Prognostic Value of Disease Risk Score Versus Gait Speed in Older Adults with Lymphoma
Notice bibliographique
Résumé
Background: Frailty is associated with poor tolerance to chemotherapy and mortality. While recent evidence suggests measures of frailty such as gait speed predict survival in older patients with hematologic malignancies such as lymphoma (Liu, Blood, 2019), its predictive ability has not been compared to disease-based prognostic risk scores. Methods: From February 2015 to April 2019, all patients aged 75 years and older who presented for initial consultation for at the Dana-Farber Cancer Institute for diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL) and Waldenstrom macroglobulinemia (WM) were approached for a screening frailty evaluation by a trained research assistant. As part of the evaluation, 4-meter gait speed was assessed (at normal pace with non-ambulatory patients recorded as having a speed of zero). Disease-specific prognostic scores were obtained retrospectively, including the International Prognostic Index (IPI) for DLBCL, the Follicular Lymphoma International Prognostic Index (FLIPI) for FL, and the International Prognostic Scoring System for Waldenstrom Macroglobulinemia (ISSWM). Univariate and multivariable Cox proportional hazard models were used to determine predictors of overall survival (OS; from date of screening geriatric assessment) including gait speed and disease-specific scores. Those with incomplete risk data excluded from disease-specific analyses. Hazard ratios and generalized R2 (higher the R2, stronger association with OS) were also calculated. Results: A total of 145 patients were included, 59 with DLBCL, 30 with FL, 56 with WM. Median age was 79 (IQR 77 to 82) and 42.1% were female. For the overall cohort, mean gait speed was 0.78 (±0.27). Risk score distribution and disease-specific gait speed are presented in the table. With a median follow-up of 20.9 months (IQR 10.2 to 34.5), median OS was 14.4 months for DLBCL, 17.2 months for FL and 32.0 months for WM. Disease-specific prognostic scores were not predictive of survival for DLBCL (p = 0.76) or FL (p = 0.22) but were for WM (p = 0.04). Overall, faster gait speed was significantly predictive of OS (HR 0.10, 95% CI 0.03 to 0.32, p < 0.0001) for all three lymphomas combined, which would mean an HR of 0.56 for an increase of 0.25 m/s of gait speed. In disease-specific analyses, faster gait speed was predictive of survival for DLBCL (HR 0.10, 95% CI 0.02 to 0.46, p = 0.003) and WM (HR 0.11, 95% CI 0.01 to 0.83, P = 0.03) but not for FL (P = 0.26). In DLBCL, gait speed explained significant variability in OS (R2 = 22.95%) compared with IPI score (R2 = 1.87%; p = 0.005) whereas it did not for WM compared with IPSSWM (R2 = 10.22% vs. 5.58%, p = 0.11) or FL compared with the FLIPI (R2= 12.21% vs. 8.10%, p = 0.35). Conclusion: Gait speed may help to further refine the prediction of outcomes in patients with aggressive lymphomas beyond standard prognostic scores but may have less of an effect for indolent lymphomas. These data suggest that gait speed should be incorporated into the standard assessment of patients with aggressive lymphomas. Table Disclosures Buckstein: Takeda: Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Soiffer:Mana therapeutic: Consultancy; Kiadis: Other: supervisory board; Juno, kiadis: Membership on an entity's Board of Directors or advisory committees, Other: DSMB; Gilead, Mana therapeutic, Cugene, Jazz: Consultancy; Cugene: Consultancy; Jazz: Consultancy.
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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 | 0,003 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».