Association of Inhaled Corticosteroids and Long-Acting Muscarinic Antagonists With Asthma Control in Patients With Uncontrolled, Persistent Asthma A Systematic Review and Meta-analysis

Juan Carlos Ivancevich Monday, 19 March 2018 21:50
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Key Points

Question  What is the efficacy associated with long-acting muscarinic antagonists (LAMAs) as add-on therapy to inhaled corticosteroids in patients with uncontrolled, persistent asthma?

Findings  In this meta-analysis that included 15 randomized clinical trials with 7122 participants 12 years or older with uncontrolled, persistent asthma, LAMA vs placebo as an add-on therapy to inhaled corticosteroids was associated with a lower risk of exacerbations requiring systemic corticosteroids (risk difference, −1.8).

Meaning  LAMA use was associated with better clinical outcomes than placebo in patients with uncontrolled, persistent asthma.


Importance  Long-acting muscarinic antagonists (LAMAs) are a potential adjunct therapy to inhaled corticosteroids in the management of persistent asthma.

Objective  To conduct a systematic review and meta-analysis of the effects associated with LAMA vs placebo or vs other controllers as an add-on therapy to inhaled corticosteroids and the use of a LAMA as add-on therapy to inhaled corticosteroids and long-acting β-agonists (LABAs; hereafter referred to as triple therapy) vs inhaled corticosteroids and LABA in patients with uncontrolled, persistent asthma.

Data Sources  MEDLINE, EMBASE, Cochrane databases, and clinical trial registries (earliest date through November 28, 2017).

Study Selection  Two reviewers selected randomized clinical trials or observational studies evaluating a LAMA vs placebo or vs another controller as an add-on therapy to inhaled corticosteroids or triple therapy vs inhaled corticosteroids and LABA in patients with uncontrolled, persistent asthma reporting on an outcome of interest.

Data Extraction and Synthesis  Meta-analyses using a random-effects model was conducted to calculate risk ratios (RRs), risk differences (RDs), and mean differences (MDs) with corresponding 95% CIs. Citation screening, data abstraction, risk assessment, and strength-of-evidence grading were completed by 2 independent reviewers.

Main Outcomes and Measures  Asthma exacerbations.

Results  Of 1326 records identified, 15 randomized clinical trials (N = 7122 patients) were included. Most trials assessed adding LAMA vs placebo or LAMA vs LABA to inhaled corticosteroids. Adding LAMA vs placebo to inhaled corticosteroids was associated with a significantly reduced risk of exacerbation requiring systemic corticosteroids (RR, 0.67 [95% CI, 0.48 to 0.92]; RD, −0.02 [95% CI, −0.04 to 0.00]). Compared with adding LABA, adding LAMA to inhaled corticosteroids was not associated with significant improvements in exacerbation risk (RR, 0.87 [95% CI, 0.53 to 1.42]; RD, 0.00 [95% CI, −0.02 to 0.02]), or any other outcomes of interest. Triple therapy was not significantly associated with improved exacerbation risk vs inhaled corticosteroids and LABA (RR, 0.84 [95% CI, 0.57 to 1.22]; RD, −0.01 [95% CI, −0.08 to 0.07]).

Conclusions and Relevance  In this systematic review and meta-analysis, the use of LAMA compared with placebo as add-on therapy to inhaled corticosteroids was associated with a lower risk of asthma exacerbations; however, the association of LAMA with benefit may not be greater than that with LABA. Triple therapy was not associated with a lower risk of exacerbations.

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An algorithmic approach for the treatment of severe uncontrolled asthma

Juan Carlos Ivancevich Friday, 09 March 2018 16:30
Eleftherios ZervasKonstantinos SamitasAndriana I. PapaioannouPetros BakakosStelios LoukidesMina Gaga


A small subgroup of patients with asthma suffers from severe disease that is either partially controlled or uncontrolled despite intensive, guideline-based treatment. These patients have significantly impaired quality of life and although they constitute <5% of all asthma patients, they are responsible for more than half of asthma-related healthcare costs. Here, we review a definition for severe asthma and present all therapeutic options currently available for these severe asthma patients. Moreover, we suggest a specific algorithmic treatment approach for the management of severe, difficult-to-treat asthma based on specific phenotype characteristics and biomarkers. The diagnosis and management of severe asthma requires specialised experience, time and effort to comprehend the needs and expectations of each individual patient and incorporate those as well as his/her specific phenotype characteristics into the management planning. Although some new treatment options are currently available for these patients, there is still a need for further research into severe asthma and yet more treatment options.

