provided in eTables S9\u0026ndash;S13. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e contains the variables that achieved statistical significance. The presence of an infiltrate on chest X-ray or CT scan emerged as an independent and significant predictor for all outcomes except ICU admission, for which a trend with p\u0026thinsp;=\u0026thinsp;0.050 was observed (eTable 10). Blood neutrophil count was selected for the prediction of ICU admission and oxygenation efficiency, whilst eosinophil count only emerged as a significant predictor for oxygenation efficiency. CRP levels were a significant predictor for hospital admission only.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eP-values of the variables selected in the LASSO models to predict the severity and outcomes of asthma exacerbations.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehospital admission\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICU admission\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNEWS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOxygenation efficiency (SpO2/FiO2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLength of hospital stay\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfiltrate on X-ray or CT-scan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (CRP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing oral corticosteroids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing theophylline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil blood count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-specific trigger exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsing leukotriene receptor antagonists\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil blood count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage of inhalation corticosteroids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eCT\u0026thinsp;=\u0026thinsp;Computed Tomography, FiO2\u0026thinsp;=\u0026thinsp;Fraction of inspired oxygen, ICU\u0026thinsp;=\u0026thinsp;Intensive Care Unit, NEWS\u0026thinsp;=\u0026thinsp;National Early Warning Score, ns\u0026thinsp;=\u0026thinsp;non-significant, SpO2\u0026thinsp;=\u0026thinsp;Saturation of Peripheral Oxygen.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor the two binary outcomes, hospital admission and ICU admission, receiver operating characteristic (ROC) curves were generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In a train cohort comprising 80% of randomly selected patients, the model yielded an area under the curve (AUC) of 0.692 for predicting hospital admission (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). On the hold-out validation cohort, the model achieved an AUC of 0.632. Although the AUC for predicting ICU admission was higher at 0.917 in the training cohort, the performance on the test cohort was substantially lower with an AUC of 0.695 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The proportion of variance explained by the selected variables in the test cohort, the fixed effects (R\u0026sup2;m), was 18.8% for NEWS, 15.2% for oxygenation efficiency and 9.0% for length of hospital stay. The proportion of variance explained by both fixed and random (patient-specific variability) effects (R\u0026sup2;c) was 36.3% for NEWS, 39.5% for oxygenation efficiency and 36.5% for length of hospital stay.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective study of adults presenting with asthma exacerbations to an emergency department of a large university hospital aimed to identify clinical predictors of exacerbation severity. While numerous studies have explored risk factors for developing subsequent exacerbations, research specifically focused on predictors of exacerbation severity remains limited\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Understanding these severity predictors is essential to enhance tailored clinical decision-making and optimize the allocation of healthcare resources. We determined the strongest predictors and their predictive value using cross-validated machine learning models applied to high-dimensional, real-world data. Our findings show that impaired baseline lung function and markers of acute infection-specifically, radiographic infiltrate, elevated CRP, and increased blood neutrophils-were most frequently associated with severe exacerbations. In contrast, factors such as blood eosinophil count, age, comorbidities, symptom duration, and exacerbation history were not significant predictors. Notably, our predictive models demonstrated only moderate accuracy. These findings highlight the complex interplay of factors influencing asthma exacerbation severity and the need to identify additional (bio)markers and patient-specific traits to improve risk stratification.