Abstract
Background
Severe asthma is associated with poor asthma control, leading to poor outcomes. However, few studies have evaluated asthma control status in patients with severe asthma in the biologic era. This study aimed to evaluate baseline asthma control in patients with severe asthma using phase 2 of the Korean Severe Asthma Registry (KoSAR-2).
Methods
Adult subjects meeting the international consensus definition of severe asthma were prospectively enrolled from 41 hospitals in Korea (March 2022 to June 2023). Data on demographics, comorbidities, lung function, inflammation and healthcare utilisation were collected. Using asthma control test (ACT) scores, patients were classified into a well-controlled asthma group (ACT ≥20) and poorly controlled asthma group (ACT <20).
Results
Among 594 severe asthma patients, 51.9% had poorly controlled asthma. Multivariable analysis showed that medical aid (adjusted odds ratio (aOR)=5.515, 95% confidence interval (CI) 2.614–13.110, p<0.001), being underweight (aOR=4.129, 95% CI 1.081–20.192), forced expiratory volume in 1 s <60% predicted (aOR=1.854, 95% CI 1.090–3.175) and prior exacerbations (aOR=3.400, 95% CI 1.987–6.012) were associated with poorly controlled asthma, while T2 inflammation was associated with well-controlled asthma (aOR=0.560, 95% CI 0.316–0.977). However, biologic use was not associated with asthma control status.
Conclusions
About half of patients with severe asthma in KoSAR-2 had poorly controlled asthma at baseline. While poor socioeconomic status reflected by medical aid was a key factor underlying poor asthma control, T2 inflammation was associated with better asthma control in severe asthma. These findings underscore the importance of both clinical management and socioeconomic support to improve asthma control outcomes.
Shareable abstract
About half of patients with severe asthma in Korea had poor asthma control at baseline. Low socioeconomic status was linked to poor control, while T2 inflammation was associated with better control, highlighting the need for clinical and social support. https://bit.ly/4hiElPQ
Introduction
Severe asthma is defined as asthma that remains uncontrolled despite high levels of treatment or that requires intensive therapy to maintain good symptom control and reduce exacerbations [1]. It affects ∼5–10% of the total asthma population; however, its impacts on individual quality of life and public healthcare cost are significant compared to mild to moderate asthma [2]. In Korea, the prevalence of severe asthma has been reported to range from 1.6% to 6.1%, and healthcare utilisation and asthma-related costs are increasing more over time for those with severe asthma than those with non-severe asthma [3, 4]. Given the substantial burden of severe asthma, a better understanding of its epidemiology, disease mechanisms and the effectiveness of novel biologics is highly needed in clinical practice. As guidelines are evidence-based and rely on randomised controlled trial data that may not fully reflect real-world settings, each country has organised national registries for severe asthma. Registry data have provided insights into country-specific epidemiological characteristics, risk factors and treatment patterns, which are important for translational research as well as healthcare policy development [5].
In Korea, a national registry of severe asthma was established in 2011 by the Korean Academy of Asthma, Allergy and Clinical Immunology (KAAACI) Working Group on Severe Asthma and named the Korean Severe Asthma Registry (KoSAR). This registry was established to estimate the burden and treatment status of patients with severe asthma and to develop better strategies for management of this condition [6]. During the past decade, novel biologics targeting type 2 (T2) inflammation became available in clinical practice, and the concept of severe asthma evolved from an umbrella term to a heterogeneous clinical syndrome. Phase 2 of the Korean Severe Asthma Registry (KoSAR-2) was initiated in 2022 to assess longitudinal outcomes in patients with severe asthma in Korea [2, 7].
Asthma control is a core concept in asthma management and is considered a critical outcome as it reflects the effectiveness of current treatment regimens and provides insights into patients' daily functioning and well-being [5]. In addition, asthma control is an important predictor of future outcomes such as exacerbations, lung function decline, oral corticosteroid use and economic costs [8]. Although T2 biologics enable better asthma control and reduce the risk of exacerbations, decreased lung function and oral corticosteroid use, a significant number of patients on biologics still have uncontrolled severe asthma [9]. The Epidemiology and Natural History of Asthma: Outcomes and Treatment Regimens (TENOR) II cohort study reported 58.1% of subjects had very poorly controlled asthma, while only 7.7% of patients achieved well-controlled asthma, according to the National Heart, Lung and Blood Institute asthma guidelines [10]. A multinational, observational and real-world study of patients with severe asthma reported that 81% of severe asthmatic patients had uncontrolled asthma (based on an Asthma Control Questionnaire-5 (ACQ-5) score of ≥1.5) [11]. Previous studies have identified several variables associated with poor asthma control, including smoking, comorbidities such as psychological problems or obesity, and socioeconomic status [12, 13]. Thus, in this era of recent advances in biologics for asthma control, registry-based studies are valuable for identifying real-world determinants of asthma control and can improve severe asthma management strategies.
This study aims to describe the demographic and clinical characteristics of patients with severe asthma in Korea based on asthma control status and to identify factors associated with poor asthma control.
Material and methods
Study participants
We analysed baseline data from KoSAR-2, a nationwide, multicentre prospective registry initiated in 2022 with support from the Korea National Institute of Health. KoSAR-2 is an expansion of the original KoSAR, established in 2011, and was renamed to reflect updated data collection protocols, the inclusion of new clinical variables, and broader participation from tertiary and secondary hospitals. The registry collects a more comprehensive range of data, including patient-reported outcomes, biomarker profiles and detailed treatment information. Patients are followed longitudinally at regular intervals to assess their clinical characteristics and long-term outcomes in severe asthma [6]. Patients aged ≥18 years with a diagnosis of severe asthma according to the European Respiratory Society (ERS)/American Thoracic Society (ATS) criteria were eligible for enrolment in this registry (supplementary figure S1) [1]. Briefly, asthma patients who required treatment for Global Initiative for Asthma (GINA) steps 4–5 asthma or who used systemic corticosteroids for ≥50% of the previous year to prevent their asthma becoming “uncontrolled” or who had “uncontrolled” asthma despite this therapy were considered to have severe asthma. Uncontrolled asthma is referred to as: 1) an asthma control test (ACT) score <20 or uncontrolled state based on GINA guidelines, 2) frequent severe exacerbations (≥2 bursts of systemic corticosteroid for ≥3 days), 3) serious exacerbations such as at least one hospitalisation, intensive care unit stay or mechanical ventilation, and 4) persistent airflow limitations. Exclusion criteria included other respiratory diseases (e.g., severe bronchiectasis, tuberculosis-destroyed lung, interstitial lung disease), serious mental illness, major comorbidities (e.g., severe heart, renal or liver disease) and chronic oral corticosteroid use for non-asthma-related conditions. The study protocol was approved by the institutional review board (IRB) of each participating hospital, and informed consent was obtained from all study participants (IRB number: HALLYM 2022-04-020).
