Evidence map›Paper›PMID 41346457›Full record

ArticleAsia Pacific allergy2025

The frequency and predictability of switching biologic agents in severe asthma: A real-world perspective.

Ali Can, Özge Atik

Abstract read
In one paragraph

Article in Asia Pacific allergy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ali CanDivision of Immunology and Allergic Diseases, Department of Internal Medicine, Van Training and Research Hospital, University of Health Sciences, Van, Turkey.ORCID https://orcid.org/0000-0001-8239-4071
Özge AtikDivision of Immunology and Allergic Diseases, Department of Internal Medicine, Van Training and Research Hospital, University of Health Sciences, Van, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: While phenotyping assists in selecting the right biologic agent for severe asthma, the frequency of patients transitioning to a different biologic is also significant. Objective: We sought to identify the factors that predict switching between biological agents. Methods: The study involved 165 adults with severe asthma and included a 3-year follow-up period. Clinical and spirometric data were evaluated alongside the demographic characteristics of the patients. In addition to skin prick tests and specific immunoglobulin E (IgE) tests, eosinophil counts and total IgE measurements were assessed. Results: The patient group comprised 124 female participants (75%); the average age was 48 ± 13 years. Over half of the patients had eosinophilic asthma (56%) and allergic asthma (52%). In the first-line treatment, 111 patients (68%) were started on omalizumab, 43 patients (26%) on mepolizumab, and 11 patients (6%) on benralizumab. A total of 37 patients required a change in their biologic treatment. The 2 key factors influencing this switch were the initial annual number of asthma attacks and the level of improvement in the asthma control test score assessed during the first evaluation after starting the biologic (odds ratio 2.23 [95% confidence interval: 1.49-3.33]; Conclusion: Patients with higher annual attacks at baseline and lower responsiveness at the first postbiologic efficacy assessment can be useful in predicting switching status. However, further well-designed studies involving a larger number of patients and conducted over a longer term are needed to identify additional factors that could assist in selecting the most appropriate biologic.

Indexed as

biologic agentpredictreal-worldsevere asthmaswitcherswitching

Identifiers

PMID41346457
PMCPMC12672185

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.