\u003c/p\u003e\u003cp\u003eIn our study, exacerbations requiring hospital admission frequently occurred in patients with a history of prior exacerbations, emergency department visits, or hospitalizations within the preceding 12 months. These findings align with previous studies identifying prior exacerbations as one of the strongest risk factors for recurrent exacerbations, often referred to as the \u0026lsquo;frequent exacerbator phenotype\u0026rsquo;\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, in our study, these features were not associated with more severe exacerbations or worse clinical outcomes. These findings suggest that prior exacerbations, emergency visits, and hospitalizations are not independent risk factors for exacerbation severity, but instead reflect a distinct phenotype of patients prone to recurrent exacerbations or poor asthma control\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBlood eosinophil count at hospital presentation was not associated with exacerbation severity in our study. Eosinophilic inflammation, with blood eosinophilia as a surrogate marker, is a well-established risk factor for future severe exacerbations and hospitalizations\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and even for the severity of concurrent exacerbations \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. While this could be due to the high proportion of patients using inhaled corticosteroids (75.5%) or oral maintenance steroids (24.7%), our findings suggests that type 2 (T2) inflammation may not play a dominant role during exacerbations. This discrepancy may reflect the evolving understanding of asthma phenotypes, where neutrophilic and non\u0026ndash;type 2 inflammatory pathways increasingly appear to drive severe exacerbations. This aligns with a previous study demonstrating that the degree of T2 inflammation at the start of a 48-week follow-up, assessed by FeNO or blood eosinophil count, was not predictive of T2 inflammation during exacerbations\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In contrast, we found that blood neutrophil count, CRP levels and the presence of an infiltrate on chest radiography were among the most important predictors of exacerbation severity. Neutrophilic inflammation, in both blood and tissue, is frequently associated with asthma exacerbations triggered by viral infections and has been linked to exacerbation severity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These observations collectively suggest that pathogen-driven and non-eosinophilic pathways, such as inflammasome activation, interferon response, or Th-17 pathway upregulation play a more prominent role in determining exacerbation severity\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Exploring these mechanisms, could help develop targeted interventions.\u003c/p\u003e\u003cp\u003eDue to the retrospective design of our study, specific inflammatory pathways were not directly investigated. Furthermore, microbiological testing, including throat swabs and sputum cultures, was performed only in a subset of exacerbations. Nevertheless, we observed that rhinovirus and enterovirus infections were associated with ICU admissions. While rhinovirus infections typically cause mild symptoms in healthy individuals, it is known to induce more severe and prolonged episodes of airway dysfunction in asthma patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The role of enteroviruses in adult asthma exacerbations is less well characterized, but similar to rhinoviruses, they belong to the \u003cem\u003ePicornaviridae\u003c/em\u003e family and have been linked to near-fatal asthma episodes\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. These findings underscore the importance of further investigating non-eosinophilic pathways and viral triggers in asthma exacerbations.\u003c/p\u003e\u003cp\u003eTheophylline maintenance use was associated with increased exacerbation severity, including higher NEWS and ICU admission, and emerged as a key predictor of these measures and oxygenation efficiency. Patients on theophylline maintenance exhibited significantly higher NEWS (median 8, IQR 4\u0026ndash;9 vs. 3, IQR 1\u0026ndash;5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), worse oxygenation efficiency (median SpO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e 460, IQR 362\u0026ndash;480 vs. 475, IQR 460\u0026ndash;490, p\u0026thinsp;=\u0026thinsp;0.033), higher ICU admission rates (35.7% vs. 3.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and greater need for mechanical ventilation (21.4% vs. 0.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to non-users. However, despite a higher body mass index (BMI; 33.2 kg/m\u0026sup2; vs. 29.2 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.023), the two groups were comparable in demographics, pulmonary function, and comorbidities. A review of the medical records revealed no evidence of cardiovascular, metabolic, or neurologic toxicity linked to theophylline. While theoretical concerns exist about theophylline causing muscle fatigue or impaired oxygenation efficiency due to enhanced respiratory drive and subtle ventilation-perfusion mismatch, these effects have not been consistently observed in clinical studies\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Therefore, it is most likely that theophylline users represent a subgroup with more severe or refractory disease.