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Enregistrement W3097024120 · doi:10.1182/blood-2020-134985

The Burkitt Lymphoma International Prognostic Index (BL-IPI)

2020· article· en· W3097024120 sur OpenAlexaffabout
Adam J. Olszewski, Lasse Hjort Jakobsen, Graham P. Collins, Kate Cwynarski, Veronika Bachanová, Kristie A. Blum, Kirsten M Boughan, Mark Bower, Alessia Dalla Pria, Alexey V. Danilov, Kevin A. David, Catherine Diefenbach, Fredrik Ellin, Narendranath Epperla, Umar Farooq, Tatyana Feldman, Alina S. Gerrie, Deepa Jagadeesh, Manali Kamdar, Reem Karmali, Shireen Kassam, Vaishalee P. Kenkre, Nadia Khan, Andreas K. Klein, Izidore S. Lossos, Matthew A. Lunning, Peter Martin, Nicolas Martinex-Calle, Silvia Montoto, Seema Naik, Neil Palmisiano, David Peace, Elizabeth H. Phillips, Tycel Phillips, Craig A. Portell, Nishitha Reddy, Anna Santarsieri, Maryam Sarraf Yazdy, Knut B. Smeland, Scott E. Smith, Stephen D. Smith, Suchitra Sundaram, Parameswaran Venugopal, Adam Zayac, Xiaoyin Zhang, Catherine Zhu, Chan Y. Cheah, Tarec Christoffer El‐Galaly, Andrew M. Evens

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensSpinal Cord Injury BCUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésInternational Prognostic IndexMedicineHazard ratioInternal medicineProportional hazards modelConfidence intervalOncologyLactate dehydrogenaseCensoring (clinical trials)Diffuse large B-cell lymphomaGastroenterologyLymphomaPathologyBiology

