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4CPS-141 Real-life impact of including montelukast as premedication on the incidence of infusion-related reactions to isatuximab and description of risk factors

2024· article· en· W4393026380 on OpenAlexfundno aff
MDC Jiménez León, JA Hernandez Ramos, M Martín Rodríguez, E Guerrero Hurtado, A Prieto Romero, F Mayo Olveira, Francisco Martínez de la Torre, MD Canales Siguero, JM Ferrari Piquero

Bibliographic record

VenueSection 4: Clinical pharmacy services · 2024
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
FundersLeslie Dan Faculty of Pharmacy, University of TorontoUniversity of Waterloo
KeywordsDiscontinuationTolerabilityMedicineRegimenPlaceboPremedicationOdds ratioAdverse effectNumber needed to treatInternal medicineConfidence intervalRelative riskAnesthesia

Abstract

fetched live from OpenAlex

Background and Importance Infusion-related reactions (IRR) are one of isatuximab’s most frequent and significant adverse reactions that may lead to treatment discontinuation despite premedicating with dexamethasone, paracetamol, and anti-H1 antihistamines. Similarly to daratumumab, adding montelukast as premedication could improve its tolerability. Additionally, there are no studies to date describing which risk factors (RF) may affect the likeliness of an isatuximab IRR. Aim and Objectives The primary objective was to assess the impact of including montelukast as premedication on the incidence of IRR (iIRR) associated with the administration of isatuximab. Secondary objectives included describing the iIRR in a real-life setting and evaluating possible risk factors: food, environmental or medicine allergies; previous IRR; and infusion bag concentration. Material and Methods Multicentric retrospective study conducted in one secondary and three tertiary hospitals. Eligibility criteria included adults having started isatuximab and excluded patients receiving off-label corticosteroid doses and those enrolled in clinical trials. Follow-up was carried out until September 2023, treatment discontinuation or death. Baseline characteristics were sex, age, treatment regimen, premedication regimen, number of isatuximab doses and occurrence of IRR. These numerical and categorical variables were expressed as number of observations and medians respectively. Odds ratios (OR) and Mann-Whitney U tests were calculated to evaluate qualitative and quantitative RF, respectively. Absolute risk reduction (ARR) and number needed to treat (NNT) were used to assess the impact of montelukast as premedication. 95% confidence intervals (95%CI) were applied. Results 40 patients were included, with a median age of 66 (54 – 72) years, 60.0% being men. The median number of isatuximab doses per patient was 8 (4–18). The iIRR for cycle-one-day-one was 7.7% for the group premedicated with montelukast and 29.6% without. OR was 0.20 (95% CI 0.02 – 1.79), ARR was 0.22 (95% CI -0.01 – 0.44) and NNT was 5. No IRR were found for second or further doses in any patient and no risk factors were found. Conclusion and Relevance In our experience, iIRR observed for isatuximab was lower compared to pivotal clinical trials. The inclusion of montelukast as premedication might reduce IRR, which should be confirmed in subsequent studies. References and/or Acknowledgements Conflict of Interest No conflict of interest.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.108
GPT teacher head0.444
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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