\u003c/p\u003e\u003cp\u003eThe key strength of this study comprises the use of high-dimensional data from a larger cohort of asthma patients in a hospital care setting. Moreover, the associations between a wide range of clinical factors and five key outcomes related to asthma exacerbation severity were systematically investigated. Another notable strength is the use of advanced prediction modelling, incorporating both linear mixed models and LASSO regression. This dual approach not only identified independent factors associated with exacerbation severity but also leveraged a machine learning technique to select the most effective predictors. LASSO regression in particular enables efficient identification of key clinical predictors while minimizing overfitting, thereby enhancing the reliability and interpretability of our predictive models. A final major strength is the inclusion of multiple presentations per patient this allowed us to capture within-patient variability and better reflect the real-world complexity of asthma exacerbations. Despite variations in the course of consecutive exacerbations between individuals, patient-specific factors demonstrated a stronger predictive value for NEWS, oxygenation efficiency, and length of hospital stay than all other selected variables combined. This is demonstrated by the substantial differences between the marginal R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, which reflects the variability explained by fixed predictors, and the conditional R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, which accounts for both fixed predictors and patient-specific random effects. These findings highlight the significance of patient-specific variability and support the inclusion of multiple study visits per patient in future research designs.\u003c/p\u003e\u003cp\u003eThis study suffers from a few limitations. Foremost, is the retrospective design which resulted in missing data for certain variables, which prevented their inclusion in the prediction models. Consequently, the predictive value of blood gas analyses, peak flow measurements during exacerbations, treatment adherence, exacerbation history, and detailed microbiological data could not be fully assessed. Notably, the missing data were not entirely random and were more common in cases of lower disease severity, potentially introducing bias. Additionally, the retrospective nature of the study limited the availability of potentially important data, particularly regarding inflammatory pathways and dynamic changes in biomarkers, which could have provided further mechanistic insights into exacerbation severity. We chose to limit data collection to the period before the national onset of the first wave of the COVID-19 pandemic to avoid potential confounding factors that could influence asthma exacerbation trends. However, this approach, combined with temporal shifts in treatment patterns, such as the increased adoption of biologic therapies (with 9.9% of patients already receiving biologics at the time of exacerbation) and the decreasing use of medications like theophylline, may impact the generalizability of our findings to contemporary clinical practice. These limitations should be considered when interpreting the study's findings.\u003c/p\u003e\u003cp\u003eWhile our models incorporated a large number of variables, their predictive performance was moderate to poor, with a relatively low percentage of variability explained. The limited predictive accuracy for ICU admission may, in part, reflect the small number of ICU admissions in the dataset. Nevertheless, our findings do not support the routine clinical application of these prediction models. Instead, they underscore that the most important predictors of asthma exacerbation severity remain unidentified. We recommend that future research incorporate longitudinal monitoring of lung function and multi-compartment biomarker or omics-based approaches to better assess the dynamic nature of airway inflammation. Combining these data with advanced machine learning techniques could provide deeper insights into the complex mechanisms underlying exacerbation severity and ultimately lead to more personalized and effective management strategies for patients with asthma exacerbations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe presence of an infiltrate on chest radiography, pre-existing impaired lung function, elevated CRP levels and increased blood neutrophil count are the most consistent variables associated with exacerbation severity. However, despite using advanced machine learning, the predictive value of clinical and demographic parameters is limited, indicating the need to identify additional biomarkers or patient-specific traits to further improve tailored exacerbation management strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC = Area Under the Curve\u003c/p\u003e\n\u003cp\u003eBED = Beclomethasone dipropionate Equivalent Dosage\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI = Body Mass Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOPD = Chronic Obstructive Pulmonary Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP = C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRSwNP = Chronic rhinosinusitis with nasal polyps\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRSsNP = Chronic rhinosinusitis without nasal polyps\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT = Computed Tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eED = Emergency Department\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDR = False Discovery Rate\u003c/p\u003e\n\u003cp\u003eFeNO = Fractional exhaled Nitric Oxide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFEV1= Forced Expiratory Volume in 1 second\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFiO2 = Fraction of inspired oxygen\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFVC = Forced Vital Capacity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGERD = Gastroesophageal Reflux Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGINA = Global Initiative for Asthma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHMPV = Human Metapneumovirus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICD = International Classification of Diseases\u003c/p\u003e\n\u003cp\u003eICS = Inhalation Corticosteroids\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICU = Intensive Care Unit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR = Interquartile Range\u003c/p\u003e\n\u003cp\u003eLAMA = Long-Acting Muscarinic Antagonist\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLASSO = Least Absolute Shrinkage and Selection operator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLMM = Linear Mixed Models\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLTRA = leukotriene receptor antagonist\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMICE = Multivariate Imputation by Chained Equations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNEWS = National Early Warning Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNRM = non-rebreathing mask\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOCS = Oral Corticosteroid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOSAS = Obstructive Sleep Apnea Syndrome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePaCO2= partial pressure of carbon dioxide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePaO2= partial pressure of oxygen\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePEF = Peak Expiratory Flow\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC = Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eRSV = Respiratory Syncytial Virus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpO2 = peripheral oxygen saturation\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for study conduction was obtained from the ethical committee of the hospital\u0026apos;s clinical research unit. Written informed consent was waived by the ethical committee.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003ePseudonymized patient data used in this study are available from the corresponding author ([email protected]) for other researchers when reuse conditions are met.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted independently, without external funding or sponsorship\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026rsquo; contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eE.D. is the guarantor of this manuscript and takes responsibility for the integrity of the data and accuracy of the data analyses. E.D, M.A. and E.W. designed the study. E.D., J.H and J.G. conducted the statistical analysis. All authors, E.D., J.G., H.C., J.H., B.H., A.S., M.A. and E.W. have contributed to the interpretation of the results, provided critical feedback and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cu\u003e\u003cbr\u003e\u003c/u\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsthma GIf. Global Initiative for Asthma (GINA) Report: Global Strategy for Asthma Management and Prevention 2024 [Available from: https://ginasthma.org/2024-report/.\u003c/li\u003e\n\u003cli\u003eMatsunaga K, Hirano T, Oka A, Tanaka A, Kanai K, Kikuchi T, et al. Progression of Irreversible Airflow Limitation in Asthma: Correlation with Severe Exacerbations. J Allergy Clin Immunol Pract. 2015;3(5):759-64 e1.\u003c/li\u003e\n\u003cli\u003eLuskin AT, Chipps BE, Rasouliyan L, Miller DP, Haselkorn T, Dorenbaum A. Impact of asthma exacerbations and asthma triggers on asthma-related quality of life in patients with severe or difficult-to-treat asthma. J Allergy Clin Immunol Pract. 2014;2(5):544-52 e1-2.\u003c/li\u003e\n\u003cli\u003eSoler M, Matz J, Townley R, Buhl R, O\u0026apos;Brien J, Fox H, et al. The anti-IgE antibody omalizumab reduces exacerbations and steroid requirement in allergic asthmatics. Eur Respir J. 2001;18(2):254-61.\u003c/li\u003e\n\u003cli\u003eCastro M, Corren J, Pavord ID, Maspero J, Wenzel S, Rabe KF, et al. Dupilumab Efficacy and Safety in Moderate-to-Severe Uncontrolled Asthma. N Engl J Med. 2018;378(26):2486-96.\u003c/li\u003e\n\u003cli\u003ePrincipe S, Porsbjerg C, Bolm Ditlev S, Kjaersgaard Klein D, Golebski K, Dyhre-Petersen N, et al. Treating severe asthma: Targeting the IL-5 pathway. Clin Exp Allergy. 2021;51(8):992-1005.\u003c/li\u003e\n\u003cli\u003eCastro M, Zangrilli J, Wechsler ME, Bateman ED, Brusselle GG, Bardin P, et al. Reslizumab for inadequately controlled asthma with elevated blood eosinophil counts: results from two multicentre, parallel, double-blind, randomised, placebo-controlled, phase 3 trials. Lancet Respir Med. 2015;3(5):355-66.