Treatment considerations and prescribing issues for obese children with asthma
Notice bibliographique
Résumé
Overweight and obesity are important risk factors for poor control and exacerbation in pediatric asthma. While overweight and obese patients with asthma appear to be characterized by a different pathological process giving rise to a distinct phenotype, national and international guidelines recommend a one-size-fits-all therapeutic approach regardless of weight. This is because it is unclear whether the observed higher morbidity associated with the obese-asthma phenotype in children are due to poor response to conventional inhaled corticosteroid (ICS) therapies, treatment nonadherence, or other confounding factors. The overall aim of my thesis work was to investigate the impact of weight status on treatment-associated factors that may influence the risk of management failure and/or exacerbation in children with asthma, using causal inference methods. Specifically, I assessed whether elevated weight status: i) modified the response to different asthma maintenance therapies, based on treatment Step-2 (first manuscript) and Step-3 (second manuscript) recommended across pediatric asthma guidelines; and (ii) was associated with asthma medication nonadherence (third manuscript). From a methodological standpoint, a bias-reduction correction method was also developed to address analytical issues when estimating total effects on the hazard ratio scale (fourth manuscript). In this thesis, I analyzed a historical cohort of children with physician-diagnosed asthma consulting at the Asthma Center of the Montreal Children's Hospital between Jan 2000 and Dec 2007, linking clinical data to administrative health and prescription claims databases. In the first manuscript, there was an increased hazard of management failure in low-dose ICS and leukotriene receptor antagonists (LTRA) maintenance monotherapy (Step-2) users with elevated body mass index (BMI) percentiles. Findings also suggested a differential response to Step-2 maintenance therapies by weight status on the multiplicative (HR) scale, with LTRA achieving maximal effectiveness at higher BMI percentiles. In the second manuscript, obesity (BMI>97th percentile) was found to be an important determinant of shorter exacerbation-free time among those initiating Step-3 therapies, i.e. higher-dose ICS or low-/medium-dose ICS in combination with LTRA or long-acting beta-2 agonists (LABA). While the assessment of treatment effect modification by obesity status on the additive and multiplicative scales were not found to be statistically significant, the marginal survival curves demonstrated a potential favorable response to combination therapy relative to higher-dose ICS monotherapy in obese compared to non-obese children. In the third manuscript, weight status (BMI percentile) and excess weight (BMI>85th percentile) were associated with primary nonadherence in those newly prescribed low-dose ICS and secondary nonadherence in those with more complex regimens. Collectively, this work sheds light on potential mechanisms that may contribute to the higher morbidity associated with obese-asthma in children. The observation that LTRA monotherapy achieved maximal response in children with higher BMIs may reflect the contribution of other pathways to the asthma phenotype of obese children with mild severity. Combination therapy appeared to provide an additional benefit in terms of prolonging exacerbation-free time than higher-dose ICS alone in obese compared to non-obese children with asthma. This could suggest that obese children with severe asthma may have a tendency to exhibit resistance to higher doses of ICS. Lastly, higher weight status was associated with medication nonadherence, which may be a result of perceived treatment ineffectiveness, socioeconomic factors, misconceptions about side effects, or weight-related psychosocial comorbidities. Further research is necessary to provide an in depth understanding of the biological drivers causing the obese-asthma 'difficult-to-treat' phenotype.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».