Definition of the Responder to Hydroxyurea Therapy: Revisited.
Notice bibliographique
Résumé
Abstract Abstract 1513 Poster Board I-536 Introduction Treatment of sickle cell anemia with hydroxyurea (HU) is associated with significant decreases in the frequency of painful crises, acute chest syndrome, morbidity, and mortality. Some patients, however, show no improvement even with prolonged HU therapy. Identifying treatment responders is important for predicting clinical improvements and for assessing the risk/benefit ratio of HU treatment for individual patients. The salutary effects of HU are thought to be the result of increasing the fetal hemoglobin (Hb F) level. NHLBI guidelines for sickle cell treatment define levels of 15%-20% Hb F as therapeutic endpoints. Research and reviews based on pediatric and adult patients have variously argued that levels from about 10% to 20% are beneficial. Patients and Methods Patients in this study were from the Multicenter Study of Hydroxyurea (MSH) in Sickle Cell Anemia, a randomized double-blind placebo controlled trial of HU. The N=299 adult patients were recruited from 21 sites across the U.S. and Canada, and were evenly distributed between males and females. Following randomization to placebo or HU, patients had biweekly follow-up visits until the trial was terminated early due to a significant reduction in painful crises (the primary study endpoint) in the HU arm. Levels of Hb F in MSH patients were assessed at baseline and again approximately 18-21 months after treatment began, with the level at each time being the average of two measurements. In the previously reported MSH study, patients were divided into quartiles of Hb F change as a measure of response to HU treatment. In this approach the bottom two quartiles showed either no or minimal positive change in Hb F levels, and fully overlapped with placebo group in the extent of change. We redefined HU patients as ‘responders’ or ‘nonresponders’ based on a 15% Hb F threshold; those with baseline HbF below 15% and follow-up above 15% were labeled ‘responders,’ while all others were labeled ‘nonresponders.’ The 15% level was chosen due to its frequent identification in previous publications as a level at which meaningful benefits could be expected. For both coding schemes, we compared the following outcomes between subgroups: rate of painful crises, proportion of days at home with pain and with opioid use, and average daily pain. Results Using the 15% rule, responders had significantly better outcomes than nonresponders on all outcome measures: rate of painful crises (p=.011), proportion of at-home days with pain (p=.025), proportion of days with analgesic use (p=.002), and average daily pain (p<.0001). Nonresponders, in turn, did not differ from the placebo group on any of these outcomes. Using the quartiles approach, the highest quartile had significantly fewer painful crises (p<.05) than the bottom two and placebo, but did not differ from the second highest; for the proportion of days with pain, the highest group did not differ from 2 of the other 3 quartiles or from the placebo group. Only for proportion of days with analgesic use and average daily pain did the highest quartile significantly differ from all other quartiles and from placebo patients. Finally, applying the 15% rule to the pl‘cebo group resulted in no placebo patients being mislabeled as treatment responders, suggesting that increases above the 15% cutoff for post-treatment Hb F levels is outside normal variability in sickle cell patients not in HU treatment. Conclusions The 15% Hb F rule successfully identified a ‘responder’ group that significantly differed from other HU patients and from placebo patients on all outcomes, including painful crises. Despite overlap with responders under the 15% rule, patients in the highest quartile for Hb F change did not consistently differ from all other quartiles or placebo on the primary outcome (painful crises) and on proportion of days with pain. Our data suggest that using the 15% Hb F threshold identifies a subset of patients with the best clinical outcomes. Disclosures No relevant conflicts of interest to declare.
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 | 0,047 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,002 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,009 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».