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Automated chart review utilizing natural language processing algorithm for asthma predictive index

Juan Carlos Ivancevich Wednesday, 14 February 2018 11:49
Harsheen KaurSunghwan SohnChung-Il WiEuijung RyuMiguel A. ParkKay BachmanHirohito KitaIvana CroghanJose A. Castro-RodriguezGretchen A. VogeHongfang Liu, Young J. Juhn



Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria.


This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma.


Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6–6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8–10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively.


NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.

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Effects of treatment changes on asthma phenotype prevalence and airway neutrophil function

Juan Carlos Ivancevich Thursday, 22 February 2018 13:19
BMC Pulmonary Medicine 
Collin R. Brooks, Christine J. Van DalenElizabeth HardingIan F. Hermans and Jeroen Douwes



Asthma inflammatory phenotypes are often defined by relative cell counts of airway eosinophils/neutrophils. However, the importance of neutrophilia remains unclear, as does the effect of ICS treatment on asthma phenotypes and airway neutrophil function. The purpose of this study was to assess asthma phenotype prevalence/characteristics in a community setting, and, in a nested preliminary study, determine how treatment changes affect phenotype stability and inflammation, with particular focus on airway neutrophils.


Fifty adult asthmatics and 39 non-asthmatics were assessed using questionnaires, skin prick tests, spirometry, exhaled nitric oxide (FENO) measurement, and sputum induction. Twenty-one asthmatics underwent further assessment following treatment optimisation (n = 11) or sub-optimisation (n = 10).


Forty percent (20/50) had eosinophilic asthma (EA) and 8% had neutrophilic asthma. EA was associated with increased FENO, bronchodilator reversibility (BDR) and reduced lung function (p < 0.05). Following optimisation/sub-optimisation, the EA/NEA (non-eosinophilic asthma) phenotype changed in 11/21 (52%) asthmatics. In particular, fewer subjects had EA post treatment optimisation, but this was not statistically significant. However, a significant (p < 0.05) reduction in FENO, ACQ7 score, and BDR was observed after treatment optimisation, as well as an increase in FEV1-% predicted (p < 0.05). It was also associated with reduced eosinophils (p < 0.05) and enhanced neutrophil phagocytosis (p < 0.05) in EA only, and enhanced neutrophil oxidative burst in both EA and NEA (p < 0.05).


In this community based population, non-eosinophilic asthma was common, less severe than EA, and at baseline most asthmatics showed no evidence of inflammation. In the nested change in treatment study, treatment optimisation was associated with reduced sputum eosinophils, improved symptoms and lung function, and enhanced neutrophil function, but a significant reduction in EA could not be demonstrated.

Trial registration

The nested change in treatment component of this study is registered at the Australia and New Zealand Clinical Trial Registry ( Registration date 27/09/2017. Retrospectively registered.

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Predictors of inappropriate and excessive use of reliever medications in asthma: a 16-year population-based study

Juan Carlos Ivancevich Tuesday, 13 February 2018 14:29
Hamid Tavakoli, J. Mark FitzGeraldLarry D. Lynd and Mohsen Sadatsafavi



Understanding factors associated with the inappropriate or excessive use of short-acting beta agonists (SABA) can help develop better policies.


We used British Columbian (BC)‘s administrative health data (1997–2014) to create a retrospective cohort of asthma patients aged between 14 and 55 years. The primary and secondary outcomes were, respectively, inappropriate and excessive use of SABA based on a previously validated definition. Exposures were categorised into groups comprising socio-demographic variables, indicators of type and quality of asthma care, and burden of comorbid conditions.


343,520 individuals (56.3% female, average age 30.5) satisfied the asthma case definition, contributing 2.6 million person-years. 7.3% of person-years were categorised as inappropriate SABA use and 0.9% as excessive use. Several factors were associated with lower likelihood of inappropriate use, including female sex, higher socio-economic status, higher continuity of care, having received pulmonary function test in the previous year, visited a specialist in the previous year, and the use of inhaled corticosteroids in the previous year. An asthma-related outpatient visit to a general practitioner in the previous year was associated with a higher likelihood of inappropriate SABA use. Similar associations were found for excessive SABA use with the exception that visit to respirologist and the use of systemic corticosteroids were associated with increased likelihood of excessive use.


Despite proven safety issues, inappropriate SABA use is still prevalent. Several factors belonging to patients’ characteristics and type/quality of care were associated with inappropriate use of SABAs and can be used to risk-stratify patients for targeted attempts to reduce this preventable cause of adverse asthma outcomes.

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Editor: Juan C. Ivancevich, MD

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