Data collection
Patients’ demographic information (age, sex, body mass index (BMI), smoking history), asthma history (symptom onset, asthma duration, asthma medications) and comorbidities (allergic rhinitis, allergic conjunctivitis, atopic dermatitis, chronic rhinosinusitis, osteoporosis, anxiety disorders) were collected using online structured questionnaires and items. The insurance type was categorised into national health insurance, medical aid type 1, medical aid type 2 and others according to the Korean National Health Insurance (NHI) System [14]. Laboratory parameters (complete blood cell counts with differential and total IgE), allergen sensitisation evaluation using skin-prick tests, the multiple allergen simultaneous test, or the immunoCAP assay for common inhalant allergens, and lung function (pre-bronchodilator forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC and forced expiratory flow (FEF) at 25–75% of FVC (FEF25–75)), and fraction of exhaled nitric oxide (FENO) were also collected. Lung function tests and FENO measurements were performed at each institution in accordance with the ATS/ERS guidelines [15, 16].
Asthma control questionnaires
Standardised questionnaires including the ACT, severe asthma questionnaire (SAQ), SAQ-visual analogue scale (VAS), and EuroQoL 5-Dimension 5-Level (EQ-5D-5L) were measured at baseline. The ACT assesses asthma control over the past 4 weeks using five items (score range: 5–25); scores ≥20 indicate well-controlled asthma, while those <20 indicate poor control [13, 17]. The SAQ consists of 16 items rated on a 7-point scale, averaged for a total score [18, 19]. The EQ-5D-5L consists of two parts: a descriptive system (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) and a visual analogue scale (EQ-VAS) [20]. The EQ-5D-5L includes a descriptive system (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) and a VAS. The index score ranges from <0 to 1 (1=perfect health), and the EQ-VAS from 0 to 100 (higher scores=better health). Symptom severity (cough, sputum, wheezing, throat discomfort, breathlessness) over the past week was assessed using self-reported VAS scores ranging from 0 to 100, with higher scores indicating greater severity [21].
Statistical analysis
Categorical variables are presented as counts and percentages, while continuous variables are presented as mean±SD or medians with interquartile ranges (Q1–Q3), as appropriate. Group differences were assessed using the χ2 test or Fisher's exact test for categorical variables, and the independent t-test or Mann–Whitney U-test for continuous variables, depending on normality. FEV1 (% pred), FENO, blood eosinophil count (BEC), sputum eosinophils and BMI were categorised based on clinically relevant thresholds. FEV1% was classified as <60% or 60–80%, reflecting the severity of airflow obstruction. Markers of T2 inflammation were categorised based on GINA guidelines: BEC ≥150 cells μL−1, FENO ≥20 ppb and sputum eosinophils ≥2% [22]. BMI was classified according to standard categories for the Asian population: underweight (<18.5 kg·m−2), normal weight (18.5–22.9 kg·m−2), overweight (23.0–24.9 kg·m−2) and obese (≥25.0 kg·m−2) [23].
Variables that showed statistically significant differences in demographic and clinical characteristics between groups based on an ACT score of 20 were included in univariable and multivariable logistic regression analyses to identify factors associated with poor asthma control. Variables with p<0.1 in univariable analysis were included in the multivariable model. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), and a two-sided p-value <0.05 was considered statistically significant.
Results
Baseline characteristics of the study subjects
A total of 594 patients were enrolled (table 1), with a mean age of 57.3±13.3 years and a female predominance (M:F=1:1.46). Of the participants, 60.9% were never-smokers, and the mean age of asthma onset was 41.1±16.5 years. Common comorbidities were allergic rhinitis (64.6%), chronic rhinosinusitis (33.2%), hypertension (31.2%) and gastroesophageal reflux (25.1%).
TABLE 1.
Comparison of baseline characteristics of severe asthmatic patients with control asthma versus those with uncontrolled asthma based on ACT score
| Total | Controlled status (ACT score ≥20) | Uncontrolled status (ACT score <20) | p-value | |
|---|---|---|---|---|
| Patients, n | 594 | 286 | 308 | |
| Age, years, mean±sd | 57.3±13.3 | 58±12.2 | 56.6±14.2 | 0.275 |
| Male sex, n (%) | 241 (40.6) | 126 (44.1) | 115 (37.3) | 0.096 |
| BMI, kg·m−2, mean±sd (n=593) | 25.1±4.3 | 24.8±3.7 | 25.5±4.8 | 0.129 |
| <18.5, n (%) | 15 (2.5) | 3 (1.1) | 12 (3.9) | 0.027 |
| 18.5 to 23, n (%) | 177 (29.9) | 93 (32.5) | 84 (27.4) | |
| 23 to 25, n (%) | 141 (23.8) | 75 (26.2) | 66 (21.5) | |
| ≥25, n (%) | 260 (43.8) | 115 (40.2) | 145 (47.2) | |
| Smoking history, n (%) | 0.620 | |||
| Never-smoker | 362 (60.9) | 170 (59.3) | 192 (62.3) | |
| Ex-smoker | 186 (31.3) | 95 (33.2) | 91 (29.6) | |
| Current smoker | 46 (7.8) | 21 (7.3) | 25 (8.1) | |
| Asthma history | ||||
| Age at symptom onset, years, mean±sd | 41.1±16.5 | 41.6±15.6 | 40.7±17.4 | 0.554 |
| Age at asthma diagnosis, years, mean±sd | 43.4±15.6 | 43.4±15 | 43.3±16.1 | 0.767 |
| Duration of diagnosis, years, mean±sd | 13.9±11.2 | 14.6±11.4 | 13.3±10.9 | 0.100 |
| Presence of unscheduled visit in the previous year, n (%) | 96 (16.2) | 25 (8.7) | 71 (23.1) | <0.001 |
| Emergency department visits | 63 (10.6) | 17 (5.9) | 46 (15.0) | <0.001 |
| Hospitalisation | 69 (11.6) | 18 (6.3) | 51 (16.6) | <0.001 |
| Intensive care unit admission | 6 (1.0) | 1 (0.4) | 5 (1.6) | 0.218 |
| Comorbidities, n (%) | ||||
| Allergic rhinitis | 381 (64.6) | 187 (65.6) | 194 (63.6) | 0.610 |
| Chronic sinusitis | 196 (33.2) | 100 (35.1) | 96 (31.5) | 0.352 |
| Hypertension | 184 (31.2) | 82 (28.8) | 102 (33.4) | 0.221 |
| Gastroesophageal reflux | 148 (25.1) | 64 (22.5) | 84 (27.5) | 0.155 |
| COPD | 81 (13.7) | 41 (14.4) | 40 (13.1) | 0.654 |
| Osteoporosis | 71 (12.0) | 40 (14.0) | 31 (10.2) | 0.149 |
| Depression or anxiety disorder | 50 (8.5) | 17 (6.0) | 33 (10.8) | 0.034 |
| Allergic conjunctivitis | 45 (7.6) | 19 (6.7) | 26 (8.5) | 0.396 |
| Atopic dermatitis | 42 (7.1) | 17 (6.0) | 25 (8.2) | 0.292 |
| History of pulmonary tuberculosis | 38 (6.4) | 12 (4.2) | 26 (8.5) | 0.033 |
| Arrhythmia | 19 (3.2) | 9 (3.2) | 10 (3.3) | 0.934 |
| Sleep apnoea | 19 (3.2) | 7 (2.5) | 12 (3.9) | 0.309 |
| Heart failure | 8 (1.4) | 4 (1.4) | 4 (1.3) | >0.999 |
| Insurance type, n (%) | <0.001 | |||
| National health | 525 (88.4) | 276 (96.4) | 249 (80.8) | |
| Medical aid type 1 | 43 (7.2) | 5 (1.8) | 38 (12.3) | |
| Medical aid type 2 | 15 (2.5) | 4 (1.4) | 11 (3.6) | |
| Others | 11 (1.9) | 1 (0.4) | 10 (3.3) | |
| Private health insurers | 363 (61.1) | 175 (61.2) | 188 (61.0) | 0.970 |
Bold for p-values denotes statistical significance. ACT: asthma control test; BMI: body mass index.
