Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Background: Benralizumab is effective in severe eosinophilic asthma (SEA), but suboptimal responses are observed in some patients. Although several factors have been associated with benralizumab response, no cluster analysis has yet been undertaken to identify different responsiveness sub-phenotypes.

Objective: To identify SEA sub-phenotypes with differential responsiveness to benralizumab.

Methods: One hundred and five patients diagnosed with SEA who had completed 6 months of benralizumab treatment were included in a hierarchical cluster analysis based on a set of clinical variables that can be easily collected in routine practice (age, age at disease onset, disease length, allergen sensitization status, blood eosinophil count, IgE levels, FEV % predicted, nasal polyposis, bronchiectasis).

Results: Four clusters were identified: Clusters 2 and 3 included patients with high levels of both IgE and eosinophils (type-2 biomarkers high), whereas Clusters 1 and 4 included patients with only one type-2 biomarker at a high level: IgE in Cluster 1 and eosinophils in Cluster 4. Clusters 2 and 3 (both type-2 biomarkers high) showed the highest response rate to benralizumab in terms of elimination of exacerbations (79% and 80% respectively) compared to Clusters 1 and 4 (52% and 60% respectively). When super-response (the absence of exacerbation without oral corticosteroid use) was assessed, Cluster 2, including patients with more preserved lung function than the other clusters, but comparable exacerbation rate, oral corticosteroid use and symptom severity, was the most responsive cluster (87.5% of patients).

Conclusions: Our cluster analysis identified benralizumab differential response sub-phenotypes in SEA, with the potential of improving disease treatment and precision management.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293293PMC
http://dx.doi.org/10.1111/cea.14026DOI Listing

Publication Analysis

Top Keywords

cluster analysis
16
severe eosinophilic
8
eosinophilic asthma
8
cluster
8
clusters included
8
included patients
8
type-2 biomarkers
8
biomarkers high
8
oral corticosteroid
8
clusters
6

Similar Publications

Semantic composition allows us to construct complex meanings (e.g., "dog house", "house dog") from simpler constituents ("dog", "house").

View Article and Find Full Text PDF

Background: Pituitary adenomas are relatively common benign intracranial tumors that may cause significant hormonal imbalances and visual impairments. Radiotherapy (RT) remains an important treatment option, particularly for patients with residual tumor after surgery, recurrent disease, or ongoing hormonal hypersecretion. This study summarizes long-term clinical outcomes and radiation-associated toxicities in patients with pituitary adenomas treated with contemporary radiotherapy techniques at a single institution.

View Article and Find Full Text PDF

Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.

View Article and Find Full Text PDF

Background: Parkinson's disease (PD) often presents with lateralized motor symptoms at onset, reflecting asymmetric degeneration of the substantia nigra (SN). Neuromelanin (NM) loss and iron accumulation are hallmarks of SN pathology in PD, but their spatial distribution and interrelationship in PD patients with right-sided (PDR) or left-sided (PDL) motor symptom onset remain unclear.

Purpose: To investigate the spatial vulnerability and interrelationship of NM and iron in the SN among PDR, PDL, and healthy controls (HCs) using MRI.

View Article and Find Full Text PDF

Acute lymphoblastic leukemia (ALL) is the most common hematologic malignancy in children. Current clinical diagnosis primarily relies on invasive detection methods, while molecular subtyping remains a complex and time-consuming process. This study innovatively employed silver nanoparticle-based surface-enhanced Raman spectroscopy (SERS) technology to systematically analyze 116 serum samples, including those with breakpoint cluster region-Abelson (-) fusion genotype, mixed-lineage leukemia (, also known as lysine methyltransferase 2A, ) gene rearrangement subtype, T-lymphoblastic ALL, and healthy controls.

View Article and Find Full Text PDF