Response to Steroids Predicts Response to Rituximab In Pediatric Chronic Immune Thrombocytopenia.
Notice bibliographique
Résumé
Abstract Abstract 3681 Background: Pediatric Immune Thrombocytopenia (ITP) has an incidence of 4–6/100,000 with 1/3 of cases becoming chronic. Treatment choice is arbitrary, because few studies are powered to identify predictors of therapy response. Increasingly, rituximab is becoming a treatment of choice in those refractory to other therapies (Neunert CE, et al. Pediatr Blood Cancer 2008; 51(4):513). Previous studies in ITP have not examined predictors of response to rituximab or whether response to prior treatments predicts response. Objective: To evaluate univariate and multivariable predictors of platelet count response to rituximab. Methods: After local IRB approval, 550 patients with chronic ITP enrolled in the longitudinal, North American Chronic ITP Registry (NACIR) between January 2004 and June 2010. Eligibility included: ages 6 months-18 years at ITP diagnosis, clinical diagnosis of ITP, and ITP duration >6 months. Primary ITP was defined as isolated thrombocytopenia without associated conditions. Secondary ITP included those patients with immune thrombocytopenia associated with other immune-mediated medical conditions, including Evans Syndrome. Treatment response was defined as a post-treatment platelet count ≥50,000/uL within 16 weeks of rituximab and within 14 days of steroids. Steroids were prescribed as 1–4 mg/kg prednisone or adult equivalent over 4–14 days with or without taper. The NACIR captured treatment responses both retrospectively prior to enrollment and then prospectively, and both periods were included in this analysis. The multivariable logistic regression modeling process utilized SAS 9.1 using binary variables which were either significant in the univariate analysis or clinically important. A backwards elimination procedure was used to select the final model. Results: Seventy-six (13.8%) patients were treated with rituximab. Demographics of the patients treated with rituximab include: 42% male; 81% Caucasian, 17% Black, and 2% Asian. The mean age at diagnosis of ITP was 8.4 ± SD 5.1 years. The median platelet count at diagnosis of acute ITP was 10,000/uL (IQR 5,000-20,000/uL). 19 (25%) patients had secondary ITP or Evans syndrome. Treatment with rituximab had an overall response rate of 63.2% (48/76). Univariate predictors of response to rituximab are shown in Table I. The strongest univariate predictor of response to rituximab was response to steroids. Gender, ethnicity, and race were not predictive of response to rituximab. Furthermore, other variables which did not predict rituximab response include: history of a bleeding score ≥3 (Buchanan and Adix, J Pediatr 2002; 141: 683), symptoms ≥1 month prior to ITP diagnosis, older age (age >5 years), platelets ≥20,000/uL at acute ITP diagnosis, and a positive ANA. In multivariable analysis, response to steroids remained a strong predictor of response to rituximab with an OR 6.2 (95% CI 1.8–21.3, p=0.004). Secondary ITP also remained a strong a predictor of a positive response to rituximab with an OR 5.9 (95% CI 1.2–33.3, p=0.03). Conclusion: In the NACIR, response to steroids and secondary ITP were strong predictors of response to rituximab, a finding not previously reported in children or adults. Although this finding requires further validation, this result may provide evidence that rituximab should be most considered in patients previously responsive to steroids. Disclosures: Off Label Use: Rituximab for chronic ITP. Lambert:Cangene: Membership on an entity's Board of Directors or advisory committees. Klaassen:Novartis: Research Funding; Cangene: Research Funding. Neufeld:Novartis, Inc: Research Funding.
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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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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) | 0,002 | 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 ».