At baseline, 48.1% of patients had well-controlled asthma (ACT ≥20), whereas 51.8% had poorly controlled asthma (ACT <20). BMI distribution varied significantly between the two groups (p=0.027). The patients with ACT <20 had a higher rate of previous pulmonary tuberculosis (TB) and depression/anxiety disorders than those with ACT ≥20 (p=0.033 and p=0.034, respectively). Insurance type was also significantly different between groups, with medical aid more common in those with ACT <20 (p<0.001). The incidence of unscheduled visits in the previous year was also significantly higher in patients with ACT <20 than in those with ACT ≥20 (p<0.001, respectively).
There was no difference between the two groups in GINA step classifications (table 2). Use of inhaled corticosteroid (ICS)/long-acting β2-agonist (LABAs), ICS/LABA/long-acting muscarinic antagonists (LAMAs) and other controller medications was similar between groups. Biologics were prescribed to 27.3% of all patients, with no significant differences between groups except for mepolizumab. While 32.8% of patients were on maintenance oral steroids, the median dose was higher among those with ACT <20 than those with ACT ≥20, though this difference did not reach statistical significance.
TABLE 2.
Comparison of asthma medication use based on ACT score
| Total | Controlled status (ACT score ≥20) | Uncontrolled status (ACT score <20) | p-value | |
|---|---|---|---|---|
| Patients, n | 594 | 286 | 308 | |
| GINA treatment step, n (%) (n=590) | 0.341 | |||
| Step 4 | 208 (35.3) | 106 (37.2) | 102 (33.4) | |
| Step 5 | 382 (64.8) | 179 (62.8) | 203 (66.6) | |
| ICS/LABA, n (%) | 571 (96.1) | 274 (95.8) | 297 (96.4) | 0.694 |
| ICS/LAMA, n (%) | 335 (56.4) | 150 (52.5) | 185 (60.1) | 0.061 |
| ICS/LABA/LAMA, n (%) | 324 (54.6) | 147 (51.4) | 177 (57.5) | 0.138 |
| LTRA, n (%) | 471 (79.3) | 225 (78.7) | 246 (79.9) | 0.719 |
| Macrolides, n (%) | 45 (7.6) | 16 (5.6) | 29 (9.4) | 0.078 |
| Phosphodiesterase 4 inhibitors, n (%) | 15 (2.5) | 5 (1.8) | 10 (3.3) | 0.245 |
| Immunosuppressants, n (%) | 6 (1.0) | 2 (0.7) | 4 (1.3) | 0.687 |
| Biologics, n (%) | 162 (27.3) | 85 (29.7) | 77 (25.1) | 0.205 |
| Mepolizumab | 13 (2.2) | 11 (3.9) | 2 (0.7) | 0.008 |
| Reslizumab | 49 (8.3) | 24 (8.4) | 25 (8.1) | 0.903 |
| Omalizumab | 41 (6.9) | 23 (8) | 18 (5.8) | 0.291 |
| Dupilumab | 53 (8.9) | 25 (8.7) | 28 (9.1) | 0.881 |
| Benralizumab | 11 (1.9) | 5 (1.8) | 6 (2.0) | 0.857 |
| Tezepelumab | 1 (0.2) | 0 (0.0) | 1 (0.3) | >0.999 |
| Systemic steroid# use as maintenance dose, n (%) (n=323) | 106 (32.8) | 44 (33.9) | 62 (32.1) | 0.747 |
| Dose, mg·day−1, median (IQR) (n=106) | 9.48 (5–20) | 6.75 (5–18) | 10 (5–20) | 0.346 |
Bold for p-values denotes statistical significance. ACT: asthma control test; GINA: Global Initiative for Asthma; ICS: inhaled corticosteroid; LABA: long-acting β2 agonist; LAMA: long-acting muscarinic antagonist; LTRA: leukotriene receptor antagonist. #: systemic steroid was calculated as the prednisolone equivalent dose.
Comparison of lung function and inflammatory markers based on ACT score
Mean FEV1 of all subjects was 66.9±18.2% predicted, the FEV1/FVC ratio was 66.6±13.9, and FEF25-75 was 1.5±1.1 L·s−1 (table 3). Patients with ACT <20 exhibited significantly lower mean FEV1, FVC and FEF25-75 values than those with ACT ≥20 (64.3±18.6% pred versus 69.6±17.3% pred for FEV1, 76.2±15.5% pred versus 80.2±14.6% pred for FVC and 1.4±1.1 L·s−1 versus 1.6±1.1 L·s−1 for FEF25-75, p<0.001, p=0.001 and p=0.026, respectively).
TABLE 3.
Comparison of lung function and inflammatory markers based on ACT score
| Total | Controlled status (ACT score ≥20) | Uncontrolled status (ACT score <20) | p-value | |
|---|---|---|---|---|
| Patients, n | 594 | 286 | 308 | |
| Pulmonary function tests, mean±sd | ||||
| FVC, L (n=590) | 3.0±0.9 | 3.1±0.9 | 2.9±0.9 | 0.001 |
| FVC % predicted (n=544) | 78.1±15.2 | 80.2±14.6 | 76.2±15.5 | 0.002 |
| FEV1, L (n=544) (n=590) | 2.0±0.7 | 2.1±0.7 | 1.9±0.7 | <0.001 |
| FEV1 % predicted (n=544) | 66.9±18.2 | 69.6±17.3 | 64.3±18.6 | <0.001 |
| FEV1/FVC (n=590) | 66.6±13.9 | 67.7±12.9 | 65.6±14.8 | 0.157 |
| FEF25–75, L·s−1 (n=588) | 1.5±1.1 | 1.6±1.1 | 1.4±1.1 | 0.026 |
| FEF25–75 % predicted (n=359) | 17.4±23.0 | 15.9±20.9 | 18.9±24.8 | 0.225 |
| Laboratory tests, median (IQR) | ||||
| WBC per µL (n=521) | 7.5 (6.1–9.2) | 7.4 (6–9.1) | 7.6 (6.2–9.3) | 0.280 |
| Blood eosinophils per μL (n=480) | 247.3 (56.8–590.0) | 300.0 (84.0–637.4) | 210.6 (45.1–500.0) | 0.015 |
| Blood eosinophils % (n=516) | 4.9 (2–10.0) | 5.9 (2.6–11.4) | 4.0 (1.7–8.2) | 0.002 |
| Sputum eosinophils % (n=126) | 3 (1–5) | 3 (2–6) | 1 (0–3) | 0.001 |
| FENO, ppb (n=367) | 47 (26–82) | 51 (30–90) | 43 (24–76) | 0.011 |
| Total IgE, IU·mL−1 (n=491) | 256.0 (89.7–571.0) | 263.0 (100.9–599.0) | 243.9 (71.0–551.0) | 0.133 |
| Type 2 inflammation#, n (%) | 473 (86.2) | 235 (89.7) | 238 (83.0) | 0.022 |
| Blood eosinophils ≥150 cells μL−1 | 299 (62.3) | 158 (68.1) | 141 (56.9) | 0.011 |
| FENO ≥20 ppb | 310 (84.5) | 154 (89.5) | 156 (80.0) | 0.012 |
| Sputum eosinophil ≥2% | 83 (65.9) | 59 (78.7) | 24 (47.1) | <0.001 |
| Skin test positivity | 150 (66.1) | 74 (71.8) | 76 (61.3) | 0.095 |
Bold for p-values denotes statistical significance. ACT: asthma control test; FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s; FEF25–75: forced expiratory flow at 25–75% of FVC; WBC: white blood cells; FENO: fractional exhaled nitric oxide. #: type 2 inflammation was defined as blood eosinophils ≥150 cells·µL−1, FENO ≥20 ppb, sputum eosinophil percentage ≥2% or a positive skin test to aeroallergens; the percentage was calculated among patients with available data for at least one of these type 2 inflammation markers.