Résumé

récupéré en direct d'OpenAlex

Background. BL is a rare, high-grade B-cell lymphoma that is often studied in trials with small sample sizes. Historical definitions of "low-risk BL" vary between studies, use arbitrary cutoffs for lactate dehydrogenase (LDH), and identify a small favorable group, leaving >80-90% of patients (pts) in an undifferentiated "high-risk" category. A validated prognostic index will help compare study cohorts and better define good-prognosis pts for whom reduced treatment would be appropriate vs a poor-prognosis group in need of new approaches. Herein, we constructed and validated a simplified prognostic model for BL applicable to diverse clinical settings across the world. Methods. We derived the BL-IPI from a large real-world evidence cohort of US adults treated for BL in 2009-2018 (Evens A, Blood 2020). Progression-free survival (PFS) from diagnosis until BL recurrence, progression, death, or censoring was the primary outcome. We first determined the best prognostic cutoffs for age, LDH (normalized to local upper limit normal, ULN), hemoglobin (Hgb), and albumin. Independent risk factors were ascertained by forward stepwise selection into Cox regression from candidate variables: age, sex, HIV+ status, ECOG performance status (PS) ≥2, advanced stage (3/4), involvement of >1 extranodal site, bone marrow, central nervous system (CNS), values of LDH, Hgb, and albumin. Derivation models used multiple imputation to mitigate bias from missing data and reported hazard ratios (HR) with 95% confidence interval (CI). BL-IPI groups, defined by inspection of survival curves, were compared using log-rank test for trend. We validated performance of the BL-IPI in an external retrospective dataset of BL pts treated contemporaneously in centers from the United Kingdom, Scandinavia, Canada, and Australia. Results. Characteristics of pts in the derivation (N= 633) and validation (N=457) cohorts are shown in the Table. Age ≥40 years (yr), LDH >3xULN, Hgb <11.5 g/dL, and albumin <3.5 g/dL were determined as optimal prognostic cutoffs. Age ≥40 yr, PS ≥2, stage 3/4, involvement of marrow, CNS, LDH >3xULN, low Hgb, and low albumin were associated with inferior PFS in univariate tests. In the multivariable model age ≥40 yr, LDH >3xULN, PS ≥2, and CNS involvement were selected as 4 independent prognostic factors; adding stage did not enhance the model. The model was simplified to 3 groups with 0 (low risk; 18% of pts), 1 (intermediate risk; 36% of pts; HR=3.14; 95%CI, 1.61-6.14), or 2-4 factors (high risk; 46% of pts; HR=6.52; 95%CI, 3.48-12.20; Fig A) with 3 yr PFS of 92%, 72%, and 53%, respectively (P<.001, Fig. B); median PFS was reached only in the high-risk group (46 months, 95%CI, 19-53). BL-IPI was similarly prognostic for overall survival (OS, P<.001; Fig. C). Among pts with stage III/IV (historically classified as "high-risk" and constituting 78% of all pts in the cohort), the BL-IPI further discriminated subgroups with 3 yr PFS of 87%, 71%, and 52%, respectively (P<.001; Fig. D), and OS of 95%, 75%, and 57%, respectively (P<.001; Fig. E). In addition, BL-IPI was prognostic regardless of HIV status, in the subcohort treated with rituximab (3 yr PFS: 92%, 73%, and 55%, respectively, P<.001), and among pts treated with specific regimens: CODOX-M/IVAC±R (3 yr PFS: 88%, 67%, 61%, respectively, P=.004), DA-EPOCH-R (3 yr PFS, 87%, 73%, 51%, respectively, P<.001), or hyperCVAD/MA±R (3yr PFS: 100%, 80%, 54%, respectively, P<.001). In the international validation cohort, fewer pts had CNS involvement; most received CODOX-M/IVAC+R; and PFS/OS estimates at 3 yr were higher. BL-IPI categories were of similar size (low-risk 15%, intermediate-risk 35%, high-risk 50%), and provided similar risk discrimination (Harrell's C=.65 in both datasets). PFS at 3 yr was 96%, 82%, and 63%, respectively (P<.001; Fig. F), and OS was 99%, 85%, and 64%, respectively (P<.001; Fig. G). In the validation cohort, BL-IPI remained prognostic in the subsets receiving rituximab (P<.001) and in advanced stage (P<.001). Conclusions. BL-IPI is a novel prognostic index specific to BL, which was validated to allow for simplified stratification and comparison of risk distribution in geographically diverse cohorts. The index identified a low-risk group with PFS >90-95%, which could be targeted with future strategies for treatment de-escalation. Conversely, only about 55-60% of pts in the high-risk group achieved cure with currently available immunochemotherapy. Disclosures Olszewski: Spectrum Pharmaceuticals: Research Funding; Genentech, Inc.: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding. Jakobsen:Takeda: Honoraria. Collins:ADC Therapeutics: Consultancy, Honoraria; Celleron: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Speakers Bureau; Amgen: Research Funding; BeiGene: Consultancy; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding; Taekda: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; Roche: Consultancy, Honoraria, Other: travel, accommodations, expenses , Speakers Bureau; Pfizer: Honoraria; Celgene: Research Funding. Cwynarski:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau. Bachanova:Incyte: Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; FATE: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding. Danilov:Abbvie: Consultancy; BeiGene: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Gilead Sciences: Research Funding; Takeda Oncology: Research Funding; Pharmacyclics: Consultancy; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Astra Zeneca: Consultancy, Research Funding; Verastem Oncology: Consultancy, Research Funding; Karyopharm: Consultancy; Aptose Biosciences: Research Funding; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy. Diefenbach:Trillium: Research Funding; Millenium/Takeda: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; Denovo: Research Funding. Epperla:Pharmacyclics: Honoraria; Verastem Oncology: Speakers Bureau. Farooq:Kite, a Gilead Company: Honoraria. Feldman:Pfizer: Research Funding; Portola: Research Funding; Janssen: Speakers Bureau; AstraZeneca: Consultancy; Cell Medica: Research Funding; Seattle Genetics, Inc.: Consultancy, Honoraria, Other: Travel expenses, Research Funding, Speakers Bureau; Viracta: Research Funding; Trillium: Research Funding; Rhizen: Research Funding; Corvus: Research Funding; BMS: Consultancy, Honoraria, Research Funding; Kite: Honoraria, Other: Travel expenses, Speakers Bureau; Celgene: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses; Amgen: Research Funding; Pharmacyclics: Honoraria, Other, Speakers Bureau; Abbvie: Honoraria; Bayer: Consultancy, Honoraria; Eisai: Research Funding; Kyowa Kirin: Consultancy, Research Funding. Gerrie:AbbVie: Consultancy, Honoraria, Research Funding; Astrazeneca: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Sandoz: Consultancy. Jagadeesh:Regeneron: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Debiopharm Group: Research Funding; MEI Pharma: Research Funding; Verastem: Membership on an entity's Board of Directors or advisory committees. Kamdar:BMS: Consultancy; Abbvie: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Pharmacyclics: Consultancy; Seattle Genetics: Speakers Bureau. Karmali:Takeda: Research Funding; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; Karyopharm: Honoraria; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Khan:Seattle Genetics: Research Funding; Janssen: Honoraria; Pharmacyclics: Honoraria; Bristol Myers Squibb: Research Funding; Celgene: Research Funding. Klein:Takeda: Membership on an entity's Board of Directors or advisory committees. Lossos:Verastem: Consultancy, Honoraria; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy, Other; Janssen Biotech: Honoraria; NCI: Research Funding; Janssen Scientific: Consultancy, O

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,241
Écart entre enseignants0,227 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission2
Résumé présentoui

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