Prognostic Value of Disease Risk Score Versus Gait Speed in Older Adults with Lymphoma
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".