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Record W4327684002 · doi:10.1111/all.15711

Comparative effectiveness of <scp>anti‐IL5</scp> and <scp>anti‐IgE</scp> biologic classes in patients with severe asthma eligible for both

2023· article· en· W4327684002 on OpenAlexafffund
Paul Pfeffer, Nasloon Ali, Ruth Murray, Charlotte Suppli Ulrik, Trung N. Tran, Jorge Máspero, Matthew Peters, George Christoff, Mohsen Sadatsafavi, Carlos A. Torres‐Duque, Alan Altraja, Lauri Lehtimäki, Nikolaos G. Papadopoulos, Sundeep Salvi, Richard W. Costello, Breda Cushen, Enrico Heffler, Takashi Iwanaga, Mona Al‐Ahmad, Désirée Larenas‐Linnemann, Piotr Kuna, João Fonseca, Riyad Al‐Lehebi, Chin Kook Rhee, Luis Pérez de Llano, Diahn‐Warng Perng Steve, Bassam Mahboub, Eileen Wang, Celine Goh, Juntao Lyu, A Newell, Marianna Alacqua, А. S. Belevskiy, Mohit Bhutani, Leif Bjermer, Unnur Steina Björnsdóttir, Arnaud Bourdin, Anna von Bülow, John Busby, Giorgio Walter Canonica, Borja G. Cosío, Delbert R. Dorscheid, Mariana Muñoz‐Esquerre, J. Mark FitzGerald, Esther García Gil, Peter G. Gibson, Mark Hew, Ole Hilberg, Flavia Hoyte, David J. Jackson, Mariko Siyue Koh, Hsin‐Kuo Ko, Jae Ha Lee, Sverre Lehmann, Cláudia Chaves Loureiro, Dóra Lúðvíksdóttir, Andrew N. Menzies‐Gow, Patrick Mitchell, Andriana Ι. Papaioannou, Todor A. Popov, Celeste Porsbjerg, Laila Salameh, Concetta Sirena, Camille Taillé, Christian Taube, Yuji Tohda, Michael E. Wechsler, David Price

Bibliographic record

VenueAllergy · 2023
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
FundersDaiichi Sankyo EuropeUCB PharmaSanofi GenzymeSeqirusTeijin PharmaUniversitetet i BergenAstellas PharmaCanadian Institutes of Health ResearchKuwait Foundation for the Advancement of SciencesDaiichi-SankyoQueen's University BelfastMichael Smith Health Research BCQueen's UniversityAstraZenecaAllergy TherapeuticsRegeneron PharmaceuticalsMylanSingapore General HospitalBritish Columbia Lung AssociationChiesi FarmaceuticiCovis PharmaGenentechUniversity of DundeeDanoneMedical Research CouncilTeva Pharmaceutical IndustriesHandokEli Lilly and CompanySanofiAmgenShionogiMeiji Seika PharmaPfizerGlaxoSmithKline
KeywordsAsthmaImmunologyMedicineImmunoglobulin EAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with severe asthma may present with characteristics representing overlapping phenotypes, making them eligible for more than one class of biologic. Our aim was to describe the profile of adult patients with severe asthma eligible for both anti-IgE and anti-IL5/5R and to compare the effectiveness of both classes of treatment in real life. METHODS: This was a prospective cohort study that included adult patients with severe asthma from 22 countries enrolled into the International Severe Asthma registry (ISAR) who were eligible for both anti-IgE and anti-IL5/5R. The effectiveness of anti-IgE and anti-IL5/5R was compared in a 1:1 matched cohort. Exacerbation rate was the primary effectiveness endpoint. Secondary endpoints included long-term-oral corticosteroid (LTOCS) use, asthma-related emergency room (ER) attendance, and hospital admissions. RESULTS: In the matched analysis (n = 350/group), the mean annualized exacerbation rate decreased by 47.1% in the anti-IL5/5R group and 38.7% in the anti-IgE group. Patients treated with anti-IL5/5R were less likely to experience a future exacerbation (adjusted IRR 0.76; 95% CI 0.64, 0.89; p < 0.001) and experienced a greater reduction in mean LTOCS dose than those treated with anti-IgE (37.44% vs. 20.55% reduction; p = 0.023). There was some evidence to suggest that patients treated with anti-IL5/5R experienced fewer asthma-related hospitalizations (IRR 0.64; 95% CI 0.38, 1.08), but not ER visits (IRR 0.94, 95% CI 0.61, 1.43). CONCLUSIONS: In real life, both anti-IgE and anti-IL5/5R improve asthma outcomes in patients eligible for both biologic classes; however, anti-IL5/5R was superior in terms of reducing asthma exacerbations and LTOCS use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.274
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2023
Admission routes2
Has abstractyes

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