Risk factors associated with safety of preschool peanut oral immunotherapy
Notice bibliographique
Résumé
BackgroundAn understanding of how patient characteristics such as age, baseline peanut-specific IgE, and atopic comorbidities may influence potential safety outcomes during peanut oral immunotherapy (P-OIT) could aid in shared decision making between clinicians and patient families.ObjectiveThis study explored the relationship between baseline patient characteristics and reactions during P-OIT using a large sample size to better understand potential risk factors influencing P-OIT safety.MethodsData were obtained from the Food Allergy Immunotherapy (FAIT) registry, which collects real-world OIT data from community and academic allergy clinics across Canada. Multivariable logistic regression modeling was performed to examine the relationship between baseline patient characteristics and reactions during P-OIT. Multiple imputation was applied to reduce potential bias caused by missingness and to maximize the use of available information to preserve statistical power.ResultsBetween April 2017 and June 2021, a total of 653 eligible patients initiated P-OIT. Multivariable regression analysis showed pre-OIT grade 2+ initial reaction (odds ratio [OR] = 1.33, 95% confidence interval [CI] 1.10, 1.61), allergic rhinitis (OR = 1.60, 95% CI 1.08, 2.38), older age (OR = 1.01, 95% CI 1.00, 1.02), and higher baseline peanut-specific IgE (OR = 1.02, 95% CI 1.02, 1.03) were associated with grade 2+ reaction during P-OIT after adjusting for potential risk factors.ConclusionOur study identified several clinically important risk factors for grade 2+ reactions during P-OIT: pre-OIT grade 2+ initial reaction, allergic rhinitis, older age, and higher baseline peanut-specific IgE. These results highlight the need for individualized risk stratification for OIT. An understanding of how patient characteristics such as age, baseline peanut-specific IgE, and atopic comorbidities may influence potential safety outcomes during peanut oral immunotherapy (P-OIT) could aid in shared decision making between clinicians and patient families. This study explored the relationship between baseline patient characteristics and reactions during P-OIT using a large sample size to better understand potential risk factors influencing P-OIT safety. Data were obtained from the Food Allergy Immunotherapy (FAIT) registry, which collects real-world OIT data from community and academic allergy clinics across Canada. Multivariable logistic regression modeling was performed to examine the relationship between baseline patient characteristics and reactions during P-OIT. Multiple imputation was applied to reduce potential bias caused by missingness and to maximize the use of available information to preserve statistical power. Between April 2017 and June 2021, a total of 653 eligible patients initiated P-OIT. Multivariable regression analysis showed pre-OIT grade 2+ initial reaction (odds ratio [OR] = 1.33, 95% confidence interval [CI] 1.10, 1.61), allergic rhinitis (OR = 1.60, 95% CI 1.08, 2.38), older age (OR = 1.01, 95% CI 1.00, 1.02), and higher baseline peanut-specific IgE (OR = 1.02, 95% CI 1.02, 1.03) were associated with grade 2+ reaction during P-OIT after adjusting for potential risk factors. Our study identified several clinically important risk factors for grade 2+ reactions during P-OIT: pre-OIT grade 2+ initial reaction, allergic rhinitis, older age, and higher baseline peanut-specific IgE. These results highlight the need for individualized risk stratification for OIT.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».