Total blood eosinophil level, and the percentages of blood eosinophils and sputum eosinophils were significantly higher in patients with ACT ≥20 than in those with ACT <20 (p=0.015 for blood eosinophil count, p=0.002 for blood eosinophils (%), p=0.001 for sputum eosinophils (%)). When defining T2 inflammation as blood eosinophils ≥150 cells·µL−1, FENO ≥20 ppb or sputum eosinophil percentage ≥2% or a positive skin test to aeroallergens, 86.2% of all subjects had T2 inflammation; however, the T2 phenotype was more prevalent in patients with ACT ≥20 than in those with ACT <20 (89.7% in patients with ACT ≥20 versus 83.0% in those with ACT <20, p=0.022).
Comparison of asthma questionnaires based on ACT score
Mean EQ-VAS and EQ-5D scores for all patients were 62.4±19.4 and 0.82±0.15, respectively. When comparing scores based on ACT score, EQ-VAS was significantly lower in patients with ACT <20 than those with ACT ≥20 (p<0.001, table 4). The EQ-5D index score, as well as SAQ score, was significantly lower in patients with ACT <20 than in those with ACT ≥20 (p<0.001, respectively). VAS scores for cough, sputum, throat discomfort, breathlessness and wheezing were lower in those with ACT <20 than in those with ACT ≥20 (p<0.001, respectively).
TABLE 4.
Comparison of EQ-VAS and EQ-5D index scores between patients with severe asthma with or without controlled asthma
| Total | Controlled status (ACT score ≥20) | Uncontrolled status (ACT score <20) | p-value | |
|---|---|---|---|---|
| Patients, n | 594 | 286 | 308 | |
| EQ-VAS | 62.4±19.4 | 72.1±15.9 | 53.4±18.1 | <0.001 |
| EQ-5D-5L index score | 0.82±0.15 | 0.90±0.10 | 0.75±0.16 | <0.001 |
| SAQ score (n=593) | 85.5±22.0 | 97.7±14.1 | 74.1±22.0 | <0.001 |
| Cough VAS | 35.7±27.6 | 23.3±23.0 | 47.3±26.4 | <0.001 |
| Sputum VAS | 40.3±28.5 | 29.8±25.9 | 50.1±27.2 | <0.001 |
| Throat discomfort VAS | 36.6±28.6 | 25.0±24.8 | 47.4±27.7 | <0.001 |
| Breathlessness VAS | 32.9±29.6 | 15.9±21.6 | 48.7±27.3 | <0.001 |
| Wheezing VAS | 30.0±29.6 | 13.7±20.5 | 45.2±28.6 | <0.001 |
Data are presented as mean±sd unless stated otherwise. Bold for p-values denotes statistical significance. EQ-VAS: EuroQol visual analogue scale; EQ-5D-5L: European Quality of Life 5-Dimension 5-Level; ACT: asthma control test; SAQ: severe asthma questionnaire; VAS: visual analogue scale.
Factors associated with uncontrolled asthma
In univariable analysis, being underweight, a history of tuberculosis or depression/anxiety disorder, insurance type, lower lung function and presence of unscheduled visit in the previous year were associated with uncontrolled asthma (ACT <20) (table 5). Conversely, the presence of T2 inflammation was linked to a decreased risk of uncontrolled asthma.
TABLE 5.
Factors associated with uncontrolled asthma (ACT score <20)
| Univariable analysis | Multivariable analysis | |||
|---|---|---|---|---|
| Odds ratio (95% CI) | p-value | Odds ratio (95% CI) | p-value | |
| Sex (versus female) | 0.757 (0.544–1.050) | 0.096 | ||
| Obesity (versus 18.5 to 23 kg·m−2) | ||||
| <18.5 kg·m−2 | 4.428 (1.352–19.920) | 0.025 | 4.129 (1.081–20.192) | 0.050 |
| 23 to 25 kg·m−2 | 0.974 (0.625–1.518) | 0.908 | 0.993 (0.592–1.664) | 0.979 |
| ≥ 25 kg·m−2 | 1.396 (0.952–2.051) | 0.088 | 1.328 (0.847–2.086) | 0.217 |
| History of pulmonary tuberculosis | 2.120 (1.071–4.435) | 0.037 | 1.697 (0.735–4.084) | 0.222 |
| Depression or anxiety disorder | 1.912 (1.054–3.591) | 0.037 | 1.405 (0.696–2.905) | 0.347 |
| National health insurance (versus yes) | ||||
| Medical aid type 1/2/others | 6.537 (3.419–13.838) | <0.001 | 5.515 (2.614–13.110) | <0.001 |
| FEV1 % predicted (versus ≥80) | ||||
| <60 | 1.937 (1.224–3.081) | 0.005 | 1.854 (1.090–3.175) | 0.023 |
| 60 to 80 | 1.383 (0.895–2.146) | 0.145 | 1.471 (0.906–2.404) | 0.121 |
| Type 2 inflammation | 0.558 (0.334–0.916) | 0.023 | 0.560 (0.316–0.977) | 0.043 |
| Blood eosinophils ≥150 cells·μL−1 | 0.617 (0.424–0.895) | 0.011 | ||
| FENO ≥20 ppb | 0.468 (0.251–0.842) | 0.013 | ||
| Sputum eosinophil ≥2% | 0.241 (0.108–0.519) | <0.001 | ||
| Skin test positivity | 0.620 (0.351–1.083) | 0.096 | ||
| Presence of unscheduled visits in the previous year | 3.128 (1.943–5.182) | <0.001 | 3.400 (1.987–6.012) | <0.001 |
| Emergency department visits | 2.789 (1.588–5.118) | <0.001 | ||
| Hospitalisations | 2.955 (1.711–5.320) | <0.001 | ||
| ICS/LAMA | 1.364 (0.985–1.890) | 0.062 | 1.098(0.734–1.641) | 0.647 |
| Macrolides | 1.754 (0.943–3.374) | 0.082 | 1.484 (0.735–3.074) | 0.276 |
| Biologics use | 0.788 (0.548–1.131) | 0.197 | ||
Bold for p-values denotes statistical significance. ACT: asthma control test; FEV1: forced expiratory volume in 1 s; FENO: fractional exhaled nitric oxide; ICS: inhaled corticosteroid; LAMA: long-acting muscarinic antagonist.