\u003c/li\u003e\n\u003cli\u003eBoomer J, Choi J, Alsup A, McGregor MC, Lieu J, Johnson C, et al. Increased Muc5AC and Decreased Ciliated Cells in Severe Asthma Partially Restored by Inhibition of IL-4Ralpha Receptor. Am J Respir Crit Care Med. 2024.\u003c/li\u003e\n\u003cli\u003eKardas G, Kuna P, Panek M. Biological Therapies of Severe Asthma and Their Possible Effects on Airway Remodeling. Front Immunol. 2020;11:1134.\u003c/li\u003e\n\u003cli\u003eHasegawa K, Craig SS, Teach SJ, Camargo CA, Jr. Management of Asthma Exacerbations in the Emergency Department. J Allergy Clin Immunol Pract. 2021;9(7):2599-610.\u003c/li\u003e\n\u003cli\u003eCamargo CA, Jr., Rachelefsky G, Schatz M. Managing asthma exacerbations in the emergency department: summary of the National Asthma Education and Prevention Program Expert Panel Report 3 guidelines for the management of asthma exacerbations. J Allergy Clin Immunol. 2009;124(2 Suppl):S5-14.\u003c/li\u003e\n\u003cli\u003eMartin MJ, Beasley R, Harrison TW. Towards a personalised treatment approach for asthma attacks. Thorax. 2020;75(12):1119-29.\u003c/li\u003e\n\u003cli\u003eRamakrishnan S, Russell REK, Mahmood HR, Krassowska K, Melhorn J, Mwasuku C, et al. Treating eosinophilic exacerbations of asthma and COPD with benralizumab (ABRA): a double-blind, double-dummy, active placebo-controlled randomised trial. Lancet Respir Med. 2025;13(1):59-68.\u003c/li\u003e\n\u003cli\u003eBloom CI, Palmer T, Feary J, Quint JK, Cullinan P. Exacerbation Patterns in Adults with Asthma in England. A Population-based Study. Am J Respir Crit Care Med. 2019;199(4):446-53.\u003c/li\u003e\n\u003cli\u003eDiMango E, Rogers L, Reibman J, Gerald LB, Brown M, Sugar EA, et al. Risk Factors for Asthma Exacerbation and Treatment Failure in Adults and Adolescents with Well-controlled Asthma during Continuation and Step-Down Therapy. Ann Am Thorac Soc. 2018;15(8):955-61.\u003c/li\u003e\n\u003cli\u003eten Brinke A, Sterk PJ, Masclee AA, Spinhoven P, Schmidt JT, Zwinderman AH, et al. Risk factors of frequent exacerbations in difficult-to-treat asthma. Eur Respir J. 2005;26(5):812-8.\u003c/li\u003e\n\u003cli\u003eWesterhof GA, de Groot JC, Amelink M, de Nijs SB, Ten Brinke A, Weersink EJ, et al. Predictors of frequent exacerbations in (ex)smoking and never smoking adults with severe asthma. Respir Med. 2016;118:122-7.\u003c/li\u003e\n\u003cli\u003eYang F, Busby J, Heaney LG, Menzies-Gow A, Pfeffer PE, Jackson DJ, et al. Factors Associated with Frequent Exacerbations in the UK Severe Asthma Registry. J Allergy Clin Immunol Pract. 2021;9(7):2691-701 e1.\u003c/li\u003e\n\u003cli\u003eZeiger RS, Schatz M, Li Q, Chen W, Khatry DB, Gossage D, et al. High blood eosinophil count is a risk factor for future asthma exacerbations in adult persistent asthma. J Allergy Clin Immunol Pract. 2014;2(6):741-50.\u003c/li\u003e\n\u003cli\u003eGroll A, Tutz G. Variable selection for generalized linear mixed models by L1-penalized estimation. Statistics and Computing. 2014;24(2):137-54.\u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1):1 - 48.\u003c/li\u003e\n\u003cli\u003eFuhlbrigge A, Peden D, Apter AJ, Boushey HA, Camargo CA, Jr., Gern J, et al. Asthma outcomes: exacerbations. J Allergy Clin Immunol. 2012;129(3 Suppl):S34-48.\u003c/li\u003e\n\u003cli\u003eKupczyk M, ten Brinke A, Sterk PJ, Bel EH, Papi A, Chanez P, et al. Frequent exacerbators--a distinct phenotype of severe asthma. Clin Exp Allergy. 2014;44(2):212-21.\u003c/li\u003e\n\u003cli\u003eMiller MK, Lee JH, Miller DP, Wenzel SE, Group TS. Recent asthma exacerbations: a key predictor of future exacerbations. Respir Med. 2007;101(3):481-9.\u003c/li\u003e\n\u003cli\u003eOguzulgen IK, Turktas H, Mullaoglu S, Ozkan S. What can predict the exacerbation severity in asthma? Allergy Asthma Proc. 2007;28(3):344-7.\u003c/li\u003e\n\u003cli\u003ePrice DB, Rigazio A, Campbell JD, Bleecker ER, Corrigan CJ, Thomas M, et al. Blood eosinophil count and prospective annual asthma disease burden: a UK cohort study. Lancet Respir Med. 2015;3(11):849-58.\u003c/li\u003e\n\u003cli\u003eVedel-Krogh S, Fallgaard Nielsen S, Lange P, Vestbo J, Nordestgaard BG. Association of Blood Eosinophil and Blood Neutrophil Counts with Asthma Exacerbations in the Copenhagen General Population Study. Clin Chem. 2017;63(4):823-32.\u003c/li\u003e\n\u003cli\u003eMcDowell PJ, Busby J, Hanratty CE, Djukanovic R, Woodcock A, Walker S, et al. Exacerbation Profile and Risk Factors in a Type-2-Low Enriched Severe Asthma Cohort: A Clinical Trial to Assess Asthma Exacerbation Phenotypes. Am J Respir Crit Care Med. 2022;206(5):545-53.\u003c/li\u003e\n\u003cli\u003eWark PA, Johnston SL, Moric I, Simpson JL, Hensley MJ, Gibson PG. Neutrophil degranulation and cell lysis is associated with clinical severity in virus-induced asthma. Eur Respir J. 2002;19(1):68-75.\u003c/li\u003e\n\u003cli\u003eRay A, Kolls JK. Neutrophilic Inflammation in Asthma and Association with Disease Severity. Trends Immunol. 2017;38(12):942-54.\u003c/li\u003e\n\u003cli\u003eLiu T, Woodruff PG, Zhou X. Advances in non-type 2 severe asthma: from molecular insights to novel treatment strategies. Eur Respir J. 2024;64(2).\u003c/li\u003e\n\u003cli\u003eJackson DJ, Gern JE. Rhinovirus Infections and Their Roles in Asthma: Etiology and Exacerbations. J Allergy Clin Immunol Pract. 2022;10(3):673-81.\u003c/li\u003e\n\u003cli\u003eCorne JM, Marshall C, Smith S, Schreiber J, Sanderson G, Holgate ST, et al. Frequency, severity, and duration of rhinovirus infections in asthmatic and non-asthmatic individuals: a longitudinal cohort study. Lancet. 2002;359(9309):831-4.\u003c/li\u003e\n\u003cli\u003eTan WC, Xiang X, Qiu D, Ng TP, Lam SF, Hegele RG. Epidemiology of respiratory viruses in patients hospitalized with near-fatal asthma, acute exacerbations of asthma, or chronic obstructive pulmonary disease. Am J Med. 2003;115(4):272-7.\u003c/li\u003e\n\u003cli\u003eMurciano D, Aubier M, Lecocguic Y, Pariente R. Effects of theophylline on diaphragmatic strength and fatigue in patients with chronic obstructive pulmonary disease. N Engl J Med. 1984;311(6):349-53.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Asthma, exacerbations, predictive modelling, machine learning, risk stratification, treatable traits","lastPublishedDoi":"10.21203/rs.3.rs-6664347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6664347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWhile predictors of asthma exacerbation risk are generally well established, predictors of exacerbation severity remain largely undefined. Identifying robust clinical predictors of exacerbation severity is essential to support tailored management strategies and optimize resource allocation. This study leverages machine learning to evaluate the predictive value of clinical factors for exacerbation severity in a real-world emergency department setting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort study was performed using medical records of 367 adults (644 exacerbations) who presented to the Amsterdam UMC emergency department between 2013 and 2020. Five severity outcomes were investigated: hospital admission, ICU admission, length of stay, oxygenation efficiency (SpO₂/FiO₂), and National Early Warning Score (NEWS). Associations were assessed using linear mixed models (LMM), and predictive modelling employed a machine learning approach combining LMMs with 5-fold cross-validated least absolute shrinkage and selection operator (LASSO) regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eExacerbation severity was most consistently associated with lung function, the presence of a radiographic chest infiltrate, C-reactive protein levels, blood neutrophil count and theophylline maintenance use. No significant associations were found for blood eosinophil count, age, comorbidities, symptom duration, triggers, allergic sensitization, ethnicity or exacerbation history within the preceding 12 months. Internally validated prediction models for hospital and intensive care admission achieved areas under the curve of 0.632 and 0.695, respectively. The strongest predictors explained 18.8% of variability in NEWS, 15.2% in oxygenation efficiency, and 9.0% in length of hospital stay. In these prediction models, a radiographic chest infiltrate, followed by theophylline maintenance use and blood neutrophil count, were most frequently associated across the five severity outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eLung function and markers of acute respiratory infection were mostly frequently associated with asthma exacerbation severity. However, clinical and demographic variables have only modest predictive value, highlighting the need to identify additional robust predictors.\u003c/p\u003e","manuscriptTitle":"Machine learning reveals limited predictive value of clinical factors for asthma exacerbations: insights from a real-world study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 10:21:42","doi":"10.21203/rs.3.rs-6664347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-31T18:23:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T12:04:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T11:35:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330120318503816400066201497687179925104","date":"2025-07-16T12:16:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275477604776596508668737238202633487145","date":"2025-07-11T06:19:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-08T17:00:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T16:55:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-29T11:00:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T09:58:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-14T12:39:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"059f7d7d-d087-4517-afa8-59759e9b29a8","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51299196,"name":"Health sciences/Diseases/Respiratory tract diseases"},{"id":51299197,"name":"Health sciences/Diseases/Respiratory tract diseases/Asthma"}],"tags":[],"updatedAt":"2025-10-13T15:58:59+00:00","versionOfRecord":{"articleIdentity":"rs-6664347","link":"https://doi.org/10.1038/s41598-025-19056-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-08 15:56:58","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2025-07-10 10:21:42","video":"","vorDoi":"10.1038/s41598-025-19056-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-19056-w","workflowStages":[]},"version":"v1","identity":"rs-6664347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6664347","identity":"rs-6664347","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}