| Literature DB >> 32607141 |
Enrico Heffler1,2, Fabiana Saccheri3, Marta Bartezaghi3, Giorgio Walter Canonica1,2.
Abstract
BACKGROUND: A significant proportion of patients with severe asthma may also suffer from nasal polyposis, which is commonly defined as chronic rhinosinusitis with nasal polyps (CRSwNP), the presence of which may adversely affect asthma treatment outcomes. The biologic agent omalizumab is effective as add-on therapy in patients with severe allergic asthma. The aim of this post hoc analysis of the PROXIMA study was to compare the efficacy of omalizumab between patients with severe allergic asthma, with and without comorbid CRSwNP.Entities:
Keywords: Biologics; Exacerbations; Nasal Polyps; Observational; Omalizumab; Severe asthma
Year: 2020 PMID: 32607141 PMCID: PMC7318524 DOI: 10.1186/s13601-020-00330-1
Source DB: PubMed Journal: Clin Transl Allergy ISSN: 2045-7022 Impact factor: 5.871
Distribution of main asthma comorbidities in the PROXIMA study population
| Comorbidity | N = 123 |
|---|---|
| Number of patients with at least one comorbidity | 77 (62.6%) |
| Allergic bronchopulmonary aspergillosis | 2 (1.6%) |
| Atopic dermatitis | 3 (2.4%) |
| Bronchiectasis | 3 (2.4%) |
| Cardiovascular disease | 26 (21.1%) |
| Chronic obstructive pulmonary disease | 4 (3.3%) |
| Chronic rhinitis | 17 (13.8%) |
| Chronic rhinosinusitis without nasal polyps (CRSsNP) | 22 (17.9%) |
| Chronic rhinosinusitis with nasal polyps (CRSwNP) | 17 (13.8%) |
| Chronic spontaneous urticaria | 1 (0.8%) |
| Chronic/recurrent respiratory infections | 2 (1.6%) |
| Gastroesophageal reflux | 16 (13.0%) |
| Hormonal disturbances | 16 (13.0%) |
| Obesity | 5 (4.1%) |
| Obstructive sleep apnea/sleep-disordered breathing | 3 (2.4%) |
| Psychologic disease (anxiety, depression, behavioral disorders) | 6 (4.9%) |
| Other | 19 (15.4%) |
Baseline clinical and demographic patient characteristics
| Patients with CRSwNP (n = 17) | Patients without CRSwNP (n = 106) | ||
|---|---|---|---|
| Age (years) | 51.6 ± 12.5 | 52.9 ± 13.7 | 0.626 |
| Female (%) | 58.8 | 62.3 | 0.786 |
| BMI (kg/m2) | 24.8 ± 4.1 | 26.4 ± 4.8 | 0.155 |
| Time from asthma diagnosis (years) | 12.9 ± 9.9 | 18.6 ± 14.0 | 0.161 |
| FEV1 (l) | 1.74 ± 0.80 | 1.70 ± 0.72 | 0.735 |
| FEV1 % predicted, median (Q1–Q3) | 61.01 (48.53–70.58) | 54.18 (45.65–69.23) | 0.690 |
| ACQ | 2.87 ± 1.60 | 2.98 ± 1.02 | 0.706 |
| No. of asthma exacerbations in the previous 12 months | 5.13 ± 4.13 | 4.54 ± 4.08 | 0.413 |
| Monthly dose of omalizumab, mg | 553.13 ± 303.57 | 521.23 ± 331.68 | 0.5233 |
Data are mean ± SD unless otherwise stated
ACQ Asthma Control Questionnaire, BMI body mass index, FEV forced expiratory volume in 1 s, CRSwNP chronic rhinosinusitis with nasal polyps, SD standard deviation
Fig. 1Mean (standard deviation) Asthma Control Questionnaire scores at baseline and 12 months after omalizumab treatment, and the change from baseline in ACQ score, in patients with severe allergic asthma, with chronic rhinosinusitis with nasal polyps (CRSwNP) or without CRSwNP (No CRSwNP). The p-values within cohorts were calculated using a signed rank test and p-values for comparisons between cohorts were calculated using an ANCOVA model on ranks
Fig. 2Lung function assessed via percent predicted forced expiratory volume in 1 s (FEV1) at baseline and 12 months’ after omalizumab treatment, and change from baseline in percent predicted FEV1, in patients with severe allergic asthma, with chronic rhinosinusitis with nasal polyps (CRSwNP) or without CRSwNP (No CRSwNP). The p-values within cohorts were calculated using a signed rank test and p-values for comparisons between cohorts were calculated using an ANCOVA model on ranks
Fig. 3The median number of annual exacerbations in the year prior to initiating omalizumab treatment (baseline) and during 12 months’ treatment with omalizumab, and change from baseline, in patients with with severe allergic asthma with chronic rhinosinusitis with nasal polyps (CRSwNP) or without CRSwNP (No CRSwNP). The p-values within cohorts were calculated using a signed rank test and p-values for comparisons between cohorts were calculated using an ANCOVA model on ranks