As shown in table 5 and figure 1, multivariable analysis revealed that receiving medical aid from the government was the strongest factor associated with uncontrolled asthma (adjusted odds ratio (aOR) 5.515; 95% confidence interval (95% CI) 2.614–13.110, p<0.001), followed by being underweight (aOR 4.129, 95% CI 1.081–20.192, p=0.050) and having an FEV1 <60% predicted (aOR 1.854, 95% CI 1.090–3.175, p=0.023). Meanwhile, the presence of T2 inflammation was related to well-controlled asthma (aOR 0.560, 95% CI 0.316–0.977, p=0.043). However, biologics use was not associated with asthma control status (OR 0.788, 95% CI 0.548–1.131, p=0.197).
FIGURE 1.
Korean Severe Asthma Registry baseline predictors of an uncontrolled state in the multivariable logistic regression model. BMI: body mass index; FEV1: forced end expiratory volume in 1 s; ICS: inhaled corticosteroid; LAMA: long-acting muscarinic antagonist.
Discussion
In this study, we found that 51.9% of patients with severe asthma had uncontrolled asthma despite receiving care at referral centres in this country. Low socioeconomic status reflected by medical aid, being underweight, lower lung function and prior exacerbations was associated with poor asthma control, while T2 inflammation was associated with better control. However, biologic use was not associated with asthma control status.
Asthma control is a key feature when determining the optimal asthma treatment required, and current asthma control can predict future clinical outcomes of asthma patients [8]. Its significance is amplified in severe asthma due to the greater risk of future events such as frequent exacerbations, accelerated lung function decline and potential consequences like increased side-effects from medications, particularly systemic corticosteroids. Accordingly, knowing the prevalence and factors associated with uncontrolled asthma is very important for its appropriate management.
In the KoSAR-2, 51.9% of patients with severe asthma had uncontrolled asthma (ACT <20), comparable to data from Japan (56.5%) but higher than data reported for Italy (36.2%) and Switzerland (42.1%) [24–26]. However, studies of multinational severe asthma registries such as the ERS Severe Heterogeneous Asthma Research collaboration, Patient-Centered (SHARP) (54.6–100%) or International Severe Asthma Registry (ISAR) (75% among patients with biologics and 56% among patients without biologic use) have reported a higher prevalence of uncontrolled asthma than national registries, based on ACQ or ACT [9, 27]. In the current study, patients with uncontrolled asthma had significantly lower SAQ scores than those with well-controlled asthma. Mean EQ-5D index and EQ-VAS scores for all patients were 0.82 and 62.14, respectively. These values are comparable to those reported for other chronic diseases, such as cardiovascular disease, cancer or chronic kidney disease [28]. Patients with uncontrolled asthma had a remarkably low mean EQ-VAS score (mean 53.1), similar to those reported in end-stage renal disease (59.4), rare genetic diseases such as Prader–Willi syndrome (51.3–62.6) and Parkinson disease (57.6) [28]. Our findings suggest that being in uncontrolled asthma significantly impacts the quality of life of severe asthma patients.
Our study revealed that those patients with poor economic status, as reflected by medical aid use, had the highest odds of having uncontrolled severe asthma. Korea operates a two-tiered universal health security system consisting of the National Health Insurance (NHI) programme and the medical aid programme. The NHI is a wage-based, contributory system that covers the majority of the population, while the medical aid programme is a government-subsidised public assistance scheme for individuals who are economically disadvantaged or medically indigent. ∼3–4% of the entire population receives medical aid [14]. Similar disparities in asthma outcomes have been observed in other countries. In the USA, several studies have consistently reported higher healthcare utilisation, including emergency department visits or hospitalisation as well as mortality, among patients with lower socioeconomic status (SES) [29, 30]. In countries such as Sweden, Canada and the UK that have universal healthcare coverage, low SES was associated with poorer asthma outcomes and mortality in disadvantaged populations [31–33]. SES may be subject to a multitude of confounders, including occupational exposure, smoking, dietary habits and obesity; thus, individual risk factors should be carefully reviewed. As healthcare systems vary considerably across countries, the observed association between medical aid and poor asthma control (ACT <20) in this study may have limited generalisability. However, limited access to specialised care and financial barriers to appropriate therapy, especially for severe asthma patients with low SES, constitute missed opportunities to improve health outcomes. These findings suggest that healthcare systems and policies should be adjusted to support the management of severe asthma better.
Our study revealed that being underweight is associated with poor asthma control in patients with severe asthma. Although the proportion of obese individuals was higher in the ACT <20 group, which is consistent with existing literature that links obesity to poor asthma outcomes, we did not observe a statistically significant association between obesity and asthma control in multivariable analysis. One possible explanation is that the mean BMI in Korean patients with severe asthma is relatively low compared to Western cohorts such as the Severe Asthma Research Program (SARP) and the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) [34]. This trend has also been observed in other East Asian countries, including Japan and China [35, 36]. Furukawa et al. [37] reported that asthma control status was worse in those patients who were underweight than in those in the normal weight group, even after adjusting for confounding factors. Furthermore, in patients with asthma, being underweight is associated with decreased FEV1 and enhanced airway responsiveness [38]. In addition, previous research demonstrated a U-shaped relationship between BMI and asthma incidence [39]. Expanding on this finding, our study is the first to identify an association between underweight and asthma control status in patients with severe asthma. This finding warrants further investigation to better understand the epidemiological patterns and underlying mechanisms, particularly in Asian populations.
Consistent with previous findings [12, 40], our study showed that poor asthma control in severe asthmatics was strongly associated with low FEV1 and a history of previous exacerbations. Low FEV1 is a reliable indicator of poor asthma control and is linked to an increased likelihood of future exacerbations [12]. Additionally, a history of recent exacerbations is a strong predictor of future exacerbations, with the frequency of exacerbations increasing as asthma severity worsens [41]. As low lung function and exacerbations may cause a vicious cycle where one condition exacerbates the other, clinicians should pay more attention to providing appropriate strategies to disconnect these associations. In addition, the mean FEF25-75 value in the current study was significantly lower in patients with uncontrolled state compared to those with controlled. This finding aligns with prior reports showing that small airway dysfunction is associated with lower ACT scores and increased risk of exacerbation, and is often prevalent in severe asthma [42, 43].
In KoSAR-2, 84.2% of patients exhibited a T2 phenotype at baseline, and a similar prevalence of T2 inflammation has been reported across different registries [25, 44–46], confirming that the T2-high phenotype predominates among severe asthma patients. Our study showed that the absence of T2 inflammation was significantly associated with poor asthma control. This may be partly explained by the well-established responsiveness of T2 inflammation to ICS, and other step 4 or step 5 controller therapies such as high-dose ICS-LABA, LAMA or macrolides [47–49]. Although T2-targeted biologics showed efficacy in clinical trials and real-world studies [9, 45], their use in our registry was not associated with better asthma control. This may reflect the poorer baseline control status at the time of biologic initiation, as also noted in the ISAR study [9], and could be influenced by the relatively low proportion of biologic users (27.3%) in our registry. Moreover, the cross-sectional design of our study limits the ability to infer causal relationships between T2 inflammation and asthma control status. These findings highlight the unmet clinical needs of patients with T2-low severe asthma and underscore the importance of identifying effective strategies to improve asthma control in this population.
The strength of this study lies in its use of well-characterised data from the KoSAR-2, which includes nationwide data collected by allergy and pulmonology specialists from 41 hospitals, reflecting real-world clinical practice. This dataset provides comprehensive demographic and clinical profiles of patients with severe asthma in Korea and enables international comparisons. However, several limitations must be considered when interpreting these findings. First, sputum granulocyte data were missing from some institutions, as induced sputum testing was limited. Instead, T2 phenotype was determined based on blood eosinophil count, FENO and atopy, as recommended by GINA [46]. Second, blood eosinophils and FENO may fluctuate and be suppressed by systemic or high-dose ICS; 32.3% of patients received systemic corticosteroids, potentially affecting T2 phenotype accuracy. Third, the cross-sectional design precludes causal inference regarding asthma control and related factors. Fourth, only 27.3% of patients in this study received biologics, which is a relatively lower figure than that reported in other countries such as the UK (68.9%), Italy (68.2%) and Germany (47.4%) [50]. This discrepancy may be due to limited insurance coverage in Korea at the time of enrolment, when only omalizumab was reimbursed. Since November 2023, national healthcare insurance coverage has expanded to partially cover anti-IL-4R and anti-IL-5 or 5R antibodies, thus subsequent studies might overcome this limitation. Finally, we used ACT to assess asthma control. Although ACT is a widely used, patient-centred tool and a foundational component of asthma management, it may not fully capture the clinical complexity of the severe asthma cases, and discrepancies exist between symptom control, lung function decline and exacerbation risk [51]. Nevertheless, ACT remains a practical and validated tool for assessing control in both clinical and research settings.
In conclusion, 51.9% of severe asthmatic patients had poorly controlled asthma in the KoSAR-2. Low socioeconomic status reflected by medical aid was the most significant factor affecting poor asthma control, followed by being underweight and having unscheduled hospital visits, whereas the presence of T2 inflammation was associated with good asthma control. Biologics use was not associated with asthma control status. This study provides valuable insights into managing severe asthma and highlights the unmet needs of patients with severe asthma in Korea.
Footnotes
Provenance: Submitted article, peer reviewed.
Members contributing to the Korean Severe Asthma Registry-2: Jung-Hyun Kim, Soonchunhyang University Seoul Hospital, Seoul; Hye Jung Park, Gangnam Severance Hospital, Seoul; Jin An, Kyung Hee University Hospital at Gangdong, Seoul; Ga Young Ban, Kangdong Sacred Heart Hospital, Seoul; Kwang-Ha You, Ji-Yong Moon and Youlim Kim, Konkuk University Medical Center, Seoul; Kyoung-Hee Sohn, Kyung Hee University School of Medicine, Seoul; Byung-Keun Kim, Korea University Anam Hospital, Seoul; Sang Hoon Kim, Nowon Eulji Medical Center, Eulji University; Min-Suk Yang, Seoul National University Boramae Medical Center, Seoul; Byung Jae Lee and Noeul Kang, Samsung Medical Center, Seoul; Sang-Heon Cho, Heung-Woo Park, Hye-Ryun Kang, and Ji-hyang Lee, Seoul National University Hospital, Seoul; Sook-Young Lee and Hwa Young Lee, the Catholic University of Korea, Seoul St. Mary's Hospital, Seoul; You-Sook Cho, Yeon-Mok Oh, Tae-Bum Kim, Hyouk-soo Kwon, Woo-Jung Song, Asan Medical Center, Seoul; Young Joo Cho and Min Hye Kim, Ewha Woman's University Mokdong Hospital, Seoul; Ji Su Shim and Kyung-Min Ahn, Ewha Woman's University Seoul Hospital, Seoul; Jae Woo Jung, Chung-Ang university Hospital, Seoul; Ha Kyeong Won, Veterans Health Service Medical Center, Seoul; Ho Joo Yoon, Sang Heon Kim, and Hyun Lee, Hanyang University Seoul Hospital, Seoul; Sung-Yoon Kang, Gachon University Gil Medical Center, Incheon; Yoon-Seok Chang, Seoul National University Bundang Hospital, Seongnam; AnSoo Jang and Jong Sook Park, Soonchunhyang University Hospital Bucheon, Bucheon; Hae-Sim Park, Youngsoo Lee, Jae-Hyuk Jang, Ajou University Hospital, Suwon; Soyoung Park, Chung-Ang university Gwang Myeong Hospital, Gwangmyeong; Soo Jie Chung, Hallym University Dongtan Sacred Heart Hospital, Hwaseong; Joo-Hee Kim, Hallym University Sacred Heart Hospital, Anyang; Jae-Woo Kwon, Kangwon National University Hospital, Chuncheon; Ji-Ho Lee and Sang-Ha Kim, Wonju Severance Christian Hospital, Wonju; Yong Chul Lee, Seoung Ju Park, and So Ri Kim, Jeonbuk National University Hospital, Jeonju; Young Il Koh, Da Woon Sim, and Ji Eun Yu, Chonnam National University Hospital, Gwangju; Su-Jeong Kim, Kyungpook National University Hospital, Daegu; Hyun Jung Jin, Yeungnam University Medical Center, Daegu; Han-Ki Park, Kyungpook National University Chilgok Hospital, Daegu; Gil Soon Choi and Ji Hyun Oh, Kosin University Gospel Hospital, Busan; Young Hee Nam, Dong-A University Hospital, Busan; Hye Kyung Park, Pusan National University Hospital, Busan; Chan Sun Park and Hyo-In Rhyou, Inje University Haeundae Paik Hospital, Busan; Seung Eun Lee, Pusan National University Yangsan Hospital, Yangsan; Taehoon Lee, Ulsan University Hospital, Ulsan; and Sun Young Yoon, Chungnam National University Sejong Hospital, Sejong.
Ethics statement: The study protocol was approved by the institutional review board (IRB) of each participating hospital including Hallym University Sacred Heart Hospital, and informed consent was obtained from all study participants (IRB number: HALLYM 2022-04-020).
Conflict of interest: W-J. Song reports grants from Daewoong Pharmaceutical, consultancy fees from GSK and Reckitt Benckiser, payment or honoraria for lectures, presentations, manuscript writing or educational events from Thermo Fisher/Immunotek, Celltrion, AstraZeneca, GSK and Organon, and is the Chief Editor for ERJ Open Research. S.R. Kim is an Associate Editor for ERJ Open Research. J. Kim, H. Lee, S. Lee, Y.S. Cho, S.Y. Park, S.E. Lee, Y.S. Lee, H.W. Park, H.Y. Lee, N. Kang, J.H. Jang, D.W. Sim, J.W. Jung, S.Y. Kang, H.K. Park, S. Kim, G.Y. Ban, J.S. Shim, T.H. Lee, K.H. Sohn, B.K. Kim, H.I. Rhyou and S.H. Kim have nothing to disclose.
Support statement: This research was supported by the Korea National Institute of Health research project (project number 2022-ER1205-00). Funding information for this article has been deposited with the Open Funder Registry.
Supplementary material
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Supplementary material
00609-2025.SUPPLEMENT
References
- 1.Chung KF, Wenzel SE, Brozek JL, et al. International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma. Eur Respir J 2014; 43: 343–373. doi: 10.1183/09031936.00202013 [DOI] [PubMed] [Google Scholar]
- 2.Park S-Y, Kang S-Y, Song W-J, et al. Evolving concept of severe asthma: transition from diagnosis to treatable traits. Allergy Asthma Immunol Res 2022; 14: 447–464. doi: 10.4168/aair.2022.14.5.447 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lee E, Kim A, Ye Y-M, et al. Increasing prevalence and mortality of asthma with age in Korea, 2002–2015: a nationwide, population-based study. Allergy Asthma Immunol Res 2020; 12: 467–484. doi: 10.4168/aair.2020.12.3.467 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kim JH, Lee H, Park SY, et al. Epidemiology of patients with asthma in Korea: analysis of the NHISS database 2006–2015. World Allergy Organ J 2023; 16: 100768. doi: 10.1016/j.waojou.2023.100768 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lee Y, Lee JH, Park SY, et al. Roles of real-world evidence in severe asthma treatment: challenges and opportunities. ERJ Open Res 2023; 9: 00248-2022. doi: 10.1183/23120541.00248-2022 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Kim SH, Lee H, Park SY, et al. The Korean Severe Asthma Registry (KoSAR): real world research in severe asthma. Korean J Intern Med 2022; 37: 249–260. doi: 10.3904/kjim.2021.403 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kang N, Kim JH, Kim SR. Letter from Korea-Oral corticosteroid stewardship in Korea: insights from the Korean Severe Asthma Registry (KoSAR). Respirology 2024; 29: 1001–1003. doi: 10.1111/resp.14806 [DOI] [PubMed] [Google Scholar]
- 8.Song WJ, Lee JH, Kang Y, et al. Future risks in patients with severe asthma. Allergy Asthma Immunol Res 2019; 11: 763–778. doi: 10.4168/aair.2019.11.6.763 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Denton E, Hew M, Peters MJ, et al. Real-world biologics response and super-response in the International Severe Asthma Registry cohort. Allergy 2024; 79: 2700–2716. doi: 10.1111/all.16178 [DOI] [PubMed] [Google Scholar]
- 10.Haselkorn T, Szefler SJ, Chipps BE, et al. Disease burden and long-term risk of persistent very poorly controlled asthma: TENOR II. J Allergy Clin Immunol Pract 2020; 8: 2243–2253. doi: 10.1016/j.jaip.2020.02.040 [DOI] [PubMed] [Google Scholar]
- 11.Müllerová H, Cockle SM, Gunsoy NB, et al. Clinical characteristics and burden of illness among adolescent and adult patients with severe asthma by asthma control: the IDEAL study. J Asthma 2021; 58: 459–470. doi: 10.1080/02770903.2019.1708095 [DOI] [PubMed] [Google Scholar]
- 12.Racine G, Forget A, Moullec G, et al. Predictors of asthma control and exacerbations: a real-world study. J Allergy Clin Immunol Pract 2021; 9: 2802–2811.e2. doi: 10.1016/j.jaip.2021.04.049 [DOI] [PubMed] [Google Scholar]
- 13.Papaioannou AI, Kostikas K, Zervas E, et al. Control of asthma in real life: still a valuable goal? Eur Respir Rev 2015; 24: 361–369. doi: 10.1183/16000617.00001615 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kim AY, Seo MS, Kang HY. Health disparity and healthcare utilization inequity among older adults living in poverty in South Korea: a cross-sectional study. BMC Geriatr 2022; 22: 999. doi: 10.1186/s12877-022-03686-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Graham BL, Steenbruggen I, Miller MR, et al. Standardization of spirometry 2019 update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am J Respir Crit Care Med 2019; 200: e70–e88. doi: 10.1164/rccm.201908-1590ST [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.American Thoracic Society; European Respiratory Society. ATS/ERS recommendations for standardized procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, 2005. Am J Respir Crit Care Med 2005; 171: 912–930. doi: 10.1164/rccm.200406-710ST [DOI] [PubMed] [Google Scholar]
- 17.Nathan RA, Sorkness CA, Kosinski M, et al. Development of the asthma control test: a survey for assessing asthma control. J Allergy Clin Immunol 2004; 113: 59–65. doi: 10.1016/j.jaci.2003.09.008 [DOI] [PubMed] [Google Scholar]
- 18.Hyland ME, Jones RC, Lanario JW, et al. The construction and validation of the Severe Asthma Questionnaire. Eur Respir J 2018; 52: 1800618. doi: 10.1183/13993003.00618-2018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Kang SY, Ahn KM, Lee JH, et al. Development and linguistic validation of the Korean version of the Severe Asthma Questionnaire. J Thorac Dis 2023; 15: 3172–3181. doi: 10.21037/jtd-22-1415 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Rabin R, Gudex C, Selai C, et al. From translation to version management: a history and review of methods for the cultural adaptation of the EuroQol five-dimensional questionnaire. Value Health 2014; 17: 70–76. doi: 10.1016/j.jval.2013.10.006 [DOI] [PubMed] [Google Scholar]
- 21.Lee HY, Lee Y, Lee JH, et al. Association of cough severity with asthma control and quality of life in patients with severe asthma. Lung 2024; 202: 405–414. doi: 10.1007/s00408-024-00710-5 [DOI] [PubMed] [Google Scholar]
- 22.Reddel HK, Bacharier LB, Bateman ED, et al. Global Initiative for Asthma Strategy 2021: executive summary and rationale for key changes. Eur Respir J 2022; 59: 2102730. doi: 10.1183/13993003.02730-2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157–163. doi: 10.1016/S0140-6736(03)15268-3 [DOI] [PubMed] [Google Scholar]
- 24.Tanosaki T, Kabata H, Matsusaka M, et al. Clinical characteristics of patients with not well-controlled severe asthma in Japan: analysis of the Keio Severe Asthma Research Program in Japanese population (KEIO-SARP) registry. Allergol Int 2021; 70: 61–67. doi: 10.1016/j.alit.2020.06.002 [DOI] [PubMed] [Google Scholar]
- 25.Heffler E, Blasi F, Latorre M, et al. The Severe Asthma Network in Italy: findings and perspectives. J Allergy Clin Immunol Pract 2019; 7: 1462–1468. doi: 10.1016/j.jaip.2018.10.016 [DOI] [PubMed] [Google Scholar]
- 26.Jaun F, Tröster LM, Giezendanne S, et al. Characteristics of severe asthma patients and predictors of asthma control in the Swiss Severe Asthma Registry. Respiration 2023; 102: 863–878. doi: 10.1159/000533474 [DOI] [PubMed] [Google Scholar]
- 27.van Bragt J, Adcock IM, Bel EHD, et al. Characteristics and treatment regimens across ERS SHARP severe asthma registries. Eur Respir J 2020; 55: 1901163. doi: 10.1183/13993003.01163-201 [DOI] [PubMed] [Google Scholar]
- 28.Zhou T, Guan H, Wang L, et al. Health-related quality of life in patients with different diseases measured with the EQ-5D-5L: a systematic review. Front Public Health 2021; 9: 675523. doi: 10.3389/fpubh.2021.675523 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Eisner MD, Katz PP, Yelin EH, et al. Risk factors for hospitalization among adults with asthma: the influence of sociodemographic factors and asthma severity. Respir Res 2001; 2: 53–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kaur BP, Lahewala S, Arora S, et al. Asthma: hospitalization trends and predictors of in-hospital mortality and hospitalization costs in the USA (2001–2010). Int Arch Allergy Immunol 2015; 168: 71–78. doi: 10.1159/000441687 [DOI] [PubMed] [Google Scholar]
- 31.Backman H, Hedman L, Stridsman C, et al. A population-based cohort of adults with asthma: mortality and participation in a long-term follow-up. Eur Clin Respir J 2017; 4: 1334508. doi: 10.1080/20018525.2017.1334508 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.To T, Simatovic J, Zhu J, et al. Asthma deaths in a large provincial health system. A 10–year population-based study. Ann Am Thorac Soc 2014; 11: 1210–1217. doi: 10.1513/AnnalsATS.201404-138OC [DOI] [PubMed] [Google Scholar]
- 33.Busby J, Price D, Al-Lehebi R, et al. Impact of socioeconomic status on adult patients with asthma: a population-based cohort study from UK primary care. J Asthma Allergy 2021; 14: 1375–1388. doi: 10.2147/JAA.S326213 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Park SY, Fowler S, Shaw DE, et al. Comparison of asthma phenotypes in severe asthma cohorts (SARP, U-BIOPRED, ProAR and COREA) from 4 continents. Allergy Asthma Immunol Res 2024; 16: 338–352. doi: 10.4168/aair.2024.16.4.338 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Koya T, Asai K, Iwanaga T, et al. Characterization of severe uncontrolled asthma in Japan: analysis of baseline data from the PROSPECT study. J Asthma Allergy 2023; 16: 597–609. doi: 10.2147/JAA.S410292 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Zhang Q, Fu X, Wang C, et al. Severe eosinophilic asthma in Chinese C-BIOPRED asthma cohort. Clin Transl Med 2022; 12: e710. doi: 10.1002/ctm2.710 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Furukawa T, Hasegawa T, Suzuki K, et al. Influence of underweight on asthma control. Allergol Int 2012; 61: 489–496. doi: 10.2332/allergolint.12-OA-0425 [DOI] [PubMed] [Google Scholar]
- 38.Schachter LM, Salome CM, Peat JK, et al. Obesity is a risk for asthma and wheeze but not airway hyperresponsiveness. Thorax 2001; 56: 4–8. doi: 10.1136/thorax.56.1.4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Luder E, Ehrlich RI, Lou WY, et al. Body mass index and the risk of asthma in adults. Respir Med 2004; 98: 29–37. doi: 10.1016/j.rmed.2003.08.004 [DOI] [PubMed] [Google Scholar]
- 40.Chan R, Lipworth BJ. Determinants of asthma control and exacerbations in moderate to severe asthma. J Allergy Clin Immunol Pract 2022; 10: 2758–2760.e1. doi: 10.1016/j.jaip.2022.06.042 [DOI] [PubMed] [Google Scholar]
- 41.Nakwan N. Impact of asthma severity as risk factor to future exacerbations in patients admitted for asthma exacerbation. Multidiscip Respir Med 2021; 16: 780. doi: 10.4081/mrm.2021.780 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Cottini M, Bondi B, Bagnasco D, et al. Impulse oscillometry defined small airway dysfunction in asthmatic patients with normal spirometry: prevalence, clinical associations, and impact on asthma control. Respir Med 2023; 218: 107391. doi: 10.1016/j.rmed.2023.107391 [DOI] [PubMed] [Google Scholar]
- 43.Galant SP, Morphew T. Adding oscillometry to spirometry in guidelines better identifies uncontrolled asthma, future exacerbations, and potential targeted therapy. Ann Allergy Asthma Immunol 2024; 132: 21–29. doi: 10.1016/j.anai.2023.08.011 [DOI] [PubMed] [Google Scholar]
- 44.Jackson DJ, Busby J, Pfeffer PE, et al. Characterisation of patients with severe asthma in the UK Severe Asthma Registry in the biologic era. Thorax 2021; 76: 220–227. doi: 10.1136/thoraxjnl-2020-215168 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Milger K, Suhling H, Skowasch D, et al. Response to biologics and clinical remission in the adult German Asthma Net severe asthma registry cohort. J Allergy Clin Immunol Pract 2023; 11: 2701–2712.e2. doi: 10.1016/j.jaip.2023.05.047 [DOI] [PubMed] [Google Scholar]
- 46.Heaney LG, Perez de Llano L, Al-Ahmad M, et al. Eosinophilic and noneosinophilic asthma: an expert consensus framework to characterize phenotypes in a global real-life severe asthma cohort. Chest 2021; 160: 814–830. doi: 10.1016/j.chest.2021.04.013 [DOI] [PubMed] [Google Scholar]
- 47.Maijers I, Kearns N, Harper J, et al. Oral steroid-sparing effect of high-dose inhaled corticosteroids in asthma. Eur Respir J 2020; 55: 1901147. doi: 10.1183/13993003.01147-2019 [DOI] [PubMed] [Google Scholar]
- 48.Muiser S, Gosens R, van den Berge M, et al. Understanding the role of long-acting muscarinic antagonists in asthma treatment. Ann Allergy Asthma Immunol 2022; 128: 352–360. doi: 10.1016/j.anai.2021.12.020 [DOI] [PubMed] [Google Scholar]
- 49.Gibson PG, Yang IA, Upham JW, et al. Efficacy of azithromycin in severe asthma from the AMAZES randomised trial. ERJ Open Res 2019; 5: 00056-2019. doi: 10.1183/23120541.00056-2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Wang E, Wechsler ME, Tran TN, et al. Characterization of severe asthma worldwide: data from the International Severe Asthma Registry. Chest 2020; 157: 790–804. doi: 10.1016/j.chest.2019.10.053 [DOI] [PubMed] [Google Scholar]
- 51.Park SY, Yoon S-Y, Shin B, et al. Clinical factors affecting discrepant correlation between asthma control test score and pulmonary function. Allergy Asthma Immunol Res 2015; 7: 83–87. doi: 10.4168/aair.2015.7.1.83 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Supplementary material
00609-2025.SUPPLEMENT