Prognostic Power of Chronic Gvhd Risk Score Model by Ibmtr Can Be Improved with Addition of Absolute Lymphocyte Counts and Eosinophil Counts At the Onset of Chronic Gvhd
Notice bibliographique
Résumé
Abstract Abstract 4184 Background: Chronic graft-versus-host disease (cGVHD) risk score by Arora et al (Blood 2011) was successfully demonstrated to stratify patients with cGVHD according to their transplantation outcomes. In this risk score model, the following variables were included: recipient age, donor type, sex mismatch, GVHD prophylaxis, prior acute GVHD, time from HSCT to cGVHD, disease status before HSCT, platelet count at cGVHD diagnosis, serum bilirubin at cGVHD onset and performance status at diagnosis of cGVHD. However, this risk score system needs to be validated in an independent cohort. Methods: A total of 425 consecutive patients who survived beyond 100 days following allogeneic hematopoietic stem cell transplantation (HSCT) at the Princess Margaret Hospital from Jan 1996 to Oct 2007, were reviewed retrospectively and evaluated for the cGVHD risk score[J1]. Results: Out of 425 patients, 317 patients developed cGVHD according to the NIH consensus criteria of GVHD. Ten patients were removed from the analysis due to missing variables. Long-term transplant outcomes of the remaining 307 patients were evaluated using the cGVHD risk score system. First, overall survival (OS) according to the risk group (RG) were 82.5±11.3 (RG1), 76.4±3.4 (RG2), 69.4±6.5 (RG3), and 27.3±13.4 (RG4) (p<0.001), while non-relapse mortality (NRM) were 10.0±9.5 (RG1), 10.4±2.4 (RG2), 21.4±5.8 (RG3), and 69.3±14.7 (RG4) (p<0.001). Significantly lower OS and higher NRM was noted in RG4. However, there is no statistical difference of the transplant outcomes among 3 groups of RG1, RG2 and RG3 for OS and NRM. Next, in order to improve a stratification power of cGVHD risk score model, two other clinical variables were incorporated into the model, absolute lymphocyte count (ALC) and eosinophil count (EOS) at the onset of cGVHD. The patients with lower (ALC, <1.0·109/L) and lower eosinophil counts (EOS, <0.5·109/L) at the onset of cGVHD were associated with inferior OS and higher risk of NRM. In a multivariate analysis, lower ALC was confirmed to be an independent variable predicting OS (HR 1.94, 95% CI 1.14–3.28, p=0.014) and NRM (HR 2.87, 95% CI 1.35–6.08, p=0.006). Similarly, EOS was also confirmed to be an independent variable predicting OS (HR 3.27, 95% CI 1.28–8.38, p=0.014). Accordingly, these two variables (i.e. ALC and EOS) were tested in a revised cGVHD risk score model and patients were stratified into revised 4 groups: rRG1 (score 0–3), rRG2 (score 4–6), rRG3 (score 7–9), rRG4 (score °Ã10). Overall survival (OS) according to the revised risk group (rRG) were 93.3±6.4 (rRG1), 81.4±4.1 (rRG2), 68.2±4.6 (rRG3), and 32.0±1.7 (rRG4) (p<0.001). Non-relapse mortality (NRM) were 0.0 (rRG1), 7.9±2.8 (rRG2), 17.2±3.8 (rRG3), and 62.2±11.8 (rRG4) (p<0.001). The revised cGVHD-risk score appears to better discriminatepatients according to their transplant outcomes (particularly OS and NRM). Conclusion: The original cGVHD risk score model has been successfully validated to stratify transplant patients according to their risk of cGVHD[J2]. In addition, revised cGVHD risk score model including ALC and EOS at cGVHD onset improved prognostic stratification of the patients according to their transplantation 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 | 0,009 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».