Geriatric Assessment and Frailty Changes in Older Patients with Newly-Diagnosed Multiple Myeloma Undergoing Treatment
Bibliographic record
Abstract
Introduction Multiple myeloma (MM) is a malignant plasma cell disease with a median age at diagnosis of 70 years. Geriatric assessment and frailty measures are increasingly being utilized at the time of diagnosis for prognostication. Gait speed at baseline has recently emerged as a powerful prognostic tool which identifies frailty and predicts outcomes independent of performance status among older patients with blood cancers including myeloma (Liu et al, Blood 2019). However, the longitudinal assessment and responsiveness of change in geriatric assessment measures and functional frailty parameters, including physical performance such as gait speed, over time remains unknown. Objectives We conducted a prospective study of patients with newly diagnosed MM aged 65 and older at two institutions. The prevalence of geriatric domains at baseline has been previously published by Wildes et al (JAGS, 2019). We aimed to conduct a secondary analysis to understand the changes in geriatric and frailty assessment including physical performance as older patients underwent treatment for their myeloma. Methods Older patients with newly-diagnosed myeloma underwent a comprehensive geriatric assessment including a gait function using the Timed Up and Go test at baseline and at 6 months between the years 2012-2014. Results At baseline, forty patients were enrolled in the study with a mean age of 71.6 years; 25 (62.5%) were males. Thirty-six patients completed the 6-month follow-up with 18 patients having undergone a stem cell transplant in the interim. Overall, there were no significant change in the measured geriatric domains, including dependence, physical activity, falls, polypharmacy and cognition, at 6 months compared to baseline. Overall mental health well-being, measured with the Mental Health Inventory-17, improved over time (Table I). Physical performance, assessed with the Timed Up and Go test, showed a trend toward improvement as patients underwent treatment (11.0 seconds at the 6-month follow-up versus 12.3 at baseline, p=0.057). Additionally, two out of four individuals who were unable to complete the Timed Up and Go test at baseline were subsequently able to complete it 6 months following treatment. Conclusion Our study suggests that, for older patients with MM, treatment does not significantly lead to geriatric impairment at 6-months of follow-up, as compared to baseline and in fact is associated with improved overall mental health well-being. Additionally, both the incremental change in Timed Up and Go test and the number of individuals able to complete it may in fact improve as patients undergo treatment. This highlights that gait speed may not be static and improve with treatment, suggesting a dynamic model of frailty. Larger studies conducted longitudinally will be required to further evaluate these findings to explore the evolving concept of frailty in myeloma. Disclosures Mian: Amgen: Consultancy; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Pond:Roche Canada: Employment, Other: Stock; Takeda (DSMC membership): Other: Honorarium. Tuchman:Alnylam: Honoraria, Research Funding; Celgene: Honoraria, Research Funding, Speakers Bureau; Karyopharm: Honoraria; Amgen: Research Funding; Sanofi: Research Funding; Merck: Research Funding; Prothena: Research Funding; Roche: Research Funding. Fiala:Incyte: Research Funding. Wildes:Janssen: Research Funding; Carvive: 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".