Risk Model Incorporating Donor IL6 and ifng Genotype and Gut Gvhd Can Discriminate High Risk Patients For Steroid Refractory Acute Gvhd
Notice bibliographique
Résumé
Introduction Steroid refractory acute GVHD (SR-aGVHD) occurs frequently and affects transplant outcomes adversely associated with high morbidities and mortalities. The present study attempted to develop a risk model predicting the risk of SR-aGVHD using both candidate single nucleotide polymorphism (SNP) and clinical risk factors. Methods A total of 268 patients were included who had diagnosis of acute GVHD and treated with systemic steroids. SR-aGVHD was defined with followings: 1) progression of GVHD after 3 days of systemic steroids; 2) No change after 7 days treatment; 3) incomplete response after 14 days of treatment initation. Patients were randomly divided into training (n=180) and validation sets (n=88) adjusted for the presence of SR-aGVHD, disease risk, grade 3/4 aGVHD, presence of gastrointestinal and liver involvement. A total of 259 SNPs in 53 genes were genotyped as previously described (Kim, Transplantation 2012). Clinical risk factors were also included to generate risk model for SR-aGVHD. Results Overall, 132 (47.3%) patients developed SR-aGVHD which was equally distributed in training and validation sets. In the training set, 85 patients (47.2%) developed SR-aGVHD. In univariate analysis, gut involvement (p<0.0001) and grade 3/4 aGVHD (p<0.0001) were identified as risk factors as well as donor genotypes of IL6 (rs1800797; p=6.15x10-4) and IFNG (rs2069727; p=4.37x10-4). Multivariate analysis confirmed that these two SNPs along with gut GVHD were independent risk factors for SR-aGVHD, but not grade 3/4 acute GVHD. A combined risk model was generating using 2 SNPs of IL6 (rs1800797), IFNG (rs2069727) and clinical risk factor of gut GVHD. A score of one was assigned to each of above risk factors and patients were divided based on these scores. Overall, the risk of SR-aGVHD increased as scores increased. Then we divided the patients into low risk (score 0, n=74) versus high risk groups (score 1, 2 and 3, n=106). Higher incidence of SR-aGVHD was noted in high risk group (61.3%; 65/106) vs low risk group (27%; 20/74; p<0.0001, OR 4.28 [95% CI 2.25-8.16]). The combined risk model was successfully replicated to stratify the groups risk of SR-aGVHD in the validation set (p=0.0045, OR 3.74 [95% CI 1.47-9.52]): incidence of SR-aGVHD was 57% in high risk group (31/54) vs 26% low risk group (9/34) in the validation set. When the combined risk model was used, using SNPs along with clinical risk factor, the risk model showed AUC of 0.738 in training set with sensitivity of 76 % and specificity of 56%. In the validation set, it showed AUC of 0.773 with sensitivity of 77 % and specificity of 52%. Conclusion The present study suggested that this risk model could identify high risk patient for SR-aGVHD with following information including donor genotype of IL6 (rs1800797) and IFNG (rs2069727) with gut involvement of GVHD. Disclosures: No relevant conflicts of interest to declare.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».