Purpose: To determine whether montelukast is as effective as fluticasone in controlling mild persistent asthma as determined by rescue-free days.
Subjects And Methods: Participants aged 15 to 85 years with mild persistent asthma (n = 400) were randomized to oral montelukast (10 mg once nightly) or inhaled fluticasone (88 mug twice daily) in a year-long, parallel-group, multicenter study with a 12-week, double-blind period, followed by a 36-week, open-label period.
Results: The mean percentage of rescue-free days was similar between treatments after 12 weeks (fluticasone: 74.9%, montelukast: 73.1%; difference = 1.8%, 95% confidence interval [CI]: -3.2% to 6.8%) but not during the open-label period (fluticasone: 77.3%, montelukast: 71.1%; difference = 6.2%, 95% CI: 0.8% to 11.7%). Although both fluticasone and montelukast significantly improved symptoms, quality of life, and symptom-free days during both treatment periods, greater improvements occurred with fluticasone in lung function during both periods and in asthma control during open-label treatment. Post hoc analyses revealed a difference in rescue-free days favoring fluticasone in participants in the quartiles for lowest lung function and greatest albuterol use at baseline.
Conclusion: In patients with mild persistent asthma, rescue-free days and most asthma control measures improved similarly with fluticasone or montelukast over the short term, but with prolonged open-label treatment, asthma control improved more with fluticasone. Improved asthma control with fluticasone appeared to occur in those with decreased lung function and greater albuterol use at baseline. In the remaining patients, the two treatments appeared to be comparable. These results suggest that classification criteria for mild persistent asthma may need to be re-evaluated.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.amjmed.2005.03.003 | DOI Listing |
BMC Public Health
January 2025
Department of Dermatology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Chronic respiratory diseases (CRD) represents a series of lung disorders and is posing a global health burden. Systemic inflammation and phenotypic ageing have been respectively reported to associate with certain CRD. However, little is known about the co-exposures and mutual associations of inflammation and ageing with CRD.
View Article and Find Full Text PDFNPJ Prim Care Respir Med
January 2025
Erasmus MC, Department of General Practice, Rotterdam, The Netherlands.
Asthma and allergic rhinitis (AR) are common disorders of the respiratory tract that often coincide. Control of AR symptoms can improve asthma outcomes in patients with co-existing diseases. Our aim is to produce a systematic review of the effectiveness of conventional anti-AR medication for asthma outcomes in patients with both diseases.
View Article and Find Full Text PDFAm J Transl Res
December 2024
Department of Respiratory Medicine, Hanzhong People's Hospital Hanzhong 723000, Shaanxi, China.
Objective: To investigate the diagnostic value of immunoglobulin E (IgE), fractional of exhaled nitric oxide (FeNO), and peripheral blood eosinophils (EOS) in adult bronchial asthma and to analyze their relationship with asthma severity.
Methods: A retrospective analysis was conducted on 336 patients diagnosed with bronchial asthma and admitted to Xi'an Fourth Hospital from January 2022 to January 2024, forming the asthma group. Additionally, another 127 healthy subjects were selected as the non-asthmatic control group.
Immunol Invest
January 2025
Department of Respiratory Medicine, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China.
Introduction: T helper 17 (Th17) cells have a significant effect in the pathogenesis of asthma, and signal transducer and activator of transcription 3 (STAT3) pathway activation is critical for Th17 cell differentiation. Timosaponin A-III (TA3) was reported to inhibit the STAT3 pathway. Here, we investigated whether TA3 improved asthma by inhibiting the STAT3